Datasets:

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ADKU

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ResearchGPT_model_dataset

like 0

[ "Ofir Brem", "David Elisha", "Eli Konen", "Michal Amitai", "Eyal Klang" ]

[ "crohn’s disease", "cd", "significant morbidity", "the need", "effective, non-invasive inflammatory assessment", "magnetic resonance enterography", "mre", "this literature review", "recent publications", "the role", "deep learning", "mre", "cd assessment", "we", "medline", "studies", "that", "the use", "deep learning algorithms", "assessment", "cd activity", "the study", "the prisma guidelines", "the risk", "bias", "the quadas‐2 tool", "five eligible studies", "468 subjects", "our study", "diverse deep learning applications", "image quality enhancement", "bowel segmentation", "disease burden quantification", "3d reconstruction", "surgical planning", "cd assessment", "the studies", "preliminary, retrospective studies", "a high risk", "bias", "at least one category", "future research", "how deep learning", "cd patient diagnostics", "the increasing integration", "such models", "hospital systems", "crohn", "five", "468", "3d", "at least one" ]

[ "Sushant Kumar Pandey", "Arya Haldar", "Anil Kumar Tripathi" ]

[ "high impact", "internet technology", "rapid change", "software systems", "it", "a tough challenge", "us", "software defects", "high accuracy", "traditional software defect prediction research", "features", "e.g., complexity metrics", "them", "machine learning classifiers", "defective code", "high prediction accuracy", "researchers", "several deep learning", "high computational models", "software defect prediction", "several critical conditions", "theoretical problems", "order", "better results", "this article", "the investigation", "sdp", "two deep learning techniques", "i.e., squeezenet and bottleneck models", "we", "seven different open-source datasets", "nasa repository", "this comparative study", "we", "f-measure", "a performance evaluator", "these methods", "the-art", "mean f-measure", "0.93 ±", "0.90 ±", "we", "these two methods", "terms", "f-measure", "large- and moderate-size projects", "they", "terms", "training time", "the size", "projects", "such deep learning methods", "two", "seven", "nasa", "eight", "0.93", "0.014", "0.90", "0.013", "two" ]

[ "Haithem Awijen", "Hachmi Ben Ameur", "Zied Ftiti", "Waël Louhichi" ]

[ "this study", "oil price forecasting", "a time", "crisis", "december", "december", "the oil market", "various shocks", "its prices dynamics", "conventional (econometric and structural) models", "a new strand", "literature", "more attention", "the last decades", "artificial intelligence methods", "this literature", "the performance accuracy", "machine learning", "deep learning methods", "we", "this study", "this literature", "the oil price forecasting", "these two approaches", "the stylized facts", "we", "the support vector machine", "long short-term memory approach", "two main models", "machine learning", "deep learning methods", "our findings", "the superiority", "the deep learning method", "the machine learning approach", "our results", "the deep lstm-prediction", "a close pattern", "the observed oil prices", "robust fitting accuracy", "mid", "forecast horizons", "crisis events", "our results", "svm machine learning", "poor memory ability", "a clearer understanding", "time-dependent volatility", "the dynamic co", "-", "movements", "actual and predicted data", "our results", "the power", "svm", "long-term predictions", "which", "distortions", "forecasting performance", "december 2007 to december 2021", "the last decades", "two", "two" ]

[ "Siddharth Kumar", "P Mukhopadhyay", "C Balaji" ]

[ "the present study", "the issue", "too frequent triggers", "deep convection", "climate models", "which", "physics-based classical trigger functions", "convective available potential energy", "cape", "cloud work function", "cwf", "this problem", "the study", "machine learning", "ml", "deep convective triggers", "an alternative", "the deep convective trigger", "a binary classification problem", "the goal", "deep convection", "two elementary classification algorithms", "vector machines", "neural networks", "this study", "a novel method", "the importance", "input variables", "the classification problem", "which", "the underlying mechanisms", "factors", "deep convection", "the accuracy", "the ml-based methods", "the widely used convective available potential energy", "cape)-based and dynamic generation", "cape", "dcape", "trigger function", "many convective parameterization schemes", "results", "the elementary machine learning-based algorithms", "the classical cape-based triggers", "the potential effectiveness", "ml-based approaches", "this issue", "a method", "the mahalanobis distance", "binary classification", "which", "the mahalanobis distance-based approach", "accuracy", "other ml-based methods", "its viability", "an alternative method", "deep convective triggers", "deep convective triggers", "ml-based approaches", "the study", "a possible solution", "the probability density", "rain", "the climate model", "this improvement", "the issue", "excessive drizzle", "many climate models", "cwf", "two" ]

[ "Nguyen Minh Tuan", "Phayung Meesad", "Ha Huy Cuong Nguyen" ]

[ "artificial intelligence-based machine translation", "traditional methods", "a machine translator", "text", "speech", "one language", "another", "machine translators", "the word mechanism", "one language", "words", "another", "verbatim translations", "a good translation", "both a sentence", "a word", "that", "a completed meaning", "accordance", "the context", "the relevant sentence", "this paper", "we", "english–vietnamese translation", "deep learning methods", "recurrent neural network", "long short-term memory", "gated recurrent units", "attention", "transformer", "the deep learning-based machine translators", "the test accuracy", "the result translation", "it", "the best deep learning-based machine translator model", "the attention mechanism", "the transformer", "the second rank", "one language", "english", "vietnamese", "second" ]

[ "Priyadarsini Samal", "Mohammad Farukh Hashmi" ]

[ "emotion", "a subjective psychophysiological reaction", "external stimuli", "which", "every aspect", "our daily lives", "the continuing development", "non-invasive and portable sensor technologies", "brain-computer interfaces", "bci", "intellectuals", "several fields", "emotion recognition techniques", "human emotions", "a variety", "behavioural cues", "gestures", "body language", "voice", "physiological markers", "people", "their genuine emotions", "more precise and objective emotion recognition", "physiological signals", "other physiological signals", "electroencephalogram", "eeg", "variation", "affective states", "various eeg-based emotion recognition methods", "this study", "eeg-based bcis", "emotion identification", "an outline", "the progress", "this field", "a summary", "the datasets", "techniques", "human emotions", "various emotion models", "we", "several eeg feature extractions", "feature selection/reduction", "machine learning", "deep learning algorithms", "accordance", "standard emotional identification process", "we", "an overview", "the human brain's eeg rhythms", "which", "emotional states", "we", "a number", "eeg-based emotion identification research", "numerous machine learning", "deep learning techniques", "conclusion", "this study", "the applications", "challenges", "potential areas", "future research", "identification", "classification", "human emotional states", "first", "three" ]

[ "Pundalik Chavan", "H. Hanumanthappa", "E. G. Satish", "Sunil Manoli", "S. Supreeth", "S. Rohith", "H. C. Ramaprasad" ]

[ "the introduction", "the attention mechanism", "the internet", "things", "wsn-iot", "the sector", "the intrusion detection mechanism capabilities", "the deep learning techniques", "the attention mechanism", "the efficacy", "efficiency", "ids", "wsns", "the proposed “enhanced hybrid intrusion detection system", "attention mechanism", "ehid-sca) underlying insight", "this", "the characteristics", "wsn data", "this", "convolutional neural networks", "cnns", "other deep learning models", "an effective and coherent architecture", "the design", "the deep hybrid network", "attention", "channel attention", "spatial attention", "some", "the main components", "the model biased capacity", "important spatial and channel information", "the sensor input", "the method", "the consideration", "these things", "it", "noise", "the proposed technique", "the way", "the internet", "things intrusion detection systems", "the automatic extraction", "useful information", "sensor data", "the utilization", "deep learning", "the attention mechanism", "the attack", "mitigation", "the network design", "that", "analysis", "the geographical and temporal context", "events", "which", "intrusion events" ]

[ "Xuguang Cao", "Kefeng Fan", "Huilin Ma" ]

[ "the incidence", "colon cancer", "the top three places", "gastrointestinal tumors", "colon polyps", "an important causative factor", "the development", "colon cancer", "colon polyps", "colon polypectomy", "the chances", "colon cancer", "the current means", "colon polyp examination", "colonoscopy", "images", "the gastrointestinal tract", "them", "which", "doctors", "advanced deep learning technology", "colon polyps", "the gastrointestinal tract", "the patient", "the polyps", "an important direction", "research", "the privacy", "medical data", "the non", "-", "interoperability", "disease information", "this paper", "a dual-branch colon polyp segmentation network", "federated learning", "which", "it", "a better training effect", "the guarantee", "data independence", "the dual-branch colon polyp segmentation network", "this paper", "the two different structures", "convolutional neural network", "cnn", "a dual-branch structure", "layer", "the advantages", "different structures", "this paper", "we", "the aggregated attention module", "aam", "the high-dimensional semantic information", "the missing information", "the lower layers", "our approach", "state", "the art", "kvasir-seg and cvc-clinicdb datasets", "three", "secondly", "two", "cnn" ]

[ "I. Sakthidevi", "G. Fathima" ]

[ "healthcare domain", "access trust", "prime importance", "effective delivery", "medical services", "it", "positive patient-provider relationships", "the advancement", "technology", "affective computing", "a promising approach", "access trust", "it", "systems", "human emotions", "the research work", "the application", "techniques", "affective computing", "access trust", "healthcare environment", "a novel algorithm", "\"belief-emo-fusion", "the understanding", "interpretation", "emotions", "healthcare", "the research", "a comprehensive simulation analysis", "the performance", "belief-emo-fusion", "existing algorithms", "simulation metrics", "modal accuracy", "ınference time", "f1-score", "the study", "the importance", "emotion recognition", "understanding", "healthcare settings", "the work", "the role", "deep learning models", "empathetic and emotionally intelligent technologies", "the challenges", "affective computing", "the proposed approach", "the development", "more effective and reliable healthcare systems", "the findings", "valuable insights", "researchers", "practitioners", "deep learning techniques", "trust", "communication", "healthcare environments" ]

[ "Om G", "Saketh Ram Billa", "Vishal Malik", "Eslavath Bharath", "Sanjeev Sharma" ]

[ "india", "grapes", "the most important crops", "business", "grapes", "their byproducts", "india’s leading exports", "the leaves", "grapes", "a variety", "diseases", "large-scale production", "grapes", "these diseases", "the purpose", "this research paper", "disease", "leaves", "deep learning", "a big picture", "it", "the advances", "leaf disease", "results", "a total", "seven different types", "pre-trained deep convolutional neural networks", "cnns", "transfer learning", "mobilenet", "inceptionresnetv2", "densenet121", "inceptionv3", "xception", "vgg16", "resnet101v2", "4062 images", "grapevine leaves", "four classes", "leaf blight", "black rot", "black measles", "the major challenge", "the leaf", "the type", "disease", "the leaf", "order", "the disease", "these images", "seven different transfer learning models", "the image classification accuracy", "99.672%", "densenet121", "it", "the highest accuracy", "any accuracy", "the literature", "it", "99.500%", "xception", "99.345%", "vgg16", "99.345%", "inceptionv3", "the proposed research paper", "grapevine disease", "an early stage", "its spread", "this process", "the production rate", "profit", "farmers", "india", "one", "one", "india", "seven", "inceptionresnetv2", "inceptionv3", "4062", "four", "seven", "99.672%", "99.500%", "99.345%", "99.345%", "inceptionv3" ]

[ "Chuan Gao", "Linyu Wu", "Wei Wu", "Yichao Huang", "Xinyue Wang", "Zhichao Sun", "Maosheng Xu", "Chen Gao" ]

[ "objectivesthe accurate detection", "precise segmentation", "lung nodules", "computed tomography", "key prerequisites", "early diagnosis", "appropriate treatment", "lung cancer", "this study", "detection and segmentation methods", "pulmonary nodules", "deep-learning techniques", "methodological gaps", "biases", "the existing literature.methodsthis study", "a systematic review", "the preferred reporting items", "systematic reviews", "meta-analyses guidelines", "embase, web", "science core collection", "the cochrane library", "may", "the quality assessment", "diagnostic accuracy studies", "2 criteria", "the risk", "bias", "the checklist", "artificial intelligence", "medical imaging", "the study", "model performance", "data sources", "task-focus information.resultsafter screening", "we", "nine studies", "our inclusion criteria", "these studies", "public datasets", "the lung image database consortium image collection and image database resource initiative", "lung", "the studies", "detection", "segmentation", "other tasks", "convolutional neural networks", "model development", "performance evaluation", "multiple metrics", "sensitivity", "the dice coefficient.conclusionsthis study", "the potential power", "deep learning", "lung nodule detection", "segmentation", "it", "the importance", "standardized data processing", "code", "data", "sharing", "the value", "external test datasets", "the need", "model complexity", "efficiency", "future research.clinical relevance statementdeep learning", "significant promise", "segmenting pulmonary nodules", "future research", "methodological shortcomings", "variability", "its clinical utility.key points", "deep learning", "potential", "the detection", "segmentation", "pulmonary nodules", "methodological gaps", "biases", "the existing literature", "factors", "external validation", "transparency", "the clinical application", "2", "nine", "between 2019 and 2023", "2016" ]

[ "Anuja Bokhare", "Tripti Kothari" ]

[ "emotions", "an important role", "identification", "the interest", "user", "respect", "product", "technology", "other parameter", "a human being", "so many emotions", "which", "emotions", "anger", "surprise", "happiness", "sadness", "fear", "today's fast growing world", "technology", "necessity", "expert system", "which", "the interest", "user", "emotions", "these systems", "various applications", "techniques", "machine learning", "deep learning", "the emerging ones", "these", "the technologies", "that", "applications", "automatic cars", "music recommendation and movie recommendation", "human emotions", "it", "the help", "techniques", "that", "machine learning", "deep learning", "current study", "human face detection", "emotion detection", "video", "which", "the person", "the mood", "current system attention", "detection", "face", "emotion", "haar cascade", "deep face technology", "the outcome", "an appropriate video", "today" ]

[ "Yao-zhong Zhang", "Seiya Imoto" ]

[ "genomic sequences", "strings", "characters", "a (adenine", "c", "cytosine", "g", "guanine", "t", "(thymine", "an alternative approach", "sequence-related information", "image representations", "chaos game representation", "pileup images", "rapid advancements", "deep learning", "computer vision", "natural language processing", "interest", "image-based dl methods", "genomic sequence analysis", "these methods", "genomic information", "images", "spatial information", "images", "the analytical process", "this review", "we", "three typical applications", "that", "image processing", "dl models", "genome analysis", "we", "the utilization", "advantages", "these image-based approaches", "three" ]

[ "Abdolhossein Fathi", "Babak Moradi", "Iman Zarei", "Afshin Shirbandi" ]

[ "the utilization", "motorcycle", "the detection", "recognition", "its license plate", "a very important role", "intelligent transportation systems", "its", "its", "traffic control", "violation monitoring", "e-payment systems", "the toll pay", "parking", "several algorithms", "this task", "each", "them", "advantages", "disadvantages", "different circumstances", "situations", "deep learning based methods", "they", "the issue", "automatic license plate detection", "recognition", "the deep learning models", "a high volume", "data", "efficient training", "each country", "its license plate template", "it", "proper dataset", "an efficient model", "it", "this end", "this research", "a new dataset", "a deep learning-based system", "iranian motorcycle license plates", "images", "that", "different dimensions", "angles", "levels", "lighting", "daytime and nighttime images", "various cities", "two datasets", "detection", "identification", "these images", "an efficient deep learning-based system", "three networks", "ssd", "faster rcnn", "detection", "identification", "license plates", "the obtained results", "the yolov8 network", "the best result", "98.5% accuracy", "the detection stage", "99% accuracy", "the identification stage", "the proposed yolov8 model", "deep learning-based methods", "better performance", "the collected dataset", "the collected dataset", "the source code", "the investigated models", "first", "iranian", "first", "daytime", "nighttime", "two", "three", "yolov8", "rcnn", "yolov8", "98.5%", "99%", "yolov8 model" ]

[ "Balambigai Subramanian", "Suresh Muthusamy", "Kokilavani Thangaraj", "Hitesh Panchal", "Elavarasi Kasirajan", "Abarna Marimuthu", "Abinaya Ravi" ]

[ "melanoma", "a form", "melanocyte cells", "a significant health risk", "it", "other types", "skin cancer", "its detection", "challenges", "expert observation", "the classification accuracy", "skin lesions", "a deep convolutional neural network", "visual geometry group model", "deep learning methods", "substantial training time", "this", "transfer learning techniques", "training duration", "data sets", "the international skin imaging collaboration", "the model", "this proposed approach", "evaluation", "classification performance", "metrics", "accuracy", "positive predictive value", "negative predictive value", "specificity", "sensitivity", "the classifier’s performance", "test data", "a confusion matrix", "the introduction", "techniques", "the deep convolutional neural network", "an improved classification accuracy", "85%", "the 81%", "a standard convolutional neural network", "85%", "the 81%" ]

[ "Shuang Chen", "Changlun Zhang", "Haibing Mu" ]

[ "deep learning model", "a multi-layered network structure", "the network parameters", "that", "the final performance", "the model", "a deep learning optimizer", "comparison", "the mainstream optimizers", "that", "integer-order derivatives", "only local information, fractional-order derivatives optimizers", "which", "global information", "attention", "the long-term estimated gradients", "fractional-order derivatives", "the influence", "recent gradients", "the optimization process", "issues", "local optima", "slower optimization speeds", "this paper", "we", "an adaptive learning rate optimizer", "adagl", "the grünwald", "–letnikov (g–l) fractional-order derivative", "it", "the direction", "step size", "parameter", "the long-term and short-term gradients information", "the problem", "local minima or saddle points", "the global memory", "fractional-order calculus", "we", "the gradient", "parameter update", "g", "fractional-order", "gradient", "better use", "the long-term curvature information", "the past", "the recent gradient information", "the optimization phase", "we", "a step size control", "coefficient", "the learning rate", "real-time", "the performance", "the proposed adagl", "the current advanced optimizers", "we", "several different deep learning tasks", "image classification", "cnns", "node classification", "graph classification", "gnns", "image generation", "gans", "language modeling", "lstm", "extensive experimental results", "adagl", "stable and fast convergence", "excellent accuracy", "good generalization performance", "adagl", "adagl" ]

[ "Soonjo Kwon", "Ji-Hyeon Hur", "Hyungki Kim" ]

[ "point clouds", "3d scanners", "product inspection", "engineering", "the quality", "the point cloud", "the scanning environment", "scanner specifications", "the quality", "the point cloud", "a significant impact", "the accuracy", "automatic or manual modeling", "response", "various point cloud post-processing technologies", "point cloud upsampling", "a technique", "the resolution", "point clouds", "the purpose", "upsampling", "additional points", "the target object", "higher detail", "this technology", "areas", "high-resolution 3d representation", "approaches", "deep learning", "attention", "deep learning-based point cloud upsampling research", "surface consolidation", "edge consolidation research", "the target regions", "supervised or self-supervised learning", "the type", "approaches", "this study", "the latest research trends", "deep learning-based point cloud sampling", "the issues", "limitations", "each research category", "future research", "directions.graphical abstract", "3d", "3d" ]

[ "Byungdu Jo", "Seung-Jae Lee" ]

[ "small gantries", "long, thin scintillation pixels", "preclinical positron emission tomography", "parallax errors", "the system’s field", "view", "this problem", "a detector", "the depth", "interaction", "doi", "addition", "conduct", "research", "methods", "doi measurement", "deep learning", "this study", "we", "a detector", "measurement", "doi", "two layers", "scintillation pixel arrays", "a method", "3-dimensional (3d) position", "deep learning", "detect2000 simulation", "the 3d-positioning accuracy", "the designed detector", "data", "detect2000 simulation", "a deep learning model", "assessment", "location specification accuracy", "data", "a new location", "the deep learning model", "the result", "the 3d-position measurement accuracy", "94.48%", "two", "3", "3d", "3d", "detect2000", "3d", "94.48%" ]

[ "Muhammet Sinan Başarslan", "Fatih Kayaalp" ]

[ "people", "social life", "this", "an increase", "the use", "social media", "the past two years", "social media", "people", "their thoughts", "various topics", "their favourite movies", "restaurants", "hotels", "this", "a huge amount", "data", "many researchers", "different sciences", "this data", "natural language processing", "nlp", "these areas", "computer science", "that", "artificial technologies", "sentiment analysis", "the tasks", "nlp", "which", "emotions", "huge post data", "this study", "sentiment analysis", "two datasets", "tweets", "coronavirus and tripadvisor hotel reviews", "a frequency-based word representation method", "term frequency-inverse document frequency", "tf-idf", "a prediction-based word2vec word", "embedding method", "the datasets", "sentiment analysis models", "single machine learning methods", "decision trees", "dt", "k-nearest neighbour-knn, naive bayes", "vector machine-svm", "single deep learning methods", "long short term memory-lstm", "recurrent neural network-rnn", "heterogeneous ensemble learning methods", "stacking and majority voting", "these single machine learning", "deep learning methods", "accuracy", "a performance measure", "the heterogeneous model", "lstm-rnn", "the other models", "accuracy values", "the coronavirus dataset", "the trip advisor dataset", "they", "results", "the literature", "it", "the use", "single methods", "an ensemble", "better results", "which", "the literature", "which", "a step", "the detection", "sentiments", "posts", "the performance", "heterogeneous ensemble learning models", "different algorithms", "sentiment analysis tasks", "future work", "the past two years", "two", "accuracy", "0.864", "0.898" ]

[ "Steffen Herbold", "Steffen Tunkel" ]

[ "differential testing", "a useful approach", "that", "different implementations", "the same algorithms", "the results", "software testing", "recent years", "this approach", "test campaigns", "deep learning frameworks", "little knowledge", "the application", "differential testing", "deep learning", "this article", "we", "this gap", "classification algorithms", "we", "a case study", "scikit-learn", "weka", "spark mllib", "which", "we", "the potential", "differential testing", "which algorithms", "multiple frameworks", "the feasibility", "pairs", "algorithms", "that", "the same behavior", "the effectiveness", "tests", "the identified pairs", "the deviations", "we", "a large potential", "popular algorithms", "the feasibility", "it", "configurations", "that", "other frameworks", "the execution", "the feasible tests", "a large number", "deviations", "the scores", "classes", "only a lenient approach", "statistical significance", "classes", "a huge amount", "test failures", "the potential", "differential testing", "deep learning", "research", "the quality", "machine learning libraries", "practitioners", "the approach", "they", "deep knowledge", "implementations", "that", "significant differences", "classes", "recent years", "weka", "spark mllib" ]

[ "Salam Fraihat", "Bushra Abu Tahon", "Bushra Alhijawi", "Arafat Awajan" ]

[ "a recommendation system", "information overload issues", "personalized predictions", "users", "rs", "the overall ratings", "users", "items", "recommendations", "them", "users", "several aspects", "items", "a multi-criteria rs", "n", "-aspects", "items", "more accurate recommendations", "a single-criteria rs", "this research paper", "two deep encoder", "decoder models", "shared learning", "a multi-criteria rs, multi-modal deep encoder", "decoder-based shared learning", "mmedsl", "multi-criteria deep encoder", "decoder-based shared learning", "mcedsl", "the shared learning technique", "the multi-modality concept", "deep learning", "the training process", "the shared learning technique", "the shared learning captures", "useful shared information", "the learning process", "-", "criteria", "inter", "-", "relationships", "a set", "experiments", "the proposed models", "recent baseline approaches", "the yahoo", "movies multi-criteria dataset", "the results", "the proposed models", "other algorithms", "addition", "the results", "the shared learning technique", "the rs", "precise recommendation predictions", "two", "yahoo" ]

[ "I. I. Kurochkin", "A. I. Prun", "A. A. Balaev" ]

[ "abstractthe paper", "the adaptation", "federated deep learning", "a desktop grid system", "the example", "an image classification problem", "restrictions", "data transfer", "the nodes", "the desktop grid", "a part", "the dataset", "the implementation", "federated deep learning", "a desktop grid system", "the boinc platform", "methods", "local datasets", "desktop grid nodes", "the results", "numerical experiments" ]

[ "Sandip Kumar Singh Modak", "Vijay Kumar Jha" ]

[ "diabetes", "the significant reasons", "public sickness", "death", "diabetes", "more than 463 million people", "the international diabetes federation report", "this figure", "early screening", "diagnosis", "diabetes patients", "great significance", "diabetes", "time", "diabetes", "a multi factorial metabolic disease", "its diagnostic criteria", "all the ethology", "damage degree", "pathogenesis", "other factors", "a situation", "uncertainty", "imprecision", "various aspects", "the medical diagnosis process", "the development", "data mining", "researchers", "an important role", "diabetes prediction research", "this paper", "-depth", "the application", "machine learning", "deep learning techniques", "the prediction", "diabetics", "addition", "this paper", "the different methodology", "machine", "deep learning", "prediction", "diabetics", "last two decades", "the methods", "their successes", "failure", "this review", "researchers", "practitioners", "the-art", "gaps", "the literature", "2019", "more than 463 million", "more than 700 million", "2040", "last two decades" ]

[ "Fumio Hashimoto", "Yuya Onishi", "Kibo Ote", "Hideaki Tashima", "Andrew J. Reader", "Taiga Yamaya" ]

[ "this review", "positron emission tomography", "pet", "the evolution", "pet image reconstruction methods", "we", "an overview", "conventional pet image reconstruction methods", "filtered backprojection", "recent iterative pet image reconstruction algorithms", "deep learning methods", "pet data", "the latest innovations", "three main categories", "the first category", "post-processing methods", "pet image denoising", "the second category", "direct image reconstruction methods", "that", "mappings", "sinograms", "the reconstructed images", "end", "the third category", "iterative reconstruction methods", "that", "conventional iterative image reconstruction", "neural-network enhancement", "we", "future perspectives", "pet imaging", "deep learning technology", "first", "three", "first", "second", "third" ]

[ "Xiaofei Shi", "Daran Xu", "Zhanhao Zhang" ]

[ "this work", "the deep learning-based numerical algorithms", "optimal hedging problems", "markets", "general convex transaction costs", "our main focus", "these algorithms", "the length", "the trading time horizon", "the comparison results", "the fbsde", "han", "jentzen", "e", "buehler", "gonon", "teichmann", "wood", "we", "a stable-transfer hedging", "st-hedging", "the convenience", "the leading-order approximation formulas", "the accuracy", "the deep learning-based algorithms", "our st-hedging algorithm", "the-art", "short and moderately long time horizon", "fbsde", "long time horizon", "previous algorithms", "the transfer learning technique", "st-hedging", "the training time", "great scalability", "high-dimensional settings", "this", "new possibilities", "model-based deep learning algorithms", "economics", "finance", "operational research", "which", "advantage", "the domain expert knowledge", "the accuracy", "the learning-based methods", "han", "2018", "teichmann", "2019" ]

[ "Haroon Ahmed Khan", "Syed Saud Naqvi", "Abeer A. K. Alharbi", "Salihah Alotaibi", "Mohammed Alkhathami" ]

[ "efficient waste management", "a crucial role", "clean and green environment", "the smart cities", "this study", "the critical role", "efficient trash classification", "sustainable solid waste management", "smart city environments", "we", "a comparative analysis", "various trash classification methods", "deep learning models", "convolutional neural networks", "cnns", "the pytorch open-source framework", "the trashbox dataset", "we", "experiments", "ten unique deep neural network models", "our approach", "training accuracy", "extensive experimentation", "we", "the consistent superiority", "the resnext-101 model", "others", "exceptional training", "validation", "test accuracies", "these findings", "the potential", "cnn-based techniques", "significantly advancing trash classification", "optimized solid waste management", "smart city initiatives", "this study", "a distributed framework", "federated learning", "that", "the performance", "a combination", "cnn models", "trash detection", "smart city", "cnn", "smart city", "cnn" ]

[ "Amirhossein Arzani", "Lingxiao Yuan", "Pania Newell", "Bei Wang" ]

[ "deep learning", "remarkable success", "various scientific machine learning applications", "its opaque nature", "concerns", "interpretability and generalization capabilities", "the training data", "interpretability", "physical systems", "extensive datasets", "that", "the entire range", "input features", "many physics-based learning tasks", "increased errors", "distribution", "this work", "the field", "functional data analysis", "fda", "we", "generalized functional linear models", "an interpretable surrogate", "a trained deep learning model", "we", "our model", "a trained neural network", "post-hoc interpretation", "training data", "interpretable operator learning", "a library", "generalized functional linear models", "different kernel functions", "sparse regression", "an interpretable surrogate model", "that", "we", "test cases", "solid mechanics", "fluid mechanics", "transport", "our results", "our model", "comparable accuracy", "deep learning", "ood generalization", "more transparency", "interpretability", "our study", "the significance", "interpretable representation", "scientific machine learning", "the potential", "functional linear models", "a tool", "interpreting", "deep learning", "fda", "linear", "linear" ]

[ "Mohammad Ehsanul Karim" ]

[ "purposepropensity score matching", "epidemiological studies", "observational data", "its estimates", "correct model-specification", "this study", "deep learning models", "autoencoders", "propensity score estimation", "them", "traditional methods", "bias", "variance accuracy", "treatment effect", "a plasmode simulation", "the right heart catheterization dataset", "a variety", "settings", "we", "1) a supervised deep learning architecture", "(2) an unsupervised autoencoder", "two traditional methods", "logistic regression", "a spline-based method", "propensity scores", "performance metrics", "bias", "standard errors", "coverage probability", "the analysis", "real-world data", "estimates", "those", "a double robust", "approach.resultsthe analysis", "supervised deep learning models", "unsupervised autoencoders", "variance estimation", "comparable levels", "bias", "these results", "analyses", "real-world data", "the supervised model’s estimates", "those", "conventional methods", "deep learning models", "traditional methods", "settings", "exposure", "deep learning models", "promise", "propensity score estimations", "epidemiological research", "nuanced confounder adjustment", "complex datasets", "we", "supervised deep learning", "epidemiological research", "reproducible codes", "widespread use", "methodological transparency", "1", "2", "two" ]

[ "Heui Seung Lee", "Jaewoong Kang", "So Eui Kim", "Ji Hee Kim", "Bum-Joo Cho" ]

[ "this study", "deep learning models", "plain skull radiograph images", "the accurate postnatal age", "infants", "12 months", "the results", "the trained deep learning models", "it", "the feasibility", "major changes", "skull x-ray images", "postnatal cranial development", "gradient-weighted class activation mapping", "we", "densenet-121 and efficientnet-v2-m", "convolutional neural network models", "4933 skull x-ray images", "1343 infants", "a ± 1 month error margin", "densenet-121", "a maximum corrected accuracy", "79.4%", "anteroposterior (ap) views", "1.5%", "lateral views", "81.1 ±", "2.9%", "efficientnet-v2", "m", "a maximum corrected accuracy", "ap views", "2.3%", "lateral views", "2.5%", "saliency maps", "critical discriminative areas", "the coronal, sagittal, and metopic sutures", "ap skull x-ray images", "the lambdoid suture", "cortical bone density", "lateral images", "them", "indicators", "cranial development", "these findings", "the precision", "deep learning", "infant age", "non-invasive methods", "the progress", "clinical diagnostics", "developmental assessment tools", "under 12 months", "4933", "1343", "1 month", "79.4%", "78.0 ±", "1.5%", "84.2%", "81.1", "2.9%", "79.1%", "77.0", "2.3%", "87.3%", "85.1", "2.5%" ]

[ "Maheswara Rao Bandi", "Laxmi Narayana Pasupuleti", "Tanmay Das", "Shyamal Guchhait" ]

[ "damage detection", "any civil engineering structure", "a part", "the interest", "the field", "engineering", "which", "one", "the stability", "lifetime", "the structure", "the advancement", "technology", "the field", "infrastructure", "damage assessment", "any structure", "the help", "convolutional neural networks", "cnns", "deep learning", "importance", "the ease", "which", "it", "the damage", "these computer-aided techniques", "we", "manpower", "damage", "inaccessible places", "that", "we", "the current study", "we", "resnet-50", "which", "a part", "a deep convolutional neural network", "50 layers", "it", "resnet-50", "a subclass", "convolutional neural networks", "image classification", "we", "the utah state university", "usa", "this data", "nearly 56,000 annotated images", "both cracked and non-cracked bridge deck images", "wall images", "pavement images", "these images", "a variety", "obstructions", "shadows", "surface roughness", "some cases", "the present study", "the images", "the accuracy", "the results", "themselves", "an increase", "training data", "we", "an algorithm", "cracks", "we", "them", "the results", "the resnet-50 architecture", "good agreement", "the developed algorithm", "resnet-50", "50", "resnet-50", "the utah state university", "utah", "usa", "nearly 56,000", "resnet-50" ]

[ "Mohammed A. H. Lubbad", "Ikbal Leblebicioglu Kurtulus", "Dervis Karaboga", "Kerem Kilic", "Alper Basturk", "Bahriye Akay", "Ozkan Ufuk Nalbantoglu", "Ozden Melis Durmaz Yilmaz", "Mustafa Ayata", "Serkan Yilmaz", "Ishak Pacal" ]

[ "this study", "an effective solution", "the autonomous identification", "dental implant brands", "a deep learning-based computer diagnostic system", "it", "the system’s potential", "clinical practices", "a strategic framework", "diagnosis and treatment processes", "implantology", "this study", "a total", "28 different deep learning models", "18 convolutional neural network (cnn) models", "vgg", "resnet", "densenet", "efficientnet", "regnet", "convnext", "10 vision transformer models", "swin and vision transformer", "the dataset", "patients", "who", "implant treatments", "erciyes university faculty", "dentistry", "it", "the training and evaluation process", "deep learning models", "prototypes", "six different implant systems", "six manufacturers", "the deep learning-based dental implant system", "high classification accuracy", "different dental implant brands", "deep learning models", "all the architectures", "the small model", "the convnext architecture", "an impressive accuracy rate", "94.2%", "a high level", "classification success.this study", "the effectiveness", "deep learning-based systems", "high classification accuracy", "dental implant types", "these findings", "the way", "advanced deep learning tools", "clinical practice", "significant improvements", "patient care and treatment outcomes", "28", "18", "cnn", "10", "1258", "erciyes university faculty of dentistry", "between 2012 and 2023", "six", "six", "94.2%" ]

[ "Ding Zhang", "BingShu Zheng", "LiuWei Xu", "YiCong Wu", "Chen Shen", "ShanLei Bao", "ZhongHua Tan", "ChunFeng Sun" ]

[ "objectivessynchronous colorectal cancer peritoneal metastasis", "crpm", "a poor prognosis", "this study", "a radiomics-boosted deep learning model", "pet/ct image", "risk assessment", "synchronous crpm.methodsa total", "220 colorectal cancer (crc) cases", "this study", "we", "the feature maps", "radiomic feature maps", "rfms", "radiomic features", "ct", "pet image", "a 2d sliding kernel", "resnet50", "a radiomics-boosted deep learning model", "pet/ct image patches", "rfms", "that", "we", "the peritumoral region", "the assessment", "crpm", "this study", "the performance", "each model", "the area", "the curves", "auc).resultsthe aucs", "the radiomics", "the training", "all validation datasets", "95% confidence interval", "ci", "0.874–0.978", "95% ci", "95% ci", "0.889 (95% ci", "this model", "consistency", "the calibration curve", "the delong test", "idi", "it", "the most predictive model.conclusionsthe radiomics-boosted deep learning model", "superior estimated performance", "preoperative prediction", "synchronous crpm", "treatment pet", "ct", "potential assistance", "the development", "more personalized treatment methods", "follow-up plans.critical relevance", "statementthe onset", "synchronous colorectal crpm", "a radiomics-boosted deep learning model", "the risk", "crpm", "treatment", "personalized clinical treatment decisions", "prognosis", "patients", "crpm", "early detection poses challenges", "the synergy", "radiomics", "deep learning", "crpm", "the radiomics", "treatment approaches", "crc patients", "graphical abstract", "crpm.methodsa", "220", "2d", "resnet50", "0.926", "95%", "0.897", "95%", "0.885", "95%", "0.795–0.975", "0.889", "95%" ]

[ "Mujahid Hussain", "Makhmoor Fiza", "Aiman Khalil", "Asad Ali Siyal", "Fayaz Ali Dharejo", "Waheeduddin Hyder", "Antonella Guzzo", "Moez Krichen", "Giancarlo Fortino" ]

[ "skin cancer", "particularly melanoma", "a severe issue", "many years", "its increasing incidences", "the rising mortality rate", "melanoma", "immediate attention", "early stages", "timely diagnosis and effective treatment", "the similar visual appearance", "malignant tumors", "normal cells", "the detection", "classification", "melanoma", "the most challenging tasks", "melanoma", "diagnosis", "treatment", "which", "patient survival", "a new dataset", "nailmelonma", "this study", "order", "various deep learning models", "an indigenous nail melanoma localization dataset", "the dermoscopic image datasets", "seven cnn-based dl architectures", "viz", ".", "vgg19", "xception", "inceptionv3", "mobilenet", "mobilenetv2", "the classification", "skin lesions", "melanoma detection", "the trained models", "key performance parameters", "i.e., accuracy", "recall", "specificity", "precision", "f1-score", "the performance", "each transfer learning model", "the results", "the proposed workflow", "more than 95% accuracy", "addition", "we", "the quantization", "such models", "them", "memory-constrained mobile/edge devices", "an accurate, timely, and faster diagnosis", "nail melanoma", "the early detection", "other types", "skin cancer", "the proposed workflow", "the early detection", "nail melanoma", "seven", "cnn", "inceptionv3", "mobilenetv2", "more than 95%" ]

[ "Wang Hongsheng" ]

[ "a great chinese thinker", "writer", "the twentieth century", "lu xun", "his literary works", "a successful cultural communication activist", "editor", "publisher", "we", "depth", "lu xun", "many aspects", "deep learning", "this paper", "an online multimedia learning system", "lu xun literature", "this system", "the relationship", "classical lu xun literature", "modern multimedia technology", "the research object", "the calculation effect", "other different types", "algorithms", "this dhraa algorithm", "the availability", "data", "the dhraa algorithm", "other algorithms", "the recommendation accuracy", "its effectiveness", "this system", "two servers", "one system", "the two servers", "database server and web server", "testing", "the system", "good bearing capacity", "the limited processing capacity", "the server", "the system", "high performance", "its performance characteristics", "the system", "the expected performance", "this paper", "lu xun’s literature", "modern multimedia", "which", "online learning services", "lu xun’s literature lovers", "scholars", "lu xun’s research field", "academic vision", "this paper", "an effective online learning system", "lu xun’s literature", "deep learning", "multimedia technology", "lu xun’s literature", "chinese", "the twentieth century", "lu xun", "lu xun", "lu xun literature", "two", "one", "two", "xun", "xun’s", "xun", "xun" ]

[ "Daryl Jacob Jerrish", "Om Nankar", "Shilpa Gite", "Shruti Patil", "Ketan Kotecha", "Ganeshsree Selvachandran", "Ajith Abraham" ]

[ "lyme disease diagnosis", "a significant challenge", "blood tests", "an alarming inaccuracy rate", "nearly 60%", "early-stage infections", "a result", "an urgent need", "improved diagnostic methods", "that", "more accurate detection outcomes", "this pressing issue", "our study", "the potential", "deep learning approaches", "model", "progressive resizing", "multiple self-supervised learning models", "this paper", "we", "a comprehensive exploration", "self-supervised learning models", "simclr", "swav", "moco", "byol", "the context", "lyme disease detection", "medical imaging", "the effectiveness", "performance", "these models", "standard metrics", "f1 score", "precision", "recall", "accuracy", "we", "the significance", "progressive resizing", "its implications", "convolutional neural networks", "cnns", "medical image analysis", "deep learning approaches", "progressive resizing", "self-supervised learning models", "the challenges", "lyme disease detection", "this study", "the application", "our novel methodology", "the execution", "a comprehensive evaluation framework", "invaluable insights", "the development", "more efficient and accurate diagnostic methods", "lyme disease", "it", "our research", "a catalyst", "interdisciplinary collaborations", "that", "progress", "the convergence", "medicine", "computing", "technology", "public health", "nearly 60%" ]

[ "Haroon Ahmed Khan", "Syed Saud Naqvi", "Abeer A. K. Alharbi", "Salihah Alotaibi", "Mohammed Alkhathami" ]

[ "efficient waste management", "a crucial role", "clean and green environment", "the smart cities", "this study", "the critical role", "efficient trash classification", "sustainable solid waste management", "smart city environments", "we", "a comparative analysis", "various trash classification methods", "deep learning models", "convolutional neural networks", "cnns", "the pytorch open-source framework", "the trashbox dataset", "we", "experiments", "ten unique deep neural network models", "our approach", "training accuracy", "extensive experimentation", "we", "the consistent superiority", "the resnext-101 model", "others", "exceptional training", "validation", "test accuracies", "these findings", "the potential", "cnn-based techniques", "significantly advancing trash classification", "optimized solid waste management", "smart city initiatives", "this study", "a distributed framework", "federated learning", "that", "the performance", "a combination", "cnn models", "trash detection", "smart city", "cnn", "smart city", "cnn" ]

[ "Mohammad Ehsanul Karim" ]

[ "purposepropensity score matching", "epidemiological studies", "observational data", "its estimates", "correct model-specification", "this study", "deep learning models", "autoencoders", "propensity score estimation", "them", "traditional methods", "bias", "variance accuracy", "treatment effect", "a plasmode simulation", "the right heart catheterization dataset", "a variety", "settings", "we", "1) a supervised deep learning architecture", "(2) an unsupervised autoencoder", "two traditional methods", "logistic regression", "a spline-based method", "propensity scores", "performance metrics", "bias", "standard errors", "coverage probability", "the analysis", "real-world data", "estimates", "those", "a double robust", "approach.resultsthe analysis", "supervised deep learning models", "unsupervised autoencoders", "variance estimation", "comparable levels", "bias", "these results", "analyses", "real-world data", "the supervised model’s estimates", "those", "conventional methods", "deep learning models", "traditional methods", "settings", "exposure", "deep learning models", "promise", "propensity score estimations", "epidemiological research", "nuanced confounder adjustment", "complex datasets", "we", "supervised deep learning", "epidemiological research", "reproducible codes", "widespread use", "methodological transparency", "1", "2", "two" ]

[ "Mayuri Gupta", "Ashish Mishra" ]

[ "the world’s most common cancers", "colorectal cancer", "the third most severe form", "cancer", "early polyp detection", "the risk", "colorectal cancer", "effective treatment", "artificial intelligence methods", "deep learning", "leading techniques", "polyp image segmentation", "that", "success", "medical image diagnosis", "this study", "a review", "the most recent research studies", "that", "deep learning methods", "models", "polyp segmentation", "a comprehensive review", "deep learning-based image segmentation models", "existing research studies", "that", "polyp segmentation", "convolutional neural networks", "encoder", "decoder models", "recurrent neural networks", "attention-based models", "generative models", "the most popular deep learning models", "which", "an essential role", "polyp", "an early stage", "this study", "a detailed classification", "prominently used polyp image", "video datasets", "the evaluation metrics", "the effectiveness", "different methods", "models", "techniques", "a statistical analysis", "deep learning models", "polyp datasets", "performance metrics", "a discussion", "future research trends", "limitations", "third" ]

[ "Maheswara Rao Bandi", "Laxmi Narayana Pasupuleti", "Tanmay Das", "Shyamal Guchhait" ]

[ "damage detection", "any civil engineering structure", "a part", "the interest", "the field", "engineering", "which", "one", "the stability", "lifetime", "the structure", "the advancement", "technology", "the field", "infrastructure", "damage assessment", "any structure", "the help", "convolutional neural networks", "cnns", "deep learning", "importance", "the ease", "which", "it", "the damage", "these computer-aided techniques", "we", "manpower", "damage", "inaccessible places", "that", "we", "the current study", "we", "resnet-50", "which", "a part", "a deep convolutional neural network", "50 layers", "it", "resnet-50", "a subclass", "convolutional neural networks", "image classification", "we", "the utah state university", "usa", "this data", "nearly 56,000 annotated images", "both cracked and non-cracked bridge deck images", "wall images", "pavement images", "these images", "a variety", "obstructions", "shadows", "surface roughness", "some cases", "the present study", "the images", "the accuracy", "the results", "themselves", "an increase", "training data", "we", "an algorithm", "cracks", "we", "them", "the results", "the resnet-50 architecture", "good agreement", "the developed algorithm", "resnet-50", "50", "resnet-50", "the utah state university", "utah", "usa", "nearly 56,000", "resnet-50" ]

[ "Mohammed A. H. Lubbad", "Ikbal Leblebicioglu Kurtulus", "Dervis Karaboga", "Kerem Kilic", "Alper Basturk", "Bahriye Akay", "Ozkan Ufuk Nalbantoglu", "Ozden Melis Durmaz Yilmaz", "Mustafa Ayata", "Serkan Yilmaz", "Ishak Pacal" ]

[ "this study", "an effective solution", "the autonomous identification", "dental implant brands", "a deep learning-based computer diagnostic system", "it", "the system’s potential", "clinical practices", "a strategic framework", "diagnosis and treatment processes", "implantology", "this study", "a total", "28 different deep learning models", "18 convolutional neural network (cnn) models", "vgg", "resnet", "densenet", "efficientnet", "regnet", "convnext", "10 vision transformer models", "swin and vision transformer", "the dataset", "patients", "who", "implant treatments", "erciyes university faculty", "dentistry", "it", "the training and evaluation process", "deep learning models", "prototypes", "six different implant systems", "six manufacturers", "the deep learning-based dental implant system", "high classification accuracy", "different dental implant brands", "deep learning models", "all the architectures", "the small model", "the convnext architecture", "an impressive accuracy rate", "94.2%", "a high level", "classification success.this study", "the effectiveness", "deep learning-based systems", "high classification accuracy", "dental implant types", "these findings", "the way", "advanced deep learning tools", "clinical practice", "significant improvements", "patient care and treatment outcomes", "28", "18", "cnn", "10", "1258", "erciyes university faculty of dentistry", "between 2012 and 2023", "six", "six", "94.2%" ]

[ "Ding Zhang", "BingShu Zheng", "LiuWei Xu", "YiCong Wu", "Chen Shen", "ShanLei Bao", "ZhongHua Tan", "ChunFeng Sun" ]

[ "objectivessynchronous colorectal cancer peritoneal metastasis", "crpm", "a poor prognosis", "this study", "a radiomics-boosted deep learning model", "pet/ct image", "risk assessment", "synchronous crpm.methodsa total", "220 colorectal cancer (crc) cases", "this study", "we", "the feature maps", "radiomic feature maps", "rfms", "radiomic features", "ct", "pet image", "a 2d sliding kernel", "resnet50", "a radiomics-boosted deep learning model", "pet/ct image patches", "rfms", "that", "we", "the peritumoral region", "the assessment", "crpm", "this study", "the performance", "each model", "the area", "the curves", "auc).resultsthe aucs", "the radiomics", "the training", "all validation datasets", "95% confidence interval", "ci", "0.874–0.978", "95% ci", "95% ci", "0.889 (95% ci", "this model", "consistency", "the calibration curve", "the delong test", "idi", "it", "the most predictive model.conclusionsthe radiomics-boosted deep learning model", "superior estimated performance", "preoperative prediction", "synchronous crpm", "treatment pet", "ct", "potential assistance", "the development", "more personalized treatment methods", "follow-up plans.critical relevance", "statementthe onset", "synchronous colorectal crpm", "a radiomics-boosted deep learning model", "the risk", "crpm", "treatment", "personalized clinical treatment decisions", "prognosis", "patients", "crpm", "early detection poses challenges", "the synergy", "radiomics", "deep learning", "crpm", "the radiomics", "treatment approaches", "crc patients", "graphical abstract", "crpm.methodsa", "220", "2d", "resnet50", "0.926", "95%", "0.897", "95%", "0.885", "95%", "0.795–0.975", "0.889", "95%" ]

[ "Mujahid Hussain", "Makhmoor Fiza", "Aiman Khalil", "Asad Ali Siyal", "Fayaz Ali Dharejo", "Waheeduddin Hyder", "Antonella Guzzo", "Moez Krichen", "Giancarlo Fortino" ]

[ "skin cancer", "particularly melanoma", "a severe issue", "many years", "its increasing incidences", "the rising mortality rate", "melanoma", "immediate attention", "early stages", "timely diagnosis and effective treatment", "the similar visual appearance", "malignant tumors", "normal cells", "the detection", "classification", "melanoma", "the most challenging tasks", "melanoma", "diagnosis", "treatment", "which", "patient survival", "a new dataset", "nailmelonma", "this study", "order", "various deep learning models", "an indigenous nail melanoma localization dataset", "the dermoscopic image datasets", "seven cnn-based dl architectures", "viz", ".", "vgg19", "xception", "inceptionv3", "mobilenet", "mobilenetv2", "the classification", "skin lesions", "melanoma detection", "the trained models", "key performance parameters", "i.e., accuracy", "recall", "specificity", "precision", "f1-score", "the performance", "each transfer learning model", "the results", "the proposed workflow", "more than 95% accuracy", "addition", "we", "the quantization", "such models", "them", "memory-constrained mobile/edge devices", "an accurate, timely, and faster diagnosis", "nail melanoma", "the early detection", "other types", "skin cancer", "the proposed workflow", "the early detection", "nail melanoma", "seven", "cnn", "inceptionv3", "mobilenetv2", "more than 95%" ]

[ "Wang Hongsheng" ]

[ "a great chinese thinker", "writer", "the twentieth century", "lu xun", "his literary works", "a successful cultural communication activist", "editor", "publisher", "we", "depth", "lu xun", "many aspects", "deep learning", "this paper", "an online multimedia learning system", "lu xun literature", "this system", "the relationship", "classical lu xun literature", "modern multimedia technology", "the research object", "the calculation effect", "other different types", "algorithms", "this dhraa algorithm", "the availability", "data", "the dhraa algorithm", "other algorithms", "the recommendation accuracy", "its effectiveness", "this system", "two servers", "one system", "the two servers", "database server and web server", "testing", "the system", "good bearing capacity", "the limited processing capacity", "the server", "the system", "high performance", "its performance characteristics", "the system", "the expected performance", "this paper", "lu xun’s literature", "modern multimedia", "which", "online learning services", "lu xun’s literature lovers", "scholars", "lu xun’s research field", "academic vision", "this paper", "an effective online learning system", "lu xun’s literature", "deep learning", "multimedia technology", "lu xun’s literature", "chinese", "the twentieth century", "lu xun", "lu xun", "lu xun literature", "two", "one", "two", "xun", "xun’s", "xun", "xun" ]

[ "Daryl Jacob Jerrish", "Om Nankar", "Shilpa Gite", "Shruti Patil", "Ketan Kotecha", "Ganeshsree Selvachandran", "Ajith Abraham" ]

[ "lyme disease diagnosis", "a significant challenge", "blood tests", "an alarming inaccuracy rate", "nearly 60%", "early-stage infections", "a result", "an urgent need", "improved diagnostic methods", "that", "more accurate detection outcomes", "this pressing issue", "our study", "the potential", "deep learning approaches", "model", "progressive resizing", "multiple self-supervised learning models", "this paper", "we", "a comprehensive exploration", "self-supervised learning models", "simclr", "swav", "moco", "byol", "the context", "lyme disease detection", "medical imaging", "the effectiveness", "performance", "these models", "standard metrics", "f1 score", "precision", "recall", "accuracy", "we", "the significance", "progressive resizing", "its implications", "convolutional neural networks", "cnns", "medical image analysis", "deep learning approaches", "progressive resizing", "self-supervised learning models", "the challenges", "lyme disease detection", "this study", "the application", "our novel methodology", "the execution", "a comprehensive evaluation framework", "invaluable insights", "the development", "more efficient and accurate diagnostic methods", "lyme disease", "it", "our research", "a catalyst", "interdisciplinary collaborations", "that", "progress", "the convergence", "medicine", "computing", "technology", "public health", "nearly 60%" ]

[ "Anisha Paul", "Asfak Ali", "Sheli Sinha Chaudhuri" ]

[ "color space", "an important role", "various aspects", "imaging tasks", "deep learning-based computer vision", "the rgb color model", "this research", "the impact", "deep convolutional neural networks", "cancer classification", "different color spaces", "the five most popular deep learning models", "training", "testing", "eleven color spaces", "yuv", "lab", "yiq", "other color models", "most cases", "rgb images", "alternative color spaces", "enhanced representation", "specific applications", "object detection", "segmentation", "this transformation", "alterations", "the features", "the color image", "variations", "pixel intensity information", "different color models", "this research", "the aforementioned principle", "the classification", "skin cancer", "deep learning networks", "images", "skin lesions", "the results", "diverse responses", "some networks", "higher accuracy", "alternative color spaces", "rgb", "others", "this study", "insights", "the classification performance", "rgb", "the research", "how deep learning", "the analysis", "skin cancer images", "different color spaces", "rgb", "five", "eleven", "yuv", "rgb", "rgb", "rgb" ]

[ "Kevin R. McKee", "Andrea Tacchetti", "Michiel A. Bakker", "Jan Balaguer", "Lucy Campbell-Gillingham", "Richard Everett", "Matthew Botvinick" ]

[ "effective approaches", "group cooperation", "an open challenge", "we", "recent advances", "deep learning", "networks", "human participants", "a group cooperation game", "we", "deep reinforcement learning and simulation methods", "a ‘social planner", "recommendations", "connections", "group members", "the strategy", "that", "it", "networks", "human participants", "= 208 participants", "13 groups", "real monetary stakes", "the social planner", "groups", "the game", "an average cooperation rate", "77.7%", "42.8%", "static networks", "11 groups", "contrast", "prior strategies", "that", "separate defectors", "cooperators", "n", "24 groups", "the social planner", "a conciliatory approach", "defectors", "them", "them", "small highly cooperative neighbourhoods", "208", "13", "77.7%", "42.8%", "176", "11", "384", "24" ]

[ "Xirui Hou", "Pengfei Guo", "Puyang Wang", "Peiying Liu", "Doris D. M. Lin", "Hongli Fan", "Yang Li", "Zhiliang Wei", "Zixuan Lin", "Dengrong Jiang", "Jin Jin", "Catherine Kelly", "Jay J. Pillai", "Judy Huang", "Marco C. Pinho", "Binu P. Thomas", "Babu G. Welch", "Denise C. Park", "Vishal M. Patel", "Argye E. Hillis", "Hanzhang Lu" ]

[ "cerebrovascular disease", "a leading cause", "death", "prevention", "early intervention", "the most effective forms", "its management", "non-invasive imaging methods", "great promises", "early stratification", "present lack", "personalized prognosis", "resting-state functional magnetic resonance imaging", "rs-fmri", "a powerful tool", "neural activity", "most hospitals", "we", "rs", "fmri", "cerebral hemodynamic function", "delineate impairment", "time variations", "breathing pattern", "rs-fmri", "deep learning", "reproducible mapping", "cerebrovascular reactivity", "cvr", "bolus arrival time", "bat", "the human brain", "resting-state co2 fluctuations", "a natural “contrast media", "the deep-learning network", "cvr", "bat maps", "a reference method", "co2-inhalation mri", "which", "data", "young and older healthy subjects", "patients", "moyamoya disease", "brain tumors", "we", "the performance", "deep-learning cerebrovascular mapping", "the detection", "vascular abnormalities", "evaluation", "revascularization effects", "vascular alterations", "normal aging", "addition", "cerebrovascular maps", "the proposed method", "excellent reproducibility", "both healthy volunteers", "stroke patients", "deep-learning resting-state vascular imaging", "the potential", "a useful tool", "clinical cerebrovascular imaging" ]

[ "Pierre Beckmann", "Guillaume Köstner", "Inês Hipólito" ]

[ "we", "a non-representationalist framework", "deep learning", "a novel method", "computational phenomenology", "a dialogue", "the first-person perspective", "phenomenology", "the mechanisms", "computational models", "we", "an alternative", "the modern cognitivist interpretation", "deep learning", "which", "artificial neural networks encode representations", "external entities", "this interpretation", "neuro-representationalism", "a position", "that", "a strong ontological commitment", "scientific theoretical entities", "the idea", "the brain", "symbolic representations", "these entities", "we", "a review", "cognitivism", "neuro-representationalism", "the field", "deep learning", "we", "a phenomenological critique", "these positions", "we", "computational phenomenology", "it", "existing alternatives", "we", "this new method", "deep learning models", "specific tasks", "order", "a conceptual framework", "deep-learning", "that", "artificial neural networks’ mechanisms", "terms", "lived experience", "first", "first" ]

[ "James M. Dolezal", "Sara Kochanny", "Emma Dyer", "Siddhi Ramesh", "Andrew Srisuwananukorn", "Matteo Sacco", "Frederick M. Howard", "Anran Li", "Prajval Mohan", "Alexander T. Pearson" ]

[ "deep learning methods", "powerful tools", "histopathological images", "current methods", "specific domains", "software environments", "few open-source options", "models", "an interactive interface", "different deep learning approaches", "software libraries", "reprocessing data", "the feasibility", "practicality", "new architectures", "we", "a flexible deep learning library", "histopathology", "a package", "which", "a broad array", "deep learning methods", "digital pathology", "a fast whole-slide interface", "trained models", "slideflow", "unique tools", "whole-slide image data processing", "efficient stain normalization", "augmentation", "weakly-supervised whole-slide classification", "uncertainty quantification", "feature generation", "feature space analysis", "explainability", "whole-slide image processing", "whole-slide tile extraction", "40x magnification", "2.5 s", "slide", "the framework-agnostic data processing pipeline", "rapid experimentation", "new methods", "either tensorflow", "pytorch", "the graphical user interface", "real-time visualization", "slides", "predictions", "heatmaps", "feature space characteristics", "a variety", "hardware devices", "arm-based devices", "the raspberry pi", "40x", "2.5" ]

[ "Rebeh Imane Ammar Aouchiche", "Fatima Boumahdi", "Mohamed Abdelkarim Remmide", "Amina Madani" ]

[ "social media platforms", "popularity", "influence", "content integrity", "user accountability issues", "authorship attribution", "(aa", "a powerful tool", "such issues", "the real author", "online posts", "this study", "an aa approach", "machine", "algorithms", "the author", "unknown posts", "social media platforms", "it", "temporal convolutional networks", "tcn", "short texts", "the effectiveness", "long short-term memory", "lstm", "convolutional neural networks", "cnn", "the use", "an autoencoder", "adaboost classifier", "this approach", "a twitter dataset", "52.77% accuracy", "aa", "multiple experiments", "various scenarios", "cnn", "52.77%" ]

[ "Linyong Wu", "Songhua Li", "Chaojun Wu", "Shaofeng Wu", "Yan Lin", "Dayou Wei" ]

[ "backgroundthe purpose", "this study", "the potential value", "the deep learning radiomics nomogram", "dlrn", "ultrasound", "mm", "mm", "ibc", "ultrasound breast imaging reporting", "data system", "pyradiomics", "resnet50 extractors", "radiomics", "deep learning features", "supervised machine learning methods", "logistic regression", "random forest", "vector machine", "unsupervised machine learning methods", "k", "clustering analysis", "the differences", "features", "mm", "ibc", "dlrn", "the performance", "dlrn", "receiver operating characteristic curve", "calibration", "clinical practicality.resultssupervised machine learning results", "radiomics models", "especially random forest models", "deep learning models", "mm", "ibc", "the area", "the curve", "auc", "the validation cohort", "the accuracy", "the sensitivity", "the specificity", "radiomics", "deep learning models", "even further improved discrimination ability", "auc", "accuracy", "training and validation cohorts", "which", "better clinical benefits", "good calibratability", "addition", "the information heterogeneity", "deep learning features", "mm", "ibc", "unsupervised machine", "clustering analysis", "mm", "a unique features", "phenotype.conclusionthe dlrn", "radiomics", "deep learning features", "ultrasound images", "potential clinical value", "mm", "ibc", "dlrn", "visual limitations", "quantifies", "more image information", "mm", "computers", "this information", "clinical decision-making", "dlrn", "an autonomous screening system", "it", "the recognition rate", "mm", "grassroots hospitals", "the possibility", "incorrect treatment", "overtreatment", "mm", "mm", "180", "4", "161", "69", "resnet50", "mm", "0.84", "0.83", "0.73", "0.83", "0.90", "0.90", "0.83", "0.88", "mm", "mm", "dlrn", "mm", "mm", "dlrn" ]

[ "Truong Dang", "Tien Thanh Nguyen", "John McCall", "Eyad Elyan", "Carlos Francisco Moreno-García" ]

[ "the most important areas", "medical image analysis", "segmentation", "which", "raw image data", "structured and meaningful regions", "further insights", "deep neural networks", "dnn", "ai-based automated segmentation algorithms", "physicians", "more effective imaging-based diagnoses", "it", "high-quality ground truths", "medical images", "dnn hyperparameters", "significant manual tuning", "the results", "dnn-based medical models", "a potential solution", "multiple dnn models", "ensemble learning", "we", "a two-layer ensemble", "deep learning models", "which", "the prediction", "each training image pixel", "each model", "the first layer", "the augmented data", "the training image", "the second layer", "the ensemble", "the prediction", "the second layer", "a weight-based scheme", "which", "linear regression problems", "our knowledge", "our paper", "the first work", "which", "a two-layer ensemble", "deep learning models", "an augmented data technique", "medical image segmentation", "experiments", "five different medical image datasets", "diverse segmentation tasks", "proposed method", "better results", "terms", "several performance metrics", "some well-known benchmark algorithms", "our proposed two-layer ensemble", "deep learning models", "segmentation", "medical images", "effectiveness", "several benchmark algorithms", "the research", "several directions", "image classification", "one", "two", "first", "second", "second", "first", "two", "five", "two" ]

[ "Li Cheng", "Yuming Xing", "Yao Li", "Zhichang Guo" ]

[ "speckle removal", "noise", "image boundaries and texture information", "recent years", "speckle removal models", "deep learning methods", "a lot", "attention", "it", "these models", "adversarial attacks", "the adversarial attack", "the image recovery", "deep learning methods", "the speckle noise distribution", "purpose", "the above problem", "we", "a diffusion equation-based speckle removal model", "that", "the robustness", "deep learning algorithms", "this paper", "the model", "a deep learning image", "an image grayscale detection operator", "the coefficient function", "the diffusion equation", "them", "a high possibility", "the deep learning image", "it", "the performance", "the properties", "the proposed diffusion equation model", "noise removal", "we", "the robustness", "the proposed diffusion equation model", "terms", "theoretical and numerical properties", "experiments", "our proposed diffusion equation speckle removal model", "adversarial attacks", "any way", "stronger robustness", "recent years" ]

[ "Su Özgür", "Mehmet Orman" ]

[ "recent years", "the widespread utilization", "biological data processing technology", "its cost-effectiveness", "(ngs", "an integral component", "biological research", "ngs technologies", "the sequencing", "billions", "nucleotides", "the entire genome", "specific target regions", "vast data matrices", "a growing demand", "deep learning (dl) approaches", "which", "multilayer artificial neural networks", "systems", "meaningful information", "these extensive data structures", "this study", "the aim", "optimized parameters", "the prediction performance", "deep learning", "machine learning", "ml", "algorithms", "binary classification", "real and simulated whole genome data", "a cloud-based system", "the art-simulated data", "paired-end ngs", "whole genome) data", "ch22", "which", "ethnicity information", "xgboost", "lightgbm", "the learning rate", "the epoch values", "the deep learning model", "the art", "the median accuracy values", "the art models", "epoch", "epoch", "comparison", "the median accuracy values", "the xgboost and lightgbm models", "the same process", "chr", "the results", "the median accuracy values", "the dl models", "epoch", "epoch", "the median accuracy values", "the xgboost and lightgbm models", "the best classification estimates", "2000 epochs", "a learning rate (lr) value", "both real and simulated data", "the xgboost algorithm", "higher performance", "the epoch value", "the lr", "class imbalance", "the dl algorithm", "similar and high recall", "precision values", "this study", "a timely resource", "genomic scientists", "guidance", "deep learning/machine learning methods", "the analysis", "human genomic data", "recent years", "billions", "ch22", "0.01", "0.001", "500, 1000", "2000", "0.6320", "0.6800", "0.7340", "0.01", "0.6920", "0.7220", "0.8020", "0.001", "0.6990", "0.6250", "chr", "22", "0.5290", "0.5420", "0.5820", "0.01", "0.5510", "0.5830", "0.6040", "0.001", "0.5760", "0.5250", "2000", "0.001", "500", "0.01" ]

[ "Mohamed Lajili", "Zakaria Belhachmi", "Maher Moakher", "Anis Theljani" ]

[ "medical image registration", "a crucial step", "computer-assisted medical diagnosis", "significant progress", "the adoption", "deep learning methods", "convolutional neural networks", "cnn", "a deep learning network", "image registration", "humans", "the training data", "it", "this article", "an innovative approach", "unsupervised deep learning-based multilevel image registration approach", "we", "a cnn", "the geometric features", "edges", "thin structures", "images", "a loss function", "the blake-zisserman energy", "this method", "the detection", "discontinuities", "different scales", "labeled data", "we", "this geometric information", "the input images", "a second loss function", "our multimodal image registration process", "we", "a novel deep neural network architecture", "multilevel image registration", "enhanced precision", "efficiency", "traditional methods", "numerical simulations", "the accuracy", "relevance", "our approach", "we", "some numerical simulations", "the accuracy", "the relevance", "our approach", "multimodal registration", "its multilevel implementation", "cnn", "cnn", "second" ]

[ "Zhengjing Ma", "Gang Mei", "Nengxiong Xu" ]

[ "data mining", "analysis", "natural hazards", "data availability", "natural hazard analysis", "unprecedented challenges", "economic, technical, and environmental constraints", "generative deep learning", "an increasingly attractive solution", "these challenges", "which", "data", "these learned complex, high-dimensional probability distributions", "data", "the last several years", "much research", "the remarkable capabilities", "generative deep learning", "data-related problems", "natural hazards analysis", "data", "deep generative models", "the evolution", "occurrence", "natural hazards", "subsequent natural hazard modeling", "we", "a comprehensive review", "generative deep learning", "data generation", "natural hazard analysis", "we", "the limitations", "data availability", "natural hazards analysis", "the fundamental motivations", "generative deep learning", "a critical response", "these challenges", "we", "several deep generative models", "that", "the problems", "limited data availability", "natural hazards analysis", "we", "advances", "generative deep learning", "data generation", "natural hazard analysis", "we", "challenges", "generative deep learning", "natural hazard analysis", "we", "further opportunities", "generative deep learning", "natural hazard analysis", "this comprehensive review", "a detailed roadmap", "scholars", "generative models", "data generation", "natural hazard analysis", "the last several years", "1", "2", "3", "4", "5" ]

[ "Marzieh Soltani", "Mehdi Khashei", "Negar Bakhtiarvand" ]

[ "classification", "the most well-known data mining branches", "diverse domains", "fields", "the literature", "many different classification techniques", "single/hybrid", "data", "systems", "different characteristics", "intelligent classification approaches", "especially deep learning classifiers", "their unique features", "accurate and efficient results", "a lot", "attention", "the learning process", "the intelligent classifiers", "a continuous distance-based cost function", "the connection weights", "the goal function", "classification problems", "a continuous cost function", "their learning process", "this paper", "a novel discrete learning", "based methodology", "the connection weights", "intelligent classifiers", "the proposed learning process", "they", "the target", "this", "contrast", "conventional continuous learning algorithms", "which", "the connection weights", "step", "the target", "the present research", "the proposed methodology", "the deep neural network", "dnn", "which", "the most recognized deep classification approaches", "a solid mathematical foundation", "strong practical results", "complex problems", "the proposed methodology", "the dnn", "it", "a general methodology", "that", "other shallow and deep intelligent classification models", "it", "the performance", "the proposed discrete learning", "based dnn (didnn) model", "its consistency property", "the conventional ones", "the proposed didnn model", "some well-known cancer classification benchmarks", "the efficiency", "the proposed model", "the empirical results", "the proposed model", "the conventional versions", "the selected deep approach", "all data sets", "the performance analysis", "the didnn model", "the performance", "the classic version", "approximately 3.39%", "this technique", "an appropriate and effective alternative", "conventional dnn-based models", "classification purposes", "linear", "approximately 3.39%" ]

[ "Wenhuan Kuang", "Zhihui Zou", "Junhui Xing", "Wei Wei" ]

[ "earthquake data", "the key means", "which", "our planet", "a large scale", "the layered structure", "the earth", "the seismic waves", "natural earthquakes", "that", "its inner core", "a local scale", "seismology", "exploration", "massive fossil energies", "the volume", "recorded seismic data", "greater, intelligent methods", "such a volume", "data", "earthquake focal mechanisms", "the severity", "tsunamis", "seismogenic faults", "the stress perturbations", "that", "a major earthquake", "we", "a novel deep reinforcement learning method", "the earthquake focal mechanism", "more typical deep learning applications", "which", "a large training dataset", "a deep reinforcement learning system", "itself", "we", "the validity", "efficacy", "the proposed deep reinforcement learning method", "it", "the mw 7.1 mainshock", "the ridgecrest earthquakes", "southern california", "the foreseeable future", "deep learning technologies", "our understanding", "the oceanographic process", "the proposed method", "us", "the mechanism", "marine earthquakes", "one", "7.1", "southern california" ]

[ "Ravikumar Sajjanar", "Umesh D. Dixit", "Vittalkumar K Vagga" ]

[ "mri", "the diagnosis", "planning", "follow-up", "patients", "brain tumours", "an effort", "efficiency", "accuracy", "a number", "machine learning", "deep learning algorithms", "time", "the segmentation process", "hybrid strategies", "which", "the advantages", "both machine learning", "deep learning", "viable options", "-depth", "the developments", "hybrid techniques", "mri segmentation", "brain tumours", "the essential ideas", "machine learning", "deep learning approaches", "an emphasis", "their individual advantages", "disadvantages", "that", "the review", "the numerous hybrid strategies", "the literature", "hybrid approaches", "various phases", "the segmentation pipeline", "machine learning", "deep learning techniques", "pre-processing, feature extraction", "post-processing", "examples", "these phases", "various combinations", "methods", "these phases", "segmentation", "deep learning models", "extraction", "conventional machine learning algorithms", "the implementation", "ensemble approaches", "which", "forecasts", "various models", "segmentation accuracy", "the research study", "the properties", "freely accessible brain tumour datasets", "which", "hybrid models", "the difficulties", "generalisation", "robustness", "brain tumour segmentation", "it", "the necessity", "datasets", "them", "conventional machine learning", "deep learning techniques", "the review", "the effectiveness", "hybrid approaches", "the literature", "this comprehensive research", "information", "recent advancements", "hybrid techniques", "mri segmenting brain tumours", "it", "the potential", "deep learning and machine learning methods", "the precision", "effectiveness", "brain tumour segmentation", "patient diagnosis and treatment planning" ]

[ "Yuanyuan Li" ]

[ "algorithms", "machines", "rules", "a large amount", "historical data", "new samples", "the future", "deep learning", "students’ understanding", "knowledge", "depth", "new knowledge", "it", "the original knowledge", "it", "new situations", "intelligent audio–visual listening", "the perspective", "deep learning", "students", "depth", "individual differences", "innovative thinking", "the main position", "ecological education", "schools", "the publicity", "education", "ecological ideas", "low-carbon concepts", "them", "education", "teaching", "students’ awareness", "environmental protection", "this study", "the effectiveness", "flipped classroom teaching model", "deep learning", "the perspective", "deep learning", "this paper", "the theory", "deep learning", "a new model", "smart classroom", "ideas", "directions", "model reform", "this study", "the flipped classroom teaching model", "deep learning", "english teaching", "an 8-week teaching experiment", "addition", "this paper", "it", "great practical significance", "environmental protection education", "the help", "english teaching", "english", "8-week", "english" ]

[ "Soonjo Kwon", "Ji-Hyeon Hur", "Hyungki Kim" ]

[ "point clouds", "3d scanners", "product inspection", "engineering", "the quality", "the point cloud", "the scanning environment", "scanner specifications", "the quality", "the point cloud", "a significant impact", "the accuracy", "automatic or manual modeling", "response", "various point cloud post-processing technologies", "point cloud upsampling", "a technique", "the resolution", "point clouds", "the purpose", "upsampling", "additional points", "the target object", "higher detail", "this technology", "areas", "high-resolution 3d representation", "approaches", "deep learning", "attention", "deep learning-based point cloud upsampling research", "surface consolidation", "edge consolidation research", "the target regions", "supervised or self-supervised learning", "the type", "approaches", "this study", "the latest research trends", "deep learning-based point cloud sampling", "the issues", "limitations", "each research category", "future research", "directions.graphical abstract", "3d", "3d" ]

[ "Kicheol Jeong", "Chulwoo Moon" ]

[ "the gearbox", "an electric vehicle", "the high load torque", "axial load", "electric vehicles", "the bearings", "that", "the shaft", "the gearbox", "several tons", "axial load", "the mileage increases", "fault", "rolling elements", "fault", "a serious impact", "comfort", "vehicle safety", "faults", "human experts", "algorithm-based electric vehicle", "fault diagnosis", "this paper", "a deep learning-based bearing vibration signal processing method", "fault", "electric vehicle gearboxes", "the proposed method", "a deep neural network learning stage", "an application stage", "the pre-trained neural network", "the deep neural network learning stage", "supervised learning", "two acceleration sensors", "the neural network application stage", "signal processing", "a single accelerometer signal", "a pre-trained neural network", "conclusion", "the pre-trained neural network", "fault signals", "these signals", "frequency characteristics", "fault", "several tons", "two" ]

[ "Muhammet Sinan Başarslan", "Fatih Kayaalp" ]

[ "people", "social life", "this", "an increase", "the use", "social media", "the past two years", "social media", "people", "their thoughts", "various topics", "their favourite movies", "restaurants", "hotels", "this", "a huge amount", "data", "many researchers", "different sciences", "this data", "natural language processing", "nlp", "these areas", "computer science", "that", "artificial technologies", "sentiment analysis", "the tasks", "nlp", "which", "emotions", "huge post data", "this study", "sentiment analysis", "two datasets", "tweets", "coronavirus and tripadvisor hotel reviews", "a frequency-based word representation method", "term frequency-inverse document frequency", "tf-idf", "a prediction-based word2vec word", "embedding method", "the datasets", "sentiment analysis models", "single machine learning methods", "decision trees", "dt", "k-nearest neighbour-knn, naive bayes", "vector machine-svm", "single deep learning methods", "long short term memory-lstm", "recurrent neural network-rnn", "heterogeneous ensemble learning methods", "stacking and majority voting", "these single machine learning", "deep learning methods", "accuracy", "a performance measure", "the heterogeneous model", "lstm-rnn", "the other models", "accuracy values", "the coronavirus dataset", "the trip advisor dataset", "they", "results", "the literature", "it", "the use", "single methods", "an ensemble", "better results", "which", "the literature", "which", "a step", "the detection", "sentiments", "posts", "the performance", "heterogeneous ensemble learning models", "different algorithms", "sentiment analysis tasks", "future work", "the past two years", "two", "accuracy", "0.864", "0.898" ]

[ "Hao-Yuan Chen", "Yen-Jui Chang", "Shih-Wei Liao", "Ching-Ray Chang" ]

[ "quantum computing", "great potential", "the limitations", "machine learning algorithms", "higher dimensions", "data", "overall training parameters", "deep learning", "(dl) models", "this study", "a trainable variational quantum circuit", "vqc", "a gate-based quantum computing model", "the potential", "quantum benefit", "a model-free reinforcement learning problem", "a comprehensive investigation", "evaluation", "the current model", "capabilities", "quantum computers", "we", "a novel hybrid quantum neural network", "the latest qiskit and pytorch framework", "we", "its performance", "a full-classical cnn", "an incorporated vqc", "our research", "insights", "the potential", "deep quantum", "a maze problem", "potentially, other reinforcement learning problems", "we", "reinforcement learning problems", "reasonable training epochs", "a comparative study", "full-classical and hybrid quantum neural networks", "these two approaches’ performance", "advantages", "disadvantages", "deep q-learning problems", "larger-scale maze problems", "4\\(\\times", "quantum", "quantum", "quantum", "quantum", "quantum", "two" ]

[ "Xiaofei Shi", "Daran Xu", "Zhanhao Zhang" ]

[ "this work", "the deep learning-based numerical algorithms", "optimal hedging problems", "markets", "general convex transaction costs", "our main focus", "these algorithms", "the length", "the trading time horizon", "the comparison results", "the fbsde", "han", "jentzen", "e", "buehler", "gonon", "teichmann", "wood", "we", "a stable-transfer hedging", "st-hedging", "the convenience", "the leading-order approximation formulas", "the accuracy", "the deep learning-based algorithms", "our st-hedging algorithm", "the-art", "short and moderately long time horizon", "fbsde", "long time horizon", "previous algorithms", "the transfer learning technique", "st-hedging", "the training time", "great scalability", "high-dimensional settings", "this", "new possibilities", "model-based deep learning algorithms", "economics", "finance", "operational research", "which", "advantage", "the domain expert knowledge", "the accuracy", "the learning-based methods", "han", "2018", "teichmann", "2019" ]

[ "V. Auxilia Osvin Nancy", "P. Prabhavathy", "Meenakshi S. Arya", "B. Shamreen Ahamed" ]

[ "exposure", "uv rays", "global warming", "sunburn", "skin damage", "skin cancer", "early prediction", "this type", "cancer", "a detailed review", "this paper", "various algorithms", "machine learning", ") techniques", "deep learning", "(dl) techniques", "deep learning strategies", "particularly cnns", "skin cancer identification", "classification", "some usage", "machine learning", "hybrid approaches", "these techniques", "effective classifiers", "skin lesions", "promising results", "early detection", "the paper", "various researchers’ reviews", "skin cancer diagnosis", "a suitable methodology", "diagnostic accuracy", "a publicly available dataset", "dermoscopic images", "the isic archive", "performance analysis", "metrics", "test", "validation accuracy", "the results", "algorithm", "other machine learning algorithms", "both scenarios", "accuracies", "58.57%", "augmentation", "87.32%", "augmentation", "mobilenetv2", "dense net", "inceptionv3", "superior performance", "training", "augmentation", "mobilenetv2", "an accuracy", "88.81%", "the ensemble model", "an accuracy", "88.80%", "augmentation techniques", "the accuracies", "97.58%", "97.50%", "experiment", "a customized convolutional neural network (cnn) model", "the number", "layers", "various hyperparameter", "methodologies", "suitable architectures", "a cnn", "7 layers", "batch normalization", "a cnn", "5 layers", "a cnn", "3 layers", "these models", "accuracies", "77.92%", "97.72%", "98.02%", "the raw data", "augmentation datasets", "the experimental results", "these techniques", "promise", "integration", "clinical settings", "further research", "validation", "the results", "the effectiveness", "transfer learning models", "high accuracy rates", "the findings", "the future adoption", "these techniques", "clinical practice", "further research", "validation", "58.57%", "87.32%", "mobilenetv2", "inceptionv3", "mobilenetv2", "88.81%", "88.80%", "97.58%", "97.50%", "cnn", "cnn", "7", "cnn", "5", "cnn", "3", "77.92%", "97.72%", "98.02%" ]

[ "Haroon Ahmed Khan", "Syed Saud Naqvi", "Abeer A. K. Alharbi", "Salihah Alotaibi", "Mohammed Alkhathami" ]

[ "efficient waste management", "a crucial role", "clean and green environment", "the smart cities", "this study", "the critical role", "efficient trash classification", "sustainable solid waste management", "smart city environments", "we", "a comparative analysis", "various trash classification methods", "deep learning models", "convolutional neural networks", "cnns", "the pytorch open-source framework", "the trashbox dataset", "we", "experiments", "ten unique deep neural network models", "our approach", "training accuracy", "extensive experimentation", "we", "the consistent superiority", "the resnext-101 model", "others", "exceptional training", "validation", "test accuracies", "these findings", "the potential", "cnn-based techniques", "significantly advancing trash classification", "optimized solid waste management", "smart city initiatives", "this study", "a distributed framework", "federated learning", "that", "the performance", "a combination", "cnn models", "trash detection", "smart city", "cnn", "smart city", "cnn" ]

[ "Robin Quillivic", "Frédérique Gayraud", "Yann Auxéméry", "Laurent Vanni", "Denis Peschanski", "Francis Eustache", "Jacques Dayan", "Salma Mesmoudi" ]

[ "post-traumatic stress disorder", "ptsd", "clear biomarkers", "clinical practice", "language", "a potential diagnostic biomarker", "ptsd", "this study", "we", "an original cohort", "148 individuals", "the november", "terrorist attacks", "paris", "the interviews", "the event", "individuals", "similar socioeconomic backgrounds", "the same incident", "identical questions", "uniform ptsd measures", "this dataset", "nuanced insights", "that", "we", "a three-step interdisciplinary methodology", "that", "expertise", "psychiatry", "linguistics", "the natural language processing (nlp) community", "the relationship", "language", "ptsd", "the first step", "a clinical psychiatrist's ability", "ptsd", "interview transcription", "the second step", "statistical analysis", "machine learning models", "language features", "psycholinguistic hypotheses", "their predictive strength", "the third step", "the application", "a hypothesis-free deep learning approach", "the classification", "ptsd", "our cohort", "results", "the clinical psychiatrist", "a diagnosis", "ptsd", "an auc", "this", "a gold standard questionnaire", "area", "curve", "auc", "≈", "the machine learning model", "a diagnostic auc", "the deep learning approach", "an auc", "an examination", "model error", "our discussion", "the study", "factors", "associations", "language", "dsm-5", "subsymptoms", "automated methods", "qualitative analysis", "this study", "a direct and methodologically robust description", "the relationship", "ptsd", "language", "our work", "the groundwork", "early and accurate diagnosis", "linguistic markers", "the effectiveness", "pharmacological treatments", "psychotherapies", "148", "november 13, 2015", "paris", "5–11 months", "three", "first", "second", "third", "0.72", "0.69", "0.64" ]

[ "Zied Ben Hazem" ]

[ "the rotary inverted pendulum system", "rips", "an underactuated mechanical system", "highly nonlinear dynamics", "it", "a rips", "the classic control models", "the last few years", "reinforcement learning", "(rl", "a popular nonlinear control method", "rl", "a powerful potential", "systems", "high non-linearity and complex dynamics", "rips", "rl control", "rips", "limited research", "the development", "evaluation", "this control method", "this paper", "rl control algorithms", "the swing-up and stabilization control", "a single-link rotary inverted pendulum", "slrip", "classic control methods", "pid", "lqr", "a physical model", "the slrip system", "the matlab/simscape toolbox", "the model", "a dynamic simulation", "matlab/simulink", "the rl agents", "an agent trainer system", "q-learning", "(ql", "deep q-network learning", "dqnl", "the data training", "agent actions", "the horizontal arm", "the system", "states", "the angles", "velocities", "the pendulum", "the horizontal arm", "the reward", "the angles", "the pendulum", "horizontal arm", "the reward", "the pendulum", "the upright position", "the rl algorithms", "a deep understanding", "the classical controllers", "the agent", "the outcome", "the effectiveness", "the ql and dqnl algorithms", "the conventional pid and lqr controllers", "the last few years", "zero" ]

[ "Tingyu Zhang", "Yongshuai Wang", "Mingwei Sun", "Zengqiang Chen" ]

[ "air combat maneuvering decision", "the sparse", "the application", "deep reinforcement learning", "the exploration efficiency", "the agents", "this challenge", "we", "an auxiliary reward function", "the impact", "angle", "range", "altitude", "we", "the influences", "the network nodes", "layers", "the learning rate", "decision system", "reasonable parameter ranges", "which", "a guideline", "four typical air combat scenarios", "good adaptability", "effectiveness", "the proposed scheme", "the auxiliary reward", "the learning ability", "deep q network", "dqn", "the agents", "the original deep deterministic policy gradient", "soft actor critic", "algorithm", "the proposed method", "superior exploration capability", "higher reward", "the trained agent", "different air combats", "good performance", "four" ]

[ "Olivier X. Miguel", "Emily Kaczmarek", "Inok Lee", "Robin Ducharme", "Alysha L. J. Dingwall-Harvey", "Ruth Rennicks White", "Brigitte Bonin", "Richard I. Aviv", "Steven Hawken", "Christine M. Armour", "Kevin Dick", "Mark C. Walker" ]

[ "deep learning algorithms", "remarkable potential", "clinical diagnostics", "the field", "medical imaging", "this study", "we", "the application", "deep learning models", "early detection", "fetal kidney anomalies", "an enhanced interpretation", "those models’ predictions", "we", "an adapted two-class representation", "a multi-class model interpretation approach", "problems", "more than two labels", "variable hierarchical grouping", "labels", "we", "the explainable ai (xai) visualization tools", "grad-cam", "hirescam", "insights", "model predictions", "reasons", "misclassifications", "the study dataset", "969 ultrasound images", "unique patients", "646 control images", "323 cases", "kidney anomalies", "259 cases", "unilateral urinary tract dilation", "64 cases", "unilateral multicystic dysplastic kidney", "the best performing model", "a cross-validated area", "the roc curve", "91.28% ±", "0.52%", "an overall accuracy", "84.03%", "±", "0.76%", "sensitivity", "77.39% ±", "1.99%", "specificity", "87.35%", "±", "1.28%", "our findings", "the potential", "deep learning models", "kidney anomalies", "limited prenatal ultrasound imagery", "the proposed adaptations", "model representation", "interpretation", "a novel solution", "multi-class prediction problems", "two", "more than two", "969", "646", "323", "259", "64", "roc", "91.28%", "0.52%", "84.03%", "0.76%", "77.39%", "1.99%", "87.35%", "1.28%" ]

[ "D. N. Sindhura", "Radhika M. Pai", "Shyamasunder N. Bhat", "Manohara M. M. Pai" ]

[ ", computer-aided decision support systems", "cads", "the analysis", "images", "a perennial technique", "the medical imaging field", "cads", "deep learning algorithms", "tasks", "classification", "identification", "patterns", "detection", "deep learning models", "feature representations", "images", "handcrafted features", "deep learning models", "the-art", "good performances", "different computer-aided decision-support systems", "medical applications", "deep learning-based generative models", "generative adversarial networks", "gans", "a novel method", "realistic-looking synthetic data", "gans", "different domains", "medical imaging generation", "the common problems", "class imbalance", "a small dataset", "healthcare", "gans", "it", "a leading area", "research", "segmentation", "reconstruction", "detection", "denoising", "registration", "the important applications", "gans", "this work", "the successes", "deep learning methods", "segmentation", "classification", "cell structure", "fracture detection", "computer-aided identification", "gans", "synthetic medical image generation", "segmentation", "reconstruction", "detection", "denoising", "registration", "recent times", "the review article", "research directions", "dl models", "gans", "medical applications" ]

[ "Juhwan Lee", "Justin N. Kim", "Luis A. P. Dallan", "Vladislav N. Zimin", "Ammar Hoori", "Neda S. Hassani", "Mohamed H. E. Makhlouf", "Giulio Guagliumi", "Hiram G. Bezerra", "David L. Wilson" ]

[ "thin-cap fibroatheroma", "(tcfa", "a prominent risk factor", "plaque rupture", "intravascular optical coherence tomography", "ivoct", "identification", "fibrous cap", "fc", "measurement", "fc thicknesses", "assessment", "plaque vulnerability", "we", "a fully-automated deep learning method", "fc segmentation", "this study", "32,531 images", "227 pullbacks", "two registries", "transform", "oct", "images", "our octopus", "expert editing", "established guidelines", "we", "preprocessing", "guidewire shadow detection", "lumen segmentation", "gaussian filtering", "raw ivoct (r,θ) images", "data", "a natural way", "θ", "spiral acquisitions", "intensity", "noise values", "we", "a modified segresnet", "comparison networks", "we", "transfer", "our existing much larger, fully-labeled calcification ivoct dataset", "deep-learning training", "a morphological operation enhanced segmentation performance", "our method", "better fc segmentation results", "dice", "0.837 ±", "other deep-learning methods", "transfer", "reduced training time", "84%", "the need", "more training samples", "our method", "a high level", "generalizability", "highly-consistent segmentations", "five-fold cross", "validation (sensitivity", "85.0 ±", "0.3%", "dice", "0.846 ±", "the held-out test", "84.9%", "dice", "addition", "we", "excellent agreement", "fc thickness", "ground truth", "2.95 ±", "µm", "clinically insignificant bias", "excellent reproducibility", "pre- and post-stenting pullbacks", "average fc angle", "200.9 ± 128.0°", "±", "our fully automated, deep-learning fc segmentation method", "excellent performance", "generalizability", "reproducibility", "multi-center datasets", "it", "multiple research purposes", "stent deployments", "that", "a stent edge", "an fc", "32,531", "227", "two", "0.837 ±", "84%", "five-fold", "85.0", "0.3%", "0.846", "0.011", "84.9%", "0.816", "2.95", "20.73", "200.9", "128.0", "121.1" ]

[ "Hao-Yuan Chen", "Yen-Jui Chang", "Shih-Wei Liao", "Ching-Ray Chang" ]

[ "quantum computing", "great potential", "the limitations", "machine learning algorithms", "higher dimensions", "data", "overall training parameters", "deep learning", "(dl) models", "this study", "a trainable variational quantum circuit", "vqc", "a gate-based quantum computing model", "the potential", "quantum benefit", "a model-free reinforcement learning problem", "a comprehensive investigation", "evaluation", "the current model", "capabilities", "quantum computers", "we", "a novel hybrid quantum neural network", "the latest qiskit and pytorch framework", "we", "its performance", "a full-classical cnn", "an incorporated vqc", "our research", "insights", "the potential", "deep quantum", "a maze problem", "potentially, other reinforcement learning problems", "we", "reinforcement learning problems", "reasonable training epochs", "a comparative study", "full-classical and hybrid quantum neural networks", "these two approaches’ performance", "advantages", "disadvantages", "deep q-learning problems", "larger-scale maze problems", "4\\(\\times", "quantum", "quantum", "quantum", "quantum", "quantum", "two" ]

[ "Kicheol Jeong", "Chulwoo Moon" ]

[ "the gearbox", "an electric vehicle", "the high load torque", "axial load", "electric vehicles", "the bearings", "that", "the shaft", "the gearbox", "several tons", "axial load", "the mileage increases", "fault", "rolling elements", "fault", "a serious impact", "comfort", "vehicle safety", "faults", "human experts", "algorithm-based electric vehicle", "fault diagnosis", "this paper", "a deep learning-based bearing vibration signal processing method", "fault", "electric vehicle gearboxes", "the proposed method", "a deep neural network learning stage", "an application stage", "the pre-trained neural network", "the deep neural network learning stage", "supervised learning", "two acceleration sensors", "the neural network application stage", "signal processing", "a single accelerometer signal", "a pre-trained neural network", "conclusion", "the pre-trained neural network", "fault signals", "these signals", "frequency characteristics", "fault", "several tons", "two" ]

[ "V. Auxilia Osvin Nancy", "P. Prabhavathy", "Meenakshi S. Arya", "B. Shamreen Ahamed" ]

[ "exposure", "uv rays", "global warming", "sunburn", "skin damage", "skin cancer", "early prediction", "this type", "cancer", "a detailed review", "this paper", "various algorithms", "machine learning", ") techniques", "deep learning", "(dl) techniques", "deep learning strategies", "particularly cnns", "skin cancer identification", "classification", "some usage", "machine learning", "hybrid approaches", "these techniques", "effective classifiers", "skin lesions", "promising results", "early detection", "the paper", "various researchers’ reviews", "skin cancer diagnosis", "a suitable methodology", "diagnostic accuracy", "a publicly available dataset", "dermoscopic images", "the isic archive", "performance analysis", "metrics", "test", "validation accuracy", "the results", "algorithm", "other machine learning algorithms", "both scenarios", "accuracies", "58.57%", "augmentation", "87.32%", "augmentation", "mobilenetv2", "dense net", "inceptionv3", "superior performance", "training", "augmentation", "mobilenetv2", "an accuracy", "88.81%", "the ensemble model", "an accuracy", "88.80%", "augmentation techniques", "the accuracies", "97.58%", "97.50%", "experiment", "a customized convolutional neural network (cnn) model", "the number", "layers", "various hyperparameter", "methodologies", "suitable architectures", "a cnn", "7 layers", "batch normalization", "a cnn", "5 layers", "a cnn", "3 layers", "these models", "accuracies", "77.92%", "97.72%", "98.02%", "the raw data", "augmentation datasets", "the experimental results", "these techniques", "promise", "integration", "clinical settings", "further research", "validation", "the results", "the effectiveness", "transfer learning models", "high accuracy rates", "the findings", "the future adoption", "these techniques", "clinical practice", "further research", "validation", "58.57%", "87.32%", "mobilenetv2", "inceptionv3", "mobilenetv2", "88.81%", "88.80%", "97.58%", "97.50%", "cnn", "cnn", "7", "cnn", "5", "cnn", "3", "77.92%", "97.72%", "98.02%" ]

[ "Rui Fan", "Xiaohong Han" ]

[ "the rapid development", "deep learning technology", "an increasing number", "people", "palmprint recognition algorithms", "deep learning", "identity authentication", "these algorithms", "factors", "palm placement", "light source", "insufficient data sampling", "poor recognition accuracy", "these issues", "this paper", "end", "recognition algorithm", "sslaul", "which", "self-supervised representation learning", "contextual prediction", "unlabeled palmprint data", "pre", "-", "training", "the trained parameters", "the downstream model", "fine-tuning", "an uncertainty loss function", "the downstream model", "the homoskedastic uncertainty", "a benchmark", "adaptive weight adjustment", "different loss functions", "channel", "spatial attention mechanisms", "highly discriminative local features", "this paper", "the algorithm", "publicly available iitd", "casia", "polyu", "datasets", "the method", "the best recognition performance", "the-art", "casia" ]

[ "Wenxin Jiang", "Vishnu Banna", "Naveen Vivek", "Abhinav Goel", "Nicholas Synovic", "George K. Thiruvathukal", "James C. Davis" ]

[ "contextmany engineering organizations", "deep neural networks", "the research community", "we", "this process", "deep learning model reengineering", "deep learning model reengineering", "the-art", "reasons", "under-documented reference models", "changing requirements", "the cost", "implementation", "testing.objectiveprior work", "the challenges", "deep learning model development", "we", "the deep learning model reengineering process", "its common challenges", "prior work", "dl systems", "a “product” view", "defects", "projects", "the engineers’ purpose", "our study", "activities", "a “process” view", "engineers", "the reengineering", "process.methodour goal", "the characteristics", "challenges", "deep learning model reengineering", "we", "a mixed-methods case study", "this phenomenon", "the context", "computer vision", "our results", "two data sources", "defects", "open-source reeengineering projects", "interviews", "practitioners", "the leaders", "a reengineering team", "the defect data source", "we", "348 defects", "27 open-source deep learning projects", "our reengineering team", "7 deep learning models", "two years", "we", "2 open-source contributors", "4 practitioners", "6 reengineering team leaders", "their experiences.resultsour results", "how deep learning-based computer vision techniques", "the distribution", "defects", "this process", "challenges", "practices", "we", "most defects", "58%", "re", "-", "users", "reproducibility-related defects", "training", "68%", "them", "our analysis", "most environment defects", "88%", "interface defects", "most environment defects", "46%", "api defects", "we", "training defects", "diverse symptoms", "root causes", "we", "four main challenges", "the dl reengineering process", "model operationalization", "performance debugging", "portability", "dl operations", "customized data pipeline", "our quantitative and qualitative data", "we", "a novel", "workflow.conclusionsour findings", "several conclusion", "standardizing model reengineering practices", "validation tools", "model reengineering", "automated support", "manual model reengineering", "additional unknown aspects", "model reengineering", "two", "348", "27", "7", "two years", "2", "4", "6", "58%", "68%", "88%", "46%", "four" ]

[ "Zied Ben Hazem" ]

[ "the rotary inverted pendulum system", "rips", "an underactuated mechanical system", "highly nonlinear dynamics", "it", "a rips", "the classic control models", "the last few years", "reinforcement learning", "(rl", "a popular nonlinear control method", "rl", "a powerful potential", "systems", "high non-linearity and complex dynamics", "rips", "rl control", "rips", "limited research", "the development", "evaluation", "this control method", "this paper", "rl control algorithms", "the swing-up and stabilization control", "a single-link rotary inverted pendulum", "slrip", "classic control methods", "pid", "lqr", "a physical model", "the slrip system", "the matlab/simscape toolbox", "the model", "a dynamic simulation", "matlab/simulink", "the rl agents", "an agent trainer system", "q-learning", "(ql", "deep q-network learning", "dqnl", "the data training", "agent actions", "the horizontal arm", "the system", "states", "the angles", "velocities", "the pendulum", "the horizontal arm", "the reward", "the angles", "the pendulum", "horizontal arm", "the reward", "the pendulum", "the upright position", "the rl algorithms", "a deep understanding", "the classical controllers", "the agent", "the outcome", "the effectiveness", "the ql and dqnl algorithms", "the conventional pid and lqr controllers", "the last few years", "zero" ]

[ "Pegah Mavaie", "Lawrence Holder", "Michael K. Skinner" ]

[ "backgroundthe performance", "classification methods", "the choice", "features", "many domains", "feature generation", "domain knowledge", "feature selection methods", "high-dimensional datasets", "deep learning", "success", "feature generation", "large datasets", "high classification accuracy", "biology domains", "these challenges", "numerous handcrafted features", "small amounts", "training data", "low volume).methoda hybrid learning approach", "a deep network", "the training data", "features", "the deep network", "these features", "the data", "input", "a non-deep learning method", "which", "the final classification.resultsthe approach", "the best layer", "the deep learning network", "which", "features", "the threshold", "training data volume", "that", "this approach", "results", "several domains", "this hybrid approach", "deep and non-deep learning methods", "low-volume, high-dimensional datasets", "the diverse collection", "datasets", "the robustness", "the approach", "different domains.conclusionsthe hybrid approach", "the strengths", "deep and non-deep learning paradigms", "high performance", "high-dimensional, low volume learning tasks", "that", "biology domains", "first" ]

[ "Tingyu Zhang", "Yongshuai Wang", "Mingwei Sun", "Zengqiang Chen" ]

[ "air combat maneuvering decision", "the sparse", "the application", "deep reinforcement learning", "the exploration efficiency", "the agents", "this challenge", "we", "an auxiliary reward function", "the impact", "angle", "range", "altitude", "we", "the influences", "the network nodes", "layers", "the learning rate", "decision system", "reasonable parameter ranges", "which", "a guideline", "four typical air combat scenarios", "good adaptability", "effectiveness", "the proposed scheme", "the auxiliary reward", "the learning ability", "deep q network", "dqn", "the agents", "the original deep deterministic policy gradient", "soft actor critic", "algorithm", "the proposed method", "superior exploration capability", "higher reward", "the trained agent", "different air combats", "good performance", "four" ]

[ "Yifu Zhang", "Qian Sun", "Ji Chen", "Huini Zhou" ]

[ "crop diseases", "the major natural disasters", "agricultural production", "that", "the growth", "development", "crops", "food security", "timely classification", "accurate identification", "the application", "methods", "the situation", "crop diseases", "the quality", "agricultural products", "the huge variety", "crops", "diseases", "differences", "the characteristics", "diseases", "each stage", "the current convolutional neural network models", "deep learning", "the higher requirement", "crop diseases", "it", "a new architecture scheme", "the recognition effect", "this study", "we", "the deep learning-based classification model", "multiple crop leaf diseases", "combined transfer learning", "the attention mechanism", "the modified model", "the smartphone", "testing", "10 types", "crops", "61 types", "diseases", "different degrees", "the algorithm structure", "resnet50", "transfer learning", "the se attention mechanism", "the classification performances", "different improvement methods", "model training", "result", "the average accuracy", "the proposed tl-se-resnet50 model", "7.7%", "96.32%", "the model", "the smartphone", "the test result", "the application", "94.8%", "the average response time", "882 ms", "the improved model", "a good effect", "the identification", "diseases", "their condition", "multiple crops", "the application", "the portable usage needs", "farmers", "this study", "reference", "more crop disease management research", "agricultural production", "10", "61", "resnet50", "7.7%", "96.32%", "94.8%", "882" ]

[ "Anurag Goel", "Angshul Majumdar" ]

[ "this work", "a research area", "a limited number", "studies", "that", "convolutional filter-learning based clustering", "an unsupervised formulation", "one", "it", "a conventional convolutional neural network", "the latter", "this work", "the recently developed framework", "the sparse", "clustering formulation", "deep convolutional transform", "end", "the proposed approach", "the problem", "hyperspectral band selection", "comparison", "a variety", "the-art", "this domain", "our proposed approach", "them", "terms", "the classification metrics" ]

[ "Jiankui Yan" ]

[ "the assessment", "sprint-like sports techniques", "tactics", "their underlying running principles", "structures", "functions", "methods", "real-world applications", "existing technology", "the context", "the fast-evolving sprint domain", "a deficiency", "evaluation expertise", "deep learning algorithms", "useful features", "information", "extensive training", "competition data", "a more accurate and objective basis", "sprint sports techniques", "tactics", "we", "manual intervention", "tedious workloads", "deep learning algorithms", "automated processing", "analysis", "intelligent training", "competition", "deep learning algorithms", "patterns", "trends", "data", "coaches", "athletes", "potential problems", "areas", "improvement", "a foundation", "athletes", "targeted training plans", "competition strategies", "the role", "deep learning", "dl", "the technological process", "great significance", "this paper", "dl algorithms", "sprint skill evaluation", "dl algorithms", "them", "optimal sprint skill performance", "effective tactical evaluation", "this study", "a new model", "the classic vggnet-16 model", "different cnn models", "the experiment", "the caffenet model", "objects", "that", "the target", "an enlarging search box", "track", "the target", "the experimental results", "a step frequency", "approximately 95%", "the highest speed", "the sdp algorithm", "the voc2007 dataset", "good results", "a mean average precision value", "69.4%", "the benchmark fast r-cnn algorithm", "we", "a traditional tracking algorithm", "the camshift algorithm", "color features", "vggnet-16", "cnn", "approximately 95%", "69.4%" ]

[ "Yi Huang", "Weixin Bian", "Biao Jie", "Zhiqiang Zhu", "Wenhu Li" ]

[ "the method", "image super-resolution reconstruction", "the dictionary", "a single-layer dictionary", "which", "the deep features", "the image", "a large trained dictionary", "the reconstruction effect", "this paper", "a new deep dictionary learning model", "the images", "the training set", "the dictionary", "the deep dictionary learning method", "the adjusted anchored neighborhood regression method", "image super-resolution reconstruction", "the proposed algorithm", "several classical algorithms", "set5 dataset", "the visualization and quantification results", "the proposed method", "psnr", "ssim", "the dictionary size", "reconstruction time", "traditional super-resolution algorithms", "firstly", "set5" ]

[ "Muhammad Mujahid", "Furqan Rustam", "Rahman Shafique", "Elizabeth Caro Montero", "Eduardo Silva Alvarado", "Isabel de la Torre Diez", "Imran Ashraf" ]

[ "malaria", "an extremely malignant disease", "the bites", "infected female mosquitoes", "this disease", "humans", "animals", "malaria", "mild symptoms", "fever", "headache", "sweating", "vomiting", "muscle discomfort", "severe symptoms", "coma", "seizures", "kidney failure", "the timely identification", "malaria parasites", "a challenging and chaotic endeavor", "health staff", "an expert technician", "the schematic blood smears", "infected red blood cells", "a microscope", "the conventional methods", "malaria", "machine learning approaches", "simple classification challenges", "complex tasks", "machine learning", "rigorous feature engineering", "the model", "patterns", "the features", "the other hand", "deep learning", "complex tasks", "low and high-level features", "the images", "disease", "this paper", "efficientnet", "a deep learning-based approach", "malaria", "that", "red blood cell images", "experiments", "performance comparison", "pre-trained deep learning models", "addition", "cross", "-", "validation", "the results", "the proposed approach", "experiments", "the proposed approach", "97.57%", "malaria", "red blood cell images", "medical healthcare staff", "97.57%" ]

[ "Avinash L. Golande", "T. Pavankumar" ]

[ "the diagnosis", "categorization", "cardiac disease", "the low-cost tool electrocardiogram", "ecg", "an intriguing study topic", "intelligent healthcare applications", "an ecg-based cardiac disease prediction system", "the deep learning methods", "automation", "accuracy", "multiple domains", "deep learning", "automatic ecg-based heart disease classification", "a challenging research problem", "solely deep learning approaches", "all", "the important beats", "the input ecg signal", "a hybrid strategy", "detection efficiency", "the main objective", "the proposed model", "the ecg-based heart disease classification efficiency", "a hybrid feature engineering approach", "the proposed model", "pre-processing, hybrid feature engineering", "classification", "an ecg", "powerline", "baseline interference", "the heartbeat", "data", "we", "a hybrid approach", "a conventional ecg beats extraction algorithm", "convolutional neural network", "cnn)-based features", "heart disease prediction", "the hybrid feature vector", "the deep learning classifier long term short memory", "lstm", "the results", "the simulations", "the proposed model", "both the number", "diagnostic errors", "the amount", "time", "each one", "the existing methods", "fed" ]

[ "Cátia Loureiro", "Lio Gonçalves", "Pedro Leite", "Pedro Franco-Gonçalo", "Ana Inês Pereira", "Bruno Colaço", "Sofia Alves-Pimenta", "Fintan McEvoy", "Mário Ginja", "Vítor Filipe" ]

[ "radiographic canine hip dysplasia (chd) diagnosis", "selection", "disease management", "progression", "the associated pain", "radiography", "the primary imaging modality", "chd diagnosis", "visual assessment", "radiographic features", "accurate diagnosis", "alterations", "femoral neck shape", "crucial radiographic signs", "existing literature", "dysplastic hips", "a greater femoral neck thickness", "fnt", "this study", "we", "a three-stage deep learning-based system", "that", "a femoral neck", "thickness index", "a key metric", "chd diagnosis", "our system", "a keypoint detection model", "a segmentation model", "landmark", "boundary coordinates", "the femur", "acetabulum", "we", "a series", "mathematical operations", "the keypoint detection model", "a mean absolute error", "mae", "training", "the femur segmentation results", "a dice score", "ds", "our three-stage deep learning-based system", "an intraclass correlation", "coefficient", "0.86 (95% confidence interval", "no significant differences", "paired t-test", "a specialist", "(p", "we", "this", "the initial study", "computer vision", "deep learning-based approaches", "which", "reliable support", "chd diagnosis", "three", "0.013", "0.978", "three", "0.86", "95%", "0.05" ]

[ "P. Ashok Babu", "Anjani Kumar Rai", "Janjhyam Venkata Naga Ramesh", "A. Nithyasri", "S. Sangeetha", "Pravin R. Kshirsagar", "A. Rajendran", "A. Rajaram", "S. Dilipkumar" ]

[ "a high death rate", "oral cancer", "a major worldwide health problem", "low- and middle-income nations", "timely detection", "diagnosis", "effective prevention", "treatment", "this challenge", "a growing need", "automated detection systems", "healthcare professionals", "regular dental examinations", "a vital role", "early detection", "transfer learning", "which", "knowledge", "related domains", "performance", "target categories", "this study", "a unique approach", "the early detection", "diagnosis", "oral cancer", "that", "use", "the exceptional sensory capabilities", "the mouth", "deep neural networks", "particularly those", "automated systems", "intricate patterns", "the disease", "various transfer", "approaches", "comparative analyses", "an optimal learning rate", "the categorization analysis", "the reference results", "detail", "our preliminary findings", "this challenging problem", "the inception-v3 algorithm", "superior accuracy", "other algorithms" ]

[ "Suryakant Tyagi", "Sándor Szénási" ]

[ "human cognitive functions", "perception", "attention", "learning", "memory", "reasoning", "problem-solving", "emotion", "emotion", "a particularly potent impact", "attention", "its selectivity", "behavior", "action motivation", "artificial emotional intelligence (aei) technologies", "computers", "a user's emotional state", "these systems", "a realistic dialogue", "people", "machines", "the current generation", "adaptive user", "interference technologies", "techniques", "data analytics", "machine learning", "ml", "namely deep learning", "(dl) artificial neural networks", "ann", "multimodal data", "videos", "facial expressions", "stance", "gesture", "voice", "bio-physiological data", "eye movement", "ecg", "respiration", "fmrt", "emg", "eye tracking", "this study", "we", "existing literature", "ml and data analytics techniques", "emotions", "speech", "the efficacy", "data analytics", "ml", "techniques", "this unique area", "multimodal data processing", "emotions", "speech", "this study", "emotional chatbots", "facial expressions", "images", "social media texts", "emotions", "prisma methodology", "the existing survey", "support vector machines", "svm", "naïve bayes", "random forests", "neural networks", "rnn", "logistic regression", "lr", "ml techniques", "emotion extraction purposes", "this study", "a new taxonomy", "the application", "ml", "ser", "the result", "long-short term memory", "lstm", "convolutional neural networks", "cnn", "the most useful methodology", "this purpose", "naïve bayes (nb", "cnn" ]

[ "P. Ashok Babu", "Anjani Kumar Rai", "Janjhyam Venkata Naga Ramesh", "A. Nithyasri", "S. Sangeetha", "Pravin R. Kshirsagar", "A. Rajendran", "A. Rajaram", "S. Dilipkumar" ]

[ "a high death rate", "oral cancer", "a major worldwide health problem", "low- and middle-income nations", "timely detection", "diagnosis", "effective prevention", "treatment", "this challenge", "a growing need", "automated detection systems", "healthcare professionals", "regular dental examinations", "a vital role", "early detection", "transfer learning", "which", "knowledge", "related domains", "performance", "target categories", "this study", "a unique approach", "the early detection", "diagnosis", "oral cancer", "that", "use", "the exceptional sensory capabilities", "the mouth", "deep neural networks", "particularly those", "automated systems", "intricate patterns", "the disease", "various transfer", "approaches", "comparative analyses", "an optimal learning rate", "the categorization analysis", "the reference results", "detail", "our preliminary findings", "this challenging problem", "the inception-v3 algorithm", "superior accuracy", "other algorithms" ]

[ "Helen K. Joy", "Manjunath R. Kounte", "Arunkumar Chandrasekhar", "Manoranjan Paul" ]

[ "the advancements", "the domain", "video coding technologies", "recent years", "the public", "the creation", "availability", "videos", "internet boom and video acquisition devices", "mobile phones", "the necessity", "video compression", "the resolution variance", "4 k", "framerate, display", "some", "the features", "that", "the importance", "compression", "compression ratio", "better efficiency", "quality", "the focus", "it", "many stumbling blocks", "it", "the era", "artificial intelligence", "neural network", "especially deep learning", "light", "the path", "video processing area", "compression", "the paper", "a precise, organized, meticulous review", "the impact", "deep learning", "video compression", "the content adaptivity quality", "deep learning", "its importance", "video compression", "traditional signal processing", "the development", "intelligent and self-trained steps", "video compression", "deep learning", "detail", "the relevant and noteworthy work", "that", "each step", "compression", "this paper", "a detailed survey", "the development", "intra- prediction", "inter", "-", "loop", "quantization", "hand", "deep learning techniques", "envisages ideas", "each field", "the future scope", "enhancement", "various stages", "compression", "relevant research scope", "deep learning", "recent years", "4", "2" ]

[ "Neda Fatima", "Syed Afzal Murtaza Rizvi", "Major Syed Bilal Abbas Rizvi" ]

[ "the prevalence", "skin illnesses", "that", "other diseases", "fungal infection", "bacteria", "allergies", "viruses", "genetic factors", "environmental factors", "important causative factors", "that", "the degree", "incidence", "skin diseases", "medical technology", "lasers", "photonics", "it", "skin illnesses", "the cost", "such a diagnosis", "developed areas", "the present paper", "a holistic, critical, and important skin disease prediction system", "that", "machine learning", "deep learning algorithms", "up to 20 different skin diseases", "a high f1 score", "efficiency", "algorithms", "xception", "inception-v3", "resnet50", "densenet121", "inception-resnet-v2", "diseases", "the images", "the training", "testing", "an enlarged dataset", "classification", "20 diseases", "the algorithm", "any inherent bias", "all classes", "the present model", "which", "the xception", "algorithm", "20 different skin conditions", "a dataset", "over 10,000 photos", "the developed system", "20 different dermatological diseases", "high accuracy", "precision", "up to 20", "resnet50", "20", "20", "over 10,000", "20" ]

[ "Prasannavenkatesan Theerthagiri", "A. Usha Ruby", "J. George Chellin Chandran", "Tanvir Habib Sardar", "Ahamed Shafeeq B. M." ]

[ "the maize leaf diseases", "severe yield reductions", "critical problems", "the maize leaf disease", "greater yield", "this work", "three main leaf diseases", "common rust", "blight", "grey leaf spot", "this approach", "labelling", "class balance", "the smote algorithm", "the maize leaf dataset", "augmentation", "these diseases", "several deep-learning pre-trained networks", "vgg16", "resnet34", "resnet50", "squeezenet", "the model", "a maize leaf dataset", "that", "various leaf classes", "mini-batch sizes", "input sizes", "performance measures", "recall", "precision", "accuracy", "f1-score", "confusion matrix", "each network", "the squeezenet learning model", "an accuracy", "97%", "four different classes", "plant leaf datasets", "the squeezenet learning model", "accuracy", "2–5%", "the mean square error", "4–11%", "vgg16", "resnet34", "deep learning models", "three", "grey leaf spot", "resnet34", "resnet50", "97%", "four", "2–5%", "4–11%", "resnet34", "resnet50" ]

[ "Nicolás Lefin", "Lisandra Herrera-Belén", "Jorge G. Farias", "Jorge F. Beltrán" ]

[ "viruses", "a constant threat", "global health", "millions", "human and animal deaths", "human history", "advances", "the discovery", "antiviral compounds", "that", "these pathogens", "a solution", "this problem", "a task", "that", "time", "financial resources", "artificial intelligence", "ai", "many areas", "the biological sciences", "it", "patterns", "amino acid sequences", "that", "different functions", "activities", "the field", "ai", ", machine learning", "deep learning algorithms", "antimicrobial peptides", "their effectiveness", "specificity", "antimicrobial peptides", "amps", "excellent promise", "various infections", "pathogens", "antiviral peptides", "avps", "a specific type", "amps", "that", "activity", "certain viruses", "the research", "the development", "tools", "methods", "the prediction", "antimicrobial peptides", "those", "the prediction", "avps", "the significance", "avps", "potential pharmaceutical options", "human and animal health", "the ongoing ai revolution", "we", "the current machine learning", "deep learning-based tools", "methods", "these types", "peptides", "millions", "avps" ]

[ "D. N. Sindhura", "Radhika M. Pai", "Shyamasunder N. Bhat", "Manohara M. M. Pai" ]

[ ", computer-aided decision support systems", "cads", "the analysis", "images", "a perennial technique", "the medical imaging field", "cads", "deep learning algorithms", "tasks", "classification", "identification", "patterns", "detection", "deep learning models", "feature representations", "images", "handcrafted features", "deep learning models", "the-art", "good performances", "different computer-aided decision-support systems", "medical applications", "deep learning-based generative models", "generative adversarial networks", "gans", "a novel method", "realistic-looking synthetic data", "gans", "different domains", "medical imaging generation", "the common problems", "class imbalance", "a small dataset", "healthcare", "gans", "it", "a leading area", "research", "segmentation", "reconstruction", "detection", "denoising", "registration", "the important applications", "gans", "this work", "the successes", "deep learning methods", "segmentation", "classification", "cell structure", "fracture detection", "computer-aided identification", "gans", "synthetic medical image generation", "segmentation", "reconstruction", "detection", "denoising", "registration", "recent times", "the review article", "research directions", "dl models", "gans", "medical applications" ]

[ "Mingyang Xie", "Bin Liu", "Xinjun Chen" ]

[ "the accuracy", "fishing ground prediction", "oceanic economic species", "the most concerning issues", "fisheries research", "recent studies", "deep learning", "superior results", "traditional methods", "the era", "big data", "the deep learning-based fishing ground prediction model", "a single environment", "the problem", "the area", "the fishing ground", "this study", "we", "a deep learning-based fishing ground prediction model", "multiple environmental factors", "neon flying squid", "ommastrephes", "northwest pacific ocean", "an example", "the modified u-net model", "the approach", "the sea surface temperature", "sea surface height", "sea surface salinity", "a", "inputs", "the center fishing ground", "the output", "the model", "data", "july", "november", "data", "we", "five temporal scales", "30 days", "seven multiple environmental factor combinations", "different cases", "we", "the optimal temporal scale", "30 days", "the optimal multiple environmental factor combination", "sst and chl a.", "the inclusion", "multiple factors", "the model", "the concentration", "the center fishing ground", "the selection", "a suitable combination", "multiple environmental factors", "the precise spatial distribution", "fishing grounds", "this study", "the understanding", "the mechanism", "environmental field influence", "fishing grounds", "the perspective", "artificial intelligence", "fishery science", "july", "november", "2002–2019", "2020", "five", "3", "6", "10", "15", "30 days", "seven", "30 days", "chl a." ]

[ "Yu Song", "Xin He", "Xiwang Tang", "Bo Yin", "Jie Du", "Jiali Liu", "Zhongbao Zhao", "Shigang Geng" ]

[ "things", "that", "innovative technology", "this research", "an intelligent garbage system", "the-art", "deep learning technologies", "garbage", "urbanization", "the rising population", "urban areas", "it", "daily trash", "homes", "living environments", "this research", "an intelligent iot-based garbage bin system", "classification", "deep learning techniques", "this smart bin", "more varieties", "garbage", "home", "more technologies", "iot and machine learning", "a need", "sustainable natural technologies", "daily waste", "the innovative iot-based garbage system", "various sensors", "humidity", "temperature", "gas", "liquid sensors", "the garbage condition", "the smart garbage bin system", "the data", "a garbage annotation application", "the deep learning method", "object detection", "classification", "garbage images", "arithmetic optimization algorithm", "aoa", "improved refinedet", "ird", "object detection", "the efficientnet-b0 model", "the classification", "garbage images", "the garbage content", "the content", "the deep learning model", "efficient classification tasks", "result evaluation", "smart bins", "real-time", "accuracy", "fine-tuning region-specific litter photos", "enhanced categorization", "today", "daily", "bin system", "daily", "bin system" ]

[ "Nourane Abderrahim", "Amira Echtioui", "Rafik Khemakhem", "Wassim Zouch", "Mohamed Ghorbel", "Ahmed Ben Hamida" ]

[ "epilepsy", "a common neurological disorder", "that", "millions", "people", "many patients", "traditional anti-epileptic drugs", "the lives", "these patients", "a need", "accurate methods", "epileptic seizures", "seizure prediction", "preictal and interictal states", "which", "a challenging classification problem", "deep learning techniques", "convolutional neural networks", "cnns", "great promise", "eeg signals", "epilepsy", "this study", "we", "four deep learning models", "s", "cnn", "modif-cnn, cnn-svm", "comb-2cnn", "epilepsy states", "which", "we", "an ieeg dataset", "the american epilepsy society database", "our models", "high accuracy rates", "the s-cnn and comb-2cnn models", "96.53%", "cnn-svm", "96.99%", "the modif-cnn model", "97.96%", "our experiments", "these findings", "deep learning models", "an effective approach", "epilepsy states", "seizure prediction methods", "the quality", "life", "people", "epilepsy", "millions", "four", "modif-cnn", "cnn", "american", "comb-2cnn", "96.53%", "cnn", "96.99%", "modif", "97.96%" ]

[ "Haitham El-Hussieny" ]

[ "our research", "deep learning", "the control", "a 3 degrees", "freedom biped robot leg", "we", "a dynamic model", "a detailed joint angles", "actuator torques", "this model", "a model predictive control", "mpc", "framework", "precise trajectory tracking", "the need", "traditional analytical dynamic models", "specific constraints", "the mpc", "we", "operational and safety standards", "the experimental results", "the effectiveness", "deep learning models", "robotic control", "precise trajectory tracking", "potential", "further integration", "deep learning", "robotic system control", "this approach", "traditional control methods", "accuracy", "efficiency", "the way", "new research", "robotics", "the potential", "deep learning models", "predictive control techniques", "3 degrees" ]