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" ]