Datasets:

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ADKU

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ResearchGPT_model_dataset

like 0

[ "Komal Sharma", "Ganesh Kumar Sethi", "Rajesh Kumar Bawa" ]

[ "rice", "an essential staple food", "human nutrition", "rice varieties", "production", "trading", "different types", "rice", "rice impurities", "rice importers", "exporters", "trust", "the development", "a rice variety identification system", "india", "a significant player", "the global rice market", "this extensive study", "the importance", "rice", "the study", "the-art", "tl classifiers", "the problems", "rice variety detection", "an enormous dataset", "more than 600,000 rice photographs", "22 different classes", "the study", "classification accuracy", "a training accuracy", "96%", "a testing accuracy", "80.5%", "resnet50", "other deep learning models", "the authors", "these models", "cnn", "deep cnn", "alexnet2", "xception", "inception v3", "densenet121", "resnet50", "the best classifiers", "varieties", "this work", "their possible uses", "rice seed production", "this paper", "the groundwork", "future research", "image-based rice categorization", "areas", "development", "ensemble strategies", "performance", "rice", "india", "more than 600,000", "22", "96%", "80.5%", "resnet50", "cnn", "cnn", "v3", "resnet50" ]

[ "Hengkang Wang", "Han Lu", "Ju Sun", "Sandra E. Safo" ]

[ "backgroundtechnological advances", "the generation", "unique and complementary types", "data", "views", "e.g. genomics", "proteomics", "metabolomics", "a new era", "multiview learning research", "the potential", "new biomedical discoveries.resultswe propose ideepviewlearn", "(interpretable deep learning method", "multiview learning", "nonlinear relationships", "data", "multiple views", "feature selection", "ideepviewlearn", "deep learning flexibility", "the statistical benefits", "data", "knowledge-driven feature selection", "interpretable results", "deep neural networks", "an optimization problem", "that", "the difference", "observed and reconstructed data", "a regularization penalty", "the reconstructed data", "the normalized laplacian", "a graph", "bilateral relationships", "variables", "each view", "selection", "related variables", "ideepviewlearn", "simulated and three real-world data", "classification", "clustering", "reconstruction tasks", "the classification tasks", "ideepviewlearn", "competitive classification results", "the-art", "various settings", "the clustering task", "we", "molecular clusters", "that", "their 10-year survival rates", "breast cancer", "the reconstruction task", "we", "handwritten images", "a few pixels", "competitive classification accuracy", "the results", "our real data application", "simulations", "small to moderate sample sizes", "that ideepviewlearn", "a useful method", "small-sample-size problems", "other deep learning methods", "multiview learning.conclusionideepviewlearn", "an innovative deep learning model", "nonlinear relationships", "data", "multiple views", "feature selection", "it", "fully open source", "https://github.com/lasandrall/ideepviewlearn", "ideepviewlearn", "three", "10-year", "https://github.com/lasandrall/ideepviewlearn" ]

[ "Frank Zijlstra", "Peter Thomas While" ]

[ "objectdeep learning", "great promise", "fast reconstruction", "accelerated mri acquisitions", "large amounts", "raw data", "raw data", "sufficient quantities", "this study", "synthetic data generation", "small datasets", "reconstruction quality.materials", "methodsan adversarial auto-encoder", "phase and coil sensitivity maps", "magnitude images", "which", "mr reconstruction task", "deep-learning-based reconstruction networks", "varying amounts", "training data", "20 to 160 scans", "test set performance", "baseline experiments", "experiments", "that", "synthetic raw data", "reconstruction errors", "increasing amounts", "training data", "this", "magnitude-only data", "real raw data", "small training sets", "synthetic data", "the mean absolute error", "mae", "up to 7.5%", "larger training sets", "the mae", "2.6%.discussionsynthetic raw data generation", "improved reconstruction quality", "scenarios", "limited training data", "a major advantage", "synthetic data generation", "it", "the reuse", "magnitude-only datasets", "which", "raw datasets", "20", "160", "up to 7.5%" ]

[ "Eve Kikas", "Gintautas Silinskas", "Eliis Härma" ]

[ "the aim", "this study", "which", "the use", "specific deep-level learning strategies", "an independent learning task", "participants", "335 estonian fourth- and sixth-grade students", "who", "light processes", "seasonal changes", "the study", "topic-related knowledge", "an open question", "seasonal changes", "learning-related knowledge", "scenario-based tasks", "expectancies", "interest", "utility values", "astronomy", "deep-level learning strategies", "questions", "the situated expectancy-value theory", "deep-level learning strategies", "drawings", "addition", "reading", "self-testing", "the reading task", "topic-related variables", "prior knowledge", "utility value", "not interest", "expectancy", "astronomy", "deep-level learning strategies", "learning-related variables", "interest", "utility value", "effective learning", "not metacognitive knowledge", "strategies", "expectancy", "deep-level learning strategies", "deep-level learning strategies", "this study", "it", "students’ knowledge", "skills", "learning strategies", "general or hypothetical questions", "it", "crucial importance", "students", "real learning situations", "335", "sixth" ]

[ "Qazi Umer Jamil", "Karam Dad Kallu", "Muhammad Jawad Khan", "Muhammad Safdar", "Amad Zafar", "Muhammad Umair Ali" ]

[ "traffic congestion", "a persistent challenge", "urban areas", "efficient traffic control strategies", "this research", "the application", "advanced reinforcement learning techniques", "specifically deep q-learning (dqn", "double deep q-learning", "(ddqn", "this issue", "a four-way traffic intersection", "the rl agents", "a reward function", "waiting times", "them", "effective traffic signal control policies", "the study", "the performance", "a simple non-reinforcement learning", "(non rl) agent", "a deep q-network (dqn) agent", "an improved double deep q-learning (ddqn) agent", "different traffic scenarios", "the non rl agent", "which", "a fixed order", "traffic phases", "limitations", "both low and high traffic situations", "inefficiencies", "queue lengths", "the other hand", "the dqn agent", "promising results", "low traffic conditions", "struggles", "high traffic", "its greedy behavior", "the ddqn agent", "an extended green light base time", "both the non rl agent", "the original dqn agent", "high traffic scenarios", "it", "real-world traffic conditions", "it", "some inefficiencies", "low traffic scenarios", "future research", "multi-agent deep reinforcement learning challenges", "attention mechanisms", "hierarchical reinforcement learning", "graph theory applications", "efficient communication protocols", "agents", "traffic control solutions", "four" ]

[ "Yehia Ibrahim Alzoubi", "Alok Mishra", "Ahmet Ercan Topcu" ]

[ "deep learning and machine learning show effectiveness", "cloud security threats", "the large number", "articles", "this field", "a dearth", "comprehensive reviews", "that", "the techniques", "trends", "challenges", "deep learning", "machine learning", "cloud computing security", "this paper", "the most updated statistics", "the development", "research", "cloud computing security", "deep learning", "machine learning", "the middle", "december", "4051 publications", "we", "the scopus database", "this paper", "key trend solutions", "cloud computing security", "machine learning", "deep learning", "anomaly detection", "security automation", "emerging technology's role", "challenges", "data privacy", "scalability", "explainability", "others", "challenges", "machine learning", "deep learning", "cloud security", "the findings", "this paper", "deep learning and machine learning", "cloud computing security", "research areas", "future research directions", "these challenges", "machine learning", "deep learning", "cloud security", "the development", "algorithms", "techniques", "that", "relevant laws", "regulations", "effective implementation", "this domain", "the middle of december 2023", "4051", "anomaly detection" ]

[ "Qiyue Yin", "Tongtong Yu", "Shengqi Shen", "Jun Yang", "Meijing Zhao", "Wancheng Ni", "Kaiqi Huang", "Bin Liang", "Liang Wang" ]

[ "the breakthrough", "alphago", "deep reinforcement learning", "a recognized technique", "sequential decision-making problems", "its reputation", "data inefficiency", "its trial and error learning mechanism", "deep reinforcement learning", "a wide range", "areas", "many methods", "sample efficient deep reinforcement learning", "environment modelling", "experience transfer", "modifications", "which", "deep reinforcement learning", "its potential", "various applications", "human-computer gaming", "intelligent transportation", "this paper", "we", "the state", "this exciting field", "the classical distributed deep reinforcement learning methods", "important components", "efficient distributed learning", "single player single agent", "deep reinforcement learning", "the most complex multiple players", "multiple agents", "deep reinforcement learning", "we", "toolboxes", "that", "distributed deep reinforcement learning", "many modifications", "their non-distributed versions", "their strengths", "weaknesses", "-", "agent", "which", "wargame", "a complex environment", "the usability", "the proposed toolbox", "multiple players", "multiple agents", "deep reinforcement learning", "complex games", "we", "challenges", "future trends", "this brief review", "a guide", "a spark", "researchers", "who", "distributed deep reinforcement learning" ]

[ "Berend C. Stoel", "Marius Staring", "Monique Reijnierse", "Annette H. M. van der Helm-van Mil" ]

[ "artificial intelligence techniques", "specifically deep learning", "daily life", "a wide range", "areas", "initial applications", "rheumatology", "deep learning", "the accuracy", "classic techniques", "classification", "regression", "low-dimensional numerical data", "images", "input", "deep learning", "it", "the majority", "conventional image-processing techniques", "the past 50 years", "any new imaging technology", "rheumatologists", "radiologists", "their arsenal", "diagnostic, prognostic and monitoring tools", "even their clinical role", "collaborations", "this adaptation", "a basic understanding", "the technical background", "deep learning", "its benefits", "its drawbacks", "pitfalls", "deep learning", "odds", "its capabilities", "such an understanding", "it", "an overview", "deep-learning techniques", "automatic image analysis", "rheumatic diseases", "currently published deep-learning applications", "radiological imaging", "rheumatology", "critical assessment", "possible limitations", "errors", "confounders", "conceivable consequences", "rheumatologists", "radiologists", "clinical practice", "the past 50 years" ]

[ "Shibhansh Dohare", "J. Fernando Hernandez-Garcia", "Qingfeng Lan", "Parash Rahman", "A. Rupam Mahmood", "Richard S. Sutton" ]

[ "artificial neural networks", "deep-learning methods", "the backpropagation algorithm1 form", "the foundation", "modern machine learning", "artificial intelligence", "these methods", "two phases", "which", "the weights", "the network", "which", "the weights", "the network", "this", "natural learning", "many applications", "which", "continual learning", "it", "not deep learning methods", "continual learning settings", "we", "they", "that standard deep-learning methods", "plasticity", "continual-learning settings", "they", "a shallow network", "we", "such loss", "plasticity", "the classic imagenet dataset and reinforcement-learning problems", "a wide range", "variations", "the network", "the learning algorithm", "plasticity", "algorithms", "that", "diversity", "the network", "our continual backpropagation algorithm", "a variation", "backpropagation", "which", "a small fraction", "less-used units", "our results", "methods", "gradient descent", "that", "deep learning", "a random, non-gradient component", "variability", "plasticity", "algorithm1", "two", "one" ]

[ "V. Nithya", "M. S. Josephine", "V. Jeyabalaraja" ]

[ "agriculture", "an expanding area", "study", "crop prediction", "agriculture", "soil and environmental factors", "rainfall", "humidity", "temperature", "farmers", "the authority", "the crop", "its development", "the optimal harvest time", "the farming community", "challenges", "its practices", "the swift alterations", "climatic conditions", "machine learning algorithms", "traditional methods", "agricultural productivity", "recent years", "optimal precision", "a specific machine learning approach", "authors", "their approach", "machine learning", "deep learning techniques", "we", "machine", "deep learning", "algorithms", "crop outcomes", "this proposed model", "we", "machine learning algorithms", "naive bayes", "decision tree", "knn", "it", "the decision tree", "algorithm", "superior performance", "the other algorithms", "an accuracy rate", "83%", "order", "the precision", "we", "a deep learning technique", "specifically a convolutional neural network", "the crops", "an accuracy", "93.54%", "this advanced deep-learning model", "recent years", "83%", "93.54%" ]

[ "Akhilesh Deep Arya", "Sourabh Singh Verma", "Prasun Chakarabarti", "Tulika Chakrabarti", "Ahmed A. Elngar", "Ali-Mohammad Kamali", "Mohammad Nami" ]

[ "alzheimer’s disease", "ad", "a brain-related disease", "which", "the condition", "the patient", "time", "ad", "a curable disease", "any medication", "it", "the death", "brain cells", "the help", "medication", "the effects", "ad", "not all mci patients", "ad", "it", "a mild cognitive impaired (mci) patient", "ad", "namely mci converter mci-c", "namely mci non-converter mci-nc", "early diagnosis", "two modalities", "positron emission tomography", "pet", "magnetic resonance image", "mri", "a physician", "the diagnosis", "alzheimer’s disease", "machine learning", "deep learning", "the field", "computer vision", "a requirement", "information", "high-dimensional data", "researchers", "deep learning models", "the field", "medicine", "diagnosis", "prognosis", "the future health", "the patient", "medication", "this study", "a systematic review", "publications", "machine learning", "deep learning methods", "early classification", "(nc", "ad).this study", "an effort", "the details", "the two most commonly used modalities", "the identification", "ad", "the performance", "both modalities", "different classifiers", "mci", "mci", "mci", "mci", "mci", "mci-nc", "two", "two" ]

[ "Sabrina Fawzia", "Azharul Karim" ]

[ "it", "that student learning", "assessment methods", "a concrete relationship", "the context", "multidisciplinary engineering education", "students", "a physiological investment", "they", "high value", "their learning", "they", "challenges", "delight", "their work", "student deep learning", "the assessment system", "it", "the relationship", "assessment systems", "factors", "deep learning", "this study", "the factors", "deep learning", "the relationships", "those factors", "a conceptual model", "a structured questionnaire", "technology", "243 responses", "the gathered data", "sem", "exploratory factor analysis", "deep learning", "environment", "course design", "content", "strong influence", "both summative and formative assessment", "learning", "this study", "engineering educators", "deep learning", "both assessment types", "the learning process", "the proposed theoretical model", "the deep learning concept", "the key practices", "modern learning methodologies", "the learning and teaching process", "600 queensland university of technology", "243", "spss" ]

[ "Alexander Tropsha", "Olexandr Isayev", "Alexandre Varnek", "Gisbert Schneider", "Artem Cherkasov" ]

[ "quantitative structure", "activity relationship", "qsar", "modelling", "an approach", "that", "computer-aided drug design", "recent years", "progress", "artificial intelligence techniques", "deep learning", "the rapid growth", "databases", "molecules", "virtual screening", "dramatic improvements", "computational power", "the emergence", "a new field", "qsar applications", "we", "a decade", "the pioneering applications", "deep qsar", "tasks", "small-molecule drug discovery", "we", "key advances", "the field", "deep generative and reinforcement learning approaches", "molecular design", "deep learning models", "synthetic planning", "the application", "deep qsar models", "structure-based virtual screening", "we", "the emergence", "quantum computing", "which", "deep qsar applications", "the need", "open-source", "democratized resources", "computer-aided drug design", "60 years ago", "recent years", "quantum" ]

[ "Wei Zhi", "Alison P. Appling", "Heather E. Golden", "Joel Podgorski", "Li Li" ]

[ "understanding", "the quality", "inland waters", "the context", "climate extremes", "the future", "these challenges", "complex processes", "that", "water quality", "arduous and expensive data collection", "that", "the issue", "data scarcity", "traditional process-based and statistical models", "water quality", "this review", "we", "deep learning", "an underutilized yet promising approach", "that", "intricate structures", "relationships", "high-dimensional data", "we", "deep learning methods", "data scarcity", "temporal and spatial gaps", "aid", "formulating and testing hypotheses", "influential drivers", "water quality", "this review", "the strengths", "limitations", "deep learning methods", "traditional approaches", "its potential", "an emerging and indispensable approach", "challenges", "new knowledge", "water-quality sciences" ]

[ "Palak Goyal", "Rinkle Rani" ]

[ "a progressive neurodegenerative ailment", "parkinson's disease", "pd", "the death", "dopamine-producing cells", "the substantia nigra area", "the brain", "the exact etiology", "pd", "it", "the presence", "lewy bodies", "abnormal protein aggregates", "affected brain regions", "the mobile symptoms", "pd", "the management", "pd", "a growing demand", "the establishment", "a descriptive system", "that", "the early detection", "pd", "this study", "we", "an extensive analysis", "machine learning", "ensemble learning", "deep learning models", "different hyperparameters", "accurate classification models", "pd prediction", "classifier performance", "address overfitting", "we", "principal component analysis", "pca", "feature selection", "various preprocessing techniques", "the dataset", "voice samples", "188 pd patients", "64 normal individuals", "our results", "the random forest", "(rf) model", "accuracy", "82.37%", "the other base classifiers", "the ensemble classifiers", "the lgbm model", "the highest accuracy", "85.90%", "both base and ensemble classifiers", "the deep learning model", "91.33% training accuracy", "85.02% testing accuracy", "deep learning models", "small datasets", "machine learning classifiers", "our findings", "the effectiveness", "machine learning", "ensemble techniques", "deep learning models", "pd", "188", "64", "82.37%", "85.90%", "91.33%", "85.02%" ]

[ "Sandeep Kumar Mathivanan", "Sridevi Sonaimuthu", "Sankar Murugesan", "Hariharan Rajadurai", "Basu Dev Shivahare", "Mohd Asif Shah" ]

[ "artificial intelligence-powered deep learning methods", "brain tumors", "high accuracy", "their ability", "large amounts", "data", "magnetic resonance imaging", "the gold standard", "brain tumor diagnosis", "machine vision", "computed tomography", "ultrasound", "x", "-ray imaging", "its effectiveness", "this", "brain tumor diagnosis", "a challenging endeavour", "the intricate structure", "the brain", "this study", "the potential", "architectures", "the accuracy", "brain tumor diagnosis", "transfer learning", "a machine learning technique", "that", "us", "pre-trained models", "new tasks", "this", "medical imaging tasks", "labelled data", "four distinct transfer learning architectures", "this study", "resnet152", "vgg19", "densenet169", "mobilenetv3", "the models", "a dataset", "benchmark database", "kaggle", "five-fold cross validation", "training", "testing", "the balance", "the dataset", "the performance", "the models", "image enhancement techniques", "the data", "the four categories", "glioma", "mobilenetv3", "the highest accuracy", "99.75%", "other existing methods", "this", "the potential", "architectures", "the field", "brain tumor diagnosis", "four", "mobilenetv3", "five-fold", "four", "mobilenetv3", "99.75%" ]

[ "Asma Abdulsalam", "Areej Alhothali", "Saleh Al-Ghamdi" ]

[ "social media platforms", "traditional communication techniques", "people", "people", "social media", "personal stories", "their opinions", "negative emotions", "thoughts", "death", "self-harm", "hardship", "younger generations", "the use", "social media", "suicidality", "proper intervention", "that", "the spread", "self-harm", "suicidal ideation", "social media", "the automated detection", "suicidal thoughts", "arabic tweets", "we", "a novel arabic suicidal tweet dataset", "several machine learning models", "word frequency", "features", "the performance", "pre-trained deep learning models", "suicidal sentiment", "the results", "the support vector machine", "character n-gram features", "the best performance", "conventional machine learning models", "an accuracy", "86%", "f1 score", "79%", "the subsequent deep learning experiment", "arabert", "all other machine", "deep learning models", "an accuracy", "91%", "f1-score", "88%", "the detection", "suicidal ideation", "the dataset", "our knowledge", "this study", "the first attempt", "an arabic suicidality detection", "twitter", "deep learning", "suicidal sentiment", "arabic posts", "arabic", "arabic", "86%", "79%", "91%", "88%", "first", "arabic", "arabic" ]

[ "Huangzhao Zhang", "Kechi Zhang", "Zhuo Li", "Jia Li", "Jia Li", "Yongmin Li", "Yunfei Zhao", "Yuqi Zhu", "Fang Liu", "Ge Li", "Zhi Jin" ]

[ "the past decade", "the powerfulness", "deep-learning techniques", "we", "a whole new era", "automated code generation", "developments", "we", "a comprehensive review", "solutions", "deep learning-based code generation", "this survey", "we", "the pipeline", "procedure", "code generation", "existing solutions", "taxonomy", "perspectives", "architecture", "model-agnostic enhancing strategy", "metrics", "tasks", "addition", "we", "the challenges", "current dominant large models", "several plausible directions", "future research", "we", "this survey", "handy guidance", "understanding", "deep learning-based code-generation techniques", "researchers", "practitioners", "the past decade" ]

[ "Chandra Sekhar Kolli", "Sreenivasu Seelamanthula", "Venkata Krishna Reddy V", "Padamata Ramesh Babu", "Mule Rama Krishna Reddy", "Babu Rao Gumpina" ]

[ "the increasing concerns", "data security", "privacy", "users", "the interest", "the research community", "privacy-preserving recommendation systems", "this backdrop", "our study", "a novel course recommendation methodology", "federated learning", "advanced deep learning techniques", "this method", "the recommendation process", "local nodes", "several stages", "agglomerative matrix formulation", "bi-level matching", "identification", "learner-preferred courses", "ultimately, course recommendation", "course clustering", "deep fuzzy clustering", "dfc", "deep convolutional neural networks", "dcnn", "the recommendation phase", "the efficacy", "our dfc-dcnn-fl approach", "several metrics", "accuracy", "false positive rate", "fpr", "loss function", "square error", "mse", "root mse", "rmse", "average precision", "(map", "the results", "remarkable performance", "scores", "rmse", "0.909", "0.116", "0.126", "0.291", "0.539", "0.925" ]

[ "Navodini Wijethilake", "Mithunjha Anandakumar", "Cheng Zheng", "Peter T. C. So", "Murat Yildirim", "Dushan N. Wadduwage" ]

[ "limited throughput", "a key challenge", "vivo deep tissue", "nonlinear optical microscopy", "point", "multiphoton microscopy", "the current gold standard", "the widefield imaging modalities", "optically cleared or thin specimens", "we", "de", "a widefield alternative", "point-scanning geometries", "patterned multiphoton excitation", "tissue", "de", "-", "scatter", "typical depths", "hundreds", "such patterned excitations", "this work", "we", "deep2", "a deep learning-based model", "that", "de-scatter images", "just tens", "patterned excitations", "hundreds", "we", "deep’s throughput", "almost an order", "magnitude", "we", "our method", "multiple numerical and experimental imaging studies", "vivo cortical vasculature", "scattering lengths", "live mice", "multiphoton", "hundreds", "deep2", "just tens", "hundreds", "4" ]

[ "Xin Huang", "Aigerim Rymbekova", "Olga Dolgova", "Oscar Lao", "Martin Kuhlwilm" ]

[ "population genetics", "the emergence", "large-scale genomic data", "various species", "populations", "new opportunities", "the evolutionary forces", "that", "genetic diversity", "statistical inference", "the era", "population genomics", "new challenges", "the massive amounts", "genomes", "variants", "deep learning", "the-art", "numerous applications", "large-scale data", "deep learning approaches", "popularity", "population genetics", "the advent", "massive genomic data sets", "powerful computational hardware", "complex deep learning architectures", "they", "population structure", "demographic history", "natural selection", "we", "common deep learning architectures", "comprehensive guidelines", "deep learning models", "population genetic inference", "we", "current challenges", "future directions", "deep learning", "population genetics", "efficiency", "robustness", "interpretability" ]

[ "Sohaib Asif", "Ming Zhao", "Fengxiao Tang", "Yusen Zhu" ]

[ "multi-class brain tumor classification", "an important area", "research", "the field", "medical imaging", "the different tumor characteristics", "one such challenging problem", "the multiclass classification", "brain tumors", "mr images", "accuracy", "classification", "computer vision researchers", "a number", "techniques", "high accuracy", "brain images", "early diagnosis", "brain tumor types", "timely treatment", "the patient’s chances", "survival", "recent years", "deep learning models", "promising results", "brain tumors", "neurologists", "this work", "a deep transfer learning model", "that", "brain tumor detection", "mr imaging", "this paper", "five popular deep learning architectures", "a system", "brain tumors", "the architectures", "this paper", "xception", "densenet201", "densenet121", "resnet152v2", "the final layer", "these architectures", "our deep dense block", "softmax layer", "the output layer", "the classification accuracy", "this article", "two main experiments", "the effectiveness", "the proposed model", "first, three-class results", "images", "patients", "glioma", "pituitary", "the results", "four classes", "images", "glioma", "patients", "the results", "the proposed model", "xception architecture", "the most suitable deep learning model", "brain tumors", "it", "a classification accuracy", "99.67%", "the 3-class dataset", "95.87%", "the 4-class dataset", "which", "the-art", "conclusion", "the proposed model", "radiologists", "an automated medical diagnostic system", "fast and accurate decisions", "one", "recent years", "five", "inceptionresnetv2", "two", "first", "three", "glioma", "second", "four", "glioma", "99.67%", "3", "95.87%", "4" ]

[ "Leonardo de Lellis Rossi", "Eric Rohmer", "Paula Dornhofer Paro Costa", "Esther Luna Colombini", "Alexandre da Silva Simões", "Ricardo Ribeiro Gudwin" ]

[ "recent advancements", "ai", "deep learning", "a growing demand", "artificial agents", "tasks", "increasingly complex environments", "the challenges", "continuous learning constraints", "knowledge capacity", "this context", "cognitive architectures", "human cognition", "significance", "this study", "existing research", "a cognitive-attentional system", "a constructive neural network-based learning approach", "continuous acquisition", "procedural knowledge", "we", "an incremental tabular reinforcement learning algorithm", "a constructive neural network deep reinforcement learning mechanism", "continuous sensorimotor knowledge acquisition", "the overall learning capacity", "the primary emphasis", "this modification centers", "memory utilization", "training time", "our study", "a learning strategy", "that", "deep reinforcement learning", "procedural learning", "the incremental learning process", "human sensorimotor development", "this approach", "the conaim cognitive-attentional architecture", "the cognitive tools", "cst", "the proposed learning mechanism", "the model", "elements", "its procedural memory", "the reuse", "previously acquired functions", "procedures", "it", "the model", "the capability", "elements", "complex scenarios", "a constructive neural network", "an initial hidden layer", "one neuron", "it", "the capacity", "its internal architecture", "response", "its performance", "procedural and sensorimotor learning tasks", "new hidden layers", "neurons", "experimentation", "simulations", "a humanoid robot", "the successful resolution", "tasks", "that", "incremental knowledge acquisition", "the training phase", "the constructive agent", "a minimum", "40% greater rewards", "8% more actions", "other agents", "the subsequent testing phase", "the constructive agent", "a 15% increase", "the number", "actions", "contrast", "its counterparts", "one", "40%", "8%", "15%" ]

[ "Siddharth Singh Chouhan", "Uday Pratap Singh", "Utkarsh Sharma", "Sanjeev Jain" ]

[ "the present situation", "a lot", "research", "the potency", "plants", "these natural resources", "characteristics", "combat", "a number", "diseases", "lack", "familiarity", "these plants", "human beings", "an appropriate advantage", "their significance", "plants", "the certain similar characteristics", "leaves", "color", "texture", "shape", "size", "them", "them", "others", "this problem", "a deep learning model", "the purpose", "classification", "different plants species", "real-time", "internet", "six different plants", "namely ashwagandha", "black pepper", "garlic", "ginger", "basil", "turmeric", "this purpose", "our proposed convolutional neural network (cnn) model", "higher performance", "an accuracy", "99%", "other benchmark deep learning models", "the performance", "deep learning", "machine learning models", "logistic regression", "decision tree", "random forest", "gaussian naïve bayes", "support vector machine results", "cnn", "outperforms", "all machine learning models", "the future study", "the automated plant growth estimation", "six", "basil", "cnn", "99%", "gaussian naïve bayes", "cnn" ]

[ "Yujie Xu", "Shengli Zhang", "Feng Zhu", "Yunyun Liang" ]

[ "a class", "biologically active molecules", "anti-inflammatory effects", "anti-inflammatory peptides", "important application value", "the medical and biotechnology fields", "their unique biological functions", "research", "the identification", "anti-inflammatory peptides", "important theoretical foundations", "practical value", "a deeper understanding", "the biological mechanisms", "inflammation", "immune regulation", "the development", "new drugs", "biotechnological applications", "it", "more advanced computational models", "anti-inflammatory peptides", "this study", "we", "a deep learning model", "dac-aips", "variational autoencoder", "contrastive learning", "accurate identification", "anti-inflammatory peptides", "the sequence", "part", "the incorporation", "multi-hot encoding", "richer sequence information", "the autoencoder", "convolutional layers", "linear layers", "latent features", "features", "variational inference", "the representation capability", "latent features", "the introduction", "contrastive learning", "the model's classification ability", "cross-validation and independent dataset testing experiments", "dac-aips", "superior performance", "the-art", "-", "validation", "the classification accuracy", "dac-aips", "around 88%", "which", "previous models", "various ablation experiments", "interpretability experiments", "the effectiveness", "dac-aips", "a user-friendly online predictor", "the practicality", "the model", "the server", "http://dac-aips.online", "linear", "around 88%", "7%" ]

[ "Akash Gandhar", "Kapil Gupta", "Aman Kumar Pandey", "Dharm Raj" ]

[ "fraudulent activities", "a major worry", "businesses", "financial organizations", "they", "significant financial losses", "reputational harm", "traditional fraud detection", "a method", "present rules", "patterns", "skilled scammer", "machine learning", "deep learning algorithms", "promising methods", "fraud", "order", "this problem", "authors", "a thorough overview", "the most recent ml and dl techniques", "fraud identification", "this article", "these approaches", "their fundamental tactics", "which", "supervised learning", "unsupervised learning", "reinforcement learning", "we", "recent developments", "each area", "their strengths", "weaknesses", "we", "attention", "some", "the major problems", "imbalanced datasets", "adversarial assaults", "the interpretability", "models", "other important research tasks", "difficulties", "fraud detection", "we", "the value", "feature science and data pre-processing techniques", "the effectiveness", "scam detection systems", "we", "a case study", "the use", "dl and ml techniques", "the financial sector", "fraud detection", "authors", "these algorithms", "fraudulent transactions", "false positives", "high precision", "scalability", "the overall aim", "this article", "a comprehensive evaluation", "the most cutting-edge ml and dl techniques", "fraud identification", "light", "potential future paths", "this field", "study" ]

[ "Chengjun Chen", "Hao Zhang", "Yong Pan", "Dongnian Li" ]

[ "this paper", "a deep reinforcement learning-based framework", "robot autonomous grasping and assembly skill learning", "a deep q-learning-based robot grasping skill", "algorithm", "a ppo-based robot assembly skill learning algorithm", "a priori knowledge information", "the grasping action", "the training time", "interaction data", "the assembly strategy", "a grasping constraint reward function", "an assembly constraint reward function", "the robot grasping and assembly quality", "the effectiveness", "the proposed framework", "algorithms", "both simulated and real environments", "the average success rate", "both environments", "up to 90%", "hole", "the assembly success rate", "86.7%", "73.3%", "the simulated environment", "the physical environment", "up to 90%", "3 mm", "86.7%", "73.3%" ]

[ "S. Divyashree", "Christy Jackson Joshua", "Abdul Quadir Md", "Senthilkumar Mohan", "A. Sheik Abdullah", "Ummul Hanan Mohamad", "Nisreen Innab", "Ali Ahmadian" ]

[ "this paper", "alphavision", "an innovative decision support model", "stock price prediction", "real-time news updates", "return", "roi", "various machine learning", "deep learning approaches", "the research", "the application", "these techniques", "the effectiveness", "stock trading and investment decisions", "stock prices", "valuable insights", "investors", "businesses", "the study", "the complexities", "challenges", "stock market analysis", "factors", "political, macroeconomic, and legal issues", "that", "market volatility", "these challenges", "we", "the methodology", "alphavision", "which", "various machine learning algorithms", "decision trees", "random forest", "naïve bayes", "k-nearest neighbors", "vector machine", "deep learning models", "multi-layer perceptron", "mlp", "artificial neural networks", "neural networks", "the effectiveness", "each model", "their accuracy", "stock prices", "experimental results", "the mlp model", "the highest accuracy", "approximately 92%", "other deep learning models", "the random forest algorithm", "promising results", "an accuracy", "around 84.6%", "these findings", "the potential", "machine learning", "deep learning techniques", "stock market analysis", "prediction", "the alphavision methodology", "this research", "investors", "businesses", "valuable tools", "informed investment decisions", "the complexities", "the stock market", "stock prices", "news updates", "roi values", "the model", "better financial management", "business sustainability", "the integration", "machine learning", "deep learning approaches", "a promising solution", "stock market analysis", "prediction", "future research", "more relevant financial features", "the model’s accuracy", "decision support models", "stock price prediction", "researchers", "practitioners", "better investment strategies", "foster economic growth", "the proposed model", "potential", "stock trading", "investment practices", "more informed and profitable decision-making", "the financial sector", "approximately 92%", "around 84.6%" ]

[ "Rabia Javed", "Tahir Abbas", "Ali Haider Khan", "Ali Daud", "Amal Bukhari", "Riad Alharbey" ]

[ "lung cancer", "the deadliest type", "cancer", "a good prognosis and efficient treatment", "early detection", "medical practitioners’ burden", "deep learning techniques", "especially deep convolutional neural networks", "dcnn", "which", "the diagnosis", "classification", "diseases", "this study", "we", "a variety", "medical imaging modalities", "x", "-", "rays", "wsi", "ct scans", "mri", "the use", "deep learning techniques", "the field", "lung cancer diagnosis", "classification", "this study", "a comprehensive systematic literature review", "slr", "deep learning techniques", "lung cancer research", "a comprehensive overview", "the methodology", "cutting-edge developments", "quality assessments", "customized deep learning approaches", "it", "data", "reputable journals", "concentrates", "the years", "deep learning techniques", "the difficulty", "abstract features", "lung cancer images", "this study", "a wide range", "deep learning methods", "lung cancer", "the most popular method", "the convolutional neural network", "cnn", "cnn", "maximum accuracy", "its multi-layer structure", "automatic learning", "weights", "capacity", "local weights", "various algorithms", "performance measures", "precision", "accuracy", "specificity", "sensitivity", "auc", "cnn", "the greatest accuracy", "the findings", "the important contributions", "dcnn", "lung cancer detection", "classification", "them", "researchers", "a greater knowledge", "deep learning’s function", "medical applications", "the years 2015–2024", "cnn", "cnn", "cnn" ]

[ "Vinod Kumar Chauhan", "Jiandong Zhou", "Ping Lu", "Soheila Molaei", "David A. Clifton" ]

[ "hypernetworks", "hypernets", "neural networks", "that", "weights", "another neural network", "the target network", "they", "a powerful deep learning technique", "that", "greater flexibility", "adaptability", "dynamism", "faster training", "information sharing", "model compression", "hypernets", "promising results", "a variety", "deep learning problems", "continual learning", "causal inference", "transfer learning", "weight pruning", "uncertainty quantification", "zero-shot learning", "natural language processing", "reinforcement learning", "their success", "different problem settings", "no comprehensive review", "researchers", "the latest developments", "hypernets", "this gap", "we", "the progress", "hypernets", "we", "an illustrative example", "deep neural networks", "hypernets", "categorizing hypernets", "five design criteria", "inputs", "outputs", "variability", "inputs", "outputs", "the architecture", "hypernets", "we", "applications", "hypernets", "different deep learning problem settings", "a discussion", "general scenarios", "hypernets", "we", "the challenges", "future directions", "that", "the field", "hypernets", "we", "hypernetworks", "the potential", "the field", "deep learning", "they", "a new way", "neural networks", "they", "the potential", "the performance", "deep learning models", "a variety", "tasks", "this review", "we", "further advancements", "deep learning", "hypernetworks", "zero", "five" ]

[ "Xixi Wang", "Bo Jiang", "Xiao Wang", "Bin Luo" ]

[ "batch-based image data representation", "context-enhanced image representation", "the core issue", "this task", "the dependences", "image samples", "each mini", "-", "batch", "message communication", "different samples", "existing approaches", "self-attention", "local self-attention models", "patch dimension", "this task", "which", "the intrinsic relationships", "samples", "mini", "-", "batch", "noises", "outliers", "this issue", "this paper", "we", "a flexible dynamic batch-graph representation (dybgr) model", "the intrinsic relationship", "samples", "contextual sample representation", "the mini", "-", "batch", "a graph", "termed batch-graph", "which", "nodes", "image samples", "edges", "the dependences", "images", "this graph", "the constraint", "similarity", "sparseness", "semantic correlation", "this", "the batch-graph", "each node representation", "both batch-graph learning and information propagation", "their respective performance", "practical, dybgr model", "a simple plug-and-play block", "dybgr block", "which", "any mini-batch based deep representation learning schemes", "extensive experiments", "deep metric learning tasks", "the effectiveness", "dybgr", "we", "the code", "https://github.com/sissiw/dybgr", "first" ]

[ "Zi-Hao Bo", "Yuchen Guo", "Jinhao Lyu", "Hengrui Liang", "Jianxing He", "Shijie Deng", "Feng Xu", "Xin Lou", "Qionghai Dai" ]

[ "big data", "the cornerstone", "real-world deep learning systems", "various domains", "medicine", "healthcare", "a single clinical site", "sufficient data", "the involvement", "multiple sites", "concerns", "data security", "privacy", "the sharing", "reuse", "data", "sites", "existing approaches", "multi-site clinical learning", "the security", "the network firewall and system implementation", "this issue", "we", "relay learning", "a secure deep-learning framework", "that", "clinical data", "external intruders", "the benefits", "multi-site big data", "we", "the efficacy", "three medical tasks", "different diseases", "anatomical structures", "structure segmentation", "retina fundus", "mediastinum tumors diagnosis", "brain midline localization", "we", "relay", "its performance", "solutions", "multi-site validation", "external validation", "a total", "41,038 medical images", "21 medical hosts", "7 external hosts", "non-uniform distributions", "we", "significant performance improvements", "relay", "all three tasks", "it", "an average performance increase", "44.4%", "24.2%", "36.7%", "retinal fundus segmentation", "mediastinum tumor diagnosis", "brain midline localization", "remarkably, relay", "central learning", "external test sets", "the meanwhile", "relay learning", "data sovereignty", "cross-site network connections", "we", "relay learning", "clinical multi-site collaboration", "the landscape", "healthcare", "the future", "three", "41,038", "21", "7", "three", "44.4%", "24.2%", "36.7%" ]

[ "Imtiaz Ahmed", "Pramod Kumar Yadav" ]

[ "apple cultivation", "the kashmir valley", "a cornerstone", "the region’s agriculture", "the economy", "substantial annual apple exports", "this study", "the application", "machine learning", "deep learning algorithms", "apple plant diseases", "orchards", "advanced computational techniques", "the research", "early detection", "diagnosis", "diseases", "proactive disease management", "the study", "a dataset", "diverse environmental and plant health factors", "the models", "key highlights", "the comparative analysis", "machine learning", "deep learning approaches", "the identification", "optimal feature sets", "the assessment", "model performance", "the findings", "the development", "efficient and accurate tools", "precision agriculture", "timely intervention", "sustainable orchard management", "the apple industry", "kashmir", "a significant challenge", "the prevalence", "various diseases", "apple trees", "one prominent disease", "that", "apple yields", "the region", "the apple scab", "the fungus venturia inadequacies", "apple scab", "dark", "scaly lesions", "leaves", "fruit", "twigs", "defoliation", "reduced fruit quality", "the disease", "cool and humid conditions", "which", "the kashmir valley", "this study", "the limitations", "traditional, labor-intensive, and time-consuming laboratory methods", "apple plant diseases", "the goal", "an accurate and efficient deep learning-based system", "the prompt identification", "prediction", "foliar diseases", "kashmiri apple plants", "our study", "the creation", "a dataset", "experts", "approximately 10,000 high-quality rgb images", "that", "key symptoms", "foliar diseases", "the next step", "an approach", "deep learning", "that", "convolutional neural networks", "cnns", "comparative analysis", "five different deep learning algorithms", "faster r-cnn", "the method", "apple diseases", "real time", "the proposed framework", "the-art", "a remarkable 92% accuracy", "apple plant diseases", "a new dataset", "that", "samples", "leaves", "kashmiri apple plants", "that", "three different illnesses", "the findings", "promise", "orchard management practices", "apple growers", "the thriving apple industry", "the kashmir valley", "the kashmir valley", "annual", "kashmir", "one", "apple scab", "the kashmir valley", "10,000", "five", "92%", "three", "the kashmir valley" ]

[ "Norbert Krämer", "Roland Frankenberger" ]

[ "deep-learning-modelle spielen", "unterschiedlichen feldern eingesetzt", "vor diesem hintergrund wurde", "der vorliegenden literaturübersicht eine systematische übersichtsarbeit einer internationalen autorengruppe", "deep-learning-modelle zur kariesdiagnostik analysierte", "bewertete", "sie kam zu dem schluss", "dass", "einer zunehmenden", "anzahl von studien die kariesdiagnostik mithilfe von deep-learning-modellen unterstützt wird", "die dokumentierte genauigkeit erscheint vielversprechend", "studien-", "und berichtsqualität derzeit unzureichend sind", "analysen durchzuführen", "bei verbesserter datenlage", "jedoch künftig deep-learning-modelle als hilfsmittel für entscheidungen über das vorhandensein von kariösen läsionen herangezogen werden", "unterschiedlichen feldern eingesetzt", "diesem", "hintergrund wurde", "literaturübersicht", "kam", "dem", "anzahl von studien", "könnten jedoch", "das vorhandensein von kariösen" ]

[ "Xiaotong Jiang", "Xin Zhou", "Michael R. Kosorok" ]

[ "precision medicine", "a framework", "that", "treatment strategies", "a patient’s individual characteristics", "helpful clinical decision support", "existing research", "various situations", "high-dimensional data", "the paradigm", "we", "a new precision medicine approach", "deep doubly robust outcome", "ddrowl", "that", "big and complex data", "this", "a machine learning tool", "that", "the optimal decision rule", "three worlds", "deep learning", "double robustness", "residual weighted learning", "two architectures", "the proposed method", "a fully-connected feedforward neural network", "the deep kernel learning model", "a gaussian process", "deep learning-filtered inputs", "we", "the performance", "limitation", "different methods", "a range", "simulations", "longitudinal and brain imaging data", "patients", "disease", "we", "the application", "the proposed method", "real-world clinical practice", "the implementation", "deep learning", "the proposed method", "the influence", "precision medicine", "high-dimensional abundant data", "greater flexibility", "computational power", "three", "two" ]

[ "Ali Zia", "Abdelwahed Khamis", "James Nichols", "Usman Bashir Tayab", "Zeeshan Hayder", "Vivien Rolland", "Eric Stone", "Lars Petersson" ]

[ "topological deep learning", "tdl", "an emerging area", "that", "the principles", "topological data analysis", "tda", "deep learning techniques", "tda", "insight", "data shape", "it", "global descriptions", "multi-dimensional data", "robustness", "deformation", "noise", "such properties", "deep learning pipelines", "they", "non-tda strategies", "this", "the difficulty", "tda constructs", "e.g. barcode and persistence diagrams", "current deep learning algorithms", "we", "a growth", "deep learning applications", "topologically-guided components", "this survey", "we", "the nascent field", "topological deep learning", "the core concepts", "tda", "we", "the use", "tda techniques", "time", "deep learning frameworks", "they", "different aspects", "deep learning", "we", "tda usage", "existing deep models", "deep topological analytics", "we", "the challenges", "future prospects", "topological deep learning", "first" ]

[ "Leon Herrmann", "Stefan Kollmannsberger" ]

[ "the rapid growth", "deep learning research", "the field", "computational mechanics", "an extensive and diverse body", "literature", "researchers", "key concepts", "methodologies", "this field", "we", "an overview", "deep learning", "deterministic computational mechanics", "five main categories", "simulation substitution", "simulation enhancement", "discretizations", "neural networks", "generative approaches", "deep reinforcement learning", "this review", "deep learning methods", "applications", "computational mechanics", "researchers", "this field", "the review", "researchers", "extensive knowledge", "deep learning", "the primary audience", "researchers", "the verge", "this field", "those", "an overview", "deep learning", "computational mechanics", "the discussed concepts", "five" ]

[ "Qingwen Gu", "Bo Fan", "Zhengning Liu", "Kaicheng Cao", "Songhai Zhang", "Shimin Hu" ]

[ "operators", "relu", "an important role", "deep neural networks", "every neural network", "a series", "differentiable operators", "existing ai benchmarks", "model training and inference performance", "deep learning systems", "specific models", "gpu hardware", "computing bottlenecks", "gpu performance", "specific deep learning tasks", "this paper", "gpu performance", "the operator level", "we", "the information", "operators", "12 representative deep learning models", "six prominent ai tasks", "an operator dataset", "the different importance", "various types", "operators", "different networks", "an operator-level benchmark, opbench", "the basis", "this dataset", "users", "a given range", "models", "the input sizes", "their demands", "this benchmark", "a detailed operator-level performance report", "ai and hardware developers", "we", "four gpu models", "opbench", "their performances", "various types", "operators", "the performance metric flops", "floating point operations", "12", "six", "four", "second" ]

[ "Boon Feng Wee", "Saaveethya Sivakumar", "King Hann Lim", "W. K. Wong", "Filbert H. Juwono" ]

[ "the increasing number", "diabetes individuals", "the globe", "the medical sector", "alternatives", "their medical technologies", "machine learning", "deep learning approaches", "active research", "intelligent and efficient diabetes detection systems", "this study profoundly investigates", "the impacts", "the latest machine learning", "deep learning approaches", "diabetes identification/classifications", "it", "diabetes data", "availability", "available databases", "lab-based and invasive test measurements", "anthropometric measurements", "non-invasive tests", "a cost-effective yet high-performance solution", "several findings", "the possibility", "the detection models", "anthropometric measurements", "non-invasive medical indicators", "this study", "the consequences", "techniques and data dimensionality reduction", "feature selection approaches", "the future direction", "the research", "feature selection approaches", "the accuracy", "reliability", "diabetes identifications" ]

[ "Dina Saif", "Amany M. Sarhan", "Nada M. Elshennawy" ]

[ "chronic kidney disease", "ckd", "today’s most serious illnesses", "this disease", "itself", "the kidney", "early detection", "many people’s lives", "the contribution", "the current paper", "three predictive models", "possible occurrence", "disease existence", "convolutional neural network", "cnn", "long short-term memory", "lstm) model", "deep ensemble model", "the deep ensemble model", "three base deep learning classifiers", "cnn", "lstm", "lstm-blstm", "majority voting technique", "the performance", "the proposed models", "several experiments", "two different public datasets", "the predictive models", "the reached results", "the deep ensemble model", "all the other models", "an accuracy", "the 6-month data", "12-month data predictions", "today", "three", "6 or 12 months", "cnn", "three", "cnn", "two", "0.993", "0.992", "6-month", "12-month" ]

[ "El-Sayed M. El-Kenawy", "Amel Ali Alhussan", "Nima Khodadadi", "Seyedali Mirjalili", "Marwa M. Eid" ]

[ "potatoes", "an important crop", "the world", "they", "the main source", "food", "a large number", "people", "an income", "many people", "the true forecasting", "potato yields", "a determining factor", "the rational use", "maximization", "agricultural practices", "responsible management", "the resources", "wider regions’ food security", "the latest discoveries", "machine learning", "deep learning", "new directions", "prediction models", "the study", "we", "different types", "predictive models", "k-nearest neighbors", "gradient boosting", "xgboost", "multilayer perceptron", "that", "machine learning", "graph neural networks", "gnns", "grus", "lstm", "which", "deep learning models", "these models", "the basis", "some performance measures", "mean squared error", "mse", "root mean squared error", "rmse", "absolute error", "mae", "they", "the potato yields", "the terminal results", "gradient boosting", "xgboost algorithms", "potato yield prediction", "gnns", "the advantage", "high accuracy", "the complex spatial and temporal patterns", "the data", "an mse", "an r2", "xgboost", "an mse", "an r2", "all deep learning models", "gnns", "an mse", "an r2", "the overall performance", "grus", "an mse", "an mse", "these findings", "the potential", "advanced predictive models", "sustainable agricultural practices", "informed decision-making", "the context", "potato farming", "0.03438", "0.49168", "0.03583", "0.02363", "0.51719", "0.03177", "0.03150" ]

[ "Yi Yang", "Qun Fang" ]

[ "deep learning", "a notable success", "mass spectrometry-based proteomics", "glycoproteomics", "various deep learning models", "fragment mass spectra", "peptides", "good accuracy", "they", "the non-linear glycan structure", "an intact glycopeptide", "we", "deepglyco", "a deep learning-based approach", "the prediction", "fragment spectra", "intact glycopeptides", "our model", "tree-structured long-short term memory networks", "the glycan moiety", "a graph neural network architecture", "potential fragmentation pathways", "a specific glycan structure", "this feature", "model explainability", "differentiation ability", "glycan structural isomers", "we", "spectral libraries", "data-independent acquisition glycoproteomics", "a supplement", "library completeness", "we", "this work", "a valuable deep learning resource", "glycoproteomics" ]

[ "Zeinab Jam", "Amir Albadvi", "Alireza Atashi" ]

[ "patients' lives", "diseases", "especially special diseases", "the chances", "a patient surviving", "early detection", "breast cancer", "the deadliest and common cancers", "breast cancer", "patients", "recurrence", "return", "the use", "modern technology", "disease recurrence", "an early stage", "patients", "treatment sooner.significant strides", "deep learning", "strong performance", "unstructured data challenges", "it", "tabular data", "deep learning", "its success", "unstructured data", "ensemble models", "gradient-boosted decision trees", "gbdt", "tabular data prediction tasks", "these gbdt-based models", "approaches.many novel deep learning techniques", "tabular data", "tabnet", "instance", "mirrors decision tree", "a neural network framework", "autoint addresses high dimensionality", "data", "embedding layers", "tab transformer", "the transformer model", "text representations", "categorical attributes", "their innovation", "these methods", "those", "image", "text data", "158 different characteristics", "5142 breast cancer patients", "we", "deep learning techniques effectiveness", "breast cancer recurrence", "examination", "evaluation metrics", "it", "deep learning approaches", "data surpass traditional machine learning algorithms", "imbalanced datasets", "the results", "each algorithm", "a review", "comparison", "the findings", "one", "sooner.significant", "autoint", "158", "5142", "from 1997 to 2019" ]

[ "Yan Xiong", "Guo Xinya", "Junjie Xu" ]

[ "learning engagement", "an essential indication", "students' learning pacification", "the class", "its automated identification technique", "the foundation", "the motive", "impact modifications", "intelligent teaching choices", "current research", "a direct link", "engagement", "emotional investment", "behavioural investment", "it", "artificial intelligence", "autonomous assessment", "the number", "relevant research", "the features", "engagement", "certain contexts", "this research", "we", "the features", "a particular application scenario", "learning engagement", "the application scenario", "learning engagement", "both the coarse-grained information", "human body position", "the fine-grained information", "facial expressions", "the basis", "this analysis", "a fine-grained learning participation recognition model", "that", "background clutter information", "this model", "coarse and fine-grained information", "the recognition", "participation", "real-world teaching situations", "the cnn-transformer model", "this study", "cnn", "fine-grained information", "facial expressions", "transformer", "coarse-grained information", "human body position", "we", "real teaching data", "the features", "engagement situations", "the data quality", "crowdsourcing and expert verification", "the experimental findings", "the cnn-transformer model", "the learning engagement", "unknown participants", "a 92.9% rate", "accuracy", "comparative trials", "the model's recognition impact", "that", "other sophisticated deep learning approaches", "our research", "a framework", "future work", "deep learning approaches", "engagement settings", "cnn", "cnn", "cnn", "92.9%" ]

[ "Fazal Waris", "Feipeng Da", "Shanghuan Liu" ]

[ "accurate and efficient gender recognition", "many applications", "surveillance", "security", "biometrics", "deep learning techniques", "remarkable advancements", "feature extraction", "various applications", "gender classification", "the numerous studies", "the problem", "robust and essential features", "face images", "them", "high accuracy", "the wild", "a challenging task", "real-world applications", "this article", "an approach", "that", "deep learning", "soft voting-based ensemble model", "automatic gender classification", "high accuracy", "an unconstrained environment", "the proposed technique", "a novel deep convolutional neural network", "dcnn", "128 high-quality and accurate features", "face images", "the standardscaler method", "these extracted features", "these preprocessed features", "soft voting ensemble learning model", "the outputs", "several machine learning classifiers", "random forest", "rf", "vector machine", "svm", "linear", "discriminant analysis", "logistic regression", "lr", "gradient", "classifier", "gbc", "xgboost", "the prediction accuracy", "the experimental study", "the utk", "the results", "the proposed approach", "all current approaches", "terms", "accuracy", "all datasets", "128", "linear", "gbc" ]

[ "Rupali Vyas", "Deepak Rao Khadatkar" ]

[ "pneumonia", "a severe health concern", "vulnerable groups", "early and correct classification", "optimal treatment", "this study", "the use", "deep learning", "machine learning classifiers", "dlxmlcs", "pneumonia classification", "chest x", "cxr", "we", "modified vgg19", "densenet121 models", "feature extraction", "five machine learning classifiers", "logistic regression", "vector machine", "decision tree", "random forest", "artificial neural network", "the approach", "we", "remarkable accuracy", "vgg19 and densenet121 models", "99.98% accuracy", "random forest", "decision tree classifiers", "resnet50v2", "99.25% accuracy", "random forest", "these results", "the advantages", "deep learning models", "machine learning classifiers", "the speedy and accurate identification", "pneumonia", "the study", "the potential", "dlxmlc systems", "diagnostic accuracy", "efficiency", "these models", "clinical practice", "healthcare practitioners", "patient care", "results", "future research", "these models", "their application", "other medical imaging tasks", "explainability methodologies", "their decision-making processes", "trust", "their clinical use", "this technique", "breakthroughs", "medical imaging and patient management", "five", "99.98%", "99.25%" ]

[ "Mohammed Elhmadany", "Islam Elmadah", "Hossam E. Abdelmunim" ]

[ "distributed deep learning", "a promising approach", "training", "large and complex deep learning models", "this paper", "a comprehensive workflow", "the yolact instance segmentation model", "big data clusters", "a toolkit", "its high-speed data processing", "ability", "deep learning models", "deployment", "a variety", "devices", "the yolact model", "the model", "a big data cluster", "bigdl", "a distributed deep learning library", "apache spark", "a high-level programming interface", "deep neural networks", "it", "large-scale deep learning applications", "distributed deep learning", "input data", "multiple machines", "parallel processing", "this approach", "several advantages", "the ability", "very large data", "that", "a distributed manner", "scalability", "processing time", "the number", "workers", "tolerance", "the proposed workflow", "virtual machines", "azure databricks", "a cloud-based platform", "big data analytics", "the results", "the workflow", "large datasets", "high performance", "azure databricks", "this study", "the benefits", "challenges", "distributed deep learning", "big data clusters", "instance segmentation", "popular distributed deep learning frameworks", "bigdl", "this study", "the practicality", "distributed deep learning", "sophisticated deep learning models", "big data clusters" ]

[ "Xingyue Wang" ]

[ "this study", "the development", "impact", "a deep learning-based personalized learning recommendation system", "t++' guzheng pedagogy", "the realm", "music education", "the context", "the traditional chinese guzheng instrument, technology-driven personalization", "the potential", "learning experiences", "the research", "data collection", "algorithm development", "integration", "a system", "that", "guzheng learning materials", "individual students' skill levels", "preferences", "results", "high recommendation accuracy", "increased user satisfaction", "positive engagement rates", "this study", "the intersection", "technology", "music education", "cultural preservation", "the promise", "personalized learning", "the realm", "guzheng pedagogy", "chinese", "guzheng", "guzheng" ]

[ "Xinqin Meng", "Chengbing Qin", "Xilong Liang", "Guofeng Zhang", "Ruiyun Chen", "Jianyong Hu", "Zhichun Yang", "Jianzhong Huo", "Liantuan Xiao", "Suotang Jia" ]

[ "the isolation", "graphene", "two-dimensional (2d) materials", "increasing interest", "their excellent chemical and physical properties", "promising applications", "particular challenges", "their further development", "the effective identification", "diverse 2d materials", "the domains", "large-scale and high-precision characterization", "also intelligent function prediction", "design", "these issues", "computational techniques", "density function theory", "molecular dynamic simulation", "which", "powerful computational resources", "high time consumption", "the booming deep learning methods", "recent years", "innovative insights", "tools", "these challenges", "this review", "the current progress", "deep learning", "the realm", "2d materials", "we", "the basic concepts", "deep learning", "commonly used architectures", "convolutional neural and generative adversarial networks", "u-net models", "the characterization", "2d materials", "deep learning methods", "defects and materials identification", "automatic thickness characterization", "the research progress", "the unique properties", "2d materials", "electronic, mechanical, and thermodynamic features", "the inverse design", "functional 2d materials", "we", "the application prospects", "opportunities", "deep learning", "other aspects", "2d materials", "this review", "some guidance", "the understanding and employing novel 2d materials", "two", "2d", "2d", "recent years", "2d", "firstly", "2d", "thirdly", "2d", "2d", "2d", "2d" ]

[ "Marwa Amara", "Nadia Smairi", "Sami Mnasri", "Abdelmalek Zidouri" ]

[ "deep learning techniques", "remarkable success", "various domains", "character classification tasks", "the performance", "deep learning models", "the availability", "large-annotated datasets", "this research work", "the need", "the difficulties", "handwritten arabic character recognition", "the constraints", "limited training data", "it", "the need", "the model’s generalizability", "unknown characteristics", "the accuracy", "deep learning character classification models", "this limitation", "we", "transfer learning and data augmentation strategies", "the character classification", "deep learning", "the proposed model transfers", "previously trained models", "transfer learning", "addresses data scarcity", "generalizable properties", "we", "a vgg16-imagenet transfer learning model", "which", "data augmentation", "three distinct models", "pre-trained imagenet weights", "a frozen backbone", "pre-trained imagenet weights", "a fine-tuned backbone", "each case", "data augmentation", "a critical role", "our experimental results", "better precision", "recall values", "most classes", "our dataset", "which", "the model’s ability", "instances", "each character", "our method", "the ifhcdb", "hacdb datasets", "we", "an impressive recognition accuracy", "96.01%", "97.15%", "this", "transfer learning and data augmentation", "the performance", "deep learning models", "small size training datasets", "recognition", "three", "96.01%", "97.15%" ]

[ "Yuanhang Zheng", "Zeshui Xu", "Tong Wu", "Zhang Yi" ]

[ "intelligent medical industry", "a rapid stage", "development", "the world", "the expanding market size", "basic theories", "intelligent medical diagnosis", "decision-making", "deep learning models", "good practical results", "medical domain", "traditional deep learning", "crisp values", "imprecise", "vague medical data", "the process", "diagnosis", "treatment", "it", "the contributions", "fuzzy deep learning", "uncertain medical data", "fuzzy deep learning", "that", "fuzzy sets", "uncertain and inaccurate information", "new viewpoints", "the presence", "noise", "artifact", "high dimensional unstructured information", "uncertain medical data", "focus", "the intersection", "both different fuzzy deep learning models", "several types", "uncertain medical data", "the paper", "four types", "frameworks", "fuzzy deep learning models", "uncertain medical data", "the status", "three aspects", "fuzzy deep learning models", "uncertain medical data", "application scenarios", "the performance evaluation metrics", "fuzzy deep learning models", "details", "this work", "some original points", "(1) four types", "frameworks", "fuzzy deep learning models", "uncertain medical data", "(2) seven fuzzy deep learning models", "five types", "uncertain medical data", "five application scenarios", "details", "(3) the advantages", "challenges", "future research directions", "fuzzy deep learning", "uncertain medical data", "valuable suggestions", "further deep research", "first", "four", "three", "1", "first", "2", "seven", "five", "five", "3" ]

[ "Thomas M. Bury", "Daniel Dylewsky", "Chris T. Bauch", "Madhur Anand", "Leon Glass", "Alvin Shrier", "Gil Bub" ]

[ "many natural and man-made systems", "critical transitions", "abrupt and potentially devastating changes", "dynamics", "deep learning classifiers", "an early warning signal", "critical transitions", "generic features", "bifurcations", "large simulated training data sets", "classifiers", "continuous-time bifurcations", "rich dynamics", "discrete-time bifurcations", "we", "a deep learning classifier", "an early warning signal", "the five local discrete-time bifurcations", "we", "the classifier", "simulation data", "discrete-time models", "physiology", "economics", "ecology", "experimental data", "chick-heart aggregates", "that", "a period-doubling bifurcation", "the classifier", "higher sensitivity", "specificity", "early warning signals", "a wide range", "noise intensities", "rates", "approach", "the bifurcation", "it", "the correct bifurcation", "most cases", "particularly high accuracy", "the period-doubling, neimark-sacker and fold bifurcations", "deep learning", "a tool", "bifurcation prediction", "its nascence", "the potential", "the way", "we", "systems", "critical transitions", "five" ]

[ "Thavamani. Subramani", "Vijayakumar. Jeganathan", "Sruthi. Kunkuma Balasubramanian" ]

[ "recent years", "the poultry production industry", "automation", "the help", "different kinds", "technological advancements", "verities", "monitoring", "sensing tools", "devices", "these advanced techniques", "numerous advantages", "poultry product production", "human resources", "an easy task", "the public health threads", "foods", "high antibiotic remains", "a problem", "zoonotic diseases", "foodborne diseases", "a crucial task", "poultry producers", "these repeated tasks concern massive and hazardous work", "a suffering work domain", "high human health safety", "labor cost", "sustainable risk", "cross", "-", "infection", "manufacturing facilities", "recent years", "artificial intelligence technology", "a vital role", "all sectors", "poultry production", "advanced machine learning", "deep learning technologies", "which", "a subfield", "ai", "this technology", "an acceptable potential", "numerous challenges", "the establishment", "information-based farming", "various task-handling systems", "the poultry production sector", "this article", "some recently established machine learning", "deep learning algorithms", "poultry management tasks", "chicken activity monitoring", "farm weather monitoring", "control", "weight prediction", "earlier identification", "diseased chickens", "it", "this proposed review work", "a piece", "helpful information", "all", "who", "recognition", "the future scope", "the ml and dl techniques", "the poultry production sectors", "recent years", "recent years" ]

[ "Ankit Rajpal", "Rashmi Mishra", "Sheetal Rajpal", "Kavita", "Varnika Bhatia", "Naveen Kumar" ]

[ "crop diseases", "agricultural productivity", "quality", "the primary cause", "these diseases", "the presence", "biotic stresses", "fungi", "viruses", "bacteria", "these causes", "early stages", "constant monitoring", "domain experts", "technological advancements", "machine learning", "deep learning methods", "the automated identification", "leaf disease-specific symptoms", "image analysis", "this paper", "image-based detection", "leaf diseases", "various deep learning-based models", "the experiment", "the plantvillage dataset", "which", "54,305 colour leaf images", "11 crop species", "38 classes", "the inception-resnet-v2-based model", "a 10-fold cross-validation accuracy", "\\(0.9991 \\pm 0.002\\", "the other deep neural architectures", "the performance", "existing models", "the-art", "each underlined model", "an independent cohort", "the inception-resnet-v2-based model", "the best 10-fold cross-validation accuracy", "\\(0.9535 \\pm 0.041\\", "other deep learning-based models", "these deep learning models", "a black box", "their leaf disease predictions", "users", "this issue", "a local interpretable framework", "the superpixels", "that", "leaf disease", "these superpixels", "the annotations", "the human expert", "54,305", "11", "38", "10-fold", "\\pm 0.002\\", "10-fold", "\\(0.9535 \\pm 0.041\\" ]

[ "Andre Weiner", "Janis Geise" ]

[ "recent years", "deep reinforcement learning", "a technique", "closed-loop flow control problems", "simulation-based environments", "reinforcement learning", "end", "the control system", "safety-critical control applications", "a deep understanding", "the control mechanisms", "reinforcement learning", "a number", "rather simple flow control benchmarks", "a major bottleneck", "real-world applications", "the high computational cost and turnaround time", "flow simulations", "this contribution", "we", "the benefits", "model-based reinforcement learning", "flow control applications", "we", "the policy", "trajectories", "flow simulations", "trajectories", "an ensemble", "environment models", "the model-based learning", "the overall training time", "\\(85\\%\\", "the fluidic pinball test case", "even larger savings", "more demanding flow simulations", "recent years" ]

[ "Abimael Guzman-Pando", "Graciela Ramirez-Alonso", "Carlos Arzate-Quintana", "Javier Camarillo-Cisneros" ]

[ "a significant increase", "the use", "deep learning techniques", "the molecular sciences", "which", "high performance", "datasets", "the ability", "data", "no model", "perfect performance", "all problems", "the pros", "cons", "each approach", "those", "the field", "this paper", "deep learning algorithms", "that", "molecular challenges", "computational chemistry", "we", "a comprehensive categorization", "that", "two primary approaches", "conventional deep learning", "geometric deep learning models", "this classification", "account", "the distinct techniques", "the algorithms", "each approach", "we", "date", "these algorithms", "their key features", "open issues", "this", "details", "input descriptors", "datasets", "open-source code availability", "task solutions", "actual research applications", "general applications", "specific ones", "drug discovery", "our report", "trends", "future directions", "molecular algorithm design", "the input descriptors", "each deep learning model", "gpu usage", "training", "forward processing time", "model parameters", "the most commonly used datasets", "libraries", "optimization schemes", "the most suitable algorithms", "a given task", "it", "a reference", "the datasets", "input data", "each algorithm technique", "addition", "it", "insights", "the benefits", "open issues", "each technique", "the development", "novel computational chemistry systems", "two" ]

[ "Zhentao Hu", "Wei Hou", "Xianxing Liu" ]

[ "entity recognition", "ner", "the required entities", "their types", "unstructured text", "which", "the construction", "knowledge graphs", "traditional methods", "manual feature engineering", "challenges", "large datasets", "complex linguistic contexts", "recent years", "the development", "deep learning", "a plethora", "ner methods", "deep learning", "this paper", "a succinct introduction", "the definition", "the problem", "the limitations", "traditional methods", "it", "ner datasets", "deep learning methods", "them", "three classes", "the complexity", "named entities", "some typical deep learning-based ner methods", "detail", "the development history", "deep learning models", "an in-depth analysis", "comparison", "methods", "outstanding performance", "representative and widely used datasets", "furthermore, the paper reproduces", "the recognition results", "some typical models", "three different types", "typical datasets", "the paper", "insights", "the future trends", "ner development", "ner", "recent years", "three", "three" ]

[ "Sukriti Singh", "José Miguel Hernández-Lobato" ]

[ "recent years", "a rapid growth", "the application", "various machine learning methods", "reaction outcome prediction", "deep learning models", "popularity", "their ability", "representations", "the molecular structure", "gaussian processes", "gps", "the other hand", "reliable uncertainty estimates", "representations", "the data", "we", "the feature learning ability", "neural networks", "nns", "uncertainty quantification", "gps", "a deep kernel learning (dkl) framework", "the reaction outcome", "the dkl model", "very good predictive performance", "different input representations", "it", "standard gps", "comparable performance", "neural networks", "uncertainty estimation", "the uncertainty", "predictions", "the dkl model", "its incorporation", "a surrogate model", "bayesian optimization", "bo", "the proposed method", "a great potential", "reaction discovery", "accurate predictive models", "that", "reliable uncertainty estimates", "bo", "recent years", "gaussian" ]

[ "Albert K. Dadzie", "Sabrina P. Iddir", "Mansour Abtahi", "Behrouz Ebrahimi", "David Le", "Sanjay Ganesh", "Taeyoon Son", "Michael J. Heiferman", "Xincheng Yao" ]

[ "backgroundreliable differentiation", "uveal melanoma", "choroidal nevi", "appropriate treatment", "unnecessary procedures", "benign lesions", "timely treatment", "potentially malignant cases", "the purpose", "this study", "deep learning classification", "uveal melanoma", "choroidal", "nevi", "the effect", "colour fusion options", "the classification performance.methodsa total", "798 ultra-widefield retinal images", "438 patients", "this retrospective study", "157 patients", "281 patients", "choroidal naevus", "colour fusion options", "early fusion", "intermediate fusion", "late fusion", "deep learning image classification", "a convolutional neural network", "cnn", "f1-score", "accuracy", "the area", "the curve", "auc", "(roc", "the classification performance.resultscolour fusion options", "the deep learning performance", "single-colour learning", "the red colour image", "superior performance", "green and blue channels", "multi-colour learning", "the intermediate fusion", "early and late fusion", "options.conclusiondeep learning", "a promising approach", "automated classification", "uveal melanoma", "choroidal", "nevi", "colour fusion options", "the classification performance", "798", "438", "157", "281", "cnn", "roc", "performance.resultscolour" ]

[ "Jinsen Li", "Tsu-Pei Chiu", "Remo Rohs" ]

[ "the mechanisms", "protein-dna binding", "comprehending gene regulation", "three-dimensional dna structure", "dna shape", "a key role", "these mechanisms", "this study", "we", "a deep learning-based method", "deep dnashape", "the current k-mer based high-throughput prediction", "dna shape features", "the influence", "extended flanking regions", "the need", "extensive molecular simulations", "structural biology experiments", "the deep dnashape method", "dna structural features", "any length", "number", "dna sequences", "a high-throughput manner", "an understanding", "the effects", "flanking regions", "dna structure", "a target region", "a sequence", "the deep dnashape method", "access", "the influence", "distant flanking regions", "a region", "interest", "our findings", "dna", "readout mechanisms", "a core target", "flanking regions", "extended flanking regions", "valuable insights", "the detailed structural readout mechanisms", "protein-dna binding", "machine learning models", "the features", "deep dnashape", "the model prediction accuracy", "deep dnashape", "versatile and powerful tool", "diverse dna", "structure-related studies", "three" ]

[ "Jaka Potočnik", "Edel Thomas", "Aonghus Lawlor", "Dearbhla Kearney", "Eric J. Heffernan", "Ronan P. Killeen", "Shane J. Foley" ]

[ "objectivesto", "the machine", "deep learning models", "the justification analysis", "radiology referrals", "accordance", "iguide categorisation", "prediction models", "multiple clinical sites", "human experts.methodsadult brain computed tomography (ct) referrals", "scans", "three ct centres", "ireland", "two radiographers", "the justification", "3000 randomly selected referrals", "iguide", "two consultant radiologists", "the referrals", "disagreement", "insufficient or duplicate referrals", "the inter-rater agreement", "radiographers", "consultants", "a random split", "machine learning", "ml", "deep learning", "(dl) techniques", "unstructured clinical indications", "multi-class classification", "the accuracy and macro-averaged f1 score", "the best-performing classifier", "each type", "the training set", "the test", "set.results42 referrals", "(64.5%", "238 (8.1%", "the agreement", "radiographers", "radiologists", "κ", "the best-performing ml model", "the bag-of-words-based gradient-boosting classifier", "a 94.4% accuracy", "a macro f1", "dl models", "bi-directional long short-term memory", "92.3% accuracy", "a macro f1", "outperforming multilayer", "necessitating clinical decision support", "ml", "dl", "multiple clinical sites", "human experts", "an artificial intelligence-based iguide interpreter", "radiology referrals.clinical relevance statementhealthcare vendors", "clinical sites", "artificial intelligence-enabled systems", "medical exposures", "this", "better implementation", "referral guidelines", "clinical practices", "population dose burden", "ct waiting lists", "wasteful use", "resources.key points", "significant variations", "human experts", "unstructured clinical indications", "patient presentations", "machine", "deep learning", "the justification analysis", "radiology referrals", "iguide categorisation", "machine", "deep learning", "retrospective and prospective justification", "better implementation", "imaging referral guidelines", "objectivesto train", "three", "ireland", "2020", "2021", "two", "3000", "two", "4:1", "set.results42", "1909", "64.5%", "811", "27.4%", "238", "8.1%", "0.268", "0.460", "94.4%", "0.94", "92.3%", "0.92" ]

[ "Fan Zhang", "Xiangyu Wang", "Xinhong Zhang" ]

[ "intersection", "education", "deep learning method", "artificial intelligence", "a hot research field", "education", "ai", "the purpose", "this review", "education practitioners", "the research frontiers", "directions", "ai applications", "education", "this paper", "the applications", "deep learning", "education", "a visualized bibliometric analysis", "the data", "this paper", "education resources information center", "eric", "web", "science", "wos", "these two data bases", "research", "deep learning-based education applications", "citespace", "the number", "publications", "authors", "institutions", "keywords", "articles", "that", "deep learning", "education", "this paper reviews", "the educational applications", "deep learning", "the following six aspects", "effect prediction", "educational game", "recommendation", "automatic scoring", "assisted teaching", "medical education", "the visualized bibliometric analysis", "the path", "inflection point", "research evolution", "the potential motivation", "research", "the frontier", "research", "present", "teachers", "teaching and personalized interactions", "students", "human instructions", "eric", "two", "2015", "citespace", "six" ]

[ "Mohammed Abdullahi", "Olaide Nathaniel Oyelade", "Armand Florentin Donfack Kana", "Mustapha Aminu Bagiwa", "Fatimah Binta Abdullahi", "Sahalu Balarabe Junaidu", "Ibrahim Iliyasu", "Ajayi Ore-ofe", "Haruna Chiroma" ]

[ "vfl", "a critical area", "research", "computer vision", "that", "the automatic extraction", "features", "patterns", "images", "videos", "the applications", "vfl", "object detection", "recognition", "facial recognition", "scene understanding", "medical image analysis", "autonomous vehicles", "this paper", "we", "extensive systematic literature review", "slr", "vfl", "deep learning algorithms", "the paper", "an slr", "deep learning algorithms", "convolutional neural networks", "cnns", "autoencoders", "generative adversarial networks", "gans", "their variants", "the review", "the importance", "vfl", "computer vision", "the limitations", "traditional feature extraction techniques", "it", "an in-depth analysis", "the strengths", "weaknesses", "various deep learning algorithms", "problems", "vfl", "the discussion", "the applications", "vfl", "an insight", "the impact", "vfl", "various industries", "domains", "the review", "the challenges", "vfl", "data scarcity", "quality", "overfitting", "generalization", "interpretability", "explainability", "the discussion", "future directions", "vfl", "hybrid techniques", "feature learning", "continual learning", "attention-based models", "explainable ai", "these techniques", "the challenges", "vfl", "the performance", "the models", "the systematic literature review", "vfl", "a rapidly evolving field", "the potential", "many industries", "domains", "the review", "the need", "further research", "vfl", "the importance", "responsible use", "vfl models", "various applications", "the review", "valuable insights", "researchers", "practitioners", "the field", "computer vision", "who", "these insights", "their work", "the responsible use", "vfl models", "vfl", "vfl", "vfl", "vfl", "vfl", "vfl", "vfl", "vfl", "vfl", "vfl", "vfl", "vfl", "vfl", "vfl" ]

[ "Yaner Qiu", "Liyun Ma", "Rahul Priyadarshi" ]

[ "this paper", "the transformative integration", "deep learning applications", "the deployment", "wireless sensor networks", "wsns", "wsns", "a pivotal role", "diverse domains", "the infusion", "deep learning techniques", "unprecedented opportunities", "enhanced data processing", "analysis", "decision-making", "the research problem", "this paper", "the challenges", "deep learning", "wsn deployment", "the methodology", "an extensive literature review", "the increasing role", "deep learning", "wsn challenges", "key findings", "the potential improvements", "energy efficiency", "data processing speed", "accuracy", "deep learning-empowered wsns", "the implications", "this research extend", "diverse applications", "environmental monitoring", "healthcare", "industrial systems", "smart agriculture", "we", "the future research agenda", "the paper", "the need", "further exploration", "areas", "adaptability", "dynamic environments", "privacy-preserving optimizations", "scalable deep learning models", "the unique constraints", "wsns", "wsns" ]

[ "Harmandeep Singh Gill", "Bikramjit Singh Bath", "Rajanbir Singh", "Amarinder Singh Riar" ]

[ "crop yield forecasting", "the present environment", "food security", "climate", "population", "climate change concerns", "machine learning", "a useful decision-making tool", "agricultural yields", "what", "crops", "what", "the crop’s growth season", "agricultural production prediction studies", "a number", "machine learning methods", "wheat", "a significant food source", "india", "the north", "the wheat crop", "deep learning techniques", "the proposed research", "the suggested system", "deep learning cnn", "lstm applications", "wheat crops", "the results", "the test accuracy", "\\(85\\%\\", "varietal level classification", "the proposed approach results", "the deployment", "such an approach", "practice", "india", "cnn" ]

[ "Neelam Sanjeev Kumar", "G. Deepika", "V. Goutham", "B. Buvaneswari", "R. Vijaya Kumar Reddy", "Sanjeevkumar Angadi", "C. Dhanamjayulu", "Ravikumar Chinthaginjala", "Faruq Mohammad", "Baseem Khan" ]

[ "a comprehensive examination", "human action recognition (har) methodologies", "the convergence", "deep learning and computer vision", "the subject", "this article", "we", "the progression", "handcrafted feature-based approaches", "end", "learning", "a particular focus", "the significance", "large-scale datasets", "research paradigms", "temporal modelling", "spatial features", "our proposed taxonomy", "the merits", "drawbacks", "each", "we", "an architecture", "multi-model deep learning", "that", "recurrent and convolutional neural networks", "attention mechanisms", "accuracy", "robustness", "the videomae v2 method", "https://github.com/opengvlab/videomaev2", "a case study", "practical implementations", "obstacles", "researchers", "practitioners", "a comprehensive understanding", "the most recent advancements", "har", "they", "computer vision", "deep learning", "this survey", "an invaluable resource" ]

[ "Byoungjoon Ahn", "Hyun-Sik Jeong", "Keun-Young Kim", "Kwan Yun" ]

[ "we", "a deep learning method", "the bulk spacetime", "boundary optical conductivity", "we", "the neural ordinary differential equation technique", "continuous functions", "the metric", "the typical class", "holographic condensed matter models", "broken translations", "linear-axion models", "we", "the bulk metric", "the boundary holographic optical conductivity", "an example", "real material", "we", "experimental optical conductivity", "upd2al3", "a representative", "heavy fermion metals", "strongly correlated electron systems", "the corresponding bulk metric", "our knowledge", "our work", "the first illustration", "deep learning bulk spacetime", "boundary holographic or experimental conductivity data", "linear", "first" ]

[ "Tala Talaei Khoei", "Hadjar Ould Slimane", "Naima Kaabouch" ]

[ "the current development", "deep learning", "an exponential transition", "automation applications", "this automation transition", "a promising framework", "higher performance", "lower complexity", "this ongoing transition", "several rapid changes", "the processing", "the data", "several studies", "it", "time-consuming and costly models", "these challenges", "several studies", "deep learning techniques", "they", "specific learning approaches", "supervised deep learning", "addition", "these studies", "other deep learning techniques", "deep unsupervised and deep reinforcement learning techniques", "the majority", "these studies", "some main methodologies", "deep learning", "transfer learning", "federated learning", "online learning", "the limitations", "the existing studies", "this study", "the deep learning techniques", "supervised, unsupervised, reinforcement, and hybrid learning-based models", "addition", "each category", "a brief description", "these categories", "their models", "some", "the critical topics", "deep learning", "namely, transfer", "online learning models", "detail", "challenges", "future directions", "wider outlooks", "future researchers" ]

[ "Albert K. Dadzie", "Sabrina P. Iddir", "Mansour Abtahi", "Behrouz Ebrahimi", "David Le", "Sanjay Ganesh", "Taeyoon Son", "Michael J. Heiferman", "Xincheng Yao" ]

[ "backgroundreliable differentiation", "uveal melanoma", "choroidal nevi", "appropriate treatment", "unnecessary procedures", "benign lesions", "timely treatment", "potentially malignant cases", "the purpose", "this study", "deep learning classification", "uveal melanoma", "choroidal", "nevi", "the effect", "colour fusion options", "the classification performance.methodsa total", "798 ultra-widefield retinal images", "438 patients", "this retrospective study", "157 patients", "281 patients", "choroidal naevus", "colour fusion options", "early fusion", "intermediate fusion", "late fusion", "deep learning image classification", "a convolutional neural network", "cnn", "f1-score", "accuracy", "the area", "the curve", "auc", "(roc", "the classification performance.resultscolour fusion options", "the deep learning performance", "single-colour learning", "the red colour image", "superior performance", "green and blue channels", "multi-colour learning", "the intermediate fusion", "early and late fusion", "options.conclusiondeep learning", "a promising approach", "automated classification", "uveal melanoma", "choroidal", "nevi", "colour fusion options", "the classification performance", "798", "438", "157", "281", "cnn", "roc", "performance.resultscolour" ]

[ "Jaka Potočnik", "Edel Thomas", "Aonghus Lawlor", "Dearbhla Kearney", "Eric J. Heffernan", "Ronan P. Killeen", "Shane J. Foley" ]

[ "objectivesto", "the machine", "deep learning models", "the justification analysis", "radiology referrals", "accordance", "iguide categorisation", "prediction models", "multiple clinical sites", "human experts.methodsadult brain computed tomography (ct) referrals", "scans", "three ct centres", "ireland", "two radiographers", "the justification", "3000 randomly selected referrals", "iguide", "two consultant radiologists", "the referrals", "disagreement", "insufficient or duplicate referrals", "the inter-rater agreement", "radiographers", "consultants", "a random split", "machine learning", "ml", "deep learning", "(dl) techniques", "unstructured clinical indications", "multi-class classification", "the accuracy and macro-averaged f1 score", "the best-performing classifier", "each type", "the training set", "the test", "set.results42 referrals", "(64.5%", "238 (8.1%", "the agreement", "radiographers", "radiologists", "κ", "the best-performing ml model", "the bag-of-words-based gradient-boosting classifier", "a 94.4% accuracy", "a macro f1", "dl models", "bi-directional long short-term memory", "92.3% accuracy", "a macro f1", "outperforming multilayer", "necessitating clinical decision support", "ml", "dl", "multiple clinical sites", "human experts", "an artificial intelligence-based iguide interpreter", "radiology referrals.clinical relevance statementhealthcare vendors", "clinical sites", "artificial intelligence-enabled systems", "medical exposures", "this", "better implementation", "referral guidelines", "clinical practices", "population dose burden", "ct waiting lists", "wasteful use", "resources.key points", "significant variations", "human experts", "unstructured clinical indications", "patient presentations", "machine", "deep learning", "the justification analysis", "radiology referrals", "iguide categorisation", "machine", "deep learning", "retrospective and prospective justification", "better implementation", "imaging referral guidelines", "objectivesto train", "three", "ireland", "2020", "2021", "two", "3000", "two", "4:1", "set.results42", "1909", "64.5%", "811", "27.4%", "238", "8.1%", "0.268", "0.460", "94.4%", "0.94", "92.3%", "0.92" ]

[ "Shilv Yu", "Peng Zhou", "Wang Xi", "Zihe Chen", "Yuheng Deng", "Xiaobing Luo", "Wangnan Li", "Junichiro Shiomi", "Run Hu" ]

[ "wavelength-selective thermal emitters", "ws-tes", "desired target emissivity spectra", "a typical emissivity engineering", "broad applications", "thermal camouflage", "cooling", "gas sensing", "previous designs", "prior knowledge", "materials", "structures", "different applications", "the designed ws-tes", "applications", "applications", "terms", "materials", "structures", "a general design framework", "emissivity engineering", "different applications", "previous designs", "the simultaneous design", "both materials", "structures", "they", "materials", "structures", "structures", "suitable materials", "we", "the deep q-learning network algorithm", "a reinforcement learning method", "deep learning framework", "multilayer ws-tes", "the general validity", "three ws-tes", "various applications", "thermal camouflage", "gas sensing", "which", "the merits", "the deep q-learning algorithm", "it", "a general design framework", "ws-tes", "one-dimensional multilayer structures", "suitable materials", "a self-built material library", "structural parameters", "the target emissivity spectra", "the present framework", "ws-tes", "different applications", "the design parameters", "materials", "structures", "dimensions", "the target functions", "a general framework", "emissivity engineering", "the way", "efficient design", "nonlinear optimization problems", "thermal metamaterials", "three", "1", "2", "3" ]

[ "Héctor J. Hortúa", "Andrés Mora-Valencia" ]

[ "deep learning techniques", "traditional statistical and machine learning models", "the first choice", "price forecasting tasks", "this paper", "we", "probabilistic deep learning", "the volatility index vix", "we", "the probabilistic counterpart", "wavenet", "temporal convolutional network", "tcn", "transformers", "we", "tcn", "all models", "an rmse", "addition", "it", "modern neural networks", "inaccurate uncertainty estimates", "this problem", "we", "the standard deviation", "the networks", "we", "that mnf", "prior outperforms reparameterization trick", "flipout", "terms", "precision and uncertainty predictions", "we", "cauchy", "prior distributions", "well-calibrated tcn", "the vix values", "one and five-step-ahead forecasting", "the probabilistic transformer model", "an adequate forecasting", "the covid-19 pandemic period", "first", "rmse", "0.189", "one", "five", "covid-19" ]

[ "Akhilesh Kumar Tripathi", "Rafeeq Ahmed", "Arvind Kumar Tiwari" ]

[ "neurological disease", "the most common types", "dementia", "that", "clinical approaches", "its premature stages", "no biomarker", "neurological disorders", "their earlier stages", "deep learning approaches", "much attention", "the scientific community", "scanned images", "they", "simple machine learning", "ml", "they", "the most favorable depiction", "untreated images", "deep learning", "the neuroimaging analysis", "neurological diseases", "subtle and dispersed changes", "it", "abstract and complicated patterns", "the current study", "a vital part", "deep learning", "past work", "that", "different ml algorithms", "that", "neurological diseases", "convolution neural networks", "generative adversarial network", "recurrent neural network", "deep belief network", "auto encoder", "other algorithms", "alzheimer’s illness prediction", "many publications", "preprocessing methods", "scaling", "correction" ]

[ "Huu-Hoa Nguyen", "Tri-Thuc Vo" ]

[ "cardiovascular conditions", "healthcare", "their significant impact", "overall wellness", "their role", "heart-related diseases", "this pressing issue", "the research community", "various methodologies", "which", "deep learning approaches", "notable effectiveness", "this potential", "effective deep learning models", "time-series health data", "these challenges", "vast amounts", "data", "iot devices", "optimal deep learning models", "appropriate parameters", "reliable systems", "cardiovascular health monitoring", "response", "our research", "an advanced cardiovascular health monitoring system", "that", "advantage", "wearable iot and deep learning technologies", "healthcare", "it", "a multi-layered architecture", "each layer", "a specific function", "integrates", "the others", "this integration", "the system’s overall functionality", "reliability", "the system", "processes", "health data collection", "deep learning analysis", "the delivery", "timely health alerts", "a critical feature", "this system", "the targeted deep learning model", "six potential algorithms", "experiments", "data", "iot-enabled smartwatches", "the selection process", "-depth", "the models’ performance", "the choice", "the most effective model", "system implementation", "our results", "the system’s effectiveness", "cardiovascular health", "its potential", "personalized healthcare", "individuals", "cardiovascular conditions", "advanced monitoring technologies", "six" ]

[ "Tang Xiangdong" ]

[ "we", "the information age", "our lives", "revolutionary transformations", "the continuous advancement", "computer technology", "data sharing", "information exchange", "the widespread adoption", "hybrid deep learning algorithms", "the emergence", "deep learning", "the progress", "artificial intelligence", "deep learning", "a tangible manifestation", "ai", "deep learning algorithms", "an important part", "the field", "robotics research", "development", "image recognition", "deep learning algorithms", "an irreplaceable role", "the technological breakthrough", "convolutional neural network", "deep learning", "image recognition", "addition", "aspects", "speech recognition", "body function monitoring", "sports data analysis", "all", "deep learning", "they", "unstoppable development momentum", "course", "the current development", "computer technology", "image data", "its basis", "it", "a substantial surpass", "quantity", "its types", "styles", "a variety", "colors", "the basis", "this series", "developments", "we", "a problem", "traditional recognition algorithms", "our next development requirements", "a new type", "algorithm", "this problem", "the research", "image recognition algorithms", "hybrid deep learning", "the research", "this article", "we", "its various algorithms", "their advantages", "disadvantages", "the further development", "image recognition" ]

[ "Abebech Jenber Belay", "Yelkal Mulualem Walle", "Melaku Bitew Haile" ]

[ "alzheimer disease", "ad", "the most chronic neurodegenerative diseases", "that", "global public health", "the prevalence", "alzheimer disease", "consequently the increased risk", "spread", "the world", "a vital threat", "human safekeeping", "early diagnosis", "ad", "a suitable action", "timely intervention", "medication", "which", "the prognosis", "quality", "life", "affected individuals", "quantum computing", "a more efficient model", "different disease classification tasks", "classical machine learning approaches", "the full potential", "quantum computing", "alzheimer’s disease classification tasks", "this study", "we", "an ensemble deep learning model", "quantum machine learning classifiers", "alzheimer’s disease", "i", "alzheimer", "disease", "neuroimaging initiative ii datasets", "the ad disease classification", "we", "important features", "the customized version", "vgg16 and resnet50 models", "the merged images", "these features", "the quantum machine", "classifier", "them", "we", "the performance", "our model", "six metrics", "accuracy", "the area", "the curve", "f1-score", "precision", "the result", "the proposed model", "the-art", "alzheimer’s disease", "an accuracy", "98.37 f1-score", "quantum", "quantum", "quantum", "resnet50", "quantum", "six", "99.89", "98.37" ]

[ "Prottoy Saha", "Shanta Kumar Das", "Rudra Das" ]

[ "brain cancer", "a disease", "the brain", "a brain tumor", "a brain tumor", "the development", "cells", "the brain", "that", "an unregulated and unnatural manner", "patients", "irreversible brain damage", "even death", "these tumors", "all types", "treatment", "positional information", "tumor size", "conventional systems", "a meticulous and automated approach", "information", "medical practitioners", "machine learning", "deep learning", "several imaging modalities", "physicians", "tumor types", "a shorter period", "the paper", "an overview", "newly developed systems", "that", "machine learning", "approaches", "various medical imaging modalities", "the case", "brain tumors", "datasets", "the authors", "partitioning strategies", "different performance evaluation matrices", "this paper", "the policy", "categorization", "we", "a taxonomy", "we", "deep learning and machine learning based systems", "respect", "single classifier", "multiple classifiers", "single dataset", "multiple dataset", "we", "the challenges", "deep learning algorithms", "brain tumor classification", "possible future trends", "this field" ]

[ "M. Tanveer", "T. Goel", "R. Sharma", "A. K. Malik", "I. Beheshti", "J. Del Ser", "P. N. Suganthan", "C. T. Lin" ]

[ "alzheimer’s disease", "which", "a continual deterioration", "cognitive abilities", "older people", "the most common form", "dementia", "neuroimaging data", "example", "magnetic resonance imaging", "positron emission tomography", "identification", "the structural and functional changes", "alzheimer’s disease", "the brain", "alzheimer’s disease", "medical settings", "it", "early intervention", "treatment planning", "our knowledge", "the dynamics", "alzheimer’s disease", "the brain", "ensemble deep learning", "the performance", "reliability", "alzheimer’s disease diagnosis", "these models", "several deep neural networks", "a prediction’s robustness", "we", "key developments", "ensemble deep learning", "its design", "the type", "ensemble", "its heterogeneity and data modalities", "its application", "ad diagnosis", "neuroimaging and genetic data", "trends", "challenges", "our knowledge", "this area" ]

[ "Krithikha Sanju Saravanan", "Velammal Bhagavathiappan" ]

[ "crop yield prediction", "the burgeoning research areas", "the agriculture domain", "the crop yield forecasting models", "productivity", "improved decision-making strategies", "the highly efficient crop yield forecasting model", "farmers", "their cultivable land", "the main objective", "the proposed research work", "a high efficacious crop yield prediction model", "the data", "the period", "21 years", "machine learning", "hybrid deep learning approaches", "two prediction models", "this research work", "the first model", "a machine learning-based model", "which", "the catboost regression model", "which", "the performance", "the yield prediction", "the optuna framework", "the second model", "the hybrid deep learning model", "which", "spatio-temporal attention-based convolutional neural network", "stacnn", "the features", "the bidirectional long short-term memory (bilstm) model", "the crop yield", "the proposed models", "the error metrics", "the latest contemporary models", "the evaluation results", "it", "the proposed models", "all other existing models", "catboost regression model", "the stacnn-bilstm model", "the r-squared value", "the period of 21 years from 1997 to 2017", "two", "first", "second", "stacnn", "stacnn", "0.99" ]

[ "Achraf Kanan", "Bruno Pereira", "Constance Hordonneau", "Lucie Cassagnes", "Eléonore Pouget", "Léon Appolinaire Tianhoun", "Benoît Chauveau", "Benoît Magnin" ]

[ "objectivesdetection", "liver metastases", "oncological management", "computed tomography", "iterative reconstructions", "this indication", "certain limitations", "image reconstructions", "dlir", "deep neural networks", "a significant noise reduction", "iterative reconstructions", "reports", "improvements", "image quality", "their impact", "liver metastases detection", "our main objective", "dlir", "the number", "detected liver metastasis", "our secondary objective", "metastases", "conspicuity", "the two reconstruction methods.methodsct images", "121 patients", "liver metastases", "a 50% adaptive statistical iterative reconstruction", "three levels", "dlir", "each reconstruction", "two double-blinded radiologists", "a maximum", "ten metastases", "visibility", "contour definitions", "comparisons", "methods", "continuous parameters", "mixed models.resultsa higher number", "metastases", "one reader", "median", "(q₁–q₃", "p", "ten patents", "more metastases", "both readers", "a third reader", "confirmation", "metastases visibility", "contour definition", "dlir", "the detection", "visibility", "liver metastases", "asir-v", "the number", "liver metastases", "detected.critical relevance", "high strength", "an increase", "liver metastases detection", "hybrid iterative reconstruction", "clinical oncology imaging", "the limitations", "ct.key", "points detection", "liver metastases", "standard ct reconstructions", "more liver metastases", "deep-learning ct reconstruction", "iterative reconstruction", "deep learning reconstructions", "hepatic metastases", "follow-up", "graphical abstract", "two", "121", "50%", "50%-asir", "three", "two", "ten", "one", "7", "2–10", "733", "5", "2–10", "ten", "third", "ct.key" ]

[ "Bitopan Mazumdar", "Pankaj Kumar Deva Sarma", "Hridoy Jyoti Mahanta" ]

[ "renal toxicity prediction", "a vital role", "drug discovery", "clinical practice", "it", "potentially harmful compounds", "adverse effects", "the renal system", "inherent renal-toxic potential", "the major concerns", "drug development", "it", "failure", "drug discovery", "nephrotoxic probabilities", "a compound", "an early stage", "the drug failure rate", "it", "a mechanism", "the renal toxicity", "a drug-candidate", "the risks", "renal toxicity", "predictive models", "machine learning", "deep learning techniques", "significant attention", "this study", "287 human renal-toxic drugs", "278 non-renal-toxic drugs", "a deep learning model", "27 machine learning models", "8 kinds", "fingerprints", "rdkit descriptors", "the deep neural network model", "better generalization scores", "five-fold cross-validation and extra-tree model", "better performance score", "test data", "structural alerts", "specific chemical substructures", "renal toxicity", "a valuable tool", "early toxicity assessment", "the substructures", "renal toxic compounds", "association rule mining technique", "frequent itemset patterns", "a method", "structural alerts", "10 structural alerts", "the method", "287", "278", "27", "8", "five-fold", "10" ]

[ "Kiyas Kayaalp" ]

[ "many agricultural products", "information technologies", "classification processes", "the desired quality", "it", "different types", "cherries", "export-type cherries", "this study", "cherries", "the important export products", "turkey", "the classification", "cherry species", "ensemble learning methods", "this study", "a new dataset", "3570 images", "seven different cherry species", "isparta region", "the generated new dataset", "six different deep learning models", "pre", "the original and incremental dataset", "a result", "the training", "incremental data", "the best result", "the densenet169 model", "an accuracy", "99.57%", "the two deep learning models", "the best results", "ensemble learning", "a 100% accuracy rate", "the maximum voting model", "one", "3570", "seven", "isparta", "six", "99.57%", "two", "100%" ]

[ "Lihua Dai", "Ben Wang", "Xuemin Cheng", "Qin Wang", "Xinsen Ni" ]

[ "this study", "the intricate challenge", "circuit layout optimization", "integrated circuit (ic) design", "the primary goals", "an optimal balance", "power consumption", "performance metrics", "chip area", "ppa optimization", "the complexity", "this task", "a multidimensional problem", "multiple constraints", "the exploration", "advanced methodologies", "response", "these challenges", "our research", "deep learning technology", "an innovative strategy", "circuit layout optimization", "we", "convolutional neural networks", "cnns", "an optimized layout strategy", "a performance prediction model", "a system", "fault detection", "real-time monitoring", "these methodologies", "the capacity", "deep learning models", "high-dimensional data representations", "multiple constraints", "extensive case studies", "rigorous experimental validations", "the efficacy", "our proposed deep learning-driven approaches", "the results", "significant enhancements", "optimization efficiency", "an average power consumption reduction", "120% and latency decrease", "1.5%", "the predictive capabilities", "a reduction", "the average absolute error", "power predictions", "3%", "comparative analyses", "the superiority", "deep learning methodologies", "conventional techniques", "several dimensions", "our findings", "the potential", "deep learning", "higher accuracy", "predictions", "stronger generalization abilities", "superior design quality", "user satisfaction", "these advancements", "the applicability", "deep learning", "design optimization", "the way", "future advancements", "the multidimensional challenges", "layout optimization", "120%", "1.5%", "3%" ]

[ "Pallavi Saikia", "Sanjib Kumar Kalita" ]

[ "deep learning", "alzheimer disease detection", "mri", "an emerging area", "research", "medical image processing", "the advent", "new technologies", "methods", "deep learning", "medical diagnosis", "certain diseases", "alzheimer’s", "a disease", "which", "date", "no cure", "the progression", "the disease", "a person", "who", "alzheimer", "future", "early prediction", "it", "early prediction", "medical professionals", "the correct diagnosis", "medical professionals", "alzheimer patients", "the progression", "the disease", "ad", "alzheimer", "(mild cognitive impairment", "literature", "many deep learning models", "the early detection", "alzheimer’s disease", "many image modalities", "mri images", "these types", "medical experiments", "the present study", "we", "the evolution", "alzheimer’s disease", "time", "advanced models", "possible recommendations", "those challenges", "the best performance model", "we", "an exhaustive and comprehensive survey", "very deep learning-based research papers", "alzheimer’s disease detection", "the present work", "researchers", "a clear direction", "future scope", "alzheimer disease detection", "analysis", "mci" ]

[ "Huu-Hoa Nguyen", "Tri-Thuc Vo" ]

[ "cardiovascular conditions", "healthcare", "their significant impact", "overall wellness", "their role", "heart-related diseases", "this pressing issue", "the research community", "various methodologies", "which", "deep learning approaches", "notable effectiveness", "this potential", "effective deep learning models", "time-series health data", "these challenges", "vast amounts", "data", "iot devices", "optimal deep learning models", "appropriate parameters", "reliable systems", "cardiovascular health monitoring", "response", "our research", "an advanced cardiovascular health monitoring system", "that", "advantage", "wearable iot and deep learning technologies", "healthcare", "it", "a multi-layered architecture", "each layer", "a specific function", "integrates", "the others", "this integration", "the system’s overall functionality", "reliability", "the system", "processes", "health data collection", "deep learning analysis", "the delivery", "timely health alerts", "a critical feature", "this system", "the targeted deep learning model", "six potential algorithms", "experiments", "data", "iot-enabled smartwatches", "the selection process", "-depth", "the models’ performance", "the choice", "the most effective model", "system implementation", "our results", "the system’s effectiveness", "cardiovascular health", "its potential", "personalized healthcare", "individuals", "cardiovascular conditions", "advanced monitoring technologies", "six" ]

[ "K. Balasamy", "V. Seethalakshmi", "S. Suganyadevi" ]

[ "deep learning", "the subject", "a significant amount", "research interest", "the development", "novel algorithms", "deep learning algorithms", "medical image processing", "a number", "medical imaging tasks", "identification", "diagnosis", "the shortage", "large-sized datasets", "that", "a key barrier", "that", "the continued advancement", "deep learning models", "medical image analysis", "the effectiveness", "these models", "the course", "the previous 5 years", "a great number", "research", "solutions", "this problem", "this work", "we", "a complete overview", "the use", "deep learning techniques", "a variety", "medical image analysis tasks", "the current research", "that", "this area", "we", "an emphasis", "the most recent developments", "contributions", "state", "the-art", "deep learning", "medical image analysis", "these advancements", "contributions", "various application scenarios", "which", "image registration", "segmentation", "classification", "detection", "addition", "this", "we", "the significant technological obstacles", "that", "some potential answers", "the ongoing study", "the previous 5 years" ]

[ "Ibtihaj Ahmad", "Saleem Riaz" ]

[ "cancer survival estimation", "post-cancer patient care", "cancer management policy building", "the development", "tailored treatment plans", "existing survival estimation methods", "censored data", "standard machine learning methods", "some censoring-based semi-machine learning methods", "these methods", "challenges", "they", "they", "single-modality features", "these drawbacks", "lower survival estimation performance", "these issues", "this work", "a framework", "deep-sea", "the-art", "deep-sea", "these features", "statistical methods", "only significant features", "the baseline hazard", "the cox model", "breslow’s estimator", "which", "stochastic gradient descent", "the risk function", "the parameters", "our model", "an ann", "time", "additional input", "ann", "it", "while training", "the patient-specific features", "it", "the state", "the-art", "we", "deep-sea", "five datasets", "head", "neck", "colorectal-liver cancer", "we", "c-index", "the state", "the-art", "results", "ablation studies", "deep-sea", "the proposed method", "cancer survival estimation", "other estimations", "cancer recurrence estimation", "ann", "five", "0.7181" ]

[ "K. V. Deepak", "R. Bharanidharan" ]

[ "osteosarcoma", "a common type", "cancer", "that", "the cells", "the bones", "osteosarcoma", "genetic mutations", "most cases", "it", "the ends", "long bones", "the arms", "legs", "periods", "rapid growth", "osteosarcoma", "histopathological examination", "microscopic images", "recent trends", "pathological images", "computer-intelligent approaches", "computational pathology", "this field", "machine learning", "deep learning algorithms", "digital pathology slides", "osteosarcoma detection", "the subject", "any review papers", "this study", "historical and current literature", "a concise review", "osteosarcoma detection", "this review", "the types", "clinical diagnosis", "modern and future treatment methods", "osteosarcoma", "a review", "machine learning", "ml", "dl", "histopathology image osteosarcoma detection", "sophisticated methods", "the future scope", "osteosarcoma detection", "comparative analysis", "results", "reviewed papers", "this review study", "this article", "fresh concepts", "more potent treatment alternatives", "osteosarcoma", "osteosarcoma", "osteosarcoma", "osteosarcoma", "osteosarcoma", "osteosarcoma", "osteosarcoma", "osteosarcoma" ]

[ "Krithikha Sanju Saravanan", "Velammal Bhagavathiappan" ]

[ "crop yield prediction", "the burgeoning research areas", "the agriculture domain", "the crop yield forecasting models", "productivity", "improved decision-making strategies", "the highly efficient crop yield forecasting model", "farmers", "their cultivable land", "the main objective", "the proposed research work", "a high efficacious crop yield prediction model", "the data", "the period", "21 years", "machine learning", "hybrid deep learning approaches", "two prediction models", "this research work", "the first model", "a machine learning-based model", "which", "the catboost regression model", "which", "the performance", "the yield prediction", "the optuna framework", "the second model", "the hybrid deep learning model", "which", "spatio-temporal attention-based convolutional neural network", "stacnn", "the features", "the bidirectional long short-term memory (bilstm) model", "the crop yield", "the proposed models", "the error metrics", "the latest contemporary models", "the evaluation results", "it", "the proposed models", "all other existing models", "catboost regression model", "the stacnn-bilstm model", "the r-squared value", "the period of 21 years from 1997 to 2017", "two", "first", "second", "stacnn", "stacnn", "0.99" ]

[ "Xia Liu" ]

[ "the outperformance", "deep nets", "we", "a deep net", "three hidden layers", "the empirical risk minimization", "erm", "this deep net", "the estimator", "the optimal learning rates", "the classical saturation problem", "other words", "the networks", "only three hidden layers", "the saturation", "the optimal learning rates", "the obtained results", "the success", "deep nets", "a theoretical guidance", "deep learning", "three", "only three" ]

[ "Serhat Kiliçarslan", "Mete Celik" ]

[ "activation functions", "meaningful relationships", "real-world problems", "the help", "deep learning models", "the development", "activation functions", "which", "deep learning models’ performances", "great interest", "researchers", "the literature", "nonlinear activation functions", "linear activation functions", "the learning performances", "the deep learning models", "non-linear activation functions", "fixed-parameter and trainable activation functions", "the activation function parameter", "the training process", "deep learning models", "the parameters", "the fixed-parameter activation functions", "the deep learning model training process", "it", "too much time", "appropriate function parameter values", "the slow convergence", "the deep learning model", "contrast", "trainable activation functions", "whose parameters", "each iteration", "deep learning models training process", "faster and better convergence", "the most suitable parameter values", "the datasets", "deep learning architectures", "this study", "parametric rsigelu (p+rsigelu) trainable activation functions", "p+rsigelu single (p+rsigelus", "(p+rsigelud", "the performance", "fixed-parameter activation function", "rsigelu", "the performances", "the proposed trainable activation functions", "the benchmark datasets", "mnist", "cifar-10", "cifar-100 datasets", "results", "the proposed activation functions", "prelu", "pelu", "alisa", "p+felu", "psigmoid", "gelu activation functions", "the literature", "the codes", "the activation function", "linear", "p+rsigelu", "cifar-10", "prelu", "pelu", "alisa, p+felu" ]

[ "Fangping Wan", "Marcelo D. T. Torres", "Jacqueline Peng", "Cesar de la Fuente-Nunez" ]

[ "-", "extinction", "molecules", "antibiotic resistance", "other present-day biological and biomedical problems", "we", "deep learning", "the proteomes", "all available extinct organisms", "the discovery", "antibiotic peptides", "we", "ensembles", "deep-learning models", "a peptide-sequence encoder", "neural networks", "the prediction", "antimicrobial activity", "it", "mine", "10,311,899 peptides", "the models", "37,176 sequences", "broad-spectrum antimicrobial activity", "which", "extant organisms", "we", "69 peptides", "their activity", "bacterial pathogens", "most peptides", "bacteria", "their cytoplasmic membrane", "known antimicrobial peptides", "which", "the outer membrane", "lead compounds", "the woolly mammoth", "the straight-tusked elephant", "hydrodamin-1", "the ancient sea cow", "the giant sloth", "megalocerin-1", "the extinct giant", "elk", "anti-infective activity", "mice", "skin abscess", "thigh infections", "molecular de-extinction", "deep learning", "the discovery", "therapeutic molecules", "10,311,899", "37,176", "11,035", "69" ]

[ "S. D. Yazd", "N. Gharib", "J. F. Derakhshandeh" ]

[ "climate change", "the key topics", "concerns", "our community", "these days", "greenhouse gases emissions", "the researchers", "a permanent solution", "this challenge", "this paper", "different methods", "machine learning", "deep learning models", "their effectiveness", "accuracy", "greenhouse gases emissions", "the accuracy", "the assessment", "the data", "101 countries", "a period", "31 years", "the official world bank sources", "this study", "a range", "matrices", "mean squared error", "root mean squared error", "absolute error", "p value", "correlation", "each model", "the results", "machine learning models", "the deep learning models", "the support vector regression polynomial model", "the statistical findings", "longitudinal regression analysis", "cereal yield", "the greenhouse gas emissions", "(p value", "renewable energy consumption", "forest areas", "greenhouse gas emissions", "p value", "(p value", "one", "these days", "a few decades ago", "101", "a period of 31 years", "0.000", "0.06", "0.000", "0.07" ]

[ "Abdullah Y. Muaad", "Hanumanthappa Jayappa Davanagere", "Jamil Hussain", "Mugahed A. Al-antari" ]

[ "the declaration", "covid-19", "an epidemic", "the world health organization", "september", "the task", "monitoring", "the spread", "misinformation", "covid-19", "social media", "it", "arabic text recognition", "tracking", "misleading information", "covid-19", "social media platforms", "significant difficulties", "the detection", "such text", "order", "our communities", "the dissemination", "false rumors", "a reliable framework", "text detection", "this research paper", "a novel deep ensemble learning framework", "that", "ten distinct categories", "arabic text", "covid-19", "rumors", "restrictions", "celebrity news", "informational news", "plans", "requests", "advice", "personal anecdotes", "others", "our framework", "we", "a dataset", "dataset1", "which", "10,000 text samples", "addition", "the dal technique", "new text samples", "dataset2", "our datasets", "we", "back translation and random insertion augmentation strategies", "datasets3", "datasets4", "each", "24,000 text samples", "the original and augmented datasets", "we", "dataset5", "which", "a total", "39,000 text samples", "the final text prediction", "three transformer-based bert models", "ensemble transfer learning", "our proposed ensemble framework", "each dataset", "it", "promising results", "the largest dataset", "dataset5", "an accuracy", "93%", "precision", "92%", "recall", "93%", "an f1-score", "91%", "our proposed model", "performance improvements", "27%", "18%", "2%", "1%", "datasets2", "the comprehensive experimental results", "our ensemble framework", "the-art", "the encouraging performance", "our framework", "arabic text", "the potential", "decision-making processes", "the identification", "information", "the development", "strategies", "such issues", "the future", "covid-19", "the world health organization", "september 2019", "covid-19", "arabic", "recognition", "covid-19", "ten", "arabic", "covid-19", "dataset1", "10,000", "dataset2", "datasets3", "24,000", "39,000", "three", "93%", "92%", "93%", "91%", "27%", "18%", "2%", "1%", "datasets2", "3", "4", "5", "arabic" ]

[ "Rusul Sabah Jebur", "Mohd Hazli Bin Mohamed Zabil", "Dalal Adulmohsin Hammood", "Lim Kok Cheng" ]

[ "deep learning", "significant interest", "image denoising", "notable distinctions", "the types", "deep learning methods", "discriminative learning", "gaussian noise", "optimization models", "real noise", "limited research", "that", "the different deep learning techniques", "image denoising", "this paper", "a comprehensive review", "techniques", "methods", "image denoising", "challenges", "existing approaches", "this paper", "a comparative study", "deep techniques", "image denoising", "the study", "a comprehensive review", "68 papers", "image denoising", "a detailed analysis", "the field’s progress", "methodologies", "a period", "5 years", "its literature review", "the paper", "a comprehensive summary", "image", "deep learning", "machine learning methods", "image denoising", "cnns", "image denoising", "additive white noisy-image denoising", "real noisy image denoising", "blind denoising", "hybrid noisy images", "state-", "the-art", "methods", "image", "deep learning", "salt and pepper noise", "non-linear filters", "digital color images", "the main objective", "this paper", "a comprehensive overview", "various approaches", "image denoising", "each", "which", "individual research studies", "the paper", "these approaches", "a systematic and organized manner", "their strengths", "weaknesses", "insights", "future research", "the field", "68", "between 2018 and 2023", "5 years" ]

[ "Muslume Beyza Yildiz", "Elham Tahsin Yasin", "Murat Koklu" ]

[ "abstractfish", "a source", "protein", "essential nutrients", "humans", "the proteins", "substances", "fish", "it", "its freshness", "fish", "an extended period", "its freshness", "the freshness", "fish", "its eyes", "smell", "skin", "gills", "this study", "artificial intelligence techniques", "fish freshness", "the author’s objective", "the freshness", "fish", "its eye characteristics", "this", "we", "a combination", "deep and machine learning models", "that", "the freshness", "fish", "an application", "that", "both deep learning", "machine learning", "the freshness", "any given fish sample", "two deep learning algorithms", "squeezenet", "vgg19", "features", "image data", "five machine learning models", "the freshness levels", "fish samples", "machine learning models", "k", ", rf, svm", "lr", "ann", "the results", "it", "the vgg19 model", "feature selection", "conjunction", "an artificial neural network", "ann", "classification yields", "the most favorable success rate", "77.3%", "the ffe dataset.graphical abstract", "two", "five", "77.3%" ]

[ "Kirsten R. Steffner", "Matthew Christensen", "George Gill", "Michael Bowdish", "Justin Rhee", "Abirami Kumaresan", "Bryan He", "James Zou", "David Ouyang" ]

[ "transesophageal echocardiography", "(tee) imaging", "a vital tool", "the evaluation", "complex cardiac pathology", "the management", "cardiac surgery patients", "a key limitation", "the application", "deep learning strategies", "to intraoperative and intraprocedural tee data", "the complexity", "unstructured nature", "these images", "the present study", "we", "a deep learning-based, multi-category tee view classification model", "that", "structure", "intraoperative and intraprocedural tee imaging data", "we", "a convolutional neural network", "cnn", "standardized tee views", "labeled intraoperative and intraprocedural tee videos", "cedars-sinai medical center", "csmc", "we", "our model", "intraoperative tee videos", "stanford university medical center", "sumc", "accuracy", "our model", "all labeled views", "the highest performance", "ventricular short axis view", "area", "the receiver operating curve", "csmc", "sumc", ", the mid-esophageal long axis view", "auc =", "csmc", "sumc", "the mid-esophageal aortic valve short axis view", "csmc", "sumc", "the mid-esophageal 4-chamber view", "csmc", "sumc", "we", "our deep learning model", "standardized tee views", "which", "further downstream deep learning analyses", "intraoperative and intraprocedural tee imaging", "cnn", "stanford university medical center", "0.971", "0.957", "0.954", "0.905", "0.946", "0.898", "4", "0.939", "0.902" ]

[ "Chung-Ming Lo", "Ting-Yi Lin" ]

[ "the quality inspection", "products", "delivery", "a critical role", "manufacturing quality", "quick and accurate inspection", "samples", "highly automated inspection", "pattern recognition", "smart manufacturing", "conventional ensemble methods", "defect detection", "this study", "further proposed synthetic mechanisms", "various features", "learning classifiers", "a database", "6000 sample images", "printed circuit board (pcb) connectors", "factories", "a novel confidence synthesis mechanism", "images", "deep learning features", "spatially connected texture features", "images", "low reliabilities", "the synthetic mechanism", "a single classifier", "the highest improvement", "accuracy", "97.83%", "the confidence-based synthesis", "the synthetic mechanism", "high accuracy", "defect detection", "automation", "smart manufacturing", "6000", "96.00", "97.83%" ]

[ "Abdullah Y. Muaad", "Hanumanthappa Jayappa Davanagere", "Jamil Hussain", "Mugahed A. Al-antari" ]

[ "the declaration", "covid-19", "an epidemic", "the world health organization", "september", "the task", "monitoring", "the spread", "misinformation", "covid-19", "social media", "it", "arabic text recognition", "tracking", "misleading information", "covid-19", "social media platforms", "significant difficulties", "the detection", "such text", "order", "our communities", "the dissemination", "false rumors", "a reliable framework", "text detection", "this research paper", "a novel deep ensemble learning framework", "that", "ten distinct categories", "arabic text", "covid-19", "rumors", "restrictions", "celebrity news", "informational news", "plans", "requests", "advice", "personal anecdotes", "others", "our framework", "we", "a dataset", "dataset1", "which", "10,000 text samples", "addition", "the dal technique", "new text samples", "dataset2", "our datasets", "we", "back translation and random insertion augmentation strategies", "datasets3", "datasets4", "each", "24,000 text samples", "the original and augmented datasets", "we", "dataset5", "which", "a total", "39,000 text samples", "the final text prediction", "three transformer-based bert models", "ensemble transfer learning", "our proposed ensemble framework", "each dataset", "it", "promising results", "the largest dataset", "dataset5", "an accuracy", "93%", "precision", "92%", "recall", "93%", "an f1-score", "91%", "our proposed model", "performance improvements", "27%", "18%", "2%", "1%", "datasets2", "the comprehensive experimental results", "our ensemble framework", "the-art", "the encouraging performance", "our framework", "arabic text", "the potential", "decision-making processes", "the identification", "information", "the development", "strategies", "such issues", "the future", "covid-19", "the world health organization", "september 2019", "covid-19", "arabic", "recognition", "covid-19", "ten", "arabic", "covid-19", "dataset1", "10,000", "dataset2", "datasets3", "24,000", "39,000", "three", "93%", "92%", "93%", "91%", "27%", "18%", "2%", "1%", "datasets2", "3", "4", "5", "arabic" ]

[ "Rusul Sabah Jebur", "Mohd Hazli Bin Mohamed Zabil", "Dalal Adulmohsin Hammood", "Lim Kok Cheng" ]

[ "deep learning", "significant interest", "image denoising", "notable distinctions", "the types", "deep learning methods", "discriminative learning", "gaussian noise", "optimization models", "real noise", "limited research", "that", "the different deep learning techniques", "image denoising", "this paper", "a comprehensive review", "techniques", "methods", "image denoising", "challenges", "existing approaches", "this paper", "a comparative study", "deep techniques", "image denoising", "the study", "a comprehensive review", "68 papers", "image denoising", "a detailed analysis", "the field’s progress", "methodologies", "a period", "5 years", "its literature review", "the paper", "a comprehensive summary", "image", "deep learning", "machine learning methods", "image denoising", "cnns", "image denoising", "additive white noisy-image denoising", "real noisy image denoising", "blind denoising", "hybrid noisy images", "state-", "the-art", "methods", "image", "deep learning", "salt and pepper noise", "non-linear filters", "digital color images", "the main objective", "this paper", "a comprehensive overview", "various approaches", "image denoising", "each", "which", "individual research studies", "the paper", "these approaches", "a systematic and organized manner", "their strengths", "weaknesses", "insights", "future research", "the field", "68", "between 2018 and 2023", "5 years" ]