Datasets:

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ADKU

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ResearchGPT_model_dataset

like 0

[ "Yongqing Wang", "Mengmeng Niu", "Kuo Liu", "Haibo Liu", "Bo Qin", "Yiming Cui" ]

[ "deep learning methods", "the field", "tool condition monitoring", "the complexity", "diversity", "working conditions", "it", "the high accuracy, strong generalization performance", "wide applicability", "monitoring models", "this article", "a deep transfer learning method", "center loss", "tool condition monitoring", "different processing conditions", "deep extreme learning machine", "(delm", "sample features", "tool condition monitoring", "deep coral", "the last feature extraction layer", "delm", "domain adaptation", "the center loss", "the transfer learning model", "the intra-class compactness", "the center loss", "a broader decision boundary", "a tool wear experiment", "a milling machine", "research", "the proposed method", "the tool condition monitoring", "different processing conditions", "the introduction", "central loss", "the separation", "samples", "different categories", "the target domain", "the model’s applicability", "other domain adaptation methods", "this method", "better accuracy", "generalization ability", "firstly", "secondly" ]

[ "Osama Bin Naeem", "Yasir Saleem", "M. Usman Ghani Khan", "Amjad Rehman Khan", "Tanzila Saba", "Saeed Ali Bahaj", "Noor Ayesha" ]

[ "(mostly mammography", "radiologists", "breast cancer", "the likelihood", "suspected cases", "false positives", "false negatives", "the experience", "the radiologist", "the kind", "device/method", "the confirmation", "the type", "tumour", "the radiologist", "histological investigation", "microscopic analysis", "a biopsy", "the tumor's grade", "stage", "which", "the latter stages", "treatment", "biopsy", "a secondary issue", "the cancer detection process", "only 15 to 30%", "instances", "that", "biopsy result", "malignant findings", "deep learning", "remarkable performance", "visual recognition challenges", "it", "a variety", "tasks", "similar examples", "deep learning applications", "healthcare", "which", "a lot", "interest", "the research community", "deep learning", "categories tumours", "breast cancer", "a significant global health concern", "the medical sciences", "more accurate diagnoses", "detections", "recent advancements", "machine learning techniques", "systems potential accuracy", "it", "optimistic outcomes", "malignant images", "imaging", "domains", "deep learning-based methods", "remarkable success", "constituent segmentation", "unet", "localization", "densenet", "classification", "vgg-19", "this study examines", "deep learning methods", "the highly accurate diagnosis", "benign or malignant tumours", "screened images", "contrast", "a mammogram", "which", "detail", "this paper", "imaging methods", "cancer detection", "early detection and cost effectiveness", "two main benefits", "machine learning", "deep learning techniques", "mammograms", "only 15 to", "two" ]

[ "Yongquan Yang", "Haijun Lv", "Ning Chen" ]

[ "the dominant position", "deep learning", "mostly deep neural networks", "various artificial intelligence applications", ", recently, ensemble learning", "deep neural networks", "ensemble deep learning", "significant performances", "the generalization", "learning system", "modern deep neural networks", "millions", "billions", "parameters", "the time and space overheads", "multiple base deep learners", "the ensemble deep learner", "that", "traditional ensemble learning", "several algorithms", "fast ensemble deep learning", "the deployment", "ensemble deep learning", "some applications", "further advances", "many applications", "specific fields", "the developing time", "computing resources", "the data", "large dimensionality", "an urgent problem", "the significant advantages", "ensemble deep learning", "the required expenses", "many more applications", "specific fields", "it", "the alleviation", "this problem", "it", "how ensemble learning", "the era", "deep learning", "this article", "we", "discussions", "data analyses", "published works", "methodologies", "recent advances", "unattainability", "traditional ensemble learning", "ensemble deep learning", "we", "this article", "the intrinsic problems", "technical challenges", "future developments", "ensemble learning", "the era", "deep learning", "millions to billions" ]

[ "Jingzhao Zhang", "Yanan Wang", "Benben Jiang", "Haowei He", "Shaobo Huang", "Chen Wang", "Yang Zhang", "Xuebing Han", "Dongxu Guo", "Guannan He", "Minggao Ouyang" ]

[ "accurate evaluation", "li-ion battery (lib) safety conditions", "unexpected cell failures", "facilitate battery deployment", "low-carbon economies", "the recent progress", "artificial intelligence", "anomaly detection methods", "realistic battery settings", "the complex failure mechanisms", "the lack", "real-world testing frameworks", "large-scale datasets", "we", "a realistic deep-learning framework", "electric vehicle (ev) lib anomaly detection", "it", "a dynamical autoencoder", "dynamical systems", "social and financial factors", "we", "our detection algorithm", "released datasets", "over 690,000 lib", "snippets", "347 evs", "our model", "the limitations", "the-art", "deep learning ones", "it", "the expected direct ev battery fault and inspection costs", "our work", "the potential", "deep learning", "lib safety", "the significance", "social and financial information", "deep learning models", "690,000", "347" ]

[ "Bin Liu" ]

[ "this study", "the vital role", "object representation", "detection", "smart retail management systems", "customer experiences", "operational efficiency", "the literature review", "a preference", "deep learning techniques", "their superior accuracy", "traditional methods", "the challenges", "high accuracy", "low computation costs", "deep learning-based object representation", "the paper", "a solution", "the yolov7 framework", "order", "the ever-changing landscape", "smart retail technologies", "the study", "the potential scalability", "flexibility", "deep learning approaches", "the method", "a custom dataset", "experimental results", "the model’s efficacy", "accurate results", "enhanced performance", "various experiments", "analyses" ]

[ "Yongqing Wang", "Mengmeng Niu", "Kuo Liu", "Haibo Liu", "Bo Qin", "Yiming Cui" ]

[ "deep learning methods", "the field", "tool condition monitoring", "the complexity", "diversity", "working conditions", "it", "the high accuracy, strong generalization performance", "wide applicability", "monitoring models", "this article", "a deep transfer learning method", "center loss", "tool condition monitoring", "different processing conditions", "deep extreme learning machine", "(delm", "sample features", "tool condition monitoring", "deep coral", "the last feature extraction layer", "delm", "domain adaptation", "the center loss", "the transfer learning model", "the intra-class compactness", "the center loss", "a broader decision boundary", "a tool wear experiment", "a milling machine", "research", "the proposed method", "the tool condition monitoring", "different processing conditions", "the introduction", "central loss", "the separation", "samples", "different categories", "the target domain", "the model’s applicability", "other domain adaptation methods", "this method", "better accuracy", "generalization ability", "firstly", "secondly" ]

[ "Osama Bin Naeem", "Yasir Saleem", "M. Usman Ghani Khan", "Amjad Rehman Khan", "Tanzila Saba", "Saeed Ali Bahaj", "Noor Ayesha" ]

[ "(mostly mammography", "radiologists", "breast cancer", "the likelihood", "suspected cases", "false positives", "false negatives", "the experience", "the radiologist", "the kind", "device/method", "the confirmation", "the type", "tumour", "the radiologist", "histological investigation", "microscopic analysis", "a biopsy", "the tumor's grade", "stage", "which", "the latter stages", "treatment", "biopsy", "a secondary issue", "the cancer detection process", "only 15 to 30%", "instances", "that", "biopsy result", "malignant findings", "deep learning", "remarkable performance", "visual recognition challenges", "it", "a variety", "tasks", "similar examples", "deep learning applications", "healthcare", "which", "a lot", "interest", "the research community", "deep learning", "categories tumours", "breast cancer", "a significant global health concern", "the medical sciences", "more accurate diagnoses", "detections", "recent advancements", "machine learning techniques", "systems potential accuracy", "it", "optimistic outcomes", "malignant images", "imaging", "domains", "deep learning-based methods", "remarkable success", "constituent segmentation", "unet", "localization", "densenet", "classification", "vgg-19", "this study examines", "deep learning methods", "the highly accurate diagnosis", "benign or malignant tumours", "screened images", "contrast", "a mammogram", "which", "detail", "this paper", "imaging methods", "cancer detection", "early detection and cost effectiveness", "two main benefits", "machine learning", "deep learning techniques", "mammograms", "only 15 to", "two" ]

[ "Wan Ying Chai", "Min Keng Tan", "Kenneth Tze Kin Teo", "Heng Jin Tham" ]

[ "fermentation", "chemical industries", "valuable products", "it", "less energy", "a lesser environmental impact", "conventional chemical processes", "however, the inherent nonlinearity", "the fermentation process", "limited comprehension", "its metabolic mechanisms", "present challenges", "control", "optimization", "the formation", "products", "reinforcement learning", "a machine learning method", "an agent", "exploration", "experience", "feedback", "the form", "rewards", "it", "an optimal policy", "which", "maximum cumulative reward", "the integration", "deep learning", "reinforcement learning", "the efficiency", "classical reinforcement learning", "continuous control", "this paper", "substrate feeding rate", "a simulated fed-batch baker’s yeast fermentation", "deep reinforcement learning", "artificial neural network", "ann", "the function approximator", "the state-action function", "the substrate feeding rate", "a large state space", "which", "substrate concentration", "yeast concentration", "the change", "ethanol concentration", "the deep reinforcement learning algorithm", "the optimization objective", "yeast production", "ethanol formation", "the performance", "the feeding strategy", "deep reinforcement learning", "a commonly used pre-determined exponential feeding profile", "the results", "the proposed feeding strategy", "the exponential feeding strategy", "the yeast yield", "25.66%", "negligible ethanol production", "addition", "the proposed algorithm", "effective handling", "various initial conditions", "the exponential feeding approach", "baker", "25.66%" ]

[ "Youngjin Lee", "Seungwan Lee", "Chanrok Park" ]

[ "deep learning-based convolutional neural networks", "cnns", "the quality", "digital tomosynthesis (dts) images", "the direct applications", "the conventional cnns", "low-dose dts imaging", "acceptable image quality", "the inaccurate recognition", "complex texture patterns", "this study", "a deep residual learning network", "the anscombe transformation", "the complex texture", "the low-dose dts image quality", "the proposed network", "convolution layers", "max-pooling layers", "up-sampling layers", "skip connections", "the network training", "the residual images", "the ground-truth and low-dose projections", "which", "the anscombe transformation", "a result", "the proposed network", "the quantitative accuracy", "dts images", "1.01–1.27 and 1.14–1.71 times", "comparison", "low-dose dts images", "other deep learning networks", "the spatial resolution", "the dts image", "the proposed network", "that", "a deep image learning network", "conclusion", "the proposed network", "the low-dose dts image quality", "an optimal model", "low-dose dts imaging", "max", "1.01–1.27", "1.14–1.71", "1.12" ]

[ "Shivendu Mishra", "Ritika Yaduvanshi", "Prince Rajpoot", "Sharad Verma", "Amit Kumar Pandey", "Digvijay Pandey" ]

[ "efficient waste management", "human well-being and environmental health", "proper disposal practices", "financial losses", "the depletion", "natural resources", "the rapid urbanization", "population growth", "an automated, innovative waste classification model", "this need", "our paper", "a novel and robust solution", "a smart waste classification model", "that", "a hybrid deep learning model", "svm", "waste items", "the trashnet datasets", "our proposed approach", "the advanced deep learning model densenet-121", "superior performance", "meaningful features", "an expanded trashnet dataset", "these features", "a support vector machine", "svm", "precise classification", "data augmentation", "classification accuracy", "the risk", "overfitting", "limited trashnet data", "the results", "our experimental evaluation", "this hybrid deep learning model", "an impressive accuracy rate", "99.84%", "this accuracy", "similar existing models", "the efficacy", "potential", "our approach", "waste classification", "a sustainable and cleaner future", "99.84%" ]

[ "Ebru Ergün" ]

[ "regions", "citrus crops", "diseases", "fungi", "bacteria", "pests", "viruses", "growers", "automated technologies", "that", "citrus anomalies", "economic losses", "recent advances", "deep learning techniques", "potential", "the accuracy", "citrus anomaly categorization", "this research", "the use", "deep learning methods", "specifically densenet", "robust models", "different types", "citrus anomalies", "the dataset", "high-resolution images", "different orange leaves", "the species citrus sinensis osbeck", "orange groves", "the states", "tamaulipas", "san luis potosi", "northeastern mexico", "study", "experimental results", "the effectiveness", "the proposed deep learning models", "12 different classes", "citrus anomalies", "evaluation metrics", "accuracy", "recall", "precision", "the confusion matrix", "the discriminative power", "the models", "the convolutional neural network architectures", "densenet", "the highest classification accuracy", "99.50%", "the study", "the potential", "scalable and effective citrus anomaly classification", "management", "deep learning-based systems", "san luis", "mexico", "12", "99.50%" ]

[ "Jitendra V. Tembhurne", "Nachiketa Hebbar", "Hemprasad Y. Patil", "Tausif Diwan" ]

[ "skin cancer", "the most common forms", "cancer", "which", "it", "it", "melanoma", "a form", "skin cancer", "that", "every 7-skin cancer related death", "hospitals", "dermatologists", "multiple cases", "skin cancer", "high possibilities", "false negatives", "diagnosis", "such incidents", "exhaustive research", "the research community", "the world", "highly accurate automated tools", "skin cancer detection", "this paper", "we", "a novel approach", "machine learning", "deep learning techniques", "the problem", "skin cancer detection", "the deep learning model", "the-art", "features", "images", "the machine learning model", "image features", "which", "the techniques", "contourlet transform", "local binary pattern histogram", "meaningful feature extraction", "any image classification roblem", "a result", "the manual and automated features", "our designed model", "a higher accuracy", "93%", "an individual recall score", "99.7%", "86%", "the benign and malignant forms", "cancer", "we", "the model", "publicly available kaggle dataset", "processed images", "isic archive dataset", "the proposed ensemble outperforms", "both expert dermatologists", "the-art", "this novel method", "high assistance", "dermatologists", "any misdiagnosis", "one", "6", "7", "93%", "99.7% and", "86%" ]

[ "Yongxin Liang", "Jianhui Sun", "Jialei Zhang", "Yuyao Wang", "Anchi Wan", "Shibo Zhang", "Zhenyu Ye", "Shengtao Lin", "Zinan Wang" ]

[ "acoustic sensing", "das", "a fiber sensing technology", "rayleigh scattering", "which", "optical fiber", "a series", "sensing units", "it", "an indispensable part", "the field", "seismic monitoring", "vehicle tracking", "pipeline monitoring", "fiber rayleigh scattering responses", "the core", "das", "few in-depth studies", "the purpose", "fiber rayleigh scattering responses", "this paper", "we", "a deep learning framework", "the bidirectional gated recurrent unit", "which", "the first time", "the fiber rayleigh scattering responses", "our knowledge", "the deep learning framework", "a numerical simulation", "it", "experimental data", "the responses", "the experimental probing pulses", "a finer spatial resolution", "demodulation", "this work", "the deep learning framework", "the characteristics", "the fiber rayleigh scattering responses", "which", "the way", "intelligent das", "das", "das", "first" ]

[ "D. L. Lima", "J. Kasakewitch", "D. Q. Nguyen", "R. Nogueira", "L. T. Cavazzola", "B. T. Heniford", "F. Malcher" ]

[ "introductionthis systematic review", "the use", "machine learning", "artificial intelligence", "hernia surgery.methodsthe prisma guidelines", "this systematic review", "the robins", "i", "rob", "2 tools", "qualitative assessment", "all studies", "this review", "recommendations", "the following pre-defined key items", "protocol", "research question", "search strategy", "study eligibility", "data extraction", "study design", "risk", "bias", "publication bias", "statistical analysis.resultsa total", "13 articles", "this review", "the use", "machine learning", "deep learning", "hernia surgery", "all studies", "articles", "the population", "type", "machine learning", "deep learning model", "dlm", "hernia type", "the thirteen included studies", "all", "either inguinal, ventral, or incisional hernias", "four studies", "recognition", "surgical steps", "inguinal hernia repair videos", "two studies", "outcomes", "image-based dmls", "seven studies", "deep learning algorithms", "outcomes", "factors", "postoperative complications.conclusionthe use", "ml", "abdominal wall reconstruction", "a promising tool", "outcomes", "factors", "that", "postoperative complications", "hernia", "2", "13", "2020", "thirteen", "four", "two", "seven" ]

[ "Hyeonseo Hwang", "Hyeonseong Jeon", "Nagyeong Yeo", "Daehyun Baek" ]

[ "the exponential growth", "big data", "rna biology", "rb", "the development", "deep learning (dl) models", "that", "crucial discoveries", "dl studies", "other fields", "the successful implementation", "dl", "the effective utilization", "large-scale datasets", "public databases", "this goal", "data encoding methods", "algorithms", "techniques", "that", "biological domain knowledge", "pivotal roles", "this review", "we", "guiding principles", "these dl concepts", "various problems", "rb", "successful examples", "associated methodologies", "we", "the remaining challenges", "dl models", "rb", "strategies", "these challenges", "this review", "the compelling potential", "dl", "ways", "this powerful technology", "the intriguing biology", "rna" ]

[ "Emrah Dönmez", "Serhat Kılıçarslan", "Aykut Diker" ]

[ "hazelnut", "an agricultural product", "that", "the economy", "the countries", "it", "the human factor", "a major role", "hazelnut classification", "the typical approach", "manual inspection", "each sample", "experts", "a process", "that", "limited sensitivity", "the deep learning techniques", "the classification", "detection", "agricultural products", "deep learning", "great potential", "the agricultural sector", "this technology", "product quality", "productivity", "farmers", "the ability", "their produce", "this", "sustainability", "efficiency", "the agricultural industry", "this paper", "the application", "deep learning algorithms", "hazelnut classification", "the need", "manual labor", "time", "cost", "the sorting process", "the study", "hazelnut images", "three different varieties", "giresun", "ordu", "van", "a dataset", "1165 images", "giresun", "ordu", "van hazelnuts", "this dataset", "an open-access dataset", "the study", "experiments", "the determination", "hazelnut varieties", "bigtransfer", "r101", "r152 × 4 models", "deep learning models", "big transfer", "classification", "the classification task", "3627 nut images", "a remarkable accuracy", "99.49%", "the bit-m", "r152", "4 model", "these innovative methods", "patentable products", "devices", "various industries", "the economic value", "the country", "three", "van", "1165", "1324", "1138", "bit)-m", "× 4", "3627", "99.49%", "× 4" ]

[ "Pengjun Wu", "Zhanzhi Zhang", "Xueyi Peng", "Ran Wang" ]

[ "the realm", "urban planning", "the integration", "deep learning technologies", "a transformative force", "the way", "cities", "this research embarks", "a multifaceted exploration", "that", "the power", "deep learning", "bayesian regularization techniques", "the performance", "reliability", "neural networks", "urban planning applications", "deep learning", "its ability", "complex patterns", "vast urban datasets", "the potential", "unprecedented insights", "urban dynamics", "transportation networks", "environmental sustainability", "the complexity", "these models", "challenges", "limited interpretability", "these issues", "bayesian regularization methods", "imbue neural networks", "a principled framework", "that", "generalization", "predictive uncertainty", "this research", "the practical implementation", "bayesian regularization", "neural networks", "applications", "traffic prediction", "urban infrastructure", "data privacy", "safety", "security", "bayesian regularization", "the aim", "model performance", "terms", "accuracy", "reliability", "planners", "decision-makers", "probabilistic insights", "the outcomes", "various urban interventions", "tandem", "quantitative assessments", "graphical analysis", "a crucial tool", "the inner workings", "deep learning models", "the context", "urban planning", "graphical representations", "network visualizations", "decision boundary analysis", "we", "bayesian regularization", "neural network architecture", "interpretability", "bayesian" ]

[ "Yingchun Guo", "Xin Su", "Gang Yan", "Ye Zhu", "Xueqi Lv" ]

[ "age transformation", "personalized facial information", "a given face", "a target age", "this technique", "extensive applications", "fields", "face recognition", "movie special effects", "social entertainment", "others", "the advancement", "deep learning", "particularly generative adversarial networks", "gans", "age transformation", "significant progress", "the emergence", "a diverse range", "deep learning-based methods", "a comprehensive and systematic literature review", "these methods", "this survey", "we", "an all-encompassing review", "deep learning methods", "facial aging", "we", "the key aspects", "feature preservation", "the age transformation process", "we", "a comprehensive overview", "facial age transformation techniques", "various deep learning network architectures", "we", "an analysis", "comparison", "commonly used face image datasets", "recommendations", "dataset selection", "we", "the qualitative and quantitative evaluation metrics", "age transformation methodologies", "experimental assessment", "we", "potential areas", "future research", "age transformation methods", "the current challenges", "limitations", "firstly" ]

[ "Lei Guan \n (关　磊)", "Dong-Sheng Li \n (李东升)", "Ji-Ye Liang \n (梁吉业)", "Wen-Jian Wang \n (王文剑)", "Ke-Shi Ge \n (葛可适)", "Xi-Cheng Lu \n (卢锡城)" ]

[ "deep learning", "the cornerstone", "artificial intelligence", "an increasingly important role", "human production", "lifestyle", "the complexity", "problem-solving increases", "deep learning models", "a proliferation", "large language models", "an astonishing number", "parameters", "pipeline model parallelism", "the mainstream approaches", "the significant challenge", "“big models", "this paper", "a comprehensive review", "pmp", "it", "the basic concepts", "main challenges", "pmp", "it", "synchronous and asynchronous pipeline schedules", "pmp approaches", "the main techniques", "load balance", "both intra-node and inter-node training", "the main techniques", "computation", "storage", "communication", "potential research directions" ]

[ "Taifei Zhao", "Yuxin Sun", "Xinzhe Lü", "Shuang Zhang" ]

[ "the problems", "pulse", "channel fading", "atmospheric scattering", "turbulence", "multiple-input multiple-output (mimo) technology", "a valid way", "a wireless ultraviolet (uv) mimo channel estimation approach", "deep learning", "this paper", "the deep learning", "the channel estimation", "the image processing", "convolutional neural network", "cnn", "attention mechanism", "am", "the learning model", "the depth features", "channel state information", "csi", "the simulation results", "the approach", "this paper", "channel estimation", "uv mimo communication", "the fading", "scattering", "turbulence", "the mimo scattering channel", "mimo channel estimation", "cnn" ]

[ "Saqib ul Sabha", "Assif Assad", "Sadaf Shafi", "Nusrat Mohi Ud Din", "Rayees Ahmad Dar", "Muzafar Rasool Bhat" ]

[ "deep learning", "computer vision", "small and imbalanced datasets", "substantial progress", "this domain", "prevailing models", "these constraints", "this", "we", "an innovative contrast-based learning strategy", "small and imbalanced data", "that", "the proficiency", "deep learning architectures", "these challenging datasets", "training images", "the effective training dataset", "n", "\\(n^2\\", "richer data", "model training", "n", "our solution", "specific loss functions", "network architectures", "its adaptability", "diverse classification scenarios", "four benchmark datasets", "our approach", "the-art", "the empirical evidence", "our method’s superior efficacy", "contemporaries", "metrics", "balanced accuracy", "f1 score", "geometric mean", "noteworthy increments", "7–16%", "the covid-19 dataset", "4–20%", "honey bees", "1–6%", "cifar-10", "1–9%", "fashionmnist", "essence", "our proposed method", "a potent remedy", "the perennial issues", "scanty", "skewed data", "deep learning", "four", "noteworthy", "7–16%", "covid-19", "4–20%", "1–6%", "cifar-10", "1–9%" ]

[ "Kavita Bathe", "Nita Patil", "Sanjay Patil", "Devanand Bathe", "Kuldeep Kumar" ]

[ "agricultural sector", "association", "its allied sectors", "pivotal role", "the progress", "a country", "spite", "notable contribution", "agricultural sector", "several challenges", "crop loss", "diseases", "the major challenges", "rice", "the major cereal crops", "india", "its production", "crop diseases", "early and accurate detection", "rice crop leaf diseases", "aforementioned issue", "the years", "conventional methods", "crop leaf disease detection", "its own limitations", "recent technological advancement", "computer vision", "deep learning", "new pathways", "agricultural sector", "deep learning models", "huge data", "which", "quite a great challenge", "such case", "a robust deep learning model", "limited and unbalanced dataset", "good generalization performances", "this study", "deep ensemble learning approach", "performance improvement", "rice crop leaf disease classification task", "the ensemble method convdepthtransensemblenet", "the effectiveness", "proposed work", "experiments", "diverse datasets", "convdepthtransensemblenet", "a lightweight model", "which", "the accuracy", "96.88%", "limited and unbalanced dataset", "the experimental results", "proposed model", "the individual classifiers", "conventional methods", "learning approach", "terms", "significant reduction", "parameters", "generalization performance", "the proposed model", "deep learning models", "resources constrained devices", "rice", "one", "india", "the years", "deep", "96.88%" ]

[ "Fengyin Li", "Xiaojiao Wang", "Yuhong Sun", "Tao Li", "Junrong Ge" ]

[ "the covid-19", "it", "great harm", "human beings", "the system", "the entrance", "public places", "shopping malls", "stations", "pedestrians", "masks", "pedestrians", "the system inspection", "cotton masks", "scarves", "the detection system", "pedestrians", "masks", "the type", "masks", "the lightweight network architecture", "this paper", "a cascaded deep learning network", "transfer learning", "a mask recognition system", "the cascaded deep learning network", "the activation function", "the mobilenetv3 output layer", "the structure", "the model", "two mobilenetv3 networks", "transfer", "the training process", "two modified mobilenetv3 networks", "a multi-task convolutional neural network", "the imagnet underlying parameters", "the network models", "advance", "which", "the computational load", "the models", "the cascaded deep learning network", "a multi-task convolutional neural network", "these two modified mobilenetv3 networks", "a multi-task convolutional neural network", "faces", "images", "two modified mobilenetv3 networks", "the backbone network", "the features", "masks", "the classification results", "the modified mobilenetv3 neural network", "the classification accuracy", "the cascading learning network", "7%", "the excellent performance", "the cascading network", "covid-19", "today", "mobilenetv3", "mobilenetv3", "two", "mobilenetv3", "two", "mobilenetv3", "two", "mobilenetv3", "two", "mobilenetv3", "mobilenetv3", "7%" ]

[ "Hongshuai Wang", "Jie Feng", "Zhihao Dong", "Lujie Jin", "Miaomiao Li", "Jianyu Yuan", "Youyong Li" ]

[ "organic photovoltaics", "worldwide interest", "their unique advantages", "low-cost, lightweight, and flexible power sources", "functional molecular design", "synthesis", "the discovery", "ideal organic semiconductors", "it", "experimental screening", "the wide organic compound space", "we", "a framework", "a deep learning model", "graph neural network", "an ensemble learning model", "light gradient boosting machine", "which", "rapid and accurate screening", "organic photovoltaic molecules", "this framework", "the relationship", "molecular structure", "molecular properties", "device efficiency", "our framework", "the chemical structure", "the organic photovoltaic molecules", "it", "density functional theory calculations", "it", "fast predictions", "the reliability", "our framework", "data", "previous reports", "our newly synthesized organic molecules", "our work", "an efficient method", "new organic optoelectronic materials", "the organic photovoltaic molecules" ]

[ "Hongfei Zhu", "Xingyu Liu", "Hao Zheng", "Lianhe Yang", "Xuchen Li", "Zhongzhi Han" ]

[ "the strawberry appearance", "an essential standard", "the quality", "it", "the strawberry appearance quality", "intelligent picking", "this study", "a new strawberry appearance quality detection", "unsupervised deep learning", "deep learning", "resnet18", "resnet50", "resnet101", "the strawberry image feature information", "(t-distribution", "the feature vectors’ dimension", "the unsupervised learning method", "gaussian mixture model", "strawberries", "feature points", "the results", "the clustering performance", "resnet101", "2-dimensional space", "the cluster accuracy", "94.89%", "the validation accuracy", "91.79%", "(2) the clustering method", "resnet50", "good performance", "the 3-dimensional space", "the cluster accuracy", "96.10%", "the validation accuracy", "93.08%", "(3) the accuracy", "deep features", "rf (random forest", "95.00%", "limited data", "this method", "intelligent picking strawberry equipment", "it", "the supervised learning drawback", "that", "image datasets", "prior knowledge", "strawberry", "firstly", "resnet18", "resnet50", "strawberry", "gaussian", "1", "2", "94.89%", "91.79%", "2", "resnet50", "3", "96.10%", "93.08%", "3", "95.00%" ]

[ "Sri Revathi B." ]

[ "the sustainability", "the earth", "renewable energy", "the output", "renewable energy", "a big impact", "we", "our power networks", "accurate forecasting", "renewable energy generation", "grid dependability", "permanence", "the risk", "cost", "the energy market", "infrastructure", "several approaches", "solar radiation", "a global scale", "the two most common ones", "algorithms", "cloud pictures", "physical models", "the objective", "a summary", "machine learning-based techniques", "solar irradiation forecasting", "this context", "renewable energy", "the world’s energy grid", "numerous strategies", "hybrids", "physical models", "statistical approaches", "artificial intelligence techniques", "the use", "renewable energy", "this paper", "methods", "renewable energy", "deep learning", "machine learning", "review", "analysis", "deep learning", "machine", "forecasts", "renewable energy", "the second paragraph", "metaheuristic optimization techniques", "renewable energy", "the third topic", "the open issue", "renewable energy", "i", "a few potential future job objectives", "earth", "two", "first", "second", "third" ]

[ "Omar AlZoubi", "Mohammad Abu Awad", "Ayman M. Abdalla", "Laaly Samrraie" ]

[ "varicocele", "a disease", "an abnormal dilation", "the scrotal venous pampiniform plexus", "it", "a common cause", "infertility", "some cases", "pain", "discomfort", "this paper", "a new approach", "automatic varicocele classification", "convolutional neural networks", "deep learning", "the available dataset", "images", "different color modes", "each color mode dataset", "a deep learning network", "experiments", "all possible combinations", "four different pre-trained models", "over three color modes", "the best combination", "that", "the highest performance", "augmentation", "the implementation results", "training", "testing", "several metrics", "the analysis", "the results", "the efficacy", "the proposed system", "it", "a relatively high accuracy", "that", "the previous works", "a significant improvement", "four", "three" ]

[ "Kambiz Rahbar", "Fatemeh Taheri" ]

[ "the increasing demand", "effective retrieval", "image datasets", "the rapid growth", "digital images", "image retrieval", "a method", "a structured database", "similar images", "a user's query image", "effective image representation", "extraction", "distinctive features", "the challenges", "image retrieval systems", "the proposed approach", "a triplet loss function", "binary cross", "-", "a siamese network", "deep metric learning", "a discriminative feature space", "maximum discrimination", "classes", "minimum intra-class distance", "this approach", "a pre-trained neural network", "siamese network", "image features", "a pre-trained convolutional neural network", "a siamese network", "a discriminative feature space", "deep metric learning", "this method", "a triplet loss function", "binary cross", "-", "anchor", "negative samples", "the transformation", "the initial feature space", "the discriminative feature space", "deep metric learning", "the siamese network", "the proposed approach", "features", "diverse classes", "the dataset", "visualization", "the feature space", "the t-sne statistical technique", "the explainability", "the proposed approach", "the shapley value", "the proposed approach", "the corel10k", "caltech101", "caltech256 datasets", "the best reported results", "the precision metric", "the corel10k dataset", "the caltech101", "caltech256", "datasets", "experimental results", "the proposed approach", "the retrieval results", "similar samples", "the query image", "discrimination", "the feature vectors", "dimensionality reduction", "siamese", "corel10k", "0.988", "corel10k", "0.951", "0.902" ]

[ "Reshma Sheik", "Sneha Rao Ganta", "S. Jaya Nirmala" ]

[ "boundary detection", "sbd", "an important first step", "natural language processing", "sentence boundaries", "downstream applications", "sentence boundaries", "legal texts", "a unique and challenging problem", "their distinct structural and linguistic features", "our approach", "deep learning models", "delimiter", "context information", "input", "precise detection", "sentence boundaries", "english legal texts", "we", "various deep learning models", "domain-specific transformer models", "legalbert", "caselawbert", "the efficacy", "our deep learning models", "we", "them", "the-art", "crf", "model size, f1-score, and inference time", "we", "the convolutional neural network model", "cnn", "the top-performing deep learning model", "performance", "we", "the features", "the cnn model", "the subsequent crf model", "a hybrid architecture", "that", "the strengths", "both models", "our experiments", "the hybrid model", "the baseline model", "a 4% improvement", "the f1-score", "additional experiments", "the superiority", "the hybrid model", "sbd open-source libraries", "domain", "these findings", "the importance", "efficient sbd", "legal texts", "the advantages", "deep learning models", "hybrid architectures", "optimal performance", "first", "english", "cnn", "cnn", "4%" ]

[ "Hao Wang", "Baoli Wang", "Caixia Cui" ]

[ "the vibration signal", "an effective diagnostic tool", "structural health monitoring", "shm) fields", "that", "abnormal states", "deep learning methods", "remarkable success", "vibration signals", "damage detection", "this paper", "a systematic review", "deep learning methods", "shm", "the utilization", "vibration signal data", "different model perspectives", "recent years", "a significant increase", "research", "deep learning", "vibration-based shm", "the accuracy", "such works", "that", "traditional machine learning approaches", "better results", "multiple approaches", "we", "transfer learning methods", "promising results", "limited data", "the model", "this paper", "deep learning research", "health monitoring", "vibration signal data", "multiple perspectives", "a particular emphasis", "transfer learning methods", "shm", "it", "the gap", "existing reviews", "the discussion", "shm", "we", "the challenges", "current research", "recommendations", "future work", "recent years" ]

[ "Pascal Mettes", "Mina Ghadimi Atigh", "Martin Keller-Ressel", "Jeffrey Gu", "Serena Yeung" ]

[ "deep representation learning", "a ubiquitous part", "modern computer vision", "euclidean space", "the de facto standard manifold", "visual representations", "hyperbolic space", "rapid traction", "computer vision", "hyperbolic learning", "a strong potential", "hierarchical structures", "limited samples", "quantify uncertainty", "robustness", "error severity", "this paper", "we", "a categorization", "-depth", "current literature", "hyperbolic learning", "computer vision", "we", "literature", "three main research themes", "each direction", "we", "hyperbolic learning", "all themes", "the main research problems", "that", "current advances", "hyperbolic learning", "computer vision", "we", "a high-level intuition", "hyperbolic geometry", "open research questions", "research", "this direction", "three" ]

[ "Mohammad Dehghani", "Zahra Yazdanparast" ]

[ "artificial intelligence", "remarkable progress", "complex tasks", "thanks", "advances", "hardware acceleration", "machine learning", "more accurate outcomes", "more complex issues", "algorithms", "more data", "this huge amount", "data", "a great deal", "computation", "these issues", "distributed machine learning", "which", "the data", "algorithm", "several machines", "considerable effort", "developing distributed machine learning algorithms", "different methods", "we", "these algorithms", "classification", "clustering", "(traditional machine learning", "deep learning", "deep reinforcement learning groups", "distributed deep learning", "more attention", "recent years", "the studies", "this approach", "we", "this category", "the investigation", "the mentioned algorithms", "we", "the limitations", "that", "future research", "recent years" ]

[ "Yikun Li", "Lunyang Liu", "Xiaoning Zhao", "Shuming Zhou", "Xuehui Wu", "Yuecheng Lai", "Zhongjun Chen", "Jizhong Chen", "Xueqing Xing" ]

[ "purposethe purpose", "this study", "deep learning methods", "saxs", "data.methodsthe deep learning algorithm", "simulated saxs data", "which", "batches", "the theoretical saxs formula", "python code", "our self-developed saxsnet", "a convolutional neural network", "pytorch", "saxs data", "various shapes", "nanoparticles", "we", "comparative analysis", "classification algorithms", "resnet-18", "resnet-34", "vision transformer", "random forest", "xgboost regression algorithms", "the nanoparticle size prediction", "we", "the aforementioned shape classification and numerical regression methods", "actual experimental data", "a pipeline segment", "the processing", "saxs data", "deep learning classification algorithms", "numerical regression", "simulated data", "the four deep learning algorithms", "a prediction accuracy", "over 96%", "the validation set", "the fine-tuned deep learning model", "robust generalization capabilities", "the shapes", "experimental data", "rapid and accurate identification", "morphological changes", "nanoparticles", "experiments", "the random forest", "xgboost regression algorithms", "faster and more accurate predictions", "nanoparticle", "size.conclusionthe pipeline segment", "this study", "deep learning classification and regression algorithms", "real-time processing", "high-throughput saxs data", "it", "the impact", "human factors", "data processing results", "the standardization", "automation", "intelligence", "synchrotron radiation experiments", "resnet-18", "resnet-34", "four", "over 96%" ]

[ "D. L. Lima", "J. Kasakewitch", "D. Q. Nguyen", "R. Nogueira", "L. T. Cavazzola", "B. T. Heniford", "F. Malcher" ]

[ "introductionthis systematic review", "the use", "machine learning", "artificial intelligence", "hernia surgery.methodsthe prisma guidelines", "this systematic review", "the robins", "i", "rob", "2 tools", "qualitative assessment", "all studies", "this review", "recommendations", "the following pre-defined key items", "protocol", "research question", "search strategy", "study eligibility", "data extraction", "study design", "risk", "bias", "publication bias", "statistical analysis.resultsa total", "13 articles", "this review", "the use", "machine learning", "deep learning", "hernia surgery", "all studies", "articles", "the population", "type", "machine learning", "deep learning model", "dlm", "hernia type", "the thirteen included studies", "all", "either inguinal, ventral, or incisional hernias", "four studies", "recognition", "surgical steps", "inguinal hernia repair videos", "two studies", "outcomes", "image-based dmls", "seven studies", "deep learning algorithms", "outcomes", "factors", "postoperative complications.conclusionthe use", "ml", "abdominal wall reconstruction", "a promising tool", "outcomes", "factors", "that", "postoperative complications", "hernia", "2", "13", "2020", "thirteen", "four", "two", "seven" ]

[ "Yifan Li", "Jing Liu" ]

[ "deep learning", "dl", "remarkable progress", "various real-world tasks", "its construction pipeline", "human scientists", "evolutionary computing", "ec", "an optimization tool", "the biological evolution mechanism", "good performance", "complex optimization problems", "it", "a new way", "dl models", "many sparks", "the dl field", "automatic machine learning", "automl", "many reviews", "automl", "recent years", "few comprehensive works", "the application", "ec", "dl", "which", "evolutionary deep learning", "edl", "a thorough investigation", "we", "edl", "four parts", "rule optimization", "(2) hyperparameter optimization", "3) neural architecture search", "(4) other edl-related works", "this work", "we", "the classic optimization methods", "the challenges", "edl", "respect", "these four parts", "the related work", "the future research prospects", "this work", "the concept", "research content", "edl", "which", "readers", "the intersection", "ec", "dl", "their inspiration", "ec", "recent years", "ec", "four", "1", "2", "3", "4", "four", "ec", "dl" ]

[ "H. James Deva Koresh" ]

[ "deep learning softwares", "artificial neural networks", "various applications", "training", "them", "an appropriate dataset", "the raw image samples", "the dataset", "noisy and unclear information", "radiation", "heat", "poor lighting conditions", "the researchers", "such noisy images", "steps", "a valid feature information", "the neural network layers", "the deep learning software", "certain claims", "roam", "the researchers", "an image", "some useful information", "it", "an appropriate filter", "enhancement technique", "the work", "the efficacy", "the methodologies", "that", "a preprocessing step", "the work", "the common reasons", "statements", "the researchers", "the preprocessing steps", "a deep learning approach", "the study", "a clarity", "the requirement", "-", "requirement", "step", "a deep learning software" ]

[ "Zhipeng Hu", "Yu Ding", "Runze Wu", "Lincheng Li", "Rongsheng Zhang", "Yujing Hu", "Feng Qiu", "Zhimeng Zhang", "Kai Wang", "Shiwei Zhao", "Yongqiang Zhang", "Ji Jiang", "Yadong Xi", "Jiashu Pu", "Wei Zhang", "Suzhen Wang", "Ke Chen", "Tianze Zhou", "Jiarui Chen", "Yan Song", "Tangjie Lv", "Changjie Fan" ]

[ "this paper", "a comprehensive review", "deep learning applications", "the video game industry", "these techniques", "game development", "experience", "operation", "computation techniques", "the game world", "an integration", "various complex data", "this", "the use", "deep learning", "various types", "game data", "the paper", "the game data", "asset data", "interaction data", "player data", "their utilization", "game development", "experience", "operation", "this paper", "deep learning applications", "asset data", "object images", "3d scenes", "avatar models", "facial animations", "interaction data", "improved text-based conversations", "decision-making behaviors", "player data", "cheat detection", "match-making purposes", "this review", "all existing applications", "deep learning", "it", "a thorough presentation", "the current state", "deep learning", "the gaming industry", "its potential", "game production", "costs", "the overall player experience", "3d" ]

[ "Emrah Dönmez", "Serhat Kılıçarslan", "Aykut Diker" ]

[ "hazelnut", "an agricultural product", "that", "the economy", "the countries", "it", "the human factor", "a major role", "hazelnut classification", "the typical approach", "manual inspection", "each sample", "experts", "a process", "that", "limited sensitivity", "the deep learning techniques", "the classification", "detection", "agricultural products", "deep learning", "great potential", "the agricultural sector", "this technology", "product quality", "productivity", "farmers", "the ability", "their produce", "this", "sustainability", "efficiency", "the agricultural industry", "this paper", "the application", "deep learning algorithms", "hazelnut classification", "the need", "manual labor", "time", "cost", "the sorting process", "the study", "hazelnut images", "three different varieties", "giresun", "ordu", "van", "a dataset", "1165 images", "giresun", "ordu", "van hazelnuts", "this dataset", "an open-access dataset", "the study", "experiments", "the determination", "hazelnut varieties", "bigtransfer", "r101", "r152 × 4 models", "deep learning models", "big transfer", "classification", "the classification task", "3627 nut images", "a remarkable accuracy", "99.49%", "the bit-m", "r152", "4 model", "these innovative methods", "patentable products", "devices", "various industries", "the economic value", "the country", "three", "van", "1165", "1324", "1138", "bit)-m", "× 4", "3627", "99.49%", "× 4" ]

[ "Ünal Çavuşoğlu", "Devrim Akgun", "Selman Hizal" ]

[ "attackers", "critical services", "cloud systems", "a vital and challenging task", "machine learning-based algorithms", "models", "the intelligent detection", "zero-day attacks", "deep learning methods", "that", "automatic feature extraction", "attacks", "this study", "we", "a new deep learning model", "cloud systems", "malicious attacks", "the developed deep transfer learning-based ids converts network traffic", "the feature maps", "the transferred and fine-tuned convolutional layers", "the deep learning model", "the dense layer", "detection", "classification", "traffic data", "the results", "the nsl-kdd test", "the developed models", "89.74% multiclass", "92.58%", "binary classification accuracy", "we", "another evaluation", "only 20%", "the training dataset", "test data", "80%", "training", "this case", "the model", "99.83%", "99.85% multiclass and binary classification accuracy", "today", "zero-day", "2d", "89.74%", "92.58%", "only 20%", "80%", "99.83%", "99.85%" ]

[ "Iqra Batool", "Tamim Ahmed Khan" ]

[ "software fault prediction", "(sfp) techniques", "faults", "the early stages", "the software development life cycle", "sdlc", "we", "techniques", "sfp", "deep learning methods", "which", "more accurate results", "deep learning", "exceptional results", "various domains", "computer vision", "natural language processing", "speech recognition", "this study", "we", "three deep learning methods", "namely, long short-term memory", "lstm", "bidirectional lstm", "bilstm", "radial basis function network", "rbfn", "software faults", "our results", "existing models", "our results", "our study", "chidamber", "kemerer (ck) metrics-based datasets", "experiments", "our proposed algorithm", "we", "lstm", "bilstm", "rbfn", "the required results", "we", "fold cross", "-", "validation", "the model evaluation", "our proposed models", "software developers", "a more accurate and efficient sfp mechanism", "three" ]

[ "Iqra Batool", "Tamim Ahmed Khan" ]

[ "software fault prediction", "(sfp) techniques", "faults", "the early stages", "the software development life cycle", "sdlc", "we", "techniques", "sfp", "deep learning methods", "which", "more accurate results", "deep learning", "exceptional results", "various domains", "computer vision", "natural language processing", "speech recognition", "this study", "we", "three deep learning methods", "namely, long short-term memory", "lstm", "bidirectional lstm", "bilstm", "radial basis function network", "rbfn", "software faults", "our results", "existing models", "our results", "our study", "chidamber", "kemerer (ck) metrics-based datasets", "experiments", "our proposed algorithm", "we", "lstm", "bilstm", "rbfn", "the required results", "we", "fold cross", "-", "validation", "the model evaluation", "our proposed models", "software developers", "a more accurate and efficient sfp mechanism", "three" ]

[ "Saqib ul Sabha", "Assif Assad", "Sadaf Shafi", "Nusrat Mohi Ud Din", "Rayees Ahmad Dar", "Muzafar Rasool Bhat" ]

[ "deep learning", "computer vision", "small and imbalanced datasets", "substantial progress", "this domain", "prevailing models", "these constraints", "this", "we", "an innovative contrast-based learning strategy", "small and imbalanced data", "that", "the proficiency", "deep learning architectures", "these challenging datasets", "training images", "the effective training dataset", "n", "\\(n^2\\", "richer data", "model training", "n", "our solution", "specific loss functions", "network architectures", "its adaptability", "diverse classification scenarios", "four benchmark datasets", "our approach", "the-art", "the empirical evidence", "our method’s superior efficacy", "contemporaries", "metrics", "balanced accuracy", "f1 score", "geometric mean", "noteworthy increments", "7–16%", "the covid-19 dataset", "4–20%", "honey bees", "1–6%", "cifar-10", "1–9%", "fashionmnist", "essence", "our proposed method", "a potent remedy", "the perennial issues", "scanty", "skewed data", "deep learning", "four", "noteworthy", "7–16%", "covid-19", "4–20%", "1–6%", "cifar-10", "1–9%" ]

[ "Mohamed Anas VT", "Mohd Omar", "Jameel Ahamad", "Khaleel Ahmad", "Mohd Anas Khan" ]

[ "traffic operation factors", "vehicle and cargo damage", "environmental effects", "access", "emergency vehicles", "transit routes", "traffic speeds", "volumes", "india", "traffic rule violations", "high-speed vehicles", "a large number", "accidents", "fatalities", "the road", "this issue", "the government", "speed breakers", "a safety measure", "most cases", "speed humps", "use", "streets", "a speed restriction", "30 mph", "50 km/h", "many speed breakers", "proper signboards", "drivers", "them", "accidents", "light", "this", "a speed breaker detection system", "deep learning", "the current scenario", "this research", "a system", "that", "deep learning image detection algorithms", "speed breakers", "real-time", "their visibility", "the risk", "accidents", "the system", "an indian dataset", "various driving scenarios", "its performance", "the results", "the proposed system", "existing methods", "the potential", "road safety", "india", "india", "30 mph", "50 km", "indian", "india" ]

[ "Francesco Camastra", "Angelo Casolaro", "Gennaro Iannuzzo" ]

[ "the paper", "a novel manifold learning algorithm", "the deep gaussian process autoencoder", "dpga", "deep gaussian processes", "deep gaussian process", "autoencoder algorithm", "the following two main characteristics", "a bottleneck structure", "variational autoencoders", "the so-called doubly stochastic variational inference", "deep gaussian processes architecture", "the main novelties", "the paper consist", "dgpa algorithm", "the experimental protocol", "it", "fact", "our knowledge", "deep gaussian processes algorithms", "manifold learning", "an experimental protocol", "mlpp", "the geometric preserved properties", "manifold learning projections", "the proposed deep gaussian process", "the ones", "the-art", "extensive experimental tests", "eleven synthetic", "five real datasets", "deep gaussian process autoencoder", "the other manifold learning competitors", "deep gaussian", "two", "deep gaussian", "eleven", "five", "deep gaussian" ]

[ "Qun-Ce Xu", "Tai-Jiang Mu", "Yong-Liang Yang" ]

[ "deep learning", "tasks", "the 2d image domain", "research", "3d computer vision", "deep geometry learning", "attention", "considerable achievements", "feature extraction", "discrimination", "3d shapes", "recent advances", "deep generative models", "generative adversarial networks", "effective generation", "3d shapes", "an active research topic", "2d images", "a regular grid structure", "3d shapes", "various representations", "voxels", "point clouds", "meshes", "implicit functions", "deep learning", "3d shapes", "shape representation", "account", "no unified representation", "that", "all tasks", "factors", "the representativeness", "geometry", "topology", "the quality", "the generated 3d shapes", "this survey", "we", "deep-learning-based 3d shape generation", "them", "terms", "the underlying shape representation", "the architecture", "the shape generator", "the advantages", "disadvantages", "each class", "we", "the 3d shape datasets", "shape generation", "we", "several potential research directions", "that", "future works", "this topic", "2d", "3d", "3d", "3d", "2d", "3d", "3d", "3d", "3d", "3d" ]

[ "Melina Koechli", "Fraser M. Callaghan", "Barbara E. U. Burkhardt", "Maélène Lohézic", "Xucheng Zhu", "Beate Rücker", "Emanuela R. Valsangiacomo Buechel", "Christian J. Kellenberger", "Julia Geiger" ]

[ "backgroundventricular volumetry", "a short-axis stack", "two-dimensional (d) cine", "steady-state free precession (bssfp) sequences", "imaging", "this task", "children", "multiple breath-holds.objectiveto", "the diagnostic performance", "accelerated 3-rr cine mri sequences", "deep learning reconstruction", "standard 2-d cine bssfp sequences.material and methodstwenty-nine consecutive patients", "(mean age", "median", "range 1–17 years", "cardiac mri", "a conventional segmented 2-d cine", "a deep learning", "cine", "(three heartbeats) acquisition", "a 1.5-tesla scanner", "short-axis volumetrics", "both datasets", "two experienced readers", "who", "image quality", "a 4-point grading scale", "scan times", "image quality", "the wilcoxon rank-sum test", "volumetrics", "linear regression", "bland–altman analyses", "measurement agreement", "intraclass correlation", "coefficient", "icc).resultsmean acquisition time", "the 3-rr deep learning cine", "the standard cine sequence", "45.5 ± 13.8 s", "218.3 ±", "44.8 s", "no significant differences", "biventricular volumetrics", "ventricular (lv) mass", "the deep learning cine", "the standard cine sequence", "33.1 g", "69.9 ±", "p", "all volumetric measurements", "an excellent agreement", "icc", "ejection fraction", "ef", "lvef", "rvef", "the image quality", "deep learning cine images", "end-diastolic and end-systolic contours", "papillary muscles", "valve depiction", "2.9 ±", "3.5 ±", "cine volumetrics", "standard cine results", "lv mass", "which", "deep learning cine", "cine sequences", "a significant reduction", "scan time", "only slightly lower image", "backgroundventricular volumetry", "two", "breath-holds.objectiveto", "3", "2", "age 11 ± 5", "12", "1–17 years", "2", "three", "1.5", "two", "4", "scan times", "3", "218.3", "44.8", "p < 0.001", "ventricular", "33.1", "69.9", "32.5", "0.9", "0.81", "0.73", "2.9 ± 0.5", "3.5", "0.4" ]

[ "Ruchika Bala", "Arun Sharma", "Nidhi Goel" ]

[ "diabetic retinopathy", "dr", "an eye disease", "excess", "sugar", "retinal blood vessels", "vision", "regular and timely diagnosis", "the severity", "diabetic retinopathy", "an initial stage", "manual diagnosis", "diabetic retinopathy", "a plethora", "work", "researchers", "the classification", "machine learning", "deep learning techniques", "the present review pivots", "the research papers", "recent and effective automated dr classification techniques", "a comparative analysis", "these papers", "the summary", "dr classification datasets", "pre-processing techniques", "various advanced classification algorithms", "their performance", "the summary", "analysis", "the classification techniques", "the present paper", "the experimentation", "eight pre-trained convolution neural network models", "dr benchmark classification datasets", "this", "the researchers", "the best pre-trained classification model", "the corresponding dr dataset", "the use", "deep learning and machine learning algorithms", "excellent performance", "researchers", "the design constraints", "the classification models", "effective results", "attention mechanisms", "vision transformers", "recent breakthroughs", "that", "classification challenges", "the objective", "this article", "a single platform", "researchers", "the-art", "the classification", "diabetic retinopathy", "the results", "various pre-trained models", "dr benchmark classification datasets", "future research prospects", "the researchers", "this challenging area", "2011 to 2022", "eight" ]

[ "Girish Shrikrushnarao Bhavekar", "Agam Das Goswami", "Chafle Pratiksha Vasantrao", "Amit K. Gaikwad", "Amol V. Zade", "Harsha Vyawahare" ]

[ "cardiovascular disease", "the position", "the foremost cause", "death", "heart disease prediction", "hdp", "a difficult task", "it", "advanced knowledge", "better experience", "it", "numerous significant challenges", "clinical data analysis", "many researchers", "heart disease", "the performance", "namely prediction accuracy", "the accurate hdp", "the person", "himself", "life threats", "the same time", "inaccurate prediction", "these issues", "this review", "several deep learning", "dl", "machine learning", "optimization", "hdp techniques", "recent times", "many researchers", "different dl", "ml", "the professionals", "health care industry", "the prediction", "heart disease", "it", "various optimization-based algorithms", "its performance analysis", "this review paper", "the optimization-based hdp algorithm", "doctors", "the occurrence", "heart disease", "advance", "suitable treatment" ]

[ "Lin Yan", "Zhiying Liang", "Hao Zhang", "Gaosong Zhang", "Weiwei Zheng", "Chunguang Han", "Dongsheng Yu", "Hanqi Zhang", "Xinxin Xie", "Chang Liu", "Wenxin Zhang", "Hui Zheng", "Jing Pei", "Dinggang Shen", "Xuejun Qian" ]

[ "deep learning", "advantages", "the automatic interpretation", "breast ultrasound images", "its black-box nature", "potential interactions", "radiologists", "obstacles", "clinical deployment.methodswe", "a domain knowledge-based interpretable deep learning system", "breast cancer risk prediction", "paired multimodal ultrasound images", "the deep learning system", "4320 multimodal breast ultrasound images", "1440 biopsy-confirmed lesions", "prospectively enrolled patients", "two hospitals", "august", "december", "the lesions", "70% training cohort", "10% validation cohort", "20% test cohort", "case recruitment", "we", "the interpretable deep learning system", "breast cancer risk", "experienced radiologists", "an area", "the receiver operating characteristic curve", "(95% confidence interval", "75.2%", "91.8%", "the test cohort", "the aid", "the deep learning system", "particularly its inherent explainable features", "junior radiologists", "better clinical outcomes", "senior radiologists", "increased confidence levels", "multimodal ultrasound images", "domain knowledge-based reasoning cues", "an effective human-machine collaboration", "a high level", "prediction", "a clinically applicable deep learning system", "future breast cancer screening", "support", "second-read workflows", "4320", "1440", "1348", "two", "august 2019", "december 2022", "70%", "10%", "20%", "0.902", "95%", "0.882", "0.921", "75.2%", "91.8%", "second" ]

[ "Artit Boonrod", "Prarinthorn Piyaprapaphan", "Nut Kittipongphat", "Daris Theerakulpisut", "Arunnit Boonrod" ]

[ "purposeosteoporosis", "a common bone disorder", "decreased bone mineral density", "bmd", "increased bone fragility", "which", "fractures", "morbidity", "mortality", "it", "great concern", "the one-year mortality rate", "osteoporotic hip fractures", "22%", "the treatment", "bmd measurement", "the standard method", "osteoporosis diagnosis", "it", "special equipment", "a plain radiograph", "it", "diagnosis", "deep learning technologies", "various medical contexts", "they", "the advanced investigative images", "computed tomography", "the purpose", "this study", "a deep learning model", "the anteroposterior hip radiograph images", "its diagnostic accuracy", "osteoporosis.methodswe", "all anteroposterior hip radiograph images", "patients", "a tertiary care hospital", "the bmd measurements", "the included patients", "the radiograph images", "that", "a time interval", "more than two years", "the measurements", "all images", "a computer-generated unequal allocation", "two datasets", "80%", "images", "the training dataset", "the remaining 20%", "the test dataset", "the t score", "bmd", "the ipsilateral femoral neck", "the same patient", "the date", "the performed radiograph", "the t", "cutoff value", "−", "osteoporosis", "five deep learning models", "the training dataset", "their diagnostic performances", "the test dataset", "the best model", "the area", "the curves", "auc).resultsa total", "363 anteroposterior hip radiograph images", "the average time interval", "the performed radiograph", "the bmd measurement", "6.6 months", "two-hundred-thirteen images", "osteoporosis", "t", "−", "the other 150 images", "osteoporosis", "t", "−", "the best-selected deep learning model", "an auc", "accuracy", "0.82.conclusionsthis study", "the potential", "deep learning", "anteroposterior hip radiographs", "the results", "the deep learning model", "a screening tool", "patients", "risk", "osteoporosis", "further bmd measurement", "one-year", "22%", "2013", "2021", "tertiary", "more than two years", "two", "80%", "the remaining 20%", "− 2.5", "five", "363", "6.6 months", "two-hundred-thirteen", "2.5", "150", "2.5", "0.91" ]

[ "Xueyan Tang", "Yuying Du", "Alan Lai", "Ze Zhang", "Lingzhi Shi" ]

[ "this paper", "the application", "deep learning", "smart contract vulnerabilities detection", "smart contracts", "an essential part", "blockchain technology", "decentralized applications", "smart contract vulnerabilities", "financial losses", "system crashes", "static analysis tools", "vulnerabilities", "smart contracts", "they", "false positives", "false negatives", "their high reliance", "predefined rules", "lack", "semantic analysis capabilities", "these predefined rules", "new data", "contrast", "deep learning methods", "predefined detection rules", "the features", "vulnerabilities", "the training process", "this paper", "we", "a solution", "lightning cat", "which", "deep learning techniques", "we", "three deep learning models", "vulnerabilities", "smart contract", "optimized-codebert", "optimized-lstm", "cnn", "experimental results", "the lightning cat", "we", "optimized-codebert model", "other methods", "an f1-score", "93.53%", "vulnerability features", "we", "segments", "vulnerable code functions", "critical vulnerability features", "the codebert pre-training model", "data", "we", "the syntax", "semantics", "the code", "the feasibility", "our proposed solution", "we", "its performance", "the solidifi-benchmark dataset", "which", "9369 vulnerable contracts", "vulnerabilities", "seven different types", "three", "93.53%", "9369", "seven" ]

[ "Chuanhao Wang", "Yongjian Sun", "Xiaohong Wang" ]

[ "the development", "industry", "more and more crucial mechanical machinery", "wildness demand", "effective fault diagnosis", "the safe operation", "the past few decades", "researchers", "a variety", "approaches", "recent years", "diagnosis", "deep learning", "which", "satisfied results", "mechanical equipment fault diagnosis", "few review", "these special image deep learning methods", "order", "this gap", "this paper", "the development", "special image", "deep learning", "mechanical equipment fault diagnosis", "past 5 years", "in general, a typical image fault diagnosis", "fault image", "deep learning", "data acquisition", "signal processing", "model construction", "feature learning", "decision-making", "the method", "signal preprocessing", "several common methods", "signals", "images", "the principles", "variants", "deep learning models", "the difficulties", "challenges", "this stage", "the future development", "potential trends", "the work", "it", "this work", "further exploration", "researchers", "this area", "the past few decades", "recent years", "past 5 years", "firstly" ]

[ "Kiramat Rahman", "Anwar Ghani", "Sanjay Misra", "Arif Ur Rahman" ]

[ "nonfunctional requirements", "requirement documents", "machine learning-based approaches", "analysts’ efforts", "labor", "stress", "however, the traditional approach", "supervised machine learning necessitates manual feature extraction", "which", "this study", "a novel deep-learning framework", "nfr classification", "these limitations", "the framework", "a more profound architecture", "that", "feature structures", "enhanced representational power", "a broader context", "shallower structures", "the effectiveness", "the proposed method", "an experiment", "two widely-used datasets", "914 nfr instances", "performance analysis", "the applied models", "the results", "various metrics", "the dreqann model", "the other models", "precision", "99.8%", "74 and 89%", "83 and 89%", "these significant results", "the exceptional efficacy", "the proposed deep learning framework", "nfr classification tasks", "its potential", "the field", "nfr analysis", "classification", "two", "914", "between 81", "99.8%", "between 74 and", "89%", "89%" ]

[ "Ce Wang", "Zhaoliang Wang", "Yifei Lu", "Tingting Hao", "Yufang Hu", "Sui Wang", "Zhiyong Guo" ]

[ "abstractin", "this work", "an image classification", "deep learning", "quantitative field determination", "aluminum ions", "al3", "+", "carbon quantum dots", "yellow fluorescence", "a one-pot hydrothermal method", "which", "al3", "enhanced green fluorescence", "the convolutional neural network model", "deep learning", "an image classification", "al3", "samples", "different concentrations", "a fitting method", "classification information", "the first time", "which", "discontinuous, semi-quantitative concentration classification information", "continuous, quantitative, and accurate concentration information", "recoveries", "92.0–110.3%", "the concentration range", "0.3–320 μm", "a lower limit", "detection", "0.3 μm", "excellent accuracy", "sensitivity", "it", "2 min", "large equipment", "the deep learning-enabled image classification", "a new way", "the determination", "metal ions", "abstractin", "quantum", "one", "first", "92.0–110.3%", "0.3", "2" ]

[ "Shigao Huang", "Ibrahim Arpaci", "Mostafa Al-Emran", "Serhat Kılıçarslan", "Mohammed A. Al-Sharafi" ]

[ "lung cancer", "the deadliest forms", "cancer", "patient survival rates", "60–70%", "its early stages", "the prediction", "lung cancer patient survival", "a popular area", "research", "medical and computer science experts", "this study", "the survival period", "lung cancer patients", "12 demographic and clinical features", "this", "a comparative analysis", "traditional machine learning", "deep learning techniques", "previous studies", "that", "ct or x-ray images", "the dataset", "10,001 lung cancer patients", "the data attributes", "gender", "age", "race", "t", "tumor size", "m", "tumor dissemination", "other organs", "(lymph node involvement", "chemo", "dx", "brain", "dx-liver", "dx-lung", "survival months", "six supervised machine learning", "deep learning techniques", "bayes classifier", "lazy-classifier (lwl", "meta-classifier", "(attributeselectedclassifier", "asc", "rule-learner", "(oner", "decision-tree", "(j48", "deep neural network", "dnn", "the findings", "dnn", "the performance", "the six traditional machine learning models", "the survival duration", "lung cancer patients", "an accuracy rate", "88.58%", "this evidence", "healthcare experts", "cost management", "timely treatment provision", "one", "60–70%", "12", "10,001", "months", "six", "bayes classifier", "meta-classifier", "six", "88.58%" ]

[ "Z. T. Al-Qaysi", "A. S. Albahri", "M. A. Ahmed", "Rula A. Hamid", "M. A. Alsalem", "O. S. Albahri", "A. H. Alamoodi", "Raad Z. Homod", "Ghadeer Ghazi Shayea", "Ali M. Duhaim" ]

[ "brain", "computer", "(bci) research", "deep learning", "insights", "diverse domains", "a notable focus", "steady-state visual evoked potential", "ssvep", "bci applications", "depth", "deep learning", "eeg research", "ssveps", "which", "normal brain responses", "visual stimuli", "investigations", "visual perception", "attention", "this paper", "an in-depth analysis", "the implications", "deep learning", "ssvep-adapted bci", "a systematic search", "four stable databases", "web", "science", "sciencedirect", "ieee", "a vast reservoir", "relevant theoretical and scientific knowledge", "a comprehensive search", "177 papers", "that", "a strict screening method", "predetermined inclusion criteria", "39 records", "these selected works", "the basis", "the study", "alternate views", "obstacles", "limitations", "interesting ideas", "a systematic presentation", "the material", "it", "a key scholarly contribution", "it", "the technical aspects", "ssvep-based bci", "eeg technologies", "complex applications", "deep learning technology", "these areas", "the study", "the latest deep learning pattern recognition techniques", "its predecessors", "progress", "data acquisition", "recording", "ssvep-based bci devices", "the realms", "deep learning technology orchestration", "pattern recognition techniques", "eeg data collection", "it", "four important research gaps", "the accessibility", "this critical material", "the results", "the study", "the form", "read", "deep learning techniques", "ssvep-based bci applications", "its downsides", "the study", "a radical framework", "which", "intelligent decision-making tools", "evaluation", "benchmarking", "a comparable or similar analogy", "this framework", "future research and pragmatic applications", "which", "ssvep-based bci applications", "responsibility", "what", "they", "four", "177", "between 2010 and 2023", "39", "four" ]

[ "Moe Hdaib", "Sutharshan Rajasegarar", "Lei Pan" ]

[ "aberrant activities", "the network traffic", "adverse consequences", "cyber security incidents", "which", "recent times", "existing research", "classical machine learning", "deep learning-based approaches", "such attacks", "the power", "quantum", "complex correlation", "features", "anomaly detection", "this paper", "we", "quantum machine learning", "quantum", "deep learning-based anomaly detection methodologies", "network attacks", "we", "three novel quantum auto-encoder-based anomaly detection frameworks", "our primary aim", "hybrid models", "that", "the strengths", "both quantum", "deep learning methodologies", "efficient anomaly recognition", "the three frameworks", "the quantum", "autoencoder", "a quantum one-class support vector machine", "a quantum random forest", "a quantum k-nearest neighbor approach", "the anomaly detection capability", "the frameworks", "benchmark datasets", "computer", "internet", "things network", "our evaluation", "all three frameworks", "a high potential", "the network traffic anomalies", "the framework", "that", "the quantum", "the quantum k-nearest neighbor", "the highest accuracy", "this", "the promising potential", "the development", "quantum frameworks", "anomaly detection", "their relevance", "future advancements", "network security", "anomaly detection methodologies", "three", "anomaly detection frameworks", "anomaly recognition", "three", "one", "quantum", "the anomaly detection capability", "three", "quantum", "quantum" ]

[ "Alexandros Tragoudas", "Marta Alloisio", "Elsayed S. Elsayed", "T. Christian Gasser", "Fadi Aldakheel" ]

[ "this work", "an efficient deep learning approach", "vascular wall fractures", "experimental data", "openly accessible source codes", "https://doi.org/10.25835/weuhha72", "reproduction", "vascular disease", "the primary cause", "death", "this day", "tissue damage", "these vascular disorders", "the diseases", "which", "careful study", "the scientific community", "significant efforts", "the properties", "vessel wall fractures", "the symmetry-constrained compact tension", "(symconct) test", "digital image correlation", "the study", "tissue fracture", "various aorta", "specimens", "different conditions", "main purpose", "the experiments", "the displacement and strain field", "the crack tip", "these experimental data", "the development", "verification", "computational models", "the fem model", "the dic information", "the material parameters identification", "the analysis", "fracture processes", "biological tissues", "extensive computational and experimental efforts", "the complex nature", "tissue behavior", "stress", "these high costs", "significant challenges", "efficient solutions", "research progress", "embedded costs", "deep learning techniques", "promise", "these challenges", "patterns", "relationships", "the input and label data", "this study", "we", "deep learning methodologies", "the attention residual u-net architecture", "fracture responses", "porcine aorta specimens", "a monte carlo dropout technique", "the network", "a sufficient amount", "data", "the model", "the features", "fracture progression", "these parameterized datasets", "pictures", "the evolution", "tissue fracture", "path", "the dic measurements", "the integration", "deep learning", "the predictive accuracy", "the computational and experimental burden", "a more efficient analysis", "fracture response", "this day" ]

[ "Weize Quan", "Jiaxi Chen", "Yanli Liu", "Dong-Ming Yan", "Peter Wonka" ]

[ "image", "a classic problem", "computer vision", "computer graphics", "the plausible and realistic content", "the missing areas", "images", "videos", "the advance", "deep learning", "this problem", "significant progress", "the goal", "this paper", "the deep learning-based methods", "image", "we", "existing methods", "different categories", "the perspective", "their high-level inpainting pipeline", "different deep learning architectures", "cnn", "vae", "gan", "diffusion models", "techniques", "module design", "we", "the training objectives", "the common benchmark datasets", "we", "evaluation metrics", "low-level pixel", "high-level perceptional similarity", "a performance evaluation", "the strengths", "weaknesses", "representative inpainting methods", "we", "related real-world applications", "we", "open challenges", "potential future research directions", "cnn", "vae, gan, diffusion models" ]

[ "Artit Boonrod", "Prarinthorn Piyaprapaphan", "Nut Kittipongphat", "Daris Theerakulpisut", "Arunnit Boonrod" ]

[ "purposeosteoporosis", "a common bone disorder", "decreased bone mineral density", "bmd", "increased bone fragility", "which", "fractures", "morbidity", "mortality", "it", "great concern", "the one-year mortality rate", "osteoporotic hip fractures", "22%", "the treatment", "bmd measurement", "the standard method", "osteoporosis diagnosis", "it", "special equipment", "a plain radiograph", "it", "diagnosis", "deep learning technologies", "various medical contexts", "they", "the advanced investigative images", "computed tomography", "the purpose", "this study", "a deep learning model", "the anteroposterior hip radiograph images", "its diagnostic accuracy", "osteoporosis.methodswe", "all anteroposterior hip radiograph images", "patients", "a tertiary care hospital", "the bmd measurements", "the included patients", "the radiograph images", "that", "a time interval", "more than two years", "the measurements", "all images", "a computer-generated unequal allocation", "two datasets", "80%", "images", "the training dataset", "the remaining 20%", "the test dataset", "the t score", "bmd", "the ipsilateral femoral neck", "the same patient", "the date", "the performed radiograph", "the t", "cutoff value", "−", "osteoporosis", "five deep learning models", "the training dataset", "their diagnostic performances", "the test dataset", "the best model", "the area", "the curves", "auc).resultsa total", "363 anteroposterior hip radiograph images", "the average time interval", "the performed radiograph", "the bmd measurement", "6.6 months", "two-hundred-thirteen images", "osteoporosis", "t", "−", "the other 150 images", "osteoporosis", "t", "−", "the best-selected deep learning model", "an auc", "accuracy", "0.82.conclusionsthis study", "the potential", "deep learning", "anteroposterior hip radiographs", "the results", "the deep learning model", "a screening tool", "patients", "risk", "osteoporosis", "further bmd measurement", "one-year", "22%", "2013", "2021", "tertiary", "more than two years", "two", "80%", "the remaining 20%", "− 2.5", "five", "363", "6.6 months", "two-hundred-thirteen", "2.5", "150", "2.5", "0.91" ]

[ "Xueyan Tang", "Yuying Du", "Alan Lai", "Ze Zhang", "Lingzhi Shi" ]

[ "this paper", "the application", "deep learning", "smart contract vulnerabilities detection", "smart contracts", "an essential part", "blockchain technology", "decentralized applications", "smart contract vulnerabilities", "financial losses", "system crashes", "static analysis tools", "vulnerabilities", "smart contracts", "they", "false positives", "false negatives", "their high reliance", "predefined rules", "lack", "semantic analysis capabilities", "these predefined rules", "new data", "contrast", "deep learning methods", "predefined detection rules", "the features", "vulnerabilities", "the training process", "this paper", "we", "a solution", "lightning cat", "which", "deep learning techniques", "we", "three deep learning models", "vulnerabilities", "smart contract", "optimized-codebert", "optimized-lstm", "cnn", "experimental results", "the lightning cat", "we", "optimized-codebert model", "other methods", "an f1-score", "93.53%", "vulnerability features", "we", "segments", "vulnerable code functions", "critical vulnerability features", "the codebert pre-training model", "data", "we", "the syntax", "semantics", "the code", "the feasibility", "our proposed solution", "we", "its performance", "the solidifi-benchmark dataset", "which", "9369 vulnerable contracts", "vulnerabilities", "seven different types", "three", "93.53%", "9369", "seven" ]

[ "Chuanhao Wang", "Yongjian Sun", "Xiaohong Wang" ]

[ "the development", "industry", "more and more crucial mechanical machinery", "wildness demand", "effective fault diagnosis", "the safe operation", "the past few decades", "researchers", "a variety", "approaches", "recent years", "diagnosis", "deep learning", "which", "satisfied results", "mechanical equipment fault diagnosis", "few review", "these special image deep learning methods", "order", "this gap", "this paper", "the development", "special image", "deep learning", "mechanical equipment fault diagnosis", "past 5 years", "in general, a typical image fault diagnosis", "fault image", "deep learning", "data acquisition", "signal processing", "model construction", "feature learning", "decision-making", "the method", "signal preprocessing", "several common methods", "signals", "images", "the principles", "variants", "deep learning models", "the difficulties", "challenges", "this stage", "the future development", "potential trends", "the work", "it", "this work", "further exploration", "researchers", "this area", "the past few decades", "recent years", "past 5 years", "firstly" ]

[ "Kiramat Rahman", "Anwar Ghani", "Sanjay Misra", "Arif Ur Rahman" ]

[ "nonfunctional requirements", "requirement documents", "machine learning-based approaches", "analysts’ efforts", "labor", "stress", "however, the traditional approach", "supervised machine learning necessitates manual feature extraction", "which", "this study", "a novel deep-learning framework", "nfr classification", "these limitations", "the framework", "a more profound architecture", "that", "feature structures", "enhanced representational power", "a broader context", "shallower structures", "the effectiveness", "the proposed method", "an experiment", "two widely-used datasets", "914 nfr instances", "performance analysis", "the applied models", "the results", "various metrics", "the dreqann model", "the other models", "precision", "99.8%", "74 and 89%", "83 and 89%", "these significant results", "the exceptional efficacy", "the proposed deep learning framework", "nfr classification tasks", "its potential", "the field", "nfr analysis", "classification", "two", "914", "between 81", "99.8%", "between 74 and", "89%", "89%" ]

[ "Ce Wang", "Zhaoliang Wang", "Yifei Lu", "Tingting Hao", "Yufang Hu", "Sui Wang", "Zhiyong Guo" ]

[ "abstractin", "this work", "an image classification", "deep learning", "quantitative field determination", "aluminum ions", "al3", "+", "carbon quantum dots", "yellow fluorescence", "a one-pot hydrothermal method", "which", "al3", "enhanced green fluorescence", "the convolutional neural network model", "deep learning", "an image classification", "al3", "samples", "different concentrations", "a fitting method", "classification information", "the first time", "which", "discontinuous, semi-quantitative concentration classification information", "continuous, quantitative, and accurate concentration information", "recoveries", "92.0–110.3%", "the concentration range", "0.3–320 μm", "a lower limit", "detection", "0.3 μm", "excellent accuracy", "sensitivity", "it", "2 min", "large equipment", "the deep learning-enabled image classification", "a new way", "the determination", "metal ions", "abstractin", "quantum", "one", "first", "92.0–110.3%", "0.3", "2" ]

[ "Shigao Huang", "Ibrahim Arpaci", "Mostafa Al-Emran", "Serhat Kılıçarslan", "Mohammed A. Al-Sharafi" ]

[ "lung cancer", "the deadliest forms", "cancer", "patient survival rates", "60–70%", "its early stages", "the prediction", "lung cancer patient survival", "a popular area", "research", "medical and computer science experts", "this study", "the survival period", "lung cancer patients", "12 demographic and clinical features", "this", "a comparative analysis", "traditional machine learning", "deep learning techniques", "previous studies", "that", "ct or x-ray images", "the dataset", "10,001 lung cancer patients", "the data attributes", "gender", "age", "race", "t", "tumor size", "m", "tumor dissemination", "other organs", "(lymph node involvement", "chemo", "dx", "brain", "dx-liver", "dx-lung", "survival months", "six supervised machine learning", "deep learning techniques", "bayes classifier", "lazy-classifier (lwl", "meta-classifier", "(attributeselectedclassifier", "asc", "rule-learner", "(oner", "decision-tree", "(j48", "deep neural network", "dnn", "the findings", "dnn", "the performance", "the six traditional machine learning models", "the survival duration", "lung cancer patients", "an accuracy rate", "88.58%", "this evidence", "healthcare experts", "cost management", "timely treatment provision", "one", "60–70%", "12", "10,001", "months", "six", "bayes classifier", "meta-classifier", "six", "88.58%" ]

[ "Z. T. Al-Qaysi", "A. S. Albahri", "M. A. Ahmed", "Rula A. Hamid", "M. A. Alsalem", "O. S. Albahri", "A. H. Alamoodi", "Raad Z. Homod", "Ghadeer Ghazi Shayea", "Ali M. Duhaim" ]

[ "brain", "computer", "(bci) research", "deep learning", "insights", "diverse domains", "a notable focus", "steady-state visual evoked potential", "ssvep", "bci applications", "depth", "deep learning", "eeg research", "ssveps", "which", "normal brain responses", "visual stimuli", "investigations", "visual perception", "attention", "this paper", "an in-depth analysis", "the implications", "deep learning", "ssvep-adapted bci", "a systematic search", "four stable databases", "web", "science", "sciencedirect", "ieee", "a vast reservoir", "relevant theoretical and scientific knowledge", "a comprehensive search", "177 papers", "that", "a strict screening method", "predetermined inclusion criteria", "39 records", "these selected works", "the basis", "the study", "alternate views", "obstacles", "limitations", "interesting ideas", "a systematic presentation", "the material", "it", "a key scholarly contribution", "it", "the technical aspects", "ssvep-based bci", "eeg technologies", "complex applications", "deep learning technology", "these areas", "the study", "the latest deep learning pattern recognition techniques", "its predecessors", "progress", "data acquisition", "recording", "ssvep-based bci devices", "the realms", "deep learning technology orchestration", "pattern recognition techniques", "eeg data collection", "it", "four important research gaps", "the accessibility", "this critical material", "the results", "the study", "the form", "read", "deep learning techniques", "ssvep-based bci applications", "its downsides", "the study", "a radical framework", "which", "intelligent decision-making tools", "evaluation", "benchmarking", "a comparable or similar analogy", "this framework", "future research and pragmatic applications", "which", "ssvep-based bci applications", "responsibility", "what", "they", "four", "177", "between 2010 and 2023", "39", "four" ]

[ "Moe Hdaib", "Sutharshan Rajasegarar", "Lei Pan" ]

[ "aberrant activities", "the network traffic", "adverse consequences", "cyber security incidents", "which", "recent times", "existing research", "classical machine learning", "deep learning-based approaches", "such attacks", "the power", "quantum", "complex correlation", "features", "anomaly detection", "this paper", "we", "quantum machine learning", "quantum", "deep learning-based anomaly detection methodologies", "network attacks", "we", "three novel quantum auto-encoder-based anomaly detection frameworks", "our primary aim", "hybrid models", "that", "the strengths", "both quantum", "deep learning methodologies", "efficient anomaly recognition", "the three frameworks", "the quantum", "autoencoder", "a quantum one-class support vector machine", "a quantum random forest", "a quantum k-nearest neighbor approach", "the anomaly detection capability", "the frameworks", "benchmark datasets", "computer", "internet", "things network", "our evaluation", "all three frameworks", "a high potential", "the network traffic anomalies", "the framework", "that", "the quantum", "the quantum k-nearest neighbor", "the highest accuracy", "this", "the promising potential", "the development", "quantum frameworks", "anomaly detection", "their relevance", "future advancements", "network security", "anomaly detection methodologies", "three", "anomaly detection frameworks", "anomaly recognition", "three", "one", "quantum", "the anomaly detection capability", "three", "quantum", "quantum" ]

[ "Chunmeng Weng", "Congying Chen", "Xianfeng Ai" ]

[ "this paper", "the design-based learning", "(dbl) approach", "the deep learning", "students", "the quality", "teaching", "engineering design education", "we", "three aspects", "research", "students", "a typical educational activity", "the first study", "students' deep learning", "the dbl approach", "terms", "deep learning status", "deep learning ability", "the second study", "the effectiveness", "the dbl approach", "comparative research", "a control class", "traditional teaching method", "an experimental class", "dbl method", "the third study", "students' evaluations", "the dbl approach", "it", "the dbl approach", "the students' motivation", "them", "study", "the students' higher-order thinking", "higher-order capabilities", "critical thinking ability", "problem-solving ability", "the same time", "they", "the dbl approach", "these findings", "the dbl approach", "students' deep learning", "the quality", "teaching", "three", "first", "second", "third" ]

[ "Weize Quan", "Jiaxi Chen", "Yanli Liu", "Dong-Ming Yan", "Peter Wonka" ]

[ "image", "a classic problem", "computer vision", "computer graphics", "the plausible and realistic content", "the missing areas", "images", "videos", "the advance", "deep learning", "this problem", "significant progress", "the goal", "this paper", "the deep learning-based methods", "image", "we", "existing methods", "different categories", "the perspective", "their high-level inpainting pipeline", "different deep learning architectures", "cnn", "vae", "gan", "diffusion models", "techniques", "module design", "we", "the training objectives", "the common benchmark datasets", "we", "evaluation metrics", "low-level pixel", "high-level perceptional similarity", "a performance evaluation", "the strengths", "weaknesses", "representative inpainting methods", "we", "related real-world applications", "we", "open challenges", "potential future research directions", "cnn", "vae, gan, diffusion models" ]

[ "Novsheena Rasool", "Javaid Iqbal Bhat" ]

[ "brain tumors", "the 1oth leading cause", "mortality", "85% to 95%", "all primary nervous system malignancies", "the prevalence", "this life-threatening disease", "the urgent need", "an early and precise diagnosis", "timely identification", "effective treatment", "patient survival chances", "delayed diagnosis", "the risk", "mortality", "the heterogeneous nature", "tumor cells", "challenges", "radiologists", "manual diagnosis", "magnetic resonance imaging", "(mri", "machine learning", "ml", "deep learning", "dl", "useful tools", "medical image analysis", "these techniques", "the automated extraction", "intricate patterns", "features", "mri images", "a more accurate and efficient tumor diagnosis", "these algorithms", "the capability", "the intricacy", "variability", "brain tumor characteristics", "the diagnostic process", "a range", "deep learning-based algorithms", "brain tumors", "impressive results", "the purpose", "this paper", "an exhaustive examination", "the latest techniques", "brain tumors", "mri imaging", "machine", "deep learning technologies", "it", "potential avenues", "future exploration", "this field", "the profound insights", "this comprehensive review", "invaluable guidance", "support", "both researchers", "medical professionals", "the healthcare industry", "85% to 95%" ]

[ "Tabasum Majeed", "Riyaz Ahmad Mir", "Rayees Ahmad Dar", "Mohd Anul Haq", "Shabana Nargis Rasool", "Assif Assad" ]

[ "accurate streamflow (qflow) forecasting", "a pivotal role", "water resource monitoring", "management", "a complex challenge", "water managers", "engineers", "effective streamflow prediction", "the optimized operation", "water resource systems", "alignment", "technological, financial, ethical, and political objectives", "traditional data-driven models", "the autoregressive model", "autoregressive moving average model", "water resource management", "these models", "limitations", "intricate nonlinear hydrological phenomena", "these limitations", "deep learning models", "promising alternatives", "their inherent ability", "nonlinearity", "nonlinearity", "time series", "modeling", "factors", "long-term trends", "seasonal variations", "cyclical oscillations", "external disturbances", "this study", "a deep neural network architecture", "neural basis expansion analysis", "time series", "-beats", "the daily qflow", "western himalayan river basins", "the study", "datasets", "the rampur station", "the satluj basin", "the pandoh and manali stations", "the beas basin", "the experimental results", "the superiority", "the proposed deep neural network model", "benchmarked conventional deep learning models", "long short-term memory", "feedforward neural network", "gated recurrent unit", "neural network", "the proposed deep neural network model", "remarkable accuracy", "a root mean square error", "0.05 m3", "qflow values", "all datasets", "the proposed deep neural network model", "n-beats", "an efficient and invaluable solution", "precise qflow prediction", "efficient water resource management", "control", "the results", "the proposed model", "streamflow prediction", "water management", "himalayan river basins", "daily", "rampur", "0.05", "m3/s" ]

[ "MohammadHossein Givkashi", "MohammadReza Naderi", "Nader Karimi", "Shahram Shirani", "Shadrokh Samavi" ]

[ "the size", "the image", "attention", "the contents", "the main obstacles", "deep learning models", "the need", "a vast labeled dataset", "labeled datasets", "deep learning models", "the image retargeting tasks", "a result", "we", "a new supervised approach", "deep learning models", "we", "the original images", "ground truth", "inputs", "the model", "the original images", "a second challenge", "different image sizes", "inference time", "normal convolutional neural networks", "images", "different sizes", "the input image", "this issue", "we", "a new method", "supervised learning", "our approach", "a mask", "the desired size", "location", "the object", "the mask", "the input image", "the network", "image retargeting methods", "our proposed method", "the model’s ability", "high-quality retargeted images", "we", "the image quality assessment score", "each output image", "different techniques", "the effectiveness", "our approach", "one", "second", "fed" ]

[ "Xu-Wen Wang", "Zheng Sun", "Huijue Jia", "Sebastian Michel-Mata", "Marco Tulio Angulo", "Lei Dai", "Xuesong He", "Scott T. Weiss", "Yang-Yu Liu" ]

[ "previous studies", "microbial communities", "keystone species", "whose removal", "a dramatic shift", "microbiome structure", "functioning", "an efficient method", "keystone species", "microbial communities", "we", "a data-driven keystone species identification", "(dki) framework", "deep learning", "this challenge", "our key idea", "the assembly rules", "microbial communities", "a particular habitat", "a deep-learning model", "microbiome samples", "this habitat", "the well-trained deep-learning model", "us", "the community-specific keystoneness", "each species", "any microbiome sample", "this habitat", "a thought experiment", "species removal", "we", "this dki framework", "synthetic data", "applied dki", "analyse real data", "we", "those taxa", "high median keystoneness", "different communities", "strong community specificity", "the presented dki framework", "the power", "a fundamental problem", "community ecology", "the way", "the data-driven management", "complex microbial communities" ]

[ "Yanming Liu", "Jinglei Liu" ]

[ "deep clustering", "traditional methods", "feature learning", "some existing deep clustering methods", "the suitability", "the learned features", "clustering", "insufficient feedback", "the clustering model", "the accuracy improvement", "these issues", "we", "a joint self-paced learning", "deep sparse", "image clustering", "our method", "two stages", "the pretraining stage", "the autoencoder", "basic features", "the feature space", "the finetuning stage", "method", "two tasks", "feature learning", "cluster assignment", "we", "the encoder", "both original and augmented data", "the local structure", "the feature space", "self-paced learning guarantees", "the most confident features", "each iteration", "the influence", "boundary samples", "sparse", "the model", "only key features", "feature learning tasks", "incorrect calculations", "redundant features", "we", "these two tasks", "the feature", "extensive experiments", "various datasets", "our approach", "existing solutions", "two", "two", "two" ]

[ "Ghada M. El-Banby", "Nourhan S. Salem", "Eman A. Tafweek", "Essam N. Abd El-Azziz" ]

[ "breast cancer", "the second most prevalent cause", "cancer death", "the most common malignancy", "women", "a life-threatening risk", "treatment", "breast cancer", "a survival chance", "90%", "the disease", "this paper", "a groundbreaking deep u-net framework", "mammography breast cancer images", "automatic detection", "abnormalities", "the objective", "segmented images", "that", "areas", "tumors", "other deep learning techniques", "the proposed framework", "three steps", "the first step", "image", "the li algorithm", "the cross", "-", "entropy", "the foreground", "the background", "contrast enhancement", "contrast-limited adaptive histogram equalization", "clahe", "normalization", "median filtering", "the second step", "data augmentation", "overfitting", "the final step", "a convolutional encoder-decoder network-based u-net architecture", "high precision", "medical image analysis", "the framework", "two comprehensive public datasets", "namely inbreast and cbis-ddsm", "several metrics", "quantitative performance assessment", "the dice score", "sensitivity", "hausdorff distance", "precision", "f1 score", "quantitative results", "the inbreast dataset", "an average dice score", "85.61%", "a sensitivity", "81.26%", "the cbis-ddsm dataset", "the average dice score", "87.98%", "the sensitivity", "90.58%", "the experimental results", "earlier and more accurate abnormality detection", "the success", "the proposed deep learning framework", "mammography", "promise", "broader applications", "medical imaging", "various radiological practices", "second", "90%", "three", "first", "li", "second", "two", "85.61%", "81.26%", "87.98%", "90.58%" ]

[ "Jiong Chen", "Abdullah Alshammari", "Mohammed Alonazi", "Aisha M. Alqahtani", "Sara A. Althubiti", "Romi Fadillah Rahmat" ]

[ "emerging technological advancements", "the door", "deep learning-based methods", "practically all spheres", "human endeavor", "their accuracy", "deep learning algorithms", "healthcare", "different illnesses", "the recent coronavirus (covid-19) outbreak", "the global medical system", "medical imaging", "pcr testing", "covid-19", "covid-19", "it", "it", "-", "covid-19 infections", "additional infections", "that", "covid-19 infections", "a deep learning-based entropy-controlled whale optimization", "(ewoa", "transfer learning", "this paper", "the created system", "three stages", "a preliminary processing phase", "noise effects", "the image", "then a deep learning architecture", "a pre-trained model", "features", "the pre-processed image", "the features", "optimization", "ewoa", "the optimum features", "a softmax layer", "the final categorization", "various activation functions", "thresholds", "optimizers", "the systems", "numerous metrics", "performance", "the performance", "the offered methodologies", "assessment", "an accuracy", "97.95%", "the suggested technique", "four classes", "covid-19", "viral pneumonia", "chest infection", "current methodologies", "the literature", "accuracy", "covid-19", "covid-19", "covid-19", "covid-19", "covid-19", "three", "97.95%", "four", "covid-19" ]

[ "Shannon L. Walston", "Hiroshi Seki", "Hirotaka Takita", "Yasuhito Mitsuyama", "Shingo Sato", "Akifumi Hagiwara", "Rintaro Ito", "Shouhei Hanaoka", "Yukio Miki", "Daiju Ueda" ]

[ "medicine", "deep learning-based artificial intelligence", "(ai) engineering", "two distinct fields", "each", "decades", "published history", "the current rapid convergence", "deep learning", "medicine", "significant advancements", "it", "ambiguity", "data", "terms", "both fields", "miscommunication", "methodological discrepancies", "this narrative review", "historical context", "these terms", "the importance", "clarity", "these terms", "medical deep learning contexts", "solutions", "misunderstandings", "readers", "either field", "an examination", "historical documents", "articles", "guidelines", "textbooks", "this review", "the divergent evolution", "terms", "data sets", "their impact", "the discordant interpretations", "the word", "validation", "ai contexts", "we", "the medical field", "terms", "the deep learning domain", "the data", "models", "the ‘training set", "the data", "tuning", "parameters", "the ‘validation (or tuning) set", "the evaluation", "models", "the test sets", "model evaluation", "internal (random splitting", "validation", "sets", "external (temporal and geographic) sets", "this review", "terms", "pragmatic solutions", "terminological confusion", "the field", "deep learning", "medicine", "we", "the accurate and standardized description", "these data sets", "the explicit definition", "data", "splitting terminologies", "each publication", "these", "crucial methods", "the robustness", "generalizability", "deep learning applications", "medicine", "this review", "the precision", "communication", "more effective and transparent research methodologies", "this interdisciplinary field", "two", "each with decades", "one" ]

[ "Sarita Mohanty", "Asha Ambhakar" ]

[ "the rapid proliferation", "internet usage", "an exponential increase", "cyber threats", "particularly malicious websites", "that", "user data and system integrity", "traditional methods", "web security", "more dynamic and adaptive approaches", "this research paper", "a comprehensive comparative study", "machine learning", "ml", "deep learning", "(dl) techniques", "the detection", "malicious websites", "a dataset", "over 420,000 web urls", "various features", "domain", "subdomain", "domain suffix", "the study", "the effectiveness", "precision", "computational efficiency", "multiple algorithms", "two convolutional neural network (cnn) models", "traditional ml algorithms", "decision trees", "random forests", "adaboost, k-nearest neighbors", ", stochastic gradient descent", "sgd", "extra trees", "gaussian naive bayes", "the models", "metrics", "accuracy", "precision", "recall", "f1-score", "preliminary results", "cnn models", "an accuracy rate", "up to 98%", "the potential", "dl", "cybersecurity applications", "the study", "the challenges", "high cardinality", "class imbalance", "the dataset", "various data", "techniques", "these issues", "feature engineering", "oversampling", "minority classes", "the research", "the field", "a detailed analysis", "each algorithm’s strengths", "weaknesses", "valuable insights", "the adaptability", "scalability", "ml", "dl techniques", "malicious web detection", "over 420,000", "suffix", "two", "cnn", "bayes", "cnn", "up to 98%" ]

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

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

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

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

[ "Mingzhen Li", "Changxi Liu", "Jianjin Liao", "Xuegui Zheng", "Hailong Yang", "Rujun Sun", "Jun Xu", "Lin Gan", "Guangwen Yang", "Zhongzhi Luan", "Depei Qian" ]

[ "the flourish", "deep learning frameworks", "hardware platforms", "an efficient compiler", "that", "the diversity", "both software", "hardware", "order", "application portability", "the existing deep learning compilers", "tvm", "its efficiency", "code generation", "optimization", "diverse hardware devices", "the meanwhile", "the sunway many-core processor", "itself", "a competitive candidate", "its attractive computational power", "both scientific computing", "deep learning workloads", "this paper", "the trends", "these two directions", "we", "swtvm", "that", "the original tvm", "time", "architecture", "cross", "-", "compilation", "sunway", "addition", "we", "the architecture", "the compilation", "core group", "massive parallelism", "dma", "high bandwidth memory transfer", "local device memory", "data locality", "order", "efficient codes", "deep learning workloads", "sunway", "the experiment results", "the codes", "swtvm", "1.79× improvement", "inference latency", "the-art", "sunway", "eight representative benchmarks", "this work", "the first attempt", "the compiler perspective", "the gap", "deep learning and sunway processor", "productivity", "efficiency", "mind", "we", "this work", "more people", "the power", "deep learning", "many-core processor", "two", "1.79×", "eight", "first" ]

[ "Fulu Pan", "Yang Liu", "Zhiqiang Luo", "Guopeng Wang", "Xueyan Li", "Huining Liu", "Shuang Yu", "Dongying Qi", "Xinyu Wang", "Xiaoyu Chai", "Qianqian Wang", "Renfang Yin", "Yanli Pan" ]

[ "traditional chinese medicine", "tcm", "distinctive advantages", "the management", "alzheimer’s disease", "a considerable gap", "our understanding", "its pharmacologically active constituents", "this study", "we", "the potential", "deep learning models", "drug-target interactions", "we", "a systematic screening", "cholinesterase (che) inhibitors", "an extensive array", "tcm ingredients", "rigorous validation", "vitro experiments", "we", "both a drug-target interactions", "(dti) model", "a blood-brain barrier permeability", "(bbbp) model", "both models", "an auprc score", "we", "a screening process", "that", "six compounds", "in vitro che inhibitory assay", "all six compounds", "a robust inhibitory effect", "acetylcholinesterase", "ache", "the six compounds", "potent inhibitory activity", "butyrylcholinesterase", "bche", "our findings", "the promise", "deep learning", "inhibitors", "tcm", "chinese", "cholinesterase (che", "dti", "0.9", "six", "six", "four", "six" ]

[ "Van-Hieu Vu" ]

[ "customers", "an important issue", "that", "banks", "the forefront", "the bank’s policies", "the fact", "banks", "customers", "who", "the service", "banks", "policies", "customers", "this paper", "we", "a combined deep learning network models", "customers", "the bank", "the proposed model", "two levels", "level", "three basic models", "three deep learning neural networks", "level", "a logistic regression model", "the proposed model", "evaluation results", "accuracy metrics", "96.60%", "precision metrics", "90.26%", "recall metrics", "91.91%", "f1 score", "91.07%", "the dataset “bank customer churn prediction", "two", "0", "three", "three", "1", "96.60%", "90.26%", "91.91%", "91.07%" ]

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

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

[ "Gang Xu", "Jinshan He", "Zhimin Lü", "Min Li", "Jinwu Xu" ]

[ "iron and steel enterprises", "spot test ward", "final product quality", "it", "line", "all products", "this traditional approach", "returns", "major economic losses", "enterprises", "order", "line", "steel products", "manufacturing process", "the prediction models", "mechanical properties", "deep learning", "this work", "the mechanical properties", "deep drawing steels", "lstm", "long short team memory", "gru", "gated recurrent unit", "network", "gpr", "gaussian process regression", "model", "prediction accuracy", "efficiency", "different models", "line", "-learning methods", "transfer learning models", "model parameters", "the experimental results", "not only the prediction accuracy", "optimized transfer learning models", "predetermining time", "real time requirements", "line", "the industrial production data", ") steel", "r2 value", "gru model", "training stage", "r2 value", "testing stage", "first", "gpr", "gaussian", "more than 0.99" ]

[ "Zakary Georgis-Yap", "Milos R. Popovic", "Shehroz S. Khan" ]

[ "epilepsy", "more than 50 million people", "it", "the world’s most prevalent neurological diseases", "the main symptom", "epilepsy", "seizures", "which", "serious injury", "death", "the ability", "the occurrence", "an epileptic seizure", "many risks", "people", "epilepsy face", "we", "the problem", "reference", "normal eeg", "a precursor", "incoming seizure", "this end", "we", "several supervised deep learning approaches model", "preictal eeg", "normal eeg", "we", "novel unsupervised deep learning approaches", "the models", "only normal eeg", "pre-seizure eeg", "an anomalous event", "these deep learning models", "two large eeg seizure datasets", "a person-specific manner", "we", "both", "approaches", "their performance", "the patient", "approach", "architecture", "this new line", "research", "the potential", "therapeutic interventions", "human lives", "more than 50 million", "two" ]

[ "Jimmy Tekli", "Bechara Al Bouna", "Gilbert Tekli", "Raphaël Couturier", "Antoine Charbel" ]

[ "obfuscation techniques", "e.g., blurring", "sensitive information", "images", "recent works", "adversaries", "dl", "obfuscated face images", "adversaries", "their goals", "knowledge", "capabilities", "(e.g., dl-assisted attacks", "the evaluation methodology", "obfuscation techniques", "the defense strategies", "adversaries", "scenario", "i.e., stronger adversaries", "a 2019 article", "the european union agency", "cybersecurity", "enisa", "adversaries", "more sophisticated and dangerous attacks", "these concerns", "our paper", "a novel privacy challenge", "the context", "image obfuscation", "we", "adversaries", "their dl-assisted attacks", "additional privacy breaches", "a target dataset", "obfuscated images", "we", "federated learning", "a collaborative attack/adversarial strategy", "(i", "the attacking capabilities", "an adversary", "ii", "the privacy breaches", "(iii", "the lack", "background knowledge and data shortage", "the need", "the local training datasets", "a centralized location", "our knowledge", "we", "collaborative and more specifically fl-based attacks", "the context", "face obfuscation", "2019", "the european union agency", "first" ]

[ "Mohamad M. A. Ashames", "Ahmet Demir", "Omer N. Gerek", "Mehmet Fidan", "M. Bilginer Gulmezoglu", "Semih Ergin", "Rifat Edizkan", "Mehmet Koc", "Atalay Barkana", "Cuneyt Calisir" ]

[ "the great success", "various deep learning methods", "image and object classification", "the biomedical image processing society", "their applications", "various automatic diagnosis cases", "the deep learning-based classification attempts", "the literature", "the aim", "extreme accuracy scores", "interpretability", "patient-wise separation", "training", "test data", "example", "most lung", "classification papers", "deep learning randomly shuffle data", "it", "training", "validation", "test sets", "certain images", "the computed tomography (ct) scan", "a person", "the training set", "the same person", "the validation or testing image sets", "this", "misleading accuracy rates", "the learning", "irrelevant features", "the real-life usability", "these models", "the deep neural networks", "the traditional, unfair data", "method", "new patient images", "it", "the trained models", "contrast", "deep neural networks", "strict patient-level separation", "their accuracy rates", "new patient images", "heat map visualizations", "the activations", "the deep neural networks", "strict patient-level separation", "a higher degree", "focus", "the relevant nodules", "we", "the research question", "the title", "a positive answer", "the deep neural networks", "images", "patients", "that", "the validation and testing patient sets" ]

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

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

[ "Yang Jeong Park", "HyunGi Kim", "Jeonghee Jo", "Sungroh Yoon" ]

[ "data-driven deep learning algorithms", "accurate prediction", "high-level quantum-chemical molecular properties", "their inputs", "the same quantum-chemical level", "geometric relaxation", "the training dataset", "their flexibility", "alternative cost-effective conformation generative methods", "domain-shift problems", "deteriorating prediction accuracy", "we", "a deep contrastive learning-based domain-adaptation method", "local atomic environment", "contrastive learning", "lacl", "lacl", "the disparities", "distribution", "the two geometric conformations", "different conformation-generation methods", "we", "lacl", "a domain-agnostic latent space", "that", "the semantics", "an atom’s local atomic environment", "lacl", "quantum-chemical accuracy", "the geometric relaxation bottleneck", "future application scenarios", "inverse molecular engineering and large-scale screening", "our approach", "small organic molecules", "long chains", "biological and pharmacological molecules", "two" ]

[ "Chunmeng Weng", "Congying Chen", "Xianfeng Ai" ]

[ "this paper", "the design-based learning", "(dbl) approach", "the deep learning", "students", "the quality", "teaching", "engineering design education", "we", "three aspects", "research", "students", "a typical educational activity", "the first study", "students' deep learning", "the dbl approach", "terms", "deep learning status", "deep learning ability", "the second study", "the effectiveness", "the dbl approach", "comparative research", "a control class", "traditional teaching method", "an experimental class", "dbl method", "the third study", "students' evaluations", "the dbl approach", "it", "the dbl approach", "the students' motivation", "them", "study", "the students' higher-order thinking", "higher-order capabilities", "critical thinking ability", "problem-solving ability", "the same time", "they", "the dbl approach", "these findings", "the dbl approach", "students' deep learning", "the quality", "teaching", "three", "first", "second", "third" ]

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

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

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

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

[ "H. Pooja", "M. P. Prabhudev Jagadeesh" ]

[ "the named entity recognition", "ner", "a method", "references", "rigid designators", "text", "that", "well-established semantic categories", "person", "place", "organisation", "many natural language e applications", "summarization", "text", "question–answer models", "machine translation", "ner", "their core", "early ner systems", "high performance", "the expense", "human engineers", "features", "rules", "that", "a certain domain", "the biomedical data", "the useful information", "the appropriate diagnosis", "these systems", "biomedical domain", "the traditional rule-based, dictionary based and machine learning based methods", "computational complexity", "deep learning", "ner systems", "the-art", "the present work", "a novel deep learning based approach", "which", "bidirectional long short term", "bilstm", "bert", "crf", "transfer learning", "multi-tasking model", "the oov problem", "biomedical domain", "the transfer learning architecture", "shared and task specific layers", "the multi-task transfer learning task", "the shared layer", "lexicon encoder", "transformer encoder", "vectors", "we", "a training loss function", "the bert model", "the proposed multi-task tlbbc approach", "numerous prevailing methods", "the proposed multi-task tlbbc approach", "average accuracy", "97.30%", "97.20%", "96.80%", "97.50%", "bc5cdr", "jnlpba", "s800 dataset", "ner", "as 97.30%", "97.20%", "96.80%", "97.50%" ]

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

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

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

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

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

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

[ "Zhan Yu", "Ding-Xuan Zhou" ]

[ "we", "a deep learning theory", "distribution regression", "deep convolutional neural networks", "dcnns", "deep learning", "structured deep neural networks", "practical applications", "generalization analysis", "regression", "dcnns", "the distribution regression problem", "which", "the input variables", "probability measures", "no mathematical model", "theoretical analysis", "dcnn-based learning theory", "the difficulties", "the classical neural network structure", "the input variable", "a euclidean vector", "the input samples", "probability distributions", "the traditional neural network structure", "a well-defined dcnn framework", "distribution regression", "this paper", "we", "the difficulty", "a novel dcnn-based learning theory", "a two-stage distribution regression model", "we", "an approximation theory", "functionals", "the set", "borel probability measures", "the proposed dcnn framework", "we", "the hypothesis space", "its compactness", "the hypothesis space", "the general dcnn framework", "distribution inputs", "a two-stage error decomposition technique", "we", "a novel dcnn-based two-stage oracle inequality", "optimal learning rates", "a logarithmic factor", "the proposed algorithm", "distribution regression", "one", "two", "firstly", "two", "two" ]

[ "Nacira Agram", "Bernt Øksendal", "Jan Rems" ]

[ "we", "a deep learning approach", "the minimal variance pricing", "hedging problem", "an incomplete jump diffusion market", "it", "a rigorous stochastic calculus derivation", "the optimal hedging portfolio", "optimal option price", "the corresponding equivalent martingale measure", "the means", "the stackelberg game approach", "a deep learning algorithm", "the combination", "the feed-forward", "lstm neural networks", "three different market models", "which", "contrast", "the complete market black–scholes model", "a benchmark", "the algorithm’s performance", "the results", "that", "the algorithm’s good performance", "we", "our results", "the special incomplete market model", "merton", "a detailed comparison", "our results", "the minimal variance principle", "the results", "merton", "a different pricing principle", "deep learning", "we", "the minimal variance principle", "typically higher option prices", "those", "the merton principle", "the other hand", "the minimal variance principle", "lower losses", "the merton principle", "three", "two", "merton", "merton" ]

[ "Debby D. Wang", "Wenhui Wu", "Ran Wang" ]

[ "the launch", "alphafold series", "deep-learning techniques", "the molecular structural science", "another crucial problem", "structure-based prediction", "protein-ligand binding affinity", "advanced computational techniques", "this problem", "we", "mainstream structure-based, deep-learning approaches", "this problem", "molecular representations", "architectures", "model interpretability", "a model taxonomy", "the lack", "valid comparisons", "those models", "we", "evaluated representatives", "a uniform basis", "the advantages", "shortcomings", "this review", "structure-based drug discovery", "related areas.graphical abstract" ]