Datasets:

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ADKU

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ResearchGPT_model_dataset

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[ "Matthias Griebel", "Dennis Segebarth", "Nikolai Stein", "Nina Schukraft", "Philip Tovote", "Robert Blum", "Christoph M. Flath" ]

[ "bioimages", "noise", "experimental conditions", "specimen characteristics", "imaging trade-offs", "reliable segmentation", "such ambiguous images", "we", "deepflash2", "a deep learning-enabled segmentation tool", "bioimage analysis", "the tool", "typical challenges", "that", "the training, evaluation", "application", "deep learning models", "ambiguous data", "the tool’s training and evaluation pipeline", "multiple expert annotations", "deep model ensembles", "accurate results", "the application pipeline", "various use-cases", "expert annotations", "a quality assurance mechanism", "the form", "uncertainty measures", "other tools", "deepflash2", "both high predictive accuracy", "efficient computational resource usage", "the tool", "established deep learning libraries", "sharing", "trained model ensembles", "the research community", "deepflash2", "the integration", "deep learning", "bioimage analysis projects", "accuracy", "reliability", "deepflash2", "deepflash2", "deepflash2" ]

[ "Hyeonjoo Kim", "Young Dae Jeon", "Ki Bong Park", "Hayeong Cha", "Moo-Sub Kim", "Juyeon You", "Se-Won Lee", "Seung-Han Shin", "Yang-Guk Chung", "Sung Bin Kang", "Won Seuk Jang", "Do-Kun Yoon" ]

[ "orthopaedic surgeons", "bone fragments", "2d/3d ct images", "trauma surgery", "advances", "deep learning technology", "good insights", "trauma surgery", "manual diagnosis", "this study", "the application", "the deeplab v3", "-based deep learning model", "the automatic segmentation", "fragments", "the fractured tibia", "ct images", "the results", "the evaluation", "the performance", "the automatic segmentation", "the deep learning model", "which", "over 11 million images", "good performance", "a global accuracy", "98.92%", "a weighted intersection", "the union", "a mean boundary f1 score", "deep learning", "the experts’ recognition", "which", "almost the same significance", "this study", "an important role", "preoperative surgical planning", "trauma surgery", "convenience", "speed", "2d/3d", "over 11 million", "98.92%", "0.9841", "0.8921", "5–8" ]

[ "Mansoor Alghamdi" ]

[ "the evolution", "big data collection", "storage", "manipulation techniques", "deep learning", "the attention", "numerous recent studies", "solutions", "smart cities", "these solutions", "energy consumption", "pollution levels", "public services", "traffic management issues", "urban evolution", "planning", "another recent concern", "smart cities", "this context", "this paper", "a hybrid model", "that", "evolutionary optimization algorithms", "teaching", "learning-based optimization", "tlbo", "the functioning process", "neural deep learning models", "recurrent neural network (rnn) networks", "the achieved simulations", "deep learning", "evolutionary optimizers", "an effective and promising method", "urban evolution", "future smart cities" ]

[ "Hongtao Zhang", "Dongbing Gu" ]

[ "the applications", "deep learning algorithms", "images", "various scenarios", "significant research attention", "application scenarios", "animal breeding managements", "this paper", "we", "a new deep learning framework", "the chest circumference", "domestic animals", "images", "this parameter", "a key metric", "the quality", "animal", "animal husbandry", "we", "a set", "feature extraction methods", "a multi-task learning framework", "the challenging issues", "the main estimation task", "the multiple tasks", "our proposed framework", "object segmentation", "keypoint estimation", "depth estimation", "cow", "monocular images", "the domain-specific features", "these tasks", "our main estimation task", "addition", "we", "unnecessary computations", "the framework design", "the cost", "subsequent practical implementation", "the developed system", "our proposed framework", "dataset", "its performance" ]

[ "Ruchika Malhotra", "Priya Singh" ]

[ "recent years", "deep learning", "a rapidly growing and stimulating field", "machine learning", "the-art", "a variety", "applications", "multiple deep learning models", "that", "distinct architectures", "capabilities", "the present", "a large number", "novel variants", "these baseline deep learning models", "the shortcomings", "the existing baseline models", "this paper", "a comprehensive review", "one hundred seven novel variants", "six baseline deep learning models", "convolutional neural network", "recurrent neural network", "long short term memory", "generative adversarial network", "autoencoder", "transformer neural network", "the current review", "the novel variants", "each", "the six baseline models", "the advancements", "them", "one or more limitations", "the respective baseline model", "it", "the novel variants", "their improved approach", "it", "the merits", "demerits", "the advancements", "novel variants", "the baseline deep learning model", "it", "the domain", "datasets", "performance measures", "the novel variants", "an overall judgment", "terms", "the improvements", "this", "the performance", "the deep learning models", "the application domain", "type", "datasets", "different performance measures", "the critical findings", "the review", "the researchers", "practitioners", "the most recent progressions", "advancements", "the baseline deep learning models", "them", "an appropriate novel variant", "the baseline", "deep learning based tasks", "a similar setting", "recent years", "one hundred seven", "six", "six", "one" ]

[ "S. Suganyadevi", "A. Shiny Pershiya", "K. Balasamy", "V. Seethalakshmi", "Saroj Bala", "Kumud Arora" ]

[ "dementia", "a range", "cognitive disorders", "alzheimer’s disease", "ad", "memory", "the progressive deterioration", "brain cells", "it", "a significant global health challenge", "early identification", "intervention", "ad", "its onset", "immense potential", "future health outcomes", "individuals", "the power", "artificial intelligence", "mri scans", "we", "advanced technology", "ad patients", "the likelihood", "them", "this life-altering condition", "this paper", "the latest advancements", "deep learning techniques", "their functions", "image analysis", "medical field", "its primary goals", "the intricacies", "medical image processing", "key findings", "recommendations", "recent research", "daily" ]

[ "Rakesh Ranjan", "Bikash Chandra Sahana", "Ashish Kumar Bhandari" ]

[ "schizophrenia", "scz", "a chronic neuropsychiatric disorder", "disruptions", "cognitive, perceptual, social, emotional, and behavioral functions", "the traditional approach", "the diagnosis", "scz", "the subject’s response", "the psychiatrist’s experience", "it", "recent medical research", "deep learning", "dl", "the diagnostic process", "performance", "inter-observer variation", "qualitative and quantitative support", "clinical decisions", "other modalities", "magnetic resonance images", "mri", "tomography (ct) scans", "electroencephalogram (eeg) signals", "better insights", "the underlying neural mechanisms", "brain biomarkers", "scz", "deep learning models", "promising results", "the utilization", "eeg signals", "an effective biomarker", "scz", "research", "numerous deep learning models", "automated scz diagnosis", "eeg signals", "a comprehensive assessment", "these approaches", "the literature", "this gap", "we", "the current advancements", "deep learning-based schizophrenia diagnosis", "eeg signals", "this review", "systematic details", "prominent components", "deep learning models", "scz eeg datasets", "data", "approaches", "input data formulations", "dl, chronological dl methodology advancement", "scz diagnosis", "design trends", "dl architecture", "few challenges", "both clinical and technical aspects", "that", "hindrances", "the full potential", "dl models", "eeg-based scz diagnosis", "future outlooks" ]

[ "Outhman Abbassi", "Soumia Ziti", "Meryam Belhiah", "Souad Najoua Lagmiri", "Yassine Zaoui Seghroucheni" ]

[ "the pharmacy industry", "drug discovery", "development", "the identification", "optimization", "potential drug candidates", "the key aspects", "this process", "the prediction", "various molecular properties", "that", "their potential effectiveness", "specific diseases", "graph neural networks", "significant attention", "their strong suitability", "complex relationships", "that", "atoms", "other molecular structures", "gnns", "significant depth", "global features", "the network", "information", "the entire graph structure", "this research study", "we", "a deep learning architecture", "a graph molecular property prediction neural network", "which", "mpnn feature extraction", "a multilayer perceptron classifier", "the deep learning architecture", "four benchmark datasets", "its performance", "the smiles transformer", "vector", "deeper graph convolutional networks", "atom-bond transformer-based message-passing neural network", "the results", "the architecture", "the other models", "the receiver operating characteristic area", "the curve metric", "these findings", "an exciting opportunity", "molecular property prediction", "drug discovery", "development", "one", "four" ]

[ "Burhan Ul Haque Sheikh", "Aasim Zafar" ]

[ "the exploration", "innovative diagnostic approaches", "the utilization", "machine learning", "ml", "deep learning", "(dl) technologies", "recent findings", "light", "the susceptibility", "deep learning-based models", "adversarial attacks", "erroneous predictions", "this study", "the vulnerability", "a deep covid-19 diagnosis model", "the fast gradient sign method", "fgsm) adversarial attack", "transfer learning", "efficientnet-b2", "a publicly available dataset", "a deep learning-based covid-19 diagnosis model", "an impressive average accuracy", "94.56%", "clean test data", "an untargeted fgsm attack", "varying epsilon values", "the model’s accuracy", "21.72%", "epsilon", "the attack", "adversarial covid-19 images", "100% confidence", "this study", "the critical need", "further research", "development", "these vulnerabilities", "the reliability", "accuracy", "deep learning models", "the diagnosis", "covid-19 patients", "covid-19", "covid-19", "covid-19", "94.56%", "21.72%", "0.008", "covid-19", "100%", "covid-19" ]

[ "Namya Aankur Gupta", "Mansi Bansal", "Seema Sharma", "Deepti Mehrotra", "Misha Kakkar" ]

[ "blockchain", "a sense", "security", "only one history", "transactions", "all the involved parties", "smart contracts", "users", "significant asset amounts", "finances", "the blockchain", "the involvement", "any intermediaries", "the conditions", "checks", "that", "smart contract", "the application", "these unique features", "some other risks", "the smart contract", "smart contracts", "several flaws", "its programmable language", "methods", "execution", "a developing technology", "smart contracts", "numerous complicated business logics", "high-level languages", "the developers", "smart contracts", "blockchain smart contract", "the most important element", "any decentralized application", "the risk", "it", "the presence", "vulnerabilities", "care", "a priority basis", "it", "detection", "vulnerabilities", "a smart contract", "it", "applications", "security", "funds", "the motive", "the paper", "how deep learning", "bug-free secure smart contracts", "objective", "the paper", "three kinds", "vulnerabilities- reentrancy", "timestamp", "infinite loop", "a deep learning model", "detection", "smart contract vulnerabilities", "graph neural networks", "the performance", "this model", "the present automated tools", "other independent methods", "it", "this model", "greater accuracy", "other methods", "the prediction", "smart contract vulnerabilities", "existing models", "three" ]

[ "Yuanfei Cui", "Fengtong Yao" ]

[ "today’s dynamic business landscape", "the integration", "supply chain management", "financial risk forecasting", "sustained success", "this research paper", "a groundbreaking approach", "that", "deep autoencoder (dae) models", "reinforcement learning", "(rl) techniques", "financial risk forecasting", "the realm", "supply chain management", "the primary objective", "this research", "financial decision-making processes", "key feature representations", "financial data", "rl", "decision optimization", "this", "the paper", "the pso-sdae model", "a novel and sophisticated approach", "financial risk forecasting", "advanced noise reduction features", "optimization algorithms", "the pso-sdae model", "the accuracy", "reliability", "financial risk predictions", "the pso-sdae model", "traditional forecasting methods", "the need", "real-time decision-making", "the rapidly evolving landscape", "financial risk management", "this", "the utilization", "a distributed rl algorithm", "which", "the processing", "supply chain data", "both efficiency", "accuracy", "the results", "our study showcase", "the exceptional precision", "the pso-sdae model", "financial risks", "its efficacy", "proactive risk management", "supply chain operations", "the augmented processing speed", "the model", "real-time analysis", "decision-making", "a critical capability", "today’s fast-paced business environment", "today", "dae", "today" ]

[ "Davood Zabihzadeh", "Mina Masoudifar" ]

[ "automatic plant disease detection", "an important role", "food security", "deep learning methods", "precisely various types", "plant diseases", "the expense", "huge amounts", "resources", "processors", "data", "few-shot or zero-shot learning methods", "deep metric learning", "dml", "a widely used technique", "few/zero shot learning", "existing dml methods", "features", "the last hidden layer", "a pre-trained deep network", "which", "the dependence", "the specific features", "the observed classes", "this paper", "the general discriminative feature learning method", "general features", "plant leaves", "a proxy-based loss", "that", "the embedding", "sampling phase", "a higher convergence rate", "the network", "the plant village dataset", "the images", "32 and 6 classes", "source", "target", "the knowledge", "the source domain", "the target", "a zero-shot setting", "a few samples", "the target domain", "the network", "a gallery", "the network", "the target domain", "the experimental results", "few or even only one sample", "new classes", "the network", "fine-tuning step", "our method", "a classification accuracy", "99%/80.64%", "few/one image(s", "class", "zero", "32", "6", "zero", "only one", "99%/80.64%", "one" ]

[ "Tarraf Torfeh", "Souha Aouadi", "SA Yoganathan", "Satheesh Paloor", "Rabih Hammoud", "Noora Al-Hammadi" ]

[ "a growing trend", "artificial intelligence", "ai", "techniques", "medical imaging", "the purpose", "quality assurance", "this research", "we", "various deep learning-based approaches", "automatic quality assurance", "magnetic resonance", "(mr) images", "the american college", "radiology", "standards.methodsthe study", "the development", "optimization", "testing", "custom convolutional neural network (cnn) models", "popular pre-trained models", "vgg16", "vgg19", "resnet50", "inceptionv3", "efficientnetb0", "efficientnetb5", "the use", "pre-trained models", "particularly those", "the imagenet dataset", "transfer learning", "two-class classification models", "spatial resolution", "geometric distortion", "an approach", "the image", "10 classes", "the number", "visible spokes", "the low contrast.resultsour results", "deep learning-based methods", "mr image quality assurance", "the performance", "these models", "the low contrast test", "the most challenging tests", "the acr phantom.conclusionsoverall", "geometric distortion", "spatial resolution", "all", "the deep learning models", "produced prediction accuracy", "80%", "the study", "the models", "scratch", "learning", "the low contrast", "our investigation", "the adaptability", "potential", "deep learning models", "the custom cnn models", "the number", "visible spokes", "commendable accuracy", "recall", "precision", "f1 scores", "backgroundin recent years", "american", "cnn", "resnet50", "inceptionv3", "efficientnetb0", "efficientnetb5", "two", "10", "80%", "cnn" ]

[ "Yang Liu" ]

[ "depression", "people’s mental health", "recent research", "social media", "decision-making support", "health-care professionals", "supplementary information", "patients’ health status", "deep learning models", "an individual’s likelihood", "depression", "data availability", "social media", "privacy concerns", "deep learning models", "more data", "this issue", "this study", "a methodological framework system", "clinical decision support", "that", "deep learning", "individuals", "depression", "intervention decisions", "clinicians", "the proposed framework", "evaluation", "datasets", "three social media platforms", "the experimental results", "our method", "the-art", "the study", "a clinical decision support system", "evolvable features", "that", "precise solutions", "health-care professionals", "medical diagnosis", "the proposed framework", "that", "social media data", "deep learning models", "valuable insights", "patients’ health status", "personalized treatment decisions", "health-care needs", "clinicians", "three" ]

[ "Junhao Zhang", "Vishwanatha M. Rao", "Ye Tian", "Yanting Yang", "Nicolas Acosta", "Zihan Wan", "Pin-Yu Lee", "Chloe Zhang", "Lawrence S. Kegeles", "Scott A. Small", "Jia Guo" ]

[ "schizophrenia", "a chronic neuropsychiatric disorder", "that", "distinct structural alterations", "the brain", "we", "deep learning", "a structural neuroimaging dataset", "disease-related alteration", "classification and diagnostic accuracy", "we", "this hypothesis", "conventional t1-weighted mri scan", "which", "we", "the 3d whole-brain structure", "standard post-processing methods", "a deep learning model", "three open datasets", "t1-weighted mri scans", "patients", "schizophrenia", "our proposed model", "the benchmark model", "which", "structural mr images", "a 3d cnn architecture", "our model", "almost perfectly (area", "the roc curve", "schizophrenia patients", "healthy controls", "unseen structural mri scans", "regional analysis", "subcortical regions", "ventricles", "the most predictive brain regions", "subcortical structures", "a pivotal role", "cognitive, affective, and social functions", "humans", "structural abnormalities", "these regions", "schizophrenia", "our finding", "schizophrenia", "widespread alterations", "subcortical brain structure", "the subcortical structural information", "prominent features", "diagnostic classification", "these results", "the potential", "deep learning", "schizophrenia diagnosis", "its structural neuroimaging signatures", "a single, standard t1-weighted brain mri", "schizophrenia", "3d", "three", "3d", "cnn", "roc", "0.987", "schizophrenia" ]

[ "Jaakko Sahlsten", "Joel Jaskari", "Kareem A. Wahid", "Sara Ahmed", "Enrico Glerean", "Renjie He", "Benjamin H. Kann", "Antti Mäkitie", "Clifton D. Fuller", "Mohamed A. Naser", "Kimmo Kaski" ]

[ "backgroundradiotherapy", "a core treatment modality", "oropharyngeal cancer", "the primary gross tumor volume", "gtvp", "high interobserver variability", "this", "reliable and trustworthy automated tools", "clinician workflow", "accurate uncertainty quantification", "its downstream utilization", "we", "uncertainty-aware deep learning", "opc", "gtvp segmentation", "the utility", "uncertainty", "multiple applications", "we", "two bayesian deep learning", "bdl) models", "eight uncertainty measures", "a large multi-institute dataset", "292 pet/ct", "our approach.resultswe show", "our uncertainty-based approach", "the quality", "the deep learning segmentation", "86.6%", "cases", "low performance cases", "semi-automated correction", "regions", "the scans", "the segmentations", "bdl-based analysis", "a first-step", "more widespread implementation", "uncertainty quantification", "gtvp segmentation", "bayesian", "eight", "292", "86.6%", "fail.conclusionsour bdl", "first" ]

[ "Muhammad Najam Dar", "Muhammad Usman Akram", "Ahmad Rauf Subhani", "Sajid Gul Khawaja", "Constantino Carlos Reyes-Aldasoro", "Sarah Gul" ]

[ "recognition", "a real-world, less constrained environment", "the principal prerequisite", "the industrial-level usefulness", "this technology", "the psychological profile", "smart, wearable electroencephalogram (eeg) sensors", "daily activities", "external stimuli", "memory-induced emotions", "a challenging research gap", "emotion recognition", "this paper", "a deep learning framework", "improved memory-induced emotion recognition", "a combination", "1d-cnn", "lstm", "feature extractors", "an extreme learning machine", "(elm) classifier", "the proposed deep learning architecture", "the eeg preprocessing", "the removal", "the average baseline signal", "each sample", "extraction", "eeg rhythms", "delta", "theta", "alpha", "beta", "gamma", "repetitive and continuous patterns", "memory-induced emotion recognition", "deep learning techniques", "this work", "eeg signals", "a wearable, ultra-mobile sports cap", "autobiographical emotional memories", "affect-denoting words", "self-annotation", "the scale", "valence", "arousal", "extensive experimentation", "the same dataset", "the proposed framework", "existing techniques", "the emerging area", "memory-induced emotion recognition", "an accuracy", "65.6%", "the eeg rhythms analysis", "delta", "theta", "alpha", "beta", "gamma", "65.5%", "52.1%", "65.1%", "64.6%", "65.0% accuracies", "classification", "four quadrants", "valence", "arousal", "these results", "the significant advancement", "our proposed method", "the real-world environment", "memory-induced emotion recognition", "daily", "1d", "65.6%", "65.5%", "52.1%", "65.1%", "64.6%", "65.0%", "four" ]

[ "Linghua Wu", "Jing Zhang", "Yilin Wang", "Rong Ding", "Yueqin Cao", "Guiqin Liu", "Changsheng Liufu", "Baowei Xie", "Shanping Kang", "Rui Liu", "Wenle Li", "Furen Guan" ]

[ "pneumonia", "a highly lethal disease", "research", "its treatment", "early screening tools", "extensive attention", "researchers", "the maturity and cost reduction", "chest x-ray technology", "the development", "artificial intelligence technology", "pneumonia identification", "deep learning", "chest x", "-", "ray", "attention", "the world", "the feature extraction capability", "deep learning", "existing deep learning object detection frameworks", "pre-defined anchors", "which", "a lot", "tuning", "their excellent results", "the face", "new applications", "data", "the influence", "anchor settings", "pneumonia detection", "this paper", "an anchor-free object detection framework", "rsna dataset", "pneumonia detection", "a data enhancement scheme", "the chest x-ray images", "an anchor-free object detection framework", "pneumonia detection", "which", "a feature pyramid", "two-branch detection head", "focal loss", "the average precision", "intersection", "union (iou) calculation", "the pneumonia detection results", "this paper", "the existing classical object detection framework", "an idea", "future research", "exploration", "first", "second", "two", "51.5" ]

[ "Hussam Hijazi", "Karim Sattar", "Hassan M. Al-Ahmadi", "Sami El-Ferik" ]

[ "road traffic accidents", "a major cause", "fatalities", "injuries", "effective classification", "accident type", "severity", "prompt post-accident protocols", "the development", "comprehensive road safety policies", "this study", "the application", "deep learning techniques", "crash injury severity", "the eastern province", "saudi arabia", "five deep learning models", "various variants", "feedforward multilayer perceptron", "a back-propagated artificial neural network", "ann", "radial basis function", "rpf", "data learning network", "tabnet", "the models", "bayesian optimization", "bo", "the synthetic minority oversampling technique", "smote", "the training dataset", "smote", "balanced accuracy", "ann", "rbf", "tabnet", "it", "precision", "increased recall", "the results", "techniques", "model performance", "significant features", "lasso", "feature importance", "permutation importance", "the results", "techniques", "model performance", "smote", "balanced accuracy", "ann", "rbf", "tabnet", "it", "precision", "increased recall", "the study's findings", "the consistent significance", "the 'number", "injuries major' feature", "a vital predictor", "deep learning models", "the selection techniques", "these results", "light", "the pivotal role", "the count", "individuals", "major injuries", "the severity", "crash injuries", "its potential relevance", "road safety policy development", "saudi arabia", "five" ]

[ "Camellia Ray", "Sambit Bakshi", "Pankaj Kumar Sa", "Ganapati Panda" ]

[ "customized healthcare", "cattle health monitoring", "which", "individual animal health", "productivity", "illness-related risks", "overall welfare", "healthcare practices", "individual requirements", "individual animals", "proper attention", "intervention", "better health outcomes", "sustainable cattle farming practices", "this regard", "the manuscript", "a visual cues-based region prediction methodology", "a customized cattle healthcare system", "the proposed automated ai healthcare system", "resource-efficient deep learning-inspired architecture", "computer vision applications", "region-wise classification", "the classification mechanism", "a cattle", "the regions", "it", "extensive experimentation", "a redesigned image dataset", "the best-suited deep-learning framework", "region classification", "livestock", "cattle", "mobilenetv2", "the-art", "an accuracy", "93%", "the regions", "the cattle", "tailoring healthcare", "mobilenetv2", "93%" ]

[ "C. Lalitha", "D. Ravindran" ]

[ "the volume", "complexity", "weather data", "missing values", "high correlation", "collected variables", "it", "efficient deep learning frameworks", "that", "data", "more features", "this", "a lack", "accurate and predictable weather forecasts", "a hybrid deep learning framework", "weather forecast", "rainfall prediction", "weather big data analytics", "high detection rates", "a modified planet optimization", "mpo", "algorithm", "data", "unwanted artifacts", "an improved tuna optimization", "ito", "algorithm", "optimal features", "data dimensionality issues", "a hybrid memory-augmented artificial neural network", "ma-ann) classifier", "weather early forecast detection rates", "the proposed framework", "standard benchmark datasets", "weather", "climate forecast system reanalysis", "cfsr", "the simulation results", "the-art", "error measures", "accuracy", "sensitivity", "specificity", "precision", "f1-measure).the ma-ann classifier accuracy", "97.65%", "98.88%", "tamilnadu", "the hybrid deep learning framework", "rainfall prediction", "weather big data analytics", "promising results", "accurate and predictable weather forecasts", "the proposed framework", "the-art", "the ma-ann classifier", "weather early forecast detection rates", "the study", "the potential", "big data techniques", "weather forecasting", "the importance", "efficient deep learning frameworks", "complex and high-dimensional weather data", "rmse", "97.65%", "wunderground.com delhi", "98.88%" ]

[ "Heba Abdel-Nabi", "Arafat Awajan", "Mostafa Z. Ali" ]

[ "a crucial natural language processing task", "this field", "research", "a sudden amount", "interest", "the integration", "the deep learning models", "the question", "systems", "which", "many advancements", "improvements", "this survey", "light", "the recent and most powerful deep learning-based question", "systems", "them", "the deep learning model", "the details", "the used word representation", "datasets", "evaluation metrics", "it", "the currently used models", "insights", "that", "future research", "this increasingly growing field" ]

[ "Ruoyu Yang", "Shubhendu Kumar Singh", "Mostafa Tavakkoli", "M. Amin Karami", "Rahul Rai" ]

[ "structural health monitoring", "shm", "the implementation", "a damage detection strategy", "structures", "fault occurrence", "these structural systems", "the operation", "precise health monitoring methods", "the necessary repairs", "maintenance", "time", "it", "the current structural health monitoring methods", "physically attached sensors", "non-contact vision-based vibration measurements", "these methods", "significant drawbacks", "the low spatial resolution", "weight influence", "the lightweight structure", "time/labor consumption", "computer-vison-based deep learning methods", "convolutional neural network", "cnn", "fully convolutional neural network", "fcn", "defect detection", "localization", "which", "the aforementioned problems", "high accuracy", "this paper", "a novel hybrid deep learning architecture", "cnn", "temporal convolutional networks", "cnn-tcn", "the computer vision-based defect detection task", "various beam samples", "five different materials", "various structural defects", "the proposed deep learning algorithms’ performance", "the proposed deep learning methods", "each pixel", "the video frame", "a sensor", "valuable features", "defect detection", "empirical results", "we", "this ’pixel-sensor’ approach", "a better defect detection performance", "different beam samples", "the-art", "cnn-long short-term memory", "lstm", "cnn-bidirectional long short-term memory", "bilstm", "multi-scale cnn-lstm", "cnn-gated recurrent unit(gru) methods", "cnn", "cnn", "cnn-tcn", "five", "cnn", "cnn", "cnn", "cnn" ]

[ "Bin Zhao", "Bin Li", "Jiqun Zhang", "Wei Cao", "Yilong Gao" ]

[ "the recommendation system", "valuable information", "user preferences", "it", "various industries", "the performance", "recommendation systems", "the problem", "data sparsity", "lightgbm", "the impact", "data sparsity", "a certain extent", "this end", "this paper", "a fusion recommendation model", "the lightgbm", "deep learning", "clgm model", "the model", "lighgbm", "cross network", "deep neural network", "the features", "the dataset", "lightgbm", "the feature", "the highest classification accuracy", "the input", "the neural network layer", "the cross network", "the deep neural network", "the linear cross combination feature relationship", "nonlinear correlation relationship", "high-order features", "the results", "the pre-order network", "the final recommendation result", "this paper", "auc", "logloss", "evaluation indicators", "the model", "the public dataset criteo", "dataset avazu", "the simulation experiment results", "the four typical recommendation models", "the recommendation effect", "this model", "first", "four" ]

[ "Xiaomeng Zhu", "Pär Mårtensson", "Lars Hanson", "Mårten Björkman", "Atsuto Maki" ]

[ "the manufacturing industry", "automatic quality inspections", "improved product quality", "productivity", "deep learning-based computer vision technologies", "their superior performance", "many applications", "a possible solution", "automatic quality inspections", "a large amount", "annotated training data", "deep learning", "processes", "various products", "human activities", "assembly", "this challenge", "we", "a method", "automated assembly quality inspection", "synthetic data", "computer-aided design (cad) models", "the method", "two steps", "automatic data generation and model implementation", "the first step", "we", "synthetic data", "two formats", "two-dimensional (2d) images", "three-dimensional (3d", "the second step", "we", "the-art", "the data", "quality inspection", "unsupervised domain adaptation", "i.e., a method", "adapting models", "different data distributions", "transfer learning", "which", "knowledge", "related tasks", "we", "the methods", "a case study", "pedal car front-wheel assembly quality inspection", "the possible optimal approach", "assembly quality inspection", "our results", "the method", "transfer learning", "2d synthetic images", "superior performance", "others", "it", "95% accuracy", "fine-tuning", "only five annotated real images", "class", "promising results", "our method", "other similar quality inspection use cases", "synthetic cad data", "our method", "the need", "manual data collection", "annotation", "our method", "test data", "different backgrounds", "it", "different manufacturing environments", "two", "first", "two", "two", "2d", "three", "3d", "second", "2d", "95%", "only five" ]

[ "Jie Sun \n (孙洁)" ]

[ "deep learning", "an effective way", "automatic classification", "cardiac arrhythmias", "clinical decision-making", ", pure data-driven methods", "black-boxes", "unsatisfactory results", "a promising solution", "domain knowledge", "deep learning", "this paper", "a flexible and extensible framework", "domain knowledge", "a deep neural network", "the model", "a deep neural network", "the statistical pattern", "input data", "the ground-truth label", "a knowledge module", "consistency", "the domain knowledge", "these two components", "both worlds", "the experiments", "the domain knowledge", "the neural network prediction", "accuracy", "two" ]

[ "Sanghun Lee", "Ki-Sub Kim", "Jeong Won Kang" ]

[ "we", "a deep learning color recognition model", "the color measurement process", "the paint industry", "spectrophotometers", "color measurements", "their accuracy", "the measurement method", "the sample", "a spectrophotometer", "our proposed method", "a webcam", "a deep learning model", "the stand", "a spectrophotometer", "deep learning models", "image", "color detection", "this study", "you", "algorithm", "real-time detection", "color samples", "various sample images", "yolo", "the model", "the sample area", "real time", "a webcam", "an open source computer vision", "(opencv) library", "the color recognition model", "the detected rgb color value", "the international commission", "illumination color space", "(cielab) value", "which", "the color measuring process", "the mirror-like reflection", "light", "a surface", "specular reflection", "it", "the color value", "a camera", "this problem", "we", "several specular removal methods", "the most suitable model", "the color recognition model", "color samples", "the accuracy", "the proposed model", "the colors", "various samples", "our proposed approach", "samples", "color values", "which", "the exact amount", "the target color", "deep" ]

[ "Jingjing Zhang", "Qiang Zhang", "Bo Zhao", "Gaofeng Shi" ]

[ "a deep learning radiomics nomogram", "multi-phase contrast-enhanced computed tomography", "cect", "neoadjuvant chemotherapy (nac) response", "locally advanced gastric cancer", "lagc", "patients.methodsthis multi-center study", "322 patients", "gastric cancer", "january", "june", "two hospitals", "handcrafted radiomics technique", "the efficientnet v2 neural network", "arterial, portal venous, and delayed phase ct images", "two-dimensional handcrafted and deep learning features", "a nomogram model", "the handcrafted signature", "the deep learning signature", "clinical features", "discriminative ability", "the receiver operating characteristics", "(roc) curve", "the precision-recall (p-r) curve", "model fitting", "calibration curves", "clinical utility", "decision curve analysis", "dca).resultsthe nomogram", "excellent performance", "the area", "the roc curve", "auc", "[95% confidence interval", "ci", "0.793–0.893", "0.802 (95%", "ci 0.688–0.889", "(95%", "ci", "the training", "internal validation", "external validation sets", "the aucs", "the p-r curves", "0.756–0.895", "(95%", "ci 0.329–0.740", "ci 0.376–0.722", "the corresponding sets", "the nomogram", "the clinical model", "signature", "all sets", "all p", "the nomogram model", "good calibration", "greater net benefit", "the relevant threshold range", "other models.conclusionthis study", "a deep learning nomogram", "cect images", "clinical data", "nac response", "lagc patients", "surgical resection", "personalized treatment insights.graphical abstract", "322", "january 2013 to june 2023", "two", "two", "roc", "roc", "0.848", "95%", "0.802", "95%", "0.688–0.889", "0.751", "95%", "0.652–0.833", "0.838", "95%", "0.541", "95%", "0.329–0.740", "0.556", "95%" ]

[ "Yuval Meir", "Yarden Tzach", "Shiri Hodassman", "Ofek Tevet", "Ido Kanter" ]

[ "the underlying mechanism", "successful deep learning", "dl", "a quantitative method", "that", "the quality", "a single filter", "each layer", "a dl model", "particularly vgg-16", "cifar-10", "this method", "each filter", "small clusters", "possible output labels", "additional noise", "labels", "the clusters", "this feature", "each layer", "noise", "snr", "which", "an increase", "the accuracy", "the dl network", "this study", "this mechanism", "vgg-16", "efficientnet-b0", "the cifar-100", "imagenet", "datasets", "the main results", "the accuracy", "the layers", "a given deep architecture", "the maximal error rate", "the number", "output labels", "similar trends", "dataset labels", "the range", "the universality", "this mechanism", "the performance", "a single filter", "its dominating features", "the way", "the deep architecture", "its overall accuracy", "this", "the filter’s cluster connections", "afcc", "cifar-10", "vgg-16", "first", "second", "third", "3", "1000" ]

[ "Burhan Ul Haque Sheikh", "Aasim Zafar" ]

[ "the exploration", "innovative diagnostic approaches", "the utilization", "machine learning", "ml", "deep learning", "(dl) technologies", "recent findings", "light", "the susceptibility", "deep learning-based models", "adversarial attacks", "erroneous predictions", "this study", "the vulnerability", "a deep covid-19 diagnosis model", "the fast gradient sign method", "fgsm) adversarial attack", "transfer learning", "efficientnet-b2", "a publicly available dataset", "a deep learning-based covid-19 diagnosis model", "an impressive average accuracy", "94.56%", "clean test data", "an untargeted fgsm attack", "varying epsilon values", "the model’s accuracy", "21.72%", "epsilon", "the attack", "adversarial covid-19 images", "100% confidence", "this study", "the critical need", "further research", "development", "these vulnerabilities", "the reliability", "accuracy", "deep learning models", "the diagnosis", "covid-19 patients", "covid-19", "covid-19", "covid-19", "94.56%", "21.72%", "0.008", "covid-19", "100%", "covid-19" ]

[ "Namya Aankur Gupta", "Mansi Bansal", "Seema Sharma", "Deepti Mehrotra", "Misha Kakkar" ]

[ "blockchain", "a sense", "security", "only one history", "transactions", "all the involved parties", "smart contracts", "users", "significant asset amounts", "finances", "the blockchain", "the involvement", "any intermediaries", "the conditions", "checks", "that", "smart contract", "the application", "these unique features", "some other risks", "the smart contract", "smart contracts", "several flaws", "its programmable language", "methods", "execution", "a developing technology", "smart contracts", "numerous complicated business logics", "high-level languages", "the developers", "smart contracts", "blockchain smart contract", "the most important element", "any decentralized application", "the risk", "it", "the presence", "vulnerabilities", "care", "a priority basis", "it", "detection", "vulnerabilities", "a smart contract", "it", "applications", "security", "funds", "the motive", "the paper", "how deep learning", "bug-free secure smart contracts", "objective", "the paper", "three kinds", "vulnerabilities- reentrancy", "timestamp", "infinite loop", "a deep learning model", "detection", "smart contract vulnerabilities", "graph neural networks", "the performance", "this model", "the present automated tools", "other independent methods", "it", "this model", "greater accuracy", "other methods", "the prediction", "smart contract vulnerabilities", "existing models", "three" ]

[ "Yuanfei Cui", "Fengtong Yao" ]

[ "today’s dynamic business landscape", "the integration", "supply chain management", "financial risk forecasting", "sustained success", "this research paper", "a groundbreaking approach", "that", "deep autoencoder (dae) models", "reinforcement learning", "(rl) techniques", "financial risk forecasting", "the realm", "supply chain management", "the primary objective", "this research", "financial decision-making processes", "key feature representations", "financial data", "rl", "decision optimization", "this", "the paper", "the pso-sdae model", "a novel and sophisticated approach", "financial risk forecasting", "advanced noise reduction features", "optimization algorithms", "the pso-sdae model", "the accuracy", "reliability", "financial risk predictions", "the pso-sdae model", "traditional forecasting methods", "the need", "real-time decision-making", "the rapidly evolving landscape", "financial risk management", "this", "the utilization", "a distributed rl algorithm", "which", "the processing", "supply chain data", "both efficiency", "accuracy", "the results", "our study showcase", "the exceptional precision", "the pso-sdae model", "financial risks", "its efficacy", "proactive risk management", "supply chain operations", "the augmented processing speed", "the model", "real-time analysis", "decision-making", "a critical capability", "today’s fast-paced business environment", "today", "dae", "today" ]

[ "Davood Zabihzadeh", "Mina Masoudifar" ]

[ "automatic plant disease detection", "an important role", "food security", "deep learning methods", "precisely various types", "plant diseases", "the expense", "huge amounts", "resources", "processors", "data", "few-shot or zero-shot learning methods", "deep metric learning", "dml", "a widely used technique", "few/zero shot learning", "existing dml methods", "features", "the last hidden layer", "a pre-trained deep network", "which", "the dependence", "the specific features", "the observed classes", "this paper", "the general discriminative feature learning method", "general features", "plant leaves", "a proxy-based loss", "that", "the embedding", "sampling phase", "a higher convergence rate", "the network", "the plant village dataset", "the images", "32 and 6 classes", "source", "target", "the knowledge", "the source domain", "the target", "a zero-shot setting", "a few samples", "the target domain", "the network", "a gallery", "the network", "the target domain", "the experimental results", "few or even only one sample", "new classes", "the network", "fine-tuning step", "our method", "a classification accuracy", "99%/80.64%", "few/one image(s", "class", "zero", "32", "6", "zero", "only one", "99%/80.64%", "one" ]

[ "Tarraf Torfeh", "Souha Aouadi", "SA Yoganathan", "Satheesh Paloor", "Rabih Hammoud", "Noora Al-Hammadi" ]

[ "a growing trend", "artificial intelligence", "ai", "techniques", "medical imaging", "the purpose", "quality assurance", "this research", "we", "various deep learning-based approaches", "automatic quality assurance", "magnetic resonance", "(mr) images", "the american college", "radiology", "standards.methodsthe study", "the development", "optimization", "testing", "custom convolutional neural network (cnn) models", "popular pre-trained models", "vgg16", "vgg19", "resnet50", "inceptionv3", "efficientnetb0", "efficientnetb5", "the use", "pre-trained models", "particularly those", "the imagenet dataset", "transfer learning", "two-class classification models", "spatial resolution", "geometric distortion", "an approach", "the image", "10 classes", "the number", "visible spokes", "the low contrast.resultsour results", "deep learning-based methods", "mr image quality assurance", "the performance", "these models", "the low contrast test", "the most challenging tests", "the acr phantom.conclusionsoverall", "geometric distortion", "spatial resolution", "all", "the deep learning models", "produced prediction accuracy", "80%", "the study", "the models", "scratch", "learning", "the low contrast", "our investigation", "the adaptability", "potential", "deep learning models", "the custom cnn models", "the number", "visible spokes", "commendable accuracy", "recall", "precision", "f1 scores", "backgroundin recent years", "american", "cnn", "resnet50", "inceptionv3", "efficientnetb0", "efficientnetb5", "two", "10", "80%", "cnn" ]

[ "Yang Liu" ]

[ "depression", "people’s mental health", "recent research", "social media", "decision-making support", "health-care professionals", "supplementary information", "patients’ health status", "deep learning models", "an individual’s likelihood", "depression", "data availability", "social media", "privacy concerns", "deep learning models", "more data", "this issue", "this study", "a methodological framework system", "clinical decision support", "that", "deep learning", "individuals", "depression", "intervention decisions", "clinicians", "the proposed framework", "evaluation", "datasets", "three social media platforms", "the experimental results", "our method", "the-art", "the study", "a clinical decision support system", "evolvable features", "that", "precise solutions", "health-care professionals", "medical diagnosis", "the proposed framework", "that", "social media data", "deep learning models", "valuable insights", "patients’ health status", "personalized treatment decisions", "health-care needs", "clinicians", "three" ]

[ "XiuLi Du", "KuanYang Liang", "YaNa Lv", "ShaoMing Qiu" ]

[ "traditional eeg signals", "the nyquist theorem", "long-time recordings", "eeg signals", "a large amount", "data", "the same time", "end", "memory space", "great pressure", "the effective transmission", "data", "the birth", "this transmission pressure", "an iterative compressed sensing reconstruction algorithm", "eeg signal reconstruction", "complex calculation problems", "slow data processing speed", "the application", "compressed sensing", "eeg signal rapid monitoring systems", "this paper", "a non-iterative and fast algorithm", "eeg signals", "compressed sensing", "deep learning techniques", "this algorithm", "the improved residual network model", "the feature information", "the eeg signal", "one-dimensional dilated convolution", "the nonlinear mapping relationship", "the measured value", "the original signal", "the eeg signal", "the method", "this paper", "simulation", "the open bci contest dataset", "it", "the proposed method", "higher reconstruction accuracy", "faster reconstruction speed", "the traditional cs reconstruction algorithm", "the existing deep learning reconstruction algorithm", "addition", "it", "the rapid reconstruction", "eeg signals", "one", "bci contest" ]

[ "Junhao Zhang", "Vishwanatha M. Rao", "Ye Tian", "Yanting Yang", "Nicolas Acosta", "Zihan Wan", "Pin-Yu Lee", "Chloe Zhang", "Lawrence S. Kegeles", "Scott A. Small", "Jia Guo" ]

[ "schizophrenia", "a chronic neuropsychiatric disorder", "that", "distinct structural alterations", "the brain", "we", "deep learning", "a structural neuroimaging dataset", "disease-related alteration", "classification and diagnostic accuracy", "we", "this hypothesis", "conventional t1-weighted mri scan", "which", "we", "the 3d whole-brain structure", "standard post-processing methods", "a deep learning model", "three open datasets", "t1-weighted mri scans", "patients", "schizophrenia", "our proposed model", "the benchmark model", "which", "structural mr images", "a 3d cnn architecture", "our model", "almost perfectly (area", "the roc curve", "schizophrenia patients", "healthy controls", "unseen structural mri scans", "regional analysis", "subcortical regions", "ventricles", "the most predictive brain regions", "subcortical structures", "a pivotal role", "cognitive, affective, and social functions", "humans", "structural abnormalities", "these regions", "schizophrenia", "our finding", "schizophrenia", "widespread alterations", "subcortical brain structure", "the subcortical structural information", "prominent features", "diagnostic classification", "these results", "the potential", "deep learning", "schizophrenia diagnosis", "its structural neuroimaging signatures", "a single, standard t1-weighted brain mri", "schizophrenia", "3d", "three", "3d", "cnn", "roc", "0.987", "schizophrenia" ]

[ "Yesi Novaria Kunang", "Siti Nurmaini", "Deris Stiawan", "Bhakti Yudho Suprapto" ]

[ "the rapid growth", "the internet", "things", "(iot) platform", "implications", "security vulnerabilities", "that", "this", "an intrusion detection system", "ids", "attacks", "the platforms", "line", "this", "numerous machine", "deep learning algorithms", "cyber-attacks", "real-time iot devices", "massive amounts", "heterogeneous data", "which", "the network", "traffic networks", "redundant and large amounts", "data", "that", "processing", "this study", "a hybrid deep learning model", "an ids", "the iot platform", "we", "unsupervised approaches", "data dimensions", "features", ", then a neural network", "classification", "several approaches", "the effectiveness", "the deep learning-based iot ids", "two scenarios", "feature extraction", "the first case", "autoencoder variants", "deep autoencoder", "dae", "deep lstm autoencoder", "lstm-dae", "deep convolutional autoencoder", "the second case", "stacked models", "feature extraction", "autoencoder", "deep belief network", "the feature extraction output", "the five models", "the fully connected layer", "the bot-iot dataset", "the results", "a good detection performance", "almost 100%", "a false positive rate", "fpr", "nearly 0%", "the cse-cic-ids2018 dataset", "the proposed deep learning model", "a transfer learning approach", "the highest detection rate", "99.17%", "the lowest fpr", "0.18%", "the model", "the feature extraction process", "attacks", "the previous approach", "two", "first", "dae", "second", "five", "almost 100%", "nearly 0%", "99.17%", "0.18%" ]

[ "Jaakko Sahlsten", "Joel Jaskari", "Kareem A. Wahid", "Sara Ahmed", "Enrico Glerean", "Renjie He", "Benjamin H. Kann", "Antti Mäkitie", "Clifton D. Fuller", "Mohamed A. Naser", "Kimmo Kaski" ]

[ "backgroundradiotherapy", "a core treatment modality", "oropharyngeal cancer", "the primary gross tumor volume", "gtvp", "high interobserver variability", "this", "reliable and trustworthy automated tools", "clinician workflow", "accurate uncertainty quantification", "its downstream utilization", "we", "uncertainty-aware deep learning", "opc", "gtvp segmentation", "the utility", "uncertainty", "multiple applications", "we", "two bayesian deep learning", "bdl) models", "eight uncertainty measures", "a large multi-institute dataset", "292 pet/ct", "our approach.resultswe show", "our uncertainty-based approach", "the quality", "the deep learning segmentation", "86.6%", "cases", "low performance cases", "semi-automated correction", "regions", "the scans", "the segmentations", "bdl-based analysis", "a first-step", "more widespread implementation", "uncertainty quantification", "gtvp segmentation", "bayesian", "eight", "292", "86.6%", "fail.conclusionsour bdl", "first" ]

[ "Muhammad Najam Dar", "Muhammad Usman Akram", "Ahmad Rauf Subhani", "Sajid Gul Khawaja", "Constantino Carlos Reyes-Aldasoro", "Sarah Gul" ]

[ "recognition", "a real-world, less constrained environment", "the principal prerequisite", "the industrial-level usefulness", "this technology", "the psychological profile", "smart, wearable electroencephalogram (eeg) sensors", "daily activities", "external stimuli", "memory-induced emotions", "a challenging research gap", "emotion recognition", "this paper", "a deep learning framework", "improved memory-induced emotion recognition", "a combination", "1d-cnn", "lstm", "feature extractors", "an extreme learning machine", "(elm) classifier", "the proposed deep learning architecture", "the eeg preprocessing", "the removal", "the average baseline signal", "each sample", "extraction", "eeg rhythms", "delta", "theta", "alpha", "beta", "gamma", "repetitive and continuous patterns", "memory-induced emotion recognition", "deep learning techniques", "this work", "eeg signals", "a wearable, ultra-mobile sports cap", "autobiographical emotional memories", "affect-denoting words", "self-annotation", "the scale", "valence", "arousal", "extensive experimentation", "the same dataset", "the proposed framework", "existing techniques", "the emerging area", "memory-induced emotion recognition", "an accuracy", "65.6%", "the eeg rhythms analysis", "delta", "theta", "alpha", "beta", "gamma", "65.5%", "52.1%", "65.1%", "64.6%", "65.0% accuracies", "classification", "four quadrants", "valence", "arousal", "these results", "the significant advancement", "our proposed method", "the real-world environment", "memory-induced emotion recognition", "daily", "1d", "65.6%", "65.5%", "52.1%", "65.1%", "64.6%", "65.0%", "four" ]

[ "Mohaddeseh Nasiri Hooshmand", "Elham Maserat" ]

[ "cancer", "a common and dangerous disease", "the world health organization", "much research", "new and effective cancer treatments", "cancer vaccines", "the prediction", "neoantigens", "machine learning", "the purpose", "this study", "articles", "that", "cancer vaccines", "this study", "a rapid review study", "search strategies", "related keywords", "google scholar", "direct databases", "august", "1250 articles", "13 articles", "this review", "we", "them", "the importance", "popularity", "machine learning", "cancer vaccines", "we", "them", "their machine learning technique", "it", "neural networks", "python", "neoantigens", "4 articles", "matlab", "2 articles", "one article", "the fontom", "one article", "perl", "one article", "r", "other studies", "data mining", "flowsom algorithm", "multiple linear regression", "logistics", "oncopepvca", "the rest", "articles", "information", "implementation tools", "neural networks", "python", "the prediction", "neoantigens", "the precision", "examination", "complex data sets", "they", "hla and peptide binding affinity", "vaccines", "new data", "the immune response", "rna", "dna sequences", "immunological analysis", "the world health organization", "google", "2010", "2021", "2021", "august 2023.", "13", "4", "2", "one", "one", "one" ]

[ "Camellia Ray", "Sambit Bakshi", "Pankaj Kumar Sa", "Ganapati Panda" ]

[ "customized healthcare", "cattle health monitoring", "which", "individual animal health", "productivity", "illness-related risks", "overall welfare", "healthcare practices", "individual requirements", "individual animals", "proper attention", "intervention", "better health outcomes", "sustainable cattle farming practices", "this regard", "the manuscript", "a visual cues-based region prediction methodology", "a customized cattle healthcare system", "the proposed automated ai healthcare system", "resource-efficient deep learning-inspired architecture", "computer vision applications", "region-wise classification", "the classification mechanism", "a cattle", "the regions", "it", "extensive experimentation", "a redesigned image dataset", "the best-suited deep-learning framework", "region classification", "livestock", "cattle", "mobilenetv2", "the-art", "an accuracy", "93%", "the regions", "the cattle", "tailoring healthcare", "mobilenetv2", "93%" ]

[ "C. Lalitha", "D. Ravindran" ]

[ "the volume", "complexity", "weather data", "missing values", "high correlation", "collected variables", "it", "efficient deep learning frameworks", "that", "data", "more features", "this", "a lack", "accurate and predictable weather forecasts", "a hybrid deep learning framework", "weather forecast", "rainfall prediction", "weather big data analytics", "high detection rates", "a modified planet optimization", "mpo", "algorithm", "data", "unwanted artifacts", "an improved tuna optimization", "ito", "algorithm", "optimal features", "data dimensionality issues", "a hybrid memory-augmented artificial neural network", "ma-ann) classifier", "weather early forecast detection rates", "the proposed framework", "standard benchmark datasets", "weather", "climate forecast system reanalysis", "cfsr", "the simulation results", "the-art", "error measures", "accuracy", "sensitivity", "specificity", "precision", "f1-measure).the ma-ann classifier accuracy", "97.65%", "98.88%", "tamilnadu", "the hybrid deep learning framework", "rainfall prediction", "weather big data analytics", "promising results", "accurate and predictable weather forecasts", "the proposed framework", "the-art", "the ma-ann classifier", "weather early forecast detection rates", "the study", "the potential", "big data techniques", "weather forecasting", "the importance", "efficient deep learning frameworks", "complex and high-dimensional weather data", "rmse", "97.65%", "wunderground.com delhi", "98.88%" ]

[ "Lei Li", "Xiao-li Yin" ]

[ "deep learning", "certain results", "image classification", "images", "factors", "lighting conditions", "angles", "complex backgrounds", "rotation transformations", "scale scaling", "image data sets", "some areas", "they", "the deep learning framework", "full play", "its generalization ability", "modeling ability", "image classification", "this paper", "a proposal region", "learning algorithm", "which", "the proposal regions", "each image", "each cluster", "the category", "the image", "different clusters", "different multi-instance learning packets", "each packet", "the multi-instance learning method", "the unsupervised image classification detector", "it", "the generalization", "modeling", "capabilities", "deep learning models", "addition", "this paper", "an unsupervised deep learning method", "which", "an unsupervised deep learning network structure and loss function", "the characteristics", "the classified image", "densely connected blocks", "features", "the source image", "it", "the multi-scale features", "the middle layer", "the classified image", "the problem", "insufficient image feature extraction information", "the lack", "image data", "it", "the accuracy", "subsequent image classification", "the experimental results", "the image classification method", "this paper", "the problem", "insufficient image data sets", "the interference", "various complex factors", "various image data sets", "the accuracy", "the image classification method", "this paper", "other mainstream deep learning methods", "first", "1.38–19%" ]

[ "Md Nur Hosain Likhon", "Sahab Uddin Rana", "Sadeka Akter", "Md. Shorup Ahmed", "Khadiza Akter Tanha", "Md. Mahbubur Rahman", "Md Emran Hussain Nayeem" ]

[ "cancer", "the leading cause", "death", "significantly impacting individuals", "healthcare systems", "recent decades", "skin cancer", "prevalence", "other major cancer types", "various factors", "texture", "color", "morphological characteristics", "structure", "different forms", "skin cancer", "traditional methods", "identification", "skin cancer classification", "machine learning", "the primary method", "convolutional neural networks", "cnns", "our ‘skinmultinet’ framework", "this study", "transfer learning principles", "the inceptionv3 and xception cnn models", "skin cancer", "image data", "other machine learning models", "resnet50", "nasnet", "mobilenet", "the 'skinmultinet' framework", "the most promising outcomes", "a publicly available dataset", "6086 skin images", "we", "our models", "proposed system", "a train generator", "image data", "our deep learning cnn models", "a learning rate reducer", "the datasets", "the model", "rigorous testing and validation procedures", "our models", "a substantial volume", "skin image data", "contrast", "conventional approaches", "our proposed architecture", "the potential", "more reliable diagnoses", "an optimal accuracy rate", "94%", "skin cancer prediction", "this advancement", "promise", "early detection", "improved patient outcomes", "therapy", "recent decades", "inceptionv3", "cnn", "resnet50", "6086", "cnn", "94%" ]

[ "Woohyung Lee", "Hyo Jung Park", "Hack-Jin Lee", "Ki Byung Song", "Dae Wook Hwang", "Jae Hoon Lee", "Kyongmook Lim", "Yousun Ko", "Hyoung Jung Kim", "Kyung Won Kim", "Song Cheol Kim" ]

[ "postoperative pancreatic fistula", "a life-threatening complication", "an unmet need", "accurate prediction", "this study", "preoperative artificial intelligence-based prediction models", "patients", "who", "pancreaticoduodenectomy", "model development", "validation sets", "surgery", "machine learning models", "clinical and body composition data", "deep learning models", "computed tomographic data", "ensemble voting", "final models", "comparison", "earlier model", "the 1333 participants", "training", "test", "postoperative pancreatic fistula", "47.8%", "31.8%", "clinically relevant postoperative pancreatic fistula", "6.7%", "the training", "test datasets", "the test dataset", "the area", "the receiver operating curve", "[auc", "95% confidence interval", "the selected preoperative model", "all and clinically relevant postoperative pancreatic fistula", "the ensemble model", "better predictive performance", "the individual ml", "dl models", "between 2016 and 2017", "2018", "1333", "881", "452", "421", "47.8%", "134", "31.8%", "59", "6.7%", "27", "6.0%", "95%", "0.75", "0.71–0.80", "0.68" ]

[ "Bina Kotiyal", "Heman Pathak", "Nipur Singh" ]

[ "fake news", "social media", "a growing concern", "its potential impact", "public opinion", "the proposed debunking multi-lingual social media posts", "(dsmpd", "a promising solution", "fake news", "the dsmpd approach", "a dataset", "english and hindi social media posts", "web scraping", "natural language processing (nlp) techniques", "this dataset", "a deep learning-based model", "that", "various features", "language models", "word and n-gram counts", "term frequency-inverse document frequency", "tf-idf", "sentiments", "polarity", "entity recognition", "ner", "these features", "the model classifies", "five categories", "the performance", "the classifiers", "the researchers", "two datasets", "over 45,000 articles", "ml", "the best option", "classification", "prediction", "english", "n-gram", "ner", "five", "two", "over 45,000" ]

[ "Owen Long", "Benjamin Nachman" ]

[ "many analyses", "particle", "nuclear physics", "simulations", "fundamental, effective, or phenomenological parameters", "the underlying physics models", "the inference", "unfolded cross sections", "the observables", "physics intuition", "heuristics", "we", "targeted observables", "machine learning", "unfolded, differential cross sections", "a neural network output", "the most information", "parameters", "interest", "construction", "the networks", "a custom loss function", "that", "outputs", "that", "the parameter(s", "interest", "outputs", "that", "particle-level", "detector-level", "detector distortions", "we", "this idea", "simulation", "two physics models", "inclusive measurements", "deep inelastic scattering", "we", "the new approach", "classical observables", "the two models", "a reduced unfolding uncertainty", "the reduced detector distortions", "two", "two" ]

[ "Yu Kang", "Jian Di", "Ming Li", "Yunbo Zhao", "Yuhui Wang" ]

[ "racing drones", "increasing attention", "their remarkable high speed", "excellent maneuverability", "autonomous multi-drone racing", "it", "quick and agile flight", "intricate surroundings", "rich drone interaction", "these issues", "we", "a novel autonomous multi-drone racing method", "deep reinforcement learning", "a new set", "reward functions", "racing drones", "the racing skills", "human experts", "previous methods", "that", "global information", "tracks", "boundary constraints", "the proposed method", "only limited localized track information", "the range", "its own onboard sensors", "the dynamic response characteristics", "racing drones", "the training environment", "the proposed method", "line", "the requirements", "real drone racing scenarios", "addition", "our method", "a low computational cost", "the requirements", "real-time racing", "the effectiveness", "superiority", "the proposed method", "extensive comparison", "the-art", "a series", "simulations", "real-world experiments" ]

[ "Yogesh Kumar", "Pertik Garg", "Manu Raj Moudgil", "Rupinder Singh", "Marcin Woźniak", "Jana Shafi", "Muhammad Fazal Ijaz" ]

[ "parasitic organisms", "a major global health threat", "regions", "that", "advanced medical facilities", "early and accurate detection", "parasitic organisms", "lives", "deep learning models", "the medical sector", "promising results", "diseases", "this paper", "the role", "deep learning techniques", "various parasitic organisms", "the research", "a dataset", "34,298 samples", "parasites", "toxoplasma gondii", "trypanosome", "plasmodium", "leishmania", "babesia", "trichomonad", "host cells", "red blood cells", "white blood cells", "these images", "rgb", "grayscale", "the computation", "morphological features", "perimeter", "height", "area", "width", "otsu thresholding and watershed techniques", "foreground", "background", "markers", "the images", "the identification", "regions", "interest", "deep transfer learning models", "vgg19", "inceptionv3", "resnet50v2", "resnet152v2", "efficientnetb3", "efficientnetb0", "mobilenetv2", "xception", "densenet169", "a hybrid model", "the parameters", "these models", "three optimizers", "sgd", "rmsprop", "adam", "experimental results", "rmsprop", "efficientnetb0", "the highest accuracy", "99.1%", "a loss", "the sgd optimizer", "inceptionv3", "the highest accuracy", "99.91%", "a loss", "the adam optimizer", "inceptionresnetv2 excels", "the highest accuracy", "99.96%", "a loss", "other optimizers", "the findings", "this research", "deep learning models", "image processing methods", "a highly accurate and efficient way", "parasitic organisms", "34,298", "leishmania", "babesia", "rgb", "inceptionv3", "efficientnetb3", "efficientnetb0", "mobilenetv2", "inceptionresnetv2", "three", "inceptionv3", "efficientnetb0", "99.1%", "0.09", "inceptionv3", "99.91%", "0.98", "inceptionresnetv2", "99.96%", "0.13" ]

[ "Ting Pang", "Peigao Li", "Lijie Zhao" ]

[ "recent advances", "deep learning", "great potential", "the automatic generation", "medical imaging reports", "deep learning techniques", "image captioning", "significant progress", "the field", "diagnostic report generation", "this paper", "a comprehensive overview", "recent research efforts", "deep learning-based medical imaging report generation", "future directions", "this field", "we", "the data set", "architecture", "application", "evaluation", "deep learning-based medical imaging report generation", "we", "the deep learning architectures", "diagnostic report generation", "hierarchical rnn-based frameworks", "attention-based frameworks", "reinforcement learning-based frameworks", "addition", "we", "potential challenges", "future research directions", "clinical applications", "decision-making", "medical imaging report generation systems", "first" ]

[ "Sanghun Lee", "Ki-Sub Kim", "Jeong Won Kang" ]

[ "we", "a deep learning color recognition model", "the color measurement process", "the paint industry", "spectrophotometers", "color measurements", "their accuracy", "the measurement method", "the sample", "a spectrophotometer", "our proposed method", "a webcam", "a deep learning model", "the stand", "a spectrophotometer", "deep learning models", "image", "color detection", "this study", "you", "algorithm", "real-time detection", "color samples", "various sample images", "yolo", "the model", "the sample area", "real time", "a webcam", "an open source computer vision", "(opencv) library", "the color recognition model", "the detected rgb color value", "the international commission", "illumination color space", "(cielab) value", "which", "the color measuring process", "the mirror-like reflection", "light", "a surface", "specular reflection", "it", "the color value", "a camera", "this problem", "we", "several specular removal methods", "the most suitable model", "the color recognition model", "color samples", "the accuracy", "the proposed model", "the colors", "various samples", "our proposed approach", "samples", "color values", "which", "the exact amount", "the target color", "deep" ]

[ "Tian Shi", "Jiahe Li", "Na Li", "Cheng Chen", "Chen Chen", "Chenjie Chang", "Shenglong Xue", "Weidong Liu", "Ainur Maimaiti Reyim", "Feng Gao", "Xiaoyi Lv" ]

[ "celiac disease", "cd", "a primary malabsorption syndrome", "the interplay", "genetic, immune, and dietary factors", "cd", "daily activities", "conditions", "osteoporosis", "malignancies", "the small intestine", "ulcerative jejunitis", "enteritis", "severe malnutrition", "an effective and rapid differentiation", "healthy individuals", "those", "celiac disease", "early diagnosis", "treatment", "this study", "raman spectroscopy", "deep learning models", "a non-invasive, rapid, and accurate diagnostic method", "celiac disease", "healthy controls", "a total", "59 plasma samples", "29 celiac disease cases", "30 healthy controls", "experimental purposes", "convolutional neural network", "cnn", "multi-scale convolutional neural network", "mcnn", "residual network", "resnet", "deep residual shrinkage network", "drsn) classification models", "the accuracy rates", "these models", "86.67%", "90.76%", "86.67%", "95.00%", "comparative validation results", "the drsn model", "the best performance", "an auc value", "accuracy", "97.60%", "95%", "this", "the superiority", "raman spectroscopy", "deep learning", "the diagnosis", "celiac disease", "daily", "59", "29", "30", "cnn", "86.67%", "90.76%", "86.67%", "95.00%", "97.60% and", "95%" ]

[ "Burhan Ul Haque Sheikh", "Aasim Zafar" ]

[ "the exploration", "innovative diagnostic approaches", "the utilization", "machine learning", "ml", "deep learning", "(dl) technologies", "recent findings", "light", "the susceptibility", "deep learning-based models", "adversarial attacks", "erroneous predictions", "this study", "the vulnerability", "a deep covid-19 diagnosis model", "the fast gradient sign method", "fgsm) adversarial attack", "transfer learning", "efficientnet-b2", "a publicly available dataset", "a deep learning-based covid-19 diagnosis model", "an impressive average accuracy", "94.56%", "clean test data", "an untargeted fgsm attack", "varying epsilon values", "the model’s accuracy", "21.72%", "epsilon", "the attack", "adversarial covid-19 images", "100% confidence", "this study", "the critical need", "further research", "development", "these vulnerabilities", "the reliability", "accuracy", "deep learning models", "the diagnosis", "covid-19 patients", "covid-19", "covid-19", "covid-19", "94.56%", "21.72%", "0.008", "covid-19", "100%", "covid-19" ]

[ "Heejoon Jeong", "Donghee Kim", "Dong Won Kim", "Seungho Baek", "Hyung-Chul Lee", "Yusung Kim", "Hyun Joo Ahn" ]

[ "purposeintraoperative hypotension", "adverse outcomes", "hypotension", "its incidence", "hypotension prediction algorithms", "artificial intelligence", "invasive arterial blood pressure monitors", "this study", "routine non-invasive monitors", "intraoperative hypotension", "algorithms.methodsan open-source database", "non-cardiac surgery patients", "https://vitadb.net/dataset", "the deep learning algorithm", "the algorithm", "external data", "a tertiary korean hospital", "intraoperative hypotension", "a systolic blood pressure", "less than 90 mmhg", "the input data", "five monitors", "non-invasive blood pressure", "electrocardiography", "photoplethysmography", "capnography", "bispectral index", "the primary outcome", "the performance", "the deep learning model", "the area", "the receiver operating characteristic curve", "auroc).resultsdata", "421 patients", "algorithm development", "external validation", "the fully connected model", "multi-head attention architecture", "the globally attentive locally recurrent model", "focal loss function", "intraoperative hypotension 5 min", "its occurrence", "the auroc", "the algorithm", "95% confidence interval", "the original data", "(95% ci", "the external validation data", "attention map", "which", "the contributions", "each monitor", "our algorithm utilized data", "each monitor", "weights", "8 to 22%", "hypotension.conclusionsa deep learning model", "multi-channel non-invasive monitors", "intraoperative hypotension", "high accuracy", "future prospective studies", "this model", "clinicians", "hypotension", "patients", "surgery", "non-invasive monitoring", "algorithms.methodsan", "tertiary", "korean", "less than", "five", "4754", "421", "5", "0.917", "95%", "0.915–0.918", "0.833", "95%", "8 to 22%", "hypotension.conclusionsa", "clinicians" ]

[ "Jingjing Zhang", "Qiang Zhang", "Bo Zhao", "Gaofeng Shi" ]

[ "a deep learning radiomics nomogram", "multi-phase contrast-enhanced computed tomography", "cect", "neoadjuvant chemotherapy (nac) response", "locally advanced gastric cancer", "lagc", "patients.methodsthis multi-center study", "322 patients", "gastric cancer", "january", "june", "two hospitals", "handcrafted radiomics technique", "the efficientnet v2 neural network", "arterial, portal venous, and delayed phase ct images", "two-dimensional handcrafted and deep learning features", "a nomogram model", "the handcrafted signature", "the deep learning signature", "clinical features", "discriminative ability", "the receiver operating characteristics", "(roc) curve", "the precision-recall (p-r) curve", "model fitting", "calibration curves", "clinical utility", "decision curve analysis", "dca).resultsthe nomogram", "excellent performance", "the area", "the roc curve", "auc", "[95% confidence interval", "ci", "0.793–0.893", "0.802 (95%", "ci 0.688–0.889", "(95%", "ci", "the training", "internal validation", "external validation sets", "the aucs", "the p-r curves", "0.756–0.895", "(95%", "ci 0.329–0.740", "ci 0.376–0.722", "the corresponding sets", "the nomogram", "the clinical model", "signature", "all sets", "all p", "the nomogram model", "good calibration", "greater net benefit", "the relevant threshold range", "other models.conclusionthis study", "a deep learning nomogram", "cect images", "clinical data", "nac response", "lagc patients", "surgical resection", "personalized treatment insights.graphical abstract", "322", "january 2013 to june 2023", "two", "two", "roc", "roc", "0.848", "95%", "0.802", "95%", "0.688–0.889", "0.751", "95%", "0.652–0.833", "0.838", "95%", "0.541", "95%", "0.329–0.740", "0.556", "95%" ]

[ "Michaela Unger", "Jakob Nikolas Kather" ]

[ "histopathology", "genomic profiling", "cornerstones", "precision oncology", "patients", "cancer", "histopathology slides", "highly trained pathologists", "genomic data", "the other hand", "engineered computational pipelines", "both applications", "the advent", "modern artificial intelligence methods", "specifically machine learning", "deep learning", "dl", "a fundamentally new way", "actionable insights", "raw data", "which", "some aspects", "traditional evaluation workflows", "this review", "we", "current and emerging applications", "dl", "histopathology", "genomics", "basic diagnostic as well as advanced prognostic tasks", "a growing body", "evidence", "we", "dl", "the groundwork", "a new kind", "workflow", "oncology and cancer research", "we", "dl models", "biases", "other flaws", "that", "users", "healthcare", "research", "we", "ways", "them" ]

[ "Xinjun Zhu", "Haichuan Zhao", "Mengkai Yuan", "Zhizhi Zhang", "Hongyi Wang", "Limei Song" ]

[ "phase", "the key roles", "fringe projection three-dimensional (3d) measurement technology", "we", "a new method", "phase", "camera array light", "fringe projection 3d measurement", "deep learning", "a multi-stream convolutional neural network", "cnn", "the mapping relationship", "camera array light", "phases", "fringe orders", "the expected central view", "the fringe order", "the phase", "experiments", "the light field fringe projection data", "the simulated camera array fringe projection measurement system", "blender", "the experimental 3×3 camera array light field fringe projection system", "the performance", "the proposed network", "light field", "phases", "multiple directions", "network input data", "the advantages", "phase", "deep learning", "light filed fringe projection", "three", "3d", "3d", "cnn", "3×3" ]

[ "Philipp Wesp", "Balthasar Maria Schachtner", "Katharina Jeblick", "Johanna Topalis", "Marvin Weber", "Florian Fischer", "Randolph Penning", "Jens Ricke", "Michael Ingrisch", "Bastian Oliver Sabel" ]

[ "backgroundradiological age assessment", "reference studies", "accuracy", "a finite number", "assignable skeletal maturation stages", "this limitation", "we", "a deep learning approach", "continuous age assessment", "clavicle ossification", "computed tomography", "(ct).methodsthoracic ct scans", "the picture archiving and communication system", "individuals", "15.0 to 30.0 years", "routine clinical practice", "all scans", "the medial clavicular epiphyseal cartilages", "a deep learning model", "a person’s chronological age", "these scans", "performance", "mean absolute error", "mae", "model performance", "an optimistic human reader performance estimate", "an established reference study", "method.resultsthe deep learning model", "4,400 scans", "1,935 patients", "training set", "mean age", "±", "1132 female", "300 scans", "300 patients", "a balanced age", "sex distribution", "mean age", "150 female", "model mae", "1.65 years", "the highest absolute error", "6.40 years", "females", "7.32 years", "males", "performance", "norm-variants", "pathologic disorders", "human reader", "mae", "1.84 years", "the highest absolute error", "3.40 years", "females", "3.78 years", "a deep learning approach", "continuous age predictions", "ct volumes", "the medial clavicular epiphyseal cartilage", "performance", "the human reader estimate", "15.0 to 30.0 years", "4,400", "1,935", "24.2 years", "4.0, 1132", "300", "300", "22.5 years", "4.4", "150", "mae", "1.65 years", "6.40 years", "7.32 years", "1.84 years", "3.40 years", "3.78 years" ]

[ "Zecheng Zhu", "Shunjin Zhao", "Jiahui Li", "Yuting Wang", "Luopiao Xu", "Yubing Jia", "Zihan Li", "Wenyuan Li", "Gang Chen", "Xifeng Wu" ]

[ "backgroundchronic obstructive pulmonary disease", "copd", "a frequently diagnosed yet treatable condition", "it", "this study", "an advanced copd diagnostic model", "deep learning and radiomics features.methodswe", "a dataset", "ct images", "2,983 participants", "which", "2,317 participants", "epidemiological data", "questionnaires", "deep learning features", "a variational autoencoder", "radiomics features", "the pyradiomics package", "multi-layer perceptrons", "models", "deep learning", "radiomics", "a fusion model", "both", "epidemiological questionnaire data", "a more comprehensive model", "the diagnostic performance", "standalone models", "the fusion model", "the comprehensive model", "metrics", "accuracy", "precision", "recall, f1-score", "brier score", "the curve", "auc).resultsthe fusion model", "outstanding performance", "an auc", "the standalone models", "deep learning features", "auc", "radiomics features", "auc =", "the comprehensive model", "deep learning features", "radiomics features", "questionnaire variables", "the highest diagnostic performance", "all models", "an auc", "0.971.conclusionwe", "a data fusion strategy", "the-art", "deep learning features", "radiomics features", "questionnaire variables", "our data fusion strategy", "the model", "clinical settings.trial registrationnot", "this study", "a clinical trial", "it", "the results", "a health care intervention", "human participants", "2,983", "2,317", "0.952", "0.844", "0.944", "0.971.conclusionwe" ]

[ "Ulzee An", "Ali Pazokitoroudi", "Marcus Alvarez", "Lianyun Huang", "Silviu Bacanu", "Andrew J. Schork", "Kenneth Kendler", "Päivi Pajukanta", "Jonathan Flint", "Noah Zaitlen", "Na Cai", "Andy Dahl", "Sriram Sankararaman" ]

[ "that", "deep phenotypic and genomic data", "many individuals", "a key resource", "human genetics", "phenotypes", "biobanks", "many individuals", "their utility", "we", "a deep learning-based imputation method", "population-scale biobank datasets", "collections", "phenotypes", "individuals", "the uk biobank", "existing methods", "three traits", "notable amounts", "missingness", "we", "autocomplete yields", "phenotypes", "that", "the originally observed phenotypes", "the effective sample size", "genome-wide association", "the resulting imputed phenotypes", "a substantial increase", "the number", "associated loci", "our results", "the utility", "deep learning-based phenotype imputation", "power", "genetic discoveries", "existing biobank datasets", "uk", "three", "about twofold" ]

[ "Vikram Rajpoot", "Akhilesh Tiwari", "Anand Singh Jalal" ]

[ "plant leaf disease detection", "long-term agricultural viability", "numerous artificial intelligence", "ai", "machine learning (ml) technologies", "rice diseases", "such methods", "slow recognition", "severe output loss", "an advanced and precise detection method", "this issue", "this study", "plant diseases", "that", "rice", "three different forms", "diseases", "bacterial leaf blight", "brown spot", "leaf smut", "the six diseases", "that", "rice plants", "the proposed approach", "a vgg-16 transfer", "faster r-cnn deep architecture", "features", "the transfer learning step", "the gathered characteristics", "the random forest method", "the random forest classifier", "the radish field", "three distinct regions", "the images", "rice plant leaves", "uci machine learning repository", "the proposed approach", "an average predicting accuracy", "97.3%", "rice disease imagery class prediction", "the extensive experiment outcomes", "the suggested technique’s validity", "it", "rice diseases", "three", "three", "six", "three", "uci", "97.3%" ]

[ "Baoxin Zhang", "Xiaopeng Wang", "Jinhan Cui", "Juntao Wu", "Zhi Xiong", "Wenpin Zhang", "Xinghua Yu" ]

[ "automated inspection", "modern industrial manufacturing", "production processes", "product quality", "welding", "a widely used joining technique", "defects", "porosity", "cracks", "product reliability", "traditional nondestructive testing (ndt) methods", "inefficiency", "limited accuracy", "many researchers", "deep learning", "defect detection", "these limitations", "this study", "traditional algorithms", "deep learning methods", "the swinunet model", "weld segmentation", "the model", "an impressive f1 score", "traditional algorithms", "feature analysis", "class activation maps", "the model's robust recognition", "generalization capabilities", "segmentation results", "different welding defects", "various models", "the recognition capabilities", "swinunet", "the findings", "the automation", "weld identification", "segmentation", "industrial production efficiency", "defect detection", "96.31" ]

[ "Michaela Unger", "Jakob Nikolas Kather" ]

[ "histopathology", "genomic profiling", "cornerstones", "precision oncology", "patients", "cancer", "histopathology slides", "highly trained pathologists", "genomic data", "the other hand", "engineered computational pipelines", "both applications", "the advent", "modern artificial intelligence methods", "specifically machine learning", "deep learning", "dl", "a fundamentally new way", "actionable insights", "raw data", "which", "some aspects", "traditional evaluation workflows", "this review", "we", "current and emerging applications", "dl", "histopathology", "genomics", "basic diagnostic as well as advanced prognostic tasks", "a growing body", "evidence", "we", "dl", "the groundwork", "a new kind", "workflow", "oncology and cancer research", "we", "dl models", "biases", "other flaws", "that", "users", "healthcare", "research", "we", "ways", "them" ]

[ "Xinjun Zhu", "Haichuan Zhao", "Mengkai Yuan", "Zhizhi Zhang", "Hongyi Wang", "Limei Song" ]

[ "phase", "the key roles", "fringe projection three-dimensional (3d) measurement technology", "we", "a new method", "phase", "camera array light", "fringe projection 3d measurement", "deep learning", "a multi-stream convolutional neural network", "cnn", "the mapping relationship", "camera array light", "phases", "fringe orders", "the expected central view", "the fringe order", "the phase", "experiments", "the light field fringe projection data", "the simulated camera array fringe projection measurement system", "blender", "the experimental 3×3 camera array light field fringe projection system", "the performance", "the proposed network", "light field", "phases", "multiple directions", "network input data", "the advantages", "phase", "deep learning", "light filed fringe projection", "three", "3d", "3d", "cnn", "3×3" ]

[ "Philipp Wesp", "Balthasar Maria Schachtner", "Katharina Jeblick", "Johanna Topalis", "Marvin Weber", "Florian Fischer", "Randolph Penning", "Jens Ricke", "Michael Ingrisch", "Bastian Oliver Sabel" ]

[ "backgroundradiological age assessment", "reference studies", "accuracy", "a finite number", "assignable skeletal maturation stages", "this limitation", "we", "a deep learning approach", "continuous age assessment", "clavicle ossification", "computed tomography", "(ct).methodsthoracic ct scans", "the picture archiving and communication system", "individuals", "15.0 to 30.0 years", "routine clinical practice", "all scans", "the medial clavicular epiphyseal cartilages", "a deep learning model", "a person’s chronological age", "these scans", "performance", "mean absolute error", "mae", "model performance", "an optimistic human reader performance estimate", "an established reference study", "method.resultsthe deep learning model", "4,400 scans", "1,935 patients", "training set", "mean age", "±", "1132 female", "300 scans", "300 patients", "a balanced age", "sex distribution", "mean age", "150 female", "model mae", "1.65 years", "the highest absolute error", "6.40 years", "females", "7.32 years", "males", "performance", "norm-variants", "pathologic disorders", "human reader", "mae", "1.84 years", "the highest absolute error", "3.40 years", "females", "3.78 years", "a deep learning approach", "continuous age predictions", "ct volumes", "the medial clavicular epiphyseal cartilage", "performance", "the human reader estimate", "15.0 to 30.0 years", "4,400", "1,935", "24.2 years", "4.0, 1132", "300", "300", "22.5 years", "4.4", "150", "mae", "1.65 years", "6.40 years", "7.32 years", "1.84 years", "3.40 years", "3.78 years" ]

[ "Zecheng Zhu", "Shunjin Zhao", "Jiahui Li", "Yuting Wang", "Luopiao Xu", "Yubing Jia", "Zihan Li", "Wenyuan Li", "Gang Chen", "Xifeng Wu" ]

[ "backgroundchronic obstructive pulmonary disease", "copd", "a frequently diagnosed yet treatable condition", "it", "this study", "an advanced copd diagnostic model", "deep learning and radiomics features.methodswe", "a dataset", "ct images", "2,983 participants", "which", "2,317 participants", "epidemiological data", "questionnaires", "deep learning features", "a variational autoencoder", "radiomics features", "the pyradiomics package", "multi-layer perceptrons", "models", "deep learning", "radiomics", "a fusion model", "both", "epidemiological questionnaire data", "a more comprehensive model", "the diagnostic performance", "standalone models", "the fusion model", "the comprehensive model", "metrics", "accuracy", "precision", "recall, f1-score", "brier score", "the curve", "auc).resultsthe fusion model", "outstanding performance", "an auc", "the standalone models", "deep learning features", "auc", "radiomics features", "auc =", "the comprehensive model", "deep learning features", "radiomics features", "questionnaire variables", "the highest diagnostic performance", "all models", "an auc", "0.971.conclusionwe", "a data fusion strategy", "the-art", "deep learning features", "radiomics features", "questionnaire variables", "our data fusion strategy", "the model", "clinical settings.trial registrationnot", "this study", "a clinical trial", "it", "the results", "a health care intervention", "human participants", "2,983", "2,317", "0.952", "0.844", "0.944", "0.971.conclusionwe" ]

[ "Ulzee An", "Ali Pazokitoroudi", "Marcus Alvarez", "Lianyun Huang", "Silviu Bacanu", "Andrew J. Schork", "Kenneth Kendler", "Päivi Pajukanta", "Jonathan Flint", "Noah Zaitlen", "Na Cai", "Andy Dahl", "Sriram Sankararaman" ]

[ "that", "deep phenotypic and genomic data", "many individuals", "a key resource", "human genetics", "phenotypes", "biobanks", "many individuals", "their utility", "we", "a deep learning-based imputation method", "population-scale biobank datasets", "collections", "phenotypes", "individuals", "the uk biobank", "existing methods", "three traits", "notable amounts", "missingness", "we", "autocomplete yields", "phenotypes", "that", "the originally observed phenotypes", "the effective sample size", "genome-wide association", "the resulting imputed phenotypes", "a substantial increase", "the number", "associated loci", "our results", "the utility", "deep learning-based phenotype imputation", "power", "genetic discoveries", "existing biobank datasets", "uk", "three", "about twofold" ]

[ "Dongfang Zhao", "Xu Huanshi", "Zhang Xun" ]

[ "the application", "reinforcement learning", "the field", "autonomous robotics", "high requirements", "sample efficiency", "the agent", "interaction", "the environment", "one method", "sample efficiency", "knowledge", "existing samples", "typical rl algorithms", "exploration", "task-specific knowledge", "exploration noise", "these methods", "current policy improvement level", "lack", "long-term planning", "we", "a novel active exploration", "deep rl algorithm", "the continuous action space problem", "active exploration deep reinforcement learning", "our method", "the gaussian process", "dynamic model", "the probability description", "prediction sample", "action selection", "the solution", "the optimization problem", "the optimization objective", "samples", "that", "the uncertainty", "the dynamic model", "active exploration", "long-term optimized action selection", "this long-term considered action exploration method", "more guidance", "intelligent agents", "more interesting action spaces", "the proposed aedrl algorithm", "several robotic control task", "classic pendulum problem", "five complex articulated robots", "the aedrl", "a controller", "fewer episodes", "performance", "sample efficiency", "one", "five" ]

[ "Vikram Rajpoot", "Akhilesh Tiwari", "Anand Singh Jalal" ]

[ "plant leaf disease detection", "long-term agricultural viability", "numerous artificial intelligence", "ai", "machine learning (ml) technologies", "rice diseases", "such methods", "slow recognition", "severe output loss", "an advanced and precise detection method", "this issue", "this study", "plant diseases", "that", "rice", "three different forms", "diseases", "bacterial leaf blight", "brown spot", "leaf smut", "the six diseases", "that", "rice plants", "the proposed approach", "a vgg-16 transfer", "faster r-cnn deep architecture", "features", "the transfer learning step", "the gathered characteristics", "the random forest method", "the random forest classifier", "the radish field", "three distinct regions", "the images", "rice plant leaves", "uci machine learning repository", "the proposed approach", "an average predicting accuracy", "97.3%", "rice disease imagery class prediction", "the extensive experiment outcomes", "the suggested technique’s validity", "it", "rice diseases", "three", "three", "six", "three", "uci", "97.3%" ]

[ "Gokhan Seyfi", "Engin Esme", "Merve Yilmaz", "Mustafa Servet Kiran" ]

[ "the invention", "the x-ray beam", "it", "useful applications", "various fields", "medical diagnosis", "radiation therapy", "tomography", "addition", "it", "prohibited or illegal materials", "x-ray imaging", "the security field", "these procedures", "the human factor", "an operator detects", "objects", "pseudo-color images", "a computer screen", "these processes", "error", "much work", "the processes", "initial research", "this topic", "machine learning", "methods", "hand-crafted features", "the newly developed deep learning methods", "this reason", "deep learning algorithms", "a trend", "recent studies", "the number", "publications", "areas", "x-ray imaging", "we", "the studies", "the literature", "deep learning-based x", "-ray imaging", "new readers", "new perspectives" ]

[ "Shern Ping Choy", "Byung Jin Kim", "Alexandra Paolino", "Wei Ren Tan", "Sarah Man Lin Lim", "Jessica Seo", "Sze Ping Tan", "Luc Francis", "Teresa Tsakok", "Michael Simpson", "Jonathan N. W. N. Barker", "Magnus D. Lynch", "Mark S. Corbett", "Catherine H. Smith", "Satveer K. Mahil" ]

[ "skin diseases", "one-third", "the global population", "a major healthcare burden", "deep learning", "healthcare workflows", "skin images", "neural networks", "predictions", "a focus", "deep learning research", "skin lesion triage", "cancer", "this", "the wider scope", "other skin diseases", "we", "studies", "deep learning", "skin images", "benign/malignant lesions", "prospero", "the primary outcome", "accuracy", "deep learning algorithms", "disease diagnosis", "severity assessment", "we", "quadas-2", "quality assessment", "13,857 references", "the most studied diseases", "urticaria", "deep learning algorithms", "high specificity", "variable sensitivity", "these conditions", "accuracy", "algorithms", "acne", "iqr", "rosacea", "94%", "90–97", "eczema", "93%", "90–99", "psoriasis", "89%", "n = 8)", "accuracy", "grading severity", "psoriasis", "n", "eczema", "88%", "acne", "67–86%", "92%", "bias", "62 (97%", "high-level applicability concerns", "only 12 (19%", "participant ethnicity/skin type", "twenty-four (37.5%", "the algorithm", "an independent dataset, clinical setting", "these data", "potential", "deep learning image analysis", "common skin diseases", "current research", "important methodological/reporting limitations", "real-world, prospectively-acquired image datasets", "external validation/testing", "deep learning", "the current experimental phase", "clinically-useful tools", "rising health and cost impacts", "skin disease", "one-third", "2000", "1/1/2000-23/6/2022", "13,857", "64", "94%", "11", "94%", "90–97", "4", "93%", "90–99", "9", "89%", "78–92", "8)", "93–100%", "2", "88%", "1", "67–86%", "4", "59", "92%", "62", "97%", "only 12", "19%", "twenty-four", "37.5%" ]

[ "Zeyu Liu", "Jiangjiang Wu", "Xu Gao", "Zhipeng Qin", "Run Tian", "Chunsheng Wang" ]

[ "backgroundthe patellar height index", "the measurement procedures", "significant variability", "observers", "we", "a deep learning-based automatic measurement system", "the patellar height", "its performance and generalization ability", "the patellar height index.methodswe", "a dataset", "3,923 lateral knee x-ray images", "all x-ray images", "three tertiary level", "a hospitals", "2,341 cases", "the analysis", "key points", "the model", "the residual network", "resnet", "high-resolution network", "hrnet", "human pose estimation", "the patellar height index", "various data enhancement techniques", "the robustness", "the model", "the root mean square error", "rmse", "object keypoint similarity", "oks", "percentage", "pck", "the training results", "addition", "we", "the intraclass correlation", "coefficient", "icc", "the consistency", "manual and automatic measurements.resultsthe hrnet model", "keypoint detection tasks", "different deep learning models", "the pose_hrnet_w48 model", "the rmse", "oks", "pck metrics", "salvati index", "isi", "this model", "the manual measurements", "intraclass correlation", "this evidence", "the accuracy", "generalizability", "this deep learning system", "a deep learning-based automatic measurement system", "the patellar height", "the system", "accuracy", "that", "experienced radiologists", "a strong generalizability", "different datasets", "it", "an essential tool", "knee diseases", "knee surgery", "the potential bias", "the selection", "datasets", "this study", "different datasets", "the future", "the model", "it", "clinical practice.trial registrationthe study", "the medical research registration and filing information system", "date", "registration", "3,923", "three", "2,341", "rmse", "oks", "isi", "mr-61", "may 04, 2023" ]

[ "Tabasum Majeed", "Tariq Ahmad Masoodi", "Muzafar Ahmad Macha", "Muzafar Rasool Bhat", "Khalid Muzaffar", "Assif Assad" ]

[ "oral cavity squamous cell carcinoma", "ocscc", "a common form", "head and neck cancer", "the mucosal lining", "the oral cavity", "advanced stages", "traditional detection methods", "hematoxylin", "eosin", "histopathological whole-slide images", "which", "expert pathology skills", "automated analysis", "diagnosis", "patient outcomes", "deep learning", "automated feature extraction", "a promising avenue", "high-level abstract features", "greater accuracy", "traditional methods", "the imbalance", "class distribution", "datasets", "the performance", "deep learning models", "training", "specialized approaches", "the issue", "various methods", "both data", "algorithmic levels", "this study", "strategies", "class imbalance", "a publicly available ocscc imbalance dataset", "we", "undersampling methods", "near miss", "neighbors", "techniques", "smote", "deep smote", "adasyn", "different imbalance ratios", "our findings", "the effectiveness", "smote", "test performance", "the efficacy", "strategic oversampling", "imbalanced medical datasets", "diagnostic accuracy", "clinical decisions", "reliance", "costly histopathological tests", "hematoxylin", "0.1", "0.15", "0.20", "0.30" ]

[ "Do Yeong Lim", "Ik Jae Jin", "In Cheol Bang" ]

[ "this study", "the application", "vision grayscale imaging", "deep learning", "enhanced abnormal detection", "nuclear power plants", "a scaled model", "the modern pressurized water reactor", "the data collection", "normal and abnormal conditions", "a drone", "dual vision", "ir cameras", "detailed operational imagery", "subtle anomalies", "the plant's primary systems", "deep learning algorithms", "these images", "component abnormals", "traditional monitoring", "the object detection model", "normal and abnormal component states", "the facility", "color-coded bounding boxes", "clarity", "models", "yolo", "mask r-cnn", "their precision", "anomaly detection", "results", "the yolo v8m model", "high accuracy", "system anomalies", "high map scores", "the integration", "drone technology", "ir imaging", "deep learning", "a significant stride", "abnormal detection", "complex industrial environments", "operational safety", "efficiency", "this approach", "the potential", "real-time monitoring", "safety", "critical settings", "a comprehensive, automated solution", "abnormal detection", "anomaly detection" ]

[ "Mahabuba Akhter", "Syed Md. Minhaz Hossain", "Rizma Sijana Nigar", "Srabanti Paul", "Khaleque Md. Aashiq Kamal", "Anik Sen", "Iqbal H. Sarker" ]

[ "people", "information", "the widespread use", "mobile networked devices", "this", "the spread", "false information", "such information", "which", "people", "incorrect decisions", "potentially crucial topics", "this", "the year", "the fatal and extremely contagious coronavirus disease", "covid-19", "the spread", "false information", "covid-19", "social media", "the world health organization", "who", "serious difficulties", "governments", "it", "a model", "fake news", "covid-19", "this paper", "we", "an effective convolutional neural network", "cnn)-based deep learning model", "word", "the best cnn architecture", "we", "account", "the optimal values", "model hyper-parameters", "grid search", "the effectiveness", "our proposed cnn model", "the-art", "covid-19 fake news detection", "them", "cnn", "96.19%", "mean accuracy", "95%", "0.985 area", "roc curve", "auc", "2020", "the year", "covid-19", "covid-19", "the world health organization", "covid-19", "cnn", "cnn", "covid-19", "cnn", "96.19%", "95%", "0.985", "roc" ]

[ "Zexin Ji", "Beiji Zou", "Xiaoyan Kui", "Jun Liu", "Wei Zhao", "Chengzhang Zhu", "Peishan Dai", "Yulan Dai" ]

[ "magnetic resonance imaging", "mri", "a medical imaging technique", "anatomical structures", "physiological processes", "the human body", "limitations", "image acquisition time", "hardware capabilities", "uncooperative patients", "the resolution", "mr images", "-", "resolution", "sr", "a crucial method", "the resolution", "images", "expensive scanning equipment", "recent years", "significant progress", "mr image", "super", "-", "resolution", "this survey", "a thorough overview", "current developments", "deep learning-based mr image super-resolution methods", "we", "the mri super-resolution methods", "single-contrast mr image sr methods", "-", "contrast mr image sr methods", "we", "the multi-task learning approaches", "the mr image", "super", "-", "resolution", "we", "other crucial topics", "the degradation model", "the definition", "the super-resolution problem", "the dataset", "loss functions", "image quality assessment", "we", "the challenges", "the field", "super", "-", "resolution", "a conclusion", "our survey", "recent years" ]

[ "Shubhankar Bhakta", "Utpal Nandi", "Madhab Mondal", "Kuheli Ray Mahapatra", "Partha Chowdhuri", "Pabitra Pal" ]

[ "deep model training", "an optimization technique", "the development", "an effective optimization method", "the most important study areas", "the diffgrad optimization method", "gradient changes", "optimization phases", "2nd order moments", "1st order moments", "the angulargrad optimization method", "the angular value", "the gradient", "which", "additional calculation", "these factors", "both", "those approaches", "zigzag trajectories", "that", "a long time", "additional calculations", "a global minimum", "those limitations", "a novel adaptive deep learning optimization method", "square", "first momentum", "(sqfm", "2nd order moments", "1st order moments", "step size", "the present gradient", "the non-negative function", "the suggested sqfm", "a smoother trajectory", "better image classification accuracy", "the empirical research", "the performance", "the proposed sqfm", "adam", "diffgrad", "angulargrad", "non-convex functions", "the suggested method", "the best convergence and parameter values", "comparison", "the rosenbrock function", "the proposed sqfm method", "the global minima", "less overshoot", "it", "the proposed sqfm", "consistently good classification accuracy", "cnn networks", "resnet16", "resnet50", "vgg34", "resnet18", "densenet121", "the cifar10", "cifar100", "mnist datasets", "contrast", "diffgrad", "adam", "angulargrad(cos", "angulargrad(tan", "the proposed method", "the best classification accuracy", "sgd", "adam", "adabelief", "yogi", "radam", "angulargrad", "the imagenet", "the resnet18 network", "source code link", "https://github.com/utpalnandi/sqfm-a-novel-adaptive-optimization-scheme-for-deep-learning-model", "2nd", "1st", "first", "2nd", "1st", "diffgrad", "adam", "diffgrad", "angulargrad(tan", "cnn", "resnet16", "resnet50", "resnet18", "cifar10", "diffgrad", "adam", "angulargrad(cos", "resnet18" ]

[ "Haitao Yu", "Zhiwen Hu", "Quanfa Zhao", "Jing Liu" ]

[ "electroencephalography", "(eeg", "high temporal resolution neural data", "brain-computer", "noninvasive electrophysiological recording", "the internal brain activity", "means", "source", "imaging techniques", "the spatial resolution", "eeg", "the reliability", "neural decoding and brain-computer interaction", "this work", "we", "a novel eeg data-driven source", "scheme", "precise and efficient estimation", "macroscale spatiotemporal brain dynamics", "thalamus and cortical regions", "deep learning methods", "a deep source imaging framework", "a convolutional-recurrent neural network", "the internal brain dynamics", "high-density eeg recordings", "a brain model", "210 cortical regions", "16 thalamic nuclei", "human brain connectome", "synthetic training data", "which", "intrinsic characteristics", "underlying brain dynamics", "spontaneous, stimulation-evoked, and pathological states", "transfer learning algorithm", "the trained network", "the dynamical differences", "synthetic and realistic eeg", "extensive experiments", "the proposed deep-learning method", "the spatial and temporal activity", "brain sources", "superior performance", "the-art", "the eeg data-driven source imaging framework", "the location", "seizure onset zone", "epilepsy", "reconstruction", "dynamical thalamocortical interactions", "sensory processing", "acupuncture stimulation", "its applicability", "brain-computer", "neuroscience research and clinical applications", "electroencephalography", "210", "16" ]

[ "Jamila Ouzine", "Manal Marzouq", "Saad Dosse Bennani", "Khadija Lahrech", "Hakim EL Fadili" ]

[ "non-intrusive load monitoring (nilm) technique or energy disaggregation", "a technique", "the appliance’s states", "their individual energy consumption", "the aggregated data", "the main smart meter", "energy efficiency", "the main goal", "the nilm techniques", "which", "energy disaggregation feedback", "the consumers", "single models", "training", "each appliance", "this work", "multi-target disaggregation", "which", "the drastic reduction", "resources", "training", "all target appliances", "this purpose", "new hybrid models", "well-known deep learning models", "convolutional neural network", "cnn", "autoencoder", "dae", "recurrent neural network", "rnn", "long short-term memory network", "lstm", "an implementation", "detailed comparative study", "the proposed hybrid deep learning models", "conventional single models", "terms", "various performance metrics", "the uk-domestic appliance-level electricity", "(ukdale", "benchmarking database", "the experimental results", "the proposed hybrid models", "the best disaggregation performances", "multi-target disaggregation", "single models", "the cnn-lstm", "the dae-lstm", "the best hybrid models", "the highest overall f1-score", "78.90%", "72.94%", "cnn", "dae", "uk", "cnn", "78.90%", "72.94%" ]

[ "Nihal Remzan", "Karim Tahiry", "Abdelmajid Farchi" ]

[ "brain tumors", "a severe health concern", "all age groups", "present challenges", "accurate grading", "health monitoring", "automated diagnosis", "mri scans", "their superior quality", "comprehensive anatomical insight", "this study", "the complexities", "brain tumor classification", "deep learning (dl) algorithms", "this field", "radiologists", "enhanced diagnostic precision", "the pivotal role", "extensive training data", "dl success", "medical image datasets", "limitations", "diversity", "quantity", "transfer learning", "a solution", "gaps", "similar yet distinct areas", "the inadequacy", "general pre-trained models", "imagenet", "medical imaging characteristics", "our recommendation", "radimagenet", "this tailored solution", "pre-trained models", "the specific demands", "medical imaging datasets", "the current mismatch", "our dataset", "mr images", "normal brain", "glioma", "meningioma", "pituitary tumors", "we", "two approaches", "feature ensemble", "2-top features", "ensemble", "svm", "extra trees", "mlp", "base learners", "logistic regression", "lr", "a meta-learner", "our initial approach", "resnet-50 and densenet121 features", "mlp", "we", "an impressive 97.71% accuracy", "a remarkable area", "the curve", "auc", "99.87%", "the stacking ensemble method", "densenet121", "the optimal feature extractor", "we", "a commendable 97.40% accuracy", "an exceptional auc", "99.83%", "these outcomes", "the efficacy", "our ensemble learning strategies", "both accuracy", "the overall discriminative power", "the model", "7023", "glioma", "two", "2", "resnet-50", "97.71%", "99.87%", "97.40%", "99.83%" ]

[ "Jawad Ahmad Dar", "Kamal Kr Srivastava", "Sajaad Ahmed Lone" ]

[ "the covid-19 prediction process", "the spread", "death", "occurred rate", "covid-19", "early and precise prediction", "covid-19", "different sizes", "resolutions", "input image", "these challenges", "problems", "traditional covid-19 detection methods", "major motivation", "sjhbo-based deep q network", "the classification issue", "respiratory sound", "a great focus", "the clinical scientists", "the community", "medical researcher", "the previous year", "the identification", "covid-19 disease", "the major contribution", "this research", "an effectual covid-19 detection model", "devised sjhbo-based deep q network", "this paper", "the covid-19 detection", "the deep learning", "optimization technique", "namely snake jaya honey badger optimization", "sjhbo", "algorithm-driven deep q network", "the sjhbo algorithm", "the incorporation", "jaya honey badger optimization", "jhbo", "snake optimization", "the covid-19", "the deep q network", "the weights", "deep q network", "the sjhbo algorithm", "jhbo", "hybrids", "which", "the jaya algorithm", "honey badger optimization", "hbo", "algorithm", "the features", "spectral contrast", "empirical mode decomposition", "emd", "algorithm", "spectral flux", "fast fourier transform", "fft", "spectral roll-off", "spectral centroid", "zero-crossing rate", "root mean square energy", "spectral flatness", "power spectral density", "mobility complexity", "fluctuation index", "relative amplitude", "the detection performance", "the developed method", "the better performance", "the accuracy", "sensitivity", "specificity", "all test results", "the k-fold cross validation method", "order", "an assessment", "the generalizability", "these results", "statistical analysis", "the performance", "the proposed method", "testing accuracy", "sensitivity", "specificity", "this paper", "the newly devised sjhbo-based deep q-net", "covid-19 detection", "this research", "the audio samples", "an input", "which", "the coswara dataset", "the sjhbo-based deep q network approach", "covid-19 detection", "the developed approach", "other features", "that", "the detection performance", "the proposed covid-19 detection method", "various applications", "developed sjhbo-enabled deep q network", "covid-19 detection", "an effective covid-19 detection technique", "hybrid optimization", "driven deep learning model", "the deep q network", "covid-19", "which", "the feature vector", "covid-19", "non", "-", "covid-19", "the deep q network", "devised sjhbo approach", "which", "the incorporation", "jaya honey badger optimization", "jhbo", "snake optimization", "covid-19", "covid-19", "covid-19", "covid-19", "the previous year", "covid-19", "covid-19", "covid-19", "jaya honey badger optimization", "covid-19", "jhbo", "mel", "zero", "0.9511", "0.9506", "0.9469", "the k-fold", "covid-19", "coswara", "covid-19", "covid-19", "covid-19", "covid-19", "covid-19", "covid-19", "non-covid-19", "jaya honey badger optimization" ]

[ "Xiaopeng Wang", "Baoxin Zhang", "Jinhan Cui", "Juntao Wu", "Yan Li", "Jinhang Li", "Yunhua Tan", "Xiaoming Chen", "Wenliang Wu", "Xinghua Yu" ]

[ "automatic detection", "welding flaws", "deep learning methods", "great interest", "the non-destructive testing", "few studies", "the characteristics", "welding flaws", "the x-ray image", "this study", "four deep learning models", "a dataset", "15,194 x-ray images", "a hybrid prediction", "logic", "the miss detection", "the miss detection rate", "0.61%", "which", "state", "the art", "quantitative analysis", "flaws’ characteristics", "the area", "aspect ratio", "variance", "the aspect ratios", "miss detected flaws", "the coefficient variances", "miss detected flaws", "the critical pixels", "x-ray images", "salt noises", "false alarmed predictions", "error analysis", "the deep learning method", "automatic welding flaws detection", "the characteristics", "flaws", "the factors", "inappropriate x-ray exposure techniques", "four", "15,194", "0.61%", "2", "0.2" ]

[ "Janjhyam Venkata Naga Ramesh", "Pavithra Roy Patibandla", "Manjula Shanbhog", "Srinivas Ambala", "Mohd Ashraf", "Ajmeera Kiran" ]

[ "the summer season", "india", "a severe shortage", "water", "which", "significant challenges", "daily usage", "agricultural practices", "unpredictable weather patterns", "irregular rainfall", "it", "water bodies", "domestic ponds", "lakes", "urban areas", "they", "clean and safe water", "regular use", "industrial pollutants", "this research paper", "we", "an innovative ensemble deep learning approach", "e", "-", "dla", "that", "deep learning models", "the turbidity", "dooskal lake", "telangana", "india", "remote sensing data", "the proposed approach", "various deep learning models", "bagging", "stacking", "the complex relationships", "remote sensing data", "turbidity levels", "the lake", "the study", "accurate and efficient predictions", "turbidity levels", "which", "the management", "conservation", "water resources", "the region", "hyperparameter tuning", "dynamic climatic features", "global protective intelligent algorithm", "the complex relationship", "situ", "measured values", "turbidity", "the measuring timeline", "the proposed approach", "accurate predictions", "turbidity levels", "the implementation", "effective control measures", "water quality standards", "experimental results", "the proposed approach", "prediction errors", "existing deep learning models", "this research", "the potential", "machine learning techniques", "monitoring", "water resources", "urban areas", "sustainable water management", "usage", "an urgent and pressing issue", "india", "the world", "the summer season", "india", "daily", "telangana", "india", "india" ]

[ "Piergiorgio Marigliano" ]

[ "this study", "we", "the application", "artificial intelligence", "(ai", "deep learning", "neural networks", "sentiments", "tourism reviews", "our dataset", "various hotel reviews", "the objective", "each textual review", "positive or negative feedback", "the primary challenge", "the textual data", "the reviews", "this prediction", "meticulous data processing", "analysis", "we", "neural network-based models", "that", "the efficacy", "deep learning", "the accurate interpretation", "reviews", "our findings", "a significant correlation", "the content", "reviews", "their overall ratings", "new insights", "the application", "ai", "understanding", "customer needs", "perceptions", "the tourism sector", "the principal contribution", "this study", "the practical demonstration", "techniques", "large volumes", "textual data", "new avenues", "marketing strategies", "service optimization", "the hospitality industry", "each review", "a client’s assessment", "a hotel", "each textual review", "we", "it", "a positive review", "the customer", "a negative review", "the customer", "the overall ratings", "the reviews", "the issue", "we", "them", "negative reviews", "overall ratings", "positive reviews", "ratings", "the challenge", "this information", "only the raw textual data", "the review", "2.5/10", "10/10", "less than 5", "5" ]

[ "Xiaomeng Zhu", "Pär Mårtensson", "Lars Hanson", "Mårten Björkman", "Atsuto Maki" ]

[ "the manufacturing industry", "automatic quality inspections", "improved product quality", "productivity", "deep learning-based computer vision technologies", "their superior performance", "many applications", "a possible solution", "automatic quality inspections", "a large amount", "annotated training data", "deep learning", "processes", "various products", "human activities", "assembly", "this challenge", "we", "a method", "automated assembly quality inspection", "synthetic data", "computer-aided design (cad) models", "the method", "two steps", "automatic data generation and model implementation", "the first step", "we", "synthetic data", "two formats", "two-dimensional (2d) images", "three-dimensional (3d", "the second step", "we", "the-art", "the data", "quality inspection", "unsupervised domain adaptation", "i.e., a method", "adapting models", "different data distributions", "transfer learning", "which", "knowledge", "related tasks", "we", "the methods", "a case study", "pedal car front-wheel assembly quality inspection", "the possible optimal approach", "assembly quality inspection", "our results", "the method", "transfer learning", "2d synthetic images", "superior performance", "others", "it", "95% accuracy", "fine-tuning", "only five annotated real images", "class", "promising results", "our method", "other similar quality inspection use cases", "synthetic cad data", "our method", "the need", "manual data collection", "annotation", "our method", "test data", "different backgrounds", "it", "different manufacturing environments", "two", "first", "two", "two", "2d", "three", "3d", "second", "2d", "95%", "only five" ]

[ "M. Hossein Eybposh", "Changjia Cai", "Aram Moossavi", "Jose Rodriguez-Romaguera", "Nicolas C. Pégard" ]

[ "the realism", "naturalness", "images", "virtual (vr", "augmented (ar) reality applications", "full reference image quality assessment", "fr-iqa", "metrics", "that", "human perception", "deep learning-based iqas", "that", "human-labeled data", "promise", "generic computer vision tasks", "their performance", "applications", "perfect matches", "the reference", "the distorted images", "distortion patterns", "specific domains", "this issue necessitates", "a task-specific neural network", "human-labeled fr-iqas", "deep learning", "substantial labeled data", "these challenges", "we", "coniqa", "that", "consistency training", "a novel data augmentation method", "both labeled and unlabeled data", "this", "contexts", "scarce labeled data", "validate coniqa", "we", "the example application", "computer-generated holography", "cgh", "where specific artifacts", "ringing", "speckle", "quantization errors", "existing iqas", "we", "a new dataset", "hqa1k", "that", "1000 natural images", "each", "an image", "various popular cgh algorithms", "thirteen human participants", "our results", "coniqa", "superior pearson", "spearman", "kendall’s tau (0.86) correlations", "fifteen fr-iqa metrics", "up to 5%", "significant improvements", "human perception", "the hqa1k dataset", "1000", "thirteen", "0.98", "0.965", "0.86", "fifteen", "up to 5%" ]

[ "Bin Zhao", "Bin Li", "Jiqun Zhang", "Wei Cao", "Yilong Gao" ]

[ "the recommendation system", "valuable information", "user preferences", "it", "various industries", "the performance", "recommendation systems", "the problem", "data sparsity", "lightgbm", "the impact", "data sparsity", "a certain extent", "this end", "this paper", "a fusion recommendation model", "the lightgbm", "deep learning", "clgm model", "the model", "lighgbm", "cross network", "deep neural network", "the features", "the dataset", "lightgbm", "the feature", "the highest classification accuracy", "the input", "the neural network layer", "the cross network", "the deep neural network", "the linear cross combination feature relationship", "nonlinear correlation relationship", "high-order features", "the results", "the pre-order network", "the final recommendation result", "this paper", "auc", "logloss", "evaluation indicators", "the model", "the public dataset criteo", "dataset avazu", "the simulation experiment results", "the four typical recommendation models", "the recommendation effect", "this model", "first", "four" ]

[ "Peter J. Illingworth", "Christos Venetis", "David K. Gardner", "Scott M. Nelson", "Jørgen Berntsen", "Mark G. Larman", "Franca Agresta", "Saran Ahitan", "Aisling Ahlström", "Fleur Cattrall", "Simon Cooke", "Kristy Demmers", "Anette Gabrielsen", "Johnny Hindkjær", "Rebecca L. Kelley", "Charlotte Knight", "Lisa Lee", "Robert Lahoud", "Manveen Mangat", "Hannah Park", "Anthony Price", "Geoffrey Trew", "Bettina Troest", "Anna Vincent", "Susanne Wennerström", "Lyndsey Zujovic", "Thorir Hardarson" ]

[ "the value", "deep learning", "the optimal embryo", "in vitro fertilization", "a multicenter", "randomized, double-blind, noninferiority parallel-group trial", "in vitro fertilization clinics", "australia", "europe", "women", "42 years", "age", "at least two early-stage blastocysts", "day", "either the control arm", "standard morphological assessment", "the study arm", "a deep learning algorithm", "intelligent data analysis score", "(idascore", "embryo selection", "the primary endpoint", "a clinical pregnancy rate", "a noninferiority margin", "5%", "the trial", "1,066 patients", "the idascore group", "the morphology group", "the idascore group", "a clinical pregnancy rate", "46.5%", "533 patients", "48.2%", "533 patients", "the morphology arm", "risk difference", "−1.7%", "95% confidence interval −7.7", "p", "this study", "noninferiority", "deep learning", "clinical pregnancy rate", "standard morphology", "a predefined prioritization scheme", "australian new zealand clinical trials registry (anzctr) registration", "14", "australia", "europe", "42 years of age", "at least two", "day 5", "5%", "1,066", "533", "533", "46.5%", "248", "533", "48.2%", "257", "533", "−1.7%", "95%", "−7.7", "4.3", "0.62", "australian", "new zealand", "379161" ]

[ "Ziqian Wang" ]

[ "series abnormalities", "signs", "upcoming problems", "new computational anomaly detection techniques", "early warning systems", "real-time system condition monitoring", "security and intrusion detection systems", "ids", "critical components", "internet", "things", "(iot) devices", "current approaches", "complex data", "unique intrusion detection systems", "idss", "today’s network security platforms", "deep learning techniques", "the cybertwin-enhanced self-attention mechanisms", "long short-term memory anomaly detection", "(dl-cyberwin-enhanced sam-lstm-ad) model", "business solutions", "that", "higher prediction accuracy", "iot devices", "the main component", "this suggested study", "it", "deep learning", "the absolute error rate threshold", "a new model", "this model", "assaults", "the cybertwin-neural network", "we", "the cse-cic-ids-2018 dataset", "the classifiers’ performance", "the model's capacity", "time series analysis", "this research", "the processed data", "its suggested framework", "these models", "the suggested model", "the high true positive rate", "tpr", "low false positive rate", "fpr", "that", "the model", "the test dataset", "important metrics", "f1-score", "roc-auc", "tpr", "fpr", "accuracy", "precision", "today", "anomaly detection (dl-cyberwin", "sam-lstm-ad", "roc" ]

[ "Amandeep Singh", "Jaspreet Kaur", "Kuldeep Singh", "Maninder Lal Singh" ]

[ "a major proportion", "the loss", "the agricultural industry originates", "the diseases", "the crop", "cultivation", "paddy crop", "the dominant crops", "which", "food", "a huge population", "this crop", "the losses", "such diseases", "30 to 90%", "the yield", "the automated detection", "different diseases", "paddy crops", "the attention", "the research community", "this context", "the present work", "a deep transfer learning solution", "the automated detection", "blast disease", "paddy", "which", "the major cause", "its yield reduction", "this purpose", "paddy crop", "these images", "five convolutional neural network-based deep transfer learning algorithms", "viz", "lenet", "alexnet", "vgg", "inception v1", "xception models", "binary classification", "the performance analysis", "given algorithms", "alexnet", "better results", "binary classification", "an average accuracy", "98.7%", "accuracies", "98.2%", "97.8%", "this deep transfer learning-based approach", "the gap", "experts", "farmers", "an automated expert advice platform", "the timely detection", "diseases", "paddy crop", "30 to 90%", "fed", "five", "16", "98.7%", "16", "98.2%", "97.8%" ]

[ "Mamatha Maddu", "Yamarthi Narasimha Rao" ]

[ "software-defined networking", "sdn", "a contemporary network strategy", "a traditional network structure", "it", "significantly more administrative efficiency", "ease", "traditional networks", "the centralized control", "sdn", "an elevated risk", "single-point failure", "that", "different kinds", "network assaults", "distributed denial", "service", "ddos", "api exploitation", "which", "a powerful intrusion detection system", "ids", "deep learning", "this study", "the detection", "mitigation", "network intrusions", "this system", "several stages", "the data augmentation method", "deep convolutional generative adversarial networks", "dcgan", "the data imbalance problem", "the features", "the input data", "a centernet-based approach", "effective characteristics", "slime mold", "algorithm", "sma", "based deep learning", "the assaults", "insdn", "iiot datasets", "the network intrusion", "the proposed defense module", "regular network connectivity", "several experiments", "the algorithm's robustness", "the outcomes", "the proposed system", "network intrusions" ]

[ "M. Chithambarathanu", "M. K. Jeyakumar" ]

[ "crop disease", "a significant issue", "that", "the growth", "yield", "crops", "financial loss", "farmers", "identification", "treatment", "crop diseases", "the increase", "the variety", "diseases", "the lack", "knowledge", "farmers", "this issue", "this investigate", "an ensembled-deep-learning paradigm", "a deep learning-based model", "crop disease identification", "an abc-cpoa", "raw images", "bilateral filter", "gamma correction", "feature extraction", "the pre-processed images", "texture feature", "local quinary pattern", "lqp", "local gradient pattern", "lgp", "enriched local binary pattern", "e", "-", "lbp", "color features", "color histogram", "color moments", "shape features", "(contour-based features", "convex hull", "optimal feature", "the extracted features", "the optimal features", "means", "a self-improved meta-heuristic optimization model", "abc-cpoa", "this abc-cpoa model", "an extended version", "standard coyote optimization algorithm", "coa", "crop disease detection phase", "a new ensembled-deep-learning paradigm", "ensembled-deep-learning paradigm", "attention-based bi", "-", "lstm", "recurrent neural networks", "rnns", "deep neural network", "o", "dnn", "the weight function", "o", "-", "dnn", "the new abc-cpoa", "precision", "recall", "sensitivity", "specificity", "addition", "tpr", "fpr", "fnr", "tnr", "f1-score", "accuracy", "the suggested approach", "the implementation", "the matlab tool", "version", "abc", "abc", "abc", "abc" ]

[ "Hongru Shen", "Meng Yang", "Jilei Liu", "Kexin Chen", "Xiangchun Li" ]

[ "accurate discrimination", "patients", "cancer", "cfdna", "early cancer diagnosis", "we", "a deep-learning-based model entitled end-motif inspection", "transformer", "individuals", "cancer", "feature representations", "cfdna end-motifs", "emit", "a self-supervised learning approach", "that", "cfdna end-motifs", "we", "4606 samples", "different types", "cfdna", "eimit", "classification performance", "linear projections", "six datasets", "an additional inhouse testing", "whole-genome, whole-genome bisulfite", "5-hydroxymethylcytosine", "the linear projection", "representations", "emit", "area", "the receiver operating curve (auroc) values", "(0.835–0.955", "0.994–0.997", "these six datasets", "its baseline", "significant margins", "we", "linear projection", "emit representations", "an auroc", "identification", "lung cancer", "an independent testing", "the findings", "this study", "a transformer-based deep learning model", "cancer-discrimative representations", "cfdna end-motifs", "the representations", "this deep learning model", "patients", "cancer", "emit", "linear", "six", "5", "0.895", "0.996", "six", "linear", "0.962" ]

[ "Baozhong Liu" ]

[ "weed detection", "a critical role", "smart and precise agriculture systems", "targeted weed management", "environmental impact", "unmanned aerial vehicles", "their associated imagery", "powerful tools", "weed detection", "traditional and deep learning methods", "weed detection", "deep learning methods", "their ability", "complex patterns", "accuracy rate", "computation cost challenges", "deep learning-based weed detection methods", "this", "we", "a method", "the basis", "the yolov5 algorithm", "high accuracy demand", "low computation cost requirements", "the approach", "model generation", "a custom dataset", "training", "validation", "testing sets", "experimental results", "performance evaluation", "the proposed method", "that", "the research", "weed detection", "smart and precise agriculture systems", "deep learning techniques", "enhanced accuracy", "efficiency", "yolov5" ]

[ "Ahmed Ben Said", "Abdel-Salam G. Abdel-Salam", "Khalifa A. Hazaa" ]

[ "the covid-19 outbreak", "schools", "universities", "online learning", "the continuation", "the learning process", "such setting", "instructors", "efficient mechanisms", "the learning gains", "insights", "difficulties", "learners", "this research work", "we", "the problem", "learner performance", "online learning", "a deep learning-based approach", "our proposed solution", "stakeholders", "the online learning", "the learner outcome", "the final assessment", "the opportunity", "proactive measures", "the learners", "we", "a two-pathway deep learning model", "learner performance", "their interaction", "the online sessions", "the form", "clickstreams", "we", "these time series", "clicks", "images", "the gramian angular field", "the learning model", "use", "the available extra demographic and assessment information", "we", "our approach", "the open university learning analytics", "comprehensive comparative study", "evaluation", "art", "different experimental settings", "we", "the importance", "extra demographic and assessment data", "the prediction process", "covid-19", "two", "gramian" ]

[ "Hossein Najafi", "Morteza Karamooz Mahdiabadi" ]

[ "purposethis study", "a novel approach", "hyper-reduction", "nonlinear structural dynamics", "the predictive capabilities", "stacked long short-term memory", "lstm", "neural networks", "hyper-reduction methods", "the limitations", "traditional model order reduction techniques", "the nonlinear behavior", "internal force vectors", "complex structures.method", "the proposed technique employs", "lstm neural networks", "training snapshots", "the energy conserving mesh sampling", "weighting", "hyper-reduction method", "the model", "a well-defined dataset", "we", "an impressive accuracy", "97.5%", "the effectiveness", "our method", "a geometrically nonlinear dynamic analysis", "a leaf spring", "only a 3.24% error", "full simulation results", "this study", "the potential", "deep learning techniques", "hyper-reduction methods", "the importance", "computational efficiency", "simulations", "complex structural dynamics.resultsthe findings", "significant advancements", "the application", "deep learning", "hyper-reduction methods", "the ability", "nonlinear structural behaviors", "computational efficiency", "this research", "valuable insights", "the integration", "advanced machine learning techniques", "the field", "structural dynamics", "97.5%", "a leaf spring", "3.24%" ]

[ "Dongping Qiao", "Lvqi Duan", "HongLei Li", "Yanqiu Xiao" ]

[ "the optimization problem", "the maximum completion time", "job shop scheduling", "a deep reinforcement learning optimization algorithm", "a deep reinforcement learning scheduling environment", "the disjunctive graph model", "three channels", "state characteristics", "the action space", "20 designed combination scheduling rules", "the reward function", "the proportional relationship", "the total work", "the scheduled operation", "the current maximum completion time", "the deep convolutional neural network", "action network", "target network", "the state features", "inputs", "the q value", "each action", "the action", "the action validity exploration and exploitation strategy", "the immediate reward", "the scheduling environment", "experiments", "benchmark instances", "the algorithm", "the results", "it", "solution quality and computation time", "the trained agent", "good generalization ability", "the scheduling problem", "the non-zero initial state", "first", "three", "20" ]

[ "J. Naskath", "G. Sivakamasundari", "A. Alif Siddiqua Begum" ]

[ "deep learning", "a wildly popular topic", "machine learning", "a series", "nonlinear layers", "that", "various levels", "data representations", "deep learning", "numerous layers", "data abstractions", "various computer models", "approaches", "discriminative models", "model transfer", "information processing", "this article", "a comprehensive review", "various deep learning algorithms", "multi layer perception", "self-organizing map", "deep belief networks", "it", "the-art", "suitable architectures", "implementation steps", "the various applications", "those algorithms", "various fields", "wireless networks", "adhoc networks", "mobile ad-hoc and vehicular ad-hoc networks", "speech recognition engineering", "medical applications", "natural language processing", "material science", "remote sensing applications", "first" ]

[ "Takfarines Guergueb", "Moulay A. Akhloufi" ]

[ "glaucoma", "the major reasons", "visual impairment", "the globe", "the recent advancements", "machine learning techniques", "the early diagnosis", "ocular diseases", "the employment", "automated systems", "several studies", "the timely detection", "glaucoma", "deep learning approaches", "a comprehensive review", "the deep learning approaches", "glaucoma detection", "retinal fundus images", "this paper", "the available retinal image datasets", "image pre-processing techniques", "the-art", "performance evaluation metrics", "the recent studies", "this systematic review", "critical insights", "potential research directions", "the ophthalmologists", "researchers", "this domain", "glaucoma" ]