Datasets:

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ADKU

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ResearchGPT_model_dataset

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[ "Simon Wiegrebe", "Philipp Kopper", "Raphael Sonabend", "Bernd Bischl", "Andreas Bender" ]

[ "the influx", "deep learning", "(dl) techniques", "the field", "survival analysis", "recent years", "substantial methodological progress", "instance", "unstructured or high-dimensional data", "images", "text", "omics", "data", "this work", "we", "a comprehensive systematic review", "dl-based methods", "event", "them", "both survival- and dl-related attributes", "summary", "the reviewed methods", "only a small subset", "tasks", "event", "e.g., single-risk right-censored data", "neglect", "more complex settings", "our findings", "an editable, open-source, interactive table", "https://survival-org.github.io/dl4survival", "this research area", "we", "community contribution", "order", "this database", "date", "recent years" ]

[ "Tulika Chutia", "Nomi Baruah" ]

[ "the growth", "internet", "its numerous potential applications", "diverse fields", "artificial intelligence", "analysis", "sa", "significant and popular research areas", "it", "a key technology", "that", "the fourth industrial revolution", "the subset", "emotion recognition systems", "communication", "users", "social media", "digital marketing", "e-commerce sites", "day", "massive amounts", "unstructured data", "marketing", "public safety", "education", "human resources", "business", "other industries", "the emotion recognition system", "it", "a large amount", "textual data", "the emotions", "them", "the paper", "a systematic literature review", "the existing literature", "text-based emotion detection", "this review", "330 research papers", "different conferences", "journals", "workshops", "dissertations", "this paper", "different approaches", "methods", "different deep learning models", "key aspects", "description", "datasets", "evaluation techniques", "future prospects", "deep learning", "challenges", "existing studies", "limitations", "practical implications", "fourth", "4.0", "4.0", "5.0", "between 2013 to 2023", "330" ]

[ "Abhay B. Upadhyay", "Saurin R. Shah", "Rajesh A. Thakkar" ]

[ "weather", "various factors", "temperature", "pressure", "air movement", "moisture/water vapor", "the earth", "motion", "accurate weather forecasting", "a high geographical resolution", "a complex and computationally expensive task", "this study", "a nowcasting approach", "meteorological radar images", "the principles", "unsupervised representation", "deep learning", "we", "the emerging field", "next-frame prediction", "computer vision", "this research", "future images", "prior image data", "applications", "robot decision-making", "autonomous driving", "we", "the latest advancements", "next-frame prediction networks", "them", "two approaches", "machine learners", "deep learners", "we", "the merits", "limitations", "each approach", "them", "various parameters", "we", "potential directions", "future research", "this field", "earth", "two" ]

[ "Georgios-Markos Chatziloizos", "Dimitrios Gunopulos", "Konstantinos Konstantinou" ]

[ "machine learning", "deep learning techniques", "researchers", "a background", "both economics", "computer science", "stock prices", "trends", "these techniques", "an alternative", "existing models", "methodologies", "their ability", "abstract features", "data", "most existing research approaches", "either numerical/economical data", "textual/sentimental data", "this article", "we", "approaches", "both numerical/economical data", "textual/sentimental data", "order", "stock market prices", "trends", "combined data", "a stock's technical analysis", "sentiment analysis", "the four tickers", "aapl", "goog", "nvda", "s&p", "information technology", "we", "historical financial data", "historical textual data", "we", "each type", "data", "unison", "which case", "the results", "we", "detail", "we", "each type", "data", "we", "it", "our results", "four" ]

[ "Nawaf Alharbe", "Manal Almalki" ]

[ "deep learning", "the internet", "things", "iot", "the healthcare industry", "this study", "the potential commercial transformation", "iot-enabled healthcare systems", "that", "deep learning", "patient monitoring", "diagnosis", "wearables", "smart sensors", "internet-connected medical devices", "doctors", "patients' vital signs", "activities", "physiological traits", "real time", "these devices", "vast and complex data", "analysis", "diagnosis", "challenging", "deep learning models", "this growing volume", "medical data", "convolutional neural networks", "cnns", "long short-term memory", "(lstm) networks", "complex patterns", "relationships", "sensor data", "electronic health records", "patient-reported information", "this capability", "clinical professionals", "illnesses", "warning signs", "tailoring treatments", "this paper", "a convolutional neural networks", "cnns", "long short-term memory", "lstm", "that", "feature extraction", "classification", "prediction", "data preparation", "it", "interpretability issues", "privacy concerns", "resource limitations", "deep learning models", "real-time healthcare settings", "the study", "the effectiveness", "convolutional neural networks", "cnns", "long short-term memory", "lstm", "real-time patient monitoring", "disease detection", "risk prediction", "therapy optimization", "these techniques", "the quality", "cost", "outcomes", "healthcare", "convolutional neural networks", "cnns", "long short-term memory", "lstm", "iot", "healthcare", "disease detection", "personalized treatment", "patient monitoring", "connected devices", "powerful analytics" ]

[ "K. J. Subha", "R. Rajavel", "B. Paulchamy" ]

[ "the realm", "biomedical engineering", "a significant challenge", "physiological changes", "the human body", "these irregularities", "which", "the intricate nature", "the methods", "identification", "the need", "early disease detection", "interest", "computer-assisted diagnostics", "the core goal", "this proposed work", "a computer-aided system", "cad", "the prompt identification", "screening", "treatment", "glaucoma", "a fundus camera", "structural characteristics", "segmented optic discs and optic cups", "which", "the characterization", "glaucoma", "the evaluation", "its severity", "a novel ensemble-based deep learning model", "which", "three modified pre-trained convolutional neural networks", "resnet50", "vggnet19", "the inception", "v3", "the proposed algorithm's performance", "a combined four distinct publicly available datasets", "which", "drishti-gs, drions", "db", "an accuracy", "98.58%", "specificity", "98.17%", "sensitivity", "98.80%", "precision", "98.86%", "f1 score", "98.83%", "auc", "the combined dataset", "an outstanding accuracy rates", "various datasets", "98.67%", "97.71%", "97.22%", "97.50%", "drions", "db", "drishti-gs", "the ensemble framework", "a significant advantage", "the-art", "a performance improvement", "up to 1.58%", "glaucoma classification", "the existing deep learning techniques", "three", "resnet50", "vggnet19", "four", "98.58%", "98.17%", "98.80%", "98.86%", "98.83%", "0.98", "98.67%", "97.71%", "97.22%", "97.50%", "up to 1.58%" ]

[ "Rui Fan", "Xiaohong Han" ]

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

[ "Kaiqiang Wang", "Li Song", "Chutian Wang", "Zhenbo Ren", "Guangyuan Zhao", "Jiazhen Dou", "Jianglei Di", "George Barbastathis", "Renjie Zhou", "Jianlin Zhao", "Edmund Y. Lam" ]

[ "phase recovery", "(pr", "the phase", "the light field", "its intensity measurements", "quantitative phase imaging", "coherent diffraction", "adaptive optics", "the refractive index distribution", "topography", "an object", "the aberration", "an imaging system", "recent years", "deep learning", "dl", "deep neural networks", "unprecedented support", "computational imaging", "more efficient solutions", "various pr problems", "this review", "we", "conventional methods", "we", "dl", "support", "pr", "the following three stages", "-processing", "processing", "we", "dl", "phase image processing", "we", "the work", "dl", "pr", "an outlook", "dl", "the reliability", "efficiency", "pr", "we", "a live-updating resource", "https://github.com/kqwang/phase-recovery", "readers", "pr", "recent years", "first", "three" ]

[ "Nan Lin", "Shaoju Wu", "Zheyang Wu", "Songbai Ji" ]

[ "the large amount", "training samples", "a deep learning brain injury model", "enormous computational resources", "we", "a transformer neural network", "tnn", "high accuracy", "pretraining samples", "a convolutional neural network (cnn) brain injury model", "computational cost", "the samples", "synthetic impacts", "real-world events", "augmented impacts", "limited measured impacts", "we", "the tnn", "the two impact types", "root mean squared error", "rmse", "voxelized peak strains", "the tnn-estimated samples", "each data type", "a cnn", "which", "directly simulated training samples", "an independent measured impact dataset", "complete capture", "impact event", "estimation accuracy", "we", "pretraining", "cnn accuracy", "transfer learning", "a baseline cnn", "it", "the finetuning dataset", "2000–4000 pretraining synthetic or augmented samples", "success rate", "500 finetuning samples", "samples", "no obvious improvement", "these results", "the tnn", "pretraining samples", "a more efficient training strategy", "future deep learning brain models", "the number", "costly direct simulations", "an alternative baseline model", "this study", "a wider adoption", "deep learning brain injury models", "large-scale predictive modeling", "safety protocols", "protective equipment", "cnn", "first", "two", "100", "0.948–0.967", "0.01", "1000–5000", "cnn", "191", "cnn", "cnn", "2000–4000", "0.72", "0.81", "500", "3000" ]

[ "Xinge Yang", "Qiang Fu", "Wolfgang Heidrich" ]

[ "deep optical optimization", "a new paradigm", "computational imaging systems", "only the output image", "the objective", "it", "either simple optical systems", "a single element", "a diffractive optical element", "metalens", "the fine-tuning", "compound lenses", "good initial designs", "we", "a deeplens design method", "curriculum learning", "which", "optical designs", "compound lenses", "ab initio", "randomly initialized surfaces", "human intervention", "the need", "a good initial design", "we", "the effectiveness", "our approach", "both classical imaging lenses", "view", "field", "a cellphone-style form factor", "highly aspheric surfaces", "a short back focal length", "deeplens" ]

[ "Guy Amir", "Osher Maayan", "Tom Zelazny", "Guy Katz", "Michael Schapira" ]

[ "deep neural networks", "dnns", "a crucial role", "the field", "machine learning", "the-art", "various application domains", "their success", "dnn-based models", "challenges", "generalization", "inputs", "that", "training", "this limitation", "a significant challenge", "it", "deep learning", "safety-critical tasks", "real-world settings", "substantial variability", "we", "a novel approach", "dnn verification technology", "dnn-driven decision rules", "that", "robust generalization", "input domains", "our method", "generalization", "an input domain", "the level", "agreement", "independently trained deep neural networks", "inputs", "this domain", "we", "our approach", "the-shelf", "dnn verification engines", "it", "dnn benchmarks", "a deep reinforcement learning (drl) system", "internet congestion control", "the applicability", "our approach", "real-world settings", "our research", "a fresh objective", "formal verification", "the prospect", "the challenges", "dnn-driven systems", "real-world scenarios" ]

[ "Munetoshi Akazawa", "Kazunori Hashimoto" ]

[ "placenta previa", "life-threatening bleeding", "accurate prediction", "severe hemorrhage", "risk stratification", "optimum allocation", "interventions", "we", "a multimodal deep learning model", "severe hemorrhage", "mri t2-weighted image", "the placenta and tabular data", "patient demographics", "preoperative blood examination data", "a multimodal deep learning model", "cases", "intraoperative blood loss", "we", "the prediction performance", "the model", "it", "that", "two machine learning methods", "only tabular data and mri images", "that", "two human expert obstetricians", "the enrolled 48 patients", "54.2%", "2000 ml", "blood", "22 (45.8%", "2000 ml", "blood", "multimodal deep learning model", "the best accuracy", "auc", "the machine learning model", "tabular data and mri images", "a class accuracy", "the human experts", "median accuracies", "multimodal deep learning models", "the two types", "information", "severe hemorrhage cases", "the model", "human expert", "the prediction", "intraoperative hemorrhage", "the case", "placenta previa", "placenta previa", "2000 ml", "two", "two", "48", "26", "54.2%", "2000 ml", "22", "45.8%", "2000 ml", "0.68", "0.74", "0.61", "0.53", "0.61", "two" ]

[ "Zixian Shi", "Renguang Zuo", "Bao Zhou" ]

[ "machine learning algorithms", "supervised and unsupervised learning ones", "mineral prospectivity mapping", "supervised learning algorithms", "the use", "numerous known mineral deposits", "the reliability", "the training results", "unsupervised learning algorithms", "areas", "rare or no known deposits", "reinforcement learning", "rl", "a type", "machine learning algorithm", "that", "supervised and unsupervised learning models", "the learning process", "the agent", "environment", "the environment", "the agent", "reward signals", "states", "the agent", "the mineralization potential", "each state", "these rewards", "this study", "a deep rl framework", "mineral prospectivity mapping", "a case study", "mapping", "gold prospectivity", "northwest hubei province", "china", "the framework", "the deep rl agent", "the information", "known mineralization", "the environment", "potential mineralization information", "the unlabeled dataset", "its comparison", "random forest and isolation forest models", "deep rl", "the number", "known mineral deposits", "its unique reward", "feedback mechanism", "the delineated high-potential areas", "a strong spatial correlation", "known gold deposits", "significant clues", "future prospecting", "the study area", "china" ]

[ "Jamal Alasadi", "Ghassan F. Bati", "Ahmed Al Hilli" ]

[ "deep learning", "5g applications", "computer vision", "the internet", "things", "iot", "intermedia classification", "the deep learning approach", "limited-resource mobile devices", "the most challenging issues", "the same time", "users’ experience", "terms", "quality", "service", "qos", "machine learning applications", "mobile edge computing", "(mec", "a cooperative approach", "computation resources", "proximity", "end-user devices", "these limitations", "this article", "a novel image reiterative extraction algorithm", "convolution neural network", "(cnn) learning", "computational task", "machine learning-based mobile applications", "resource-limited and uncertain environments", "we", "the framework", "image retrieval extraction", "three approaches", "privacy preservation", "personal data", "feature matching time", "our simulation results", "real-time experiments", "a small-scale mec server", "the effectiveness", "the proposed deep learning-based approach", "existing schemes", "the source code", "https://github.com/jamalalasadi/cnn_image_retrieval", "5", "cnn", "three", "first", "second", "third" ]

[ "Won-Jun Noh", "Mu Sook Lee", "Byoung-Dai Lee" ]

[ "this study", "a deep learning-based system", "the automatic measurement", "angles", "(specifically, meary’s angle", "calcaneal pitch", "weight-bearing lateral radiographs", "the foot", "flatfoot diagnosis", "we", "3960 lateral radiographs", "the left or right foot", "a pool", "4000 patients", "a deep learning-based model", "these radiographs", "june", "november", "patients", "who", "total ankle replacement surgery", "ankle arthrodesis surgery", "various methods", "correlation analysis", "bland–altman plots", "t-tests", "the concordance", "the angles", "the system", "those", "clinical experts", "the evaluation dataset", "150 weight-bearing", "150 patients", "all test cases", "the angles", "the deep learning-based system", "good agreement", "the reference standards", "meary’s angle", "pcc", "intraclass correlation coefficient", "icc", "concordance correlation coefficient", "ccc", "absolute error", "mae", "1.59°", "calcaneal pitch", "the average time", "angle measurement", "only the cpu", "the deep learning-based system", "11 ±", "the deep learning-based automatic angle measurement system", "a tool", "comparable accuracy", "reliability", "the results", "medical professionals", "patients", "internal fixation devices", "3960", "4000", "between june and november 2021", "150", "150", "0.964", "0.963", "0.963", "0.632", "1.59", "0.988", "0.987", "0.987", "0.055", "0.63", "11", "1" ]

[ "Zhiguo Jiang", "Waneeza Zaheer", "Aamir Wali", "S. A. M. Gilani" ]

[ "a growing trend", "users", "social media platforms", "their emotions", "visual content", "visual sentiment analysis", "the process", "the emotional polarity", "images", "videos", "a challenging problem", "artificial intelligence", "the existing models", "robust machine learning", "deep learning techniques", "the idea", "deep transfer", "techniques", "visual sentiment analysis", "this paper", "we", "a new approach", "data-augmented-transfer learning architecture", "a pre-trained vgg16 model", "that", "svm", "augmented training data", "fine-tuning", "evaluation", "we", "two twitter image datasets", "we", "the proposed model", "a third dataset", "the commonly used geometric augmentation methods", "rotation", "zoom", "range", ", width shift", "height shift", "shear range", "horizontal flip", "we", "our proposed vgg16-svm model", "the-art", "transfer learning", "4 machine learning models", "svm", "fine-tuning", "the results", "vgg16-svm", "the overall best accuracy", "94%", "96%", "all transfer learning and machine learning pairs", "we", "our proposed model", "all previous studies", "that", "the same dataset", "two", "third", "3", "4", "94%", "96%" ]

[ "Lauri Holmstrom", "Matthew Christensen", "Neal Yuan", "J. Weston Hughes", "John Theurer", "Melvin Jujjavarapu", "Pedram Fatehi", "Alan Kwan", "Roopinder K. Sandhu", "Joseph Ebinger", "Susan Cheng", "James Zou", "Sumeet S. Chugh", "David Ouyang" ]

[ "backgroundundiagnosed chronic kidney disease", "ckd", "a common and usually asymptomatic disorder", "that", "a high burden", "morbidity", "early mortality", "we", "a deep learning model", "ckd", "routinely acquired ecgs.methodswe collected data", "a primary cohort", "111,370 patients", "which", "247,655 ecgs", "this data", "we", "a deep learning model", "an ecg", "one year", "the patient", "a ckd diagnosis", "the model", "an external cohort", "another healthcare system", "which", "312,145 patients", "896,620 ecgs", "our deep learning", "algorithm", "discrimination", "ckd", "any stage", "an auc", "95%", "ci 0.760–0.773", "an auc", "(0.708–0.710", "the external cohort", "our 12-lead ecg-based model performance", "the severity", "ckd", "an auc", "(0.735–0.770", "mild ckd", "auc", "0.750–0.767", "moderate-severe ckd", "an auc", "esrd", "patients", "our model", "high performance", "any stage ckd", "both 12-lead", "auc", "deep learning algorithm", "ckd", "ecg waveforms", "stronger performance", "younger patients", "more severe ckd stages", "this ecg algorithm", "the potential", "augment", "ckd", "111,370", "247,655", "between 2005 and 2019", "one year", "312,145", "896,620", "between 2005", "2018.resultsusing", "12", "0.767", "95%", "0.760–0.773", "0.709", "12", "0.753", "0.735–0.770", "0.759", "0.750–0.767", "0.783", "0.773–0.793", "under 60 years old", "12", "0.843", "1", "0.824" ]

[ "Rui Yu", "Yue Cao", "Jennifer Martin", "Otto Chiang", "YuMing Zhang" ]

[ "double-electrode gas metal arc welding", "de", "-", "gmaw", "gmaw", "a second electrode", "a portion", "the current", "the wire", "this", "the heat input", "the workpiece", "successful bypassing", "the relative position", "the bypass", "the continuously varying wire tip", "proper operation", "we", "the system", "a follower robot", "the bypass", "the primary information", "this process", "the arc image", "which", "desired and undesired modes", "a robust algorithm", "the complex arc image", "a deep learning approach", "numerous arc images", "the corresponding de-gmaw modes", "which", "alternative labels", "we", "arc phenomena", "various de-gmaw modes", "them", "distinct arc systems", "varying voltages", "these voltages", "automatically derived labels", "the deep-learning network", "the results", "reliable process monitoring", "second" ]

[ "Tran Khanh Dang", "Phat T. Tran-Truong" ]

[ "the increasing use", "technology", "our daily lives", "data privacy", "a critical issue", "it", "technologies", "the protection", "people’s personal information", "fact", "what", "we", "privacy-enhancing technologies", "pets", "technologies", "themselves", "artificial intelligence", "deep learning technologies", "which", "societal locomotives", "no exception", "practitioners", "privacy concerns", "this gap", "we", "a pragmatic privacy-preserving deep learning framework", "that", "ai practitioners", "our proposed framework", "differential privacy", "a rigorous standard", "privacy", "it", "a setting", "private aggregation", "teacher ensembles", "which", "we", "several improvements", "a better level", "accuracy", "privacy protection", "we", "a differential private aggregation mechanism", "sparse vector technique", "it", "several other improvements", "the-loop", "our proposed solution", "the possibility", "privacy-preserving models", "that", "a fixed privacy budget", "these models", "a large number", "training requests", "them", "deep learning training processes", "our framework", "training settings", "we", "our work", "practitioners", "pets", "technologies", "privacy", "mind" ]

[ "Aman Sharma", "Raghav Dalmia", "Aarush Saxena", "Rajni Mohana" ]

[ "purposeplant diseases", "the main factors", "food production", "production losses", "they", "deep learning algorithms", "association", "computer vision techniques", "usage", "the diagnosis", "plant diseases", "a potent tool", "highly accurate results", "the objective", "this study", "a stacking ensemble-based solution", "several algorithms", "the process", "plant diseases", "trends", "gaps.methodthe stacking ensemble", "deep learning algorithms", "multi-layered perceptron", "meta classifier", "this regard", "we", "more than 15 studies", "the previous three years", "that", "problems", "disease detection", "dataset characteristics", "crops", "pathogens", "various ways.resultsthe proposed ensemble model", "a maximum accuracy", "98.13%", "the conventional architectures", "the results", "various performance metrics", "accuracy", "loss", "which", "the results", "the deep learning algorithms", "the data", "table", "5.conclusionthe suggested framework", "the presence", "disease", "a sample", "plant leaves", "a preventative strategy", "the results", "the results", "existing literature", "one", "four", "meta classifier", "more than 15", "the previous three years", "98.13%" ]

[ "Tiago E. Pratas", "Filipe R. Ramos", "Lihki Rubio" ]

[ "this study", "forecasting properties", "classic methodologies", "arch and garch models", "comparison", "deep learning methodologies", "mlp", "rnn", "lstm architectures", "bitcoin's volatility", "a new asset class", "unique characteristics", "bitcoin's high volatility", "structural breaks", "forecasting", "2753 observations", "this study", "bitcoin logarithmic returns", "results", "deep learning methodologies", "advantages", "terms", "forecast quality", "significant computational costs", "both mlp", "models", "smoother forecasts", "less fluctuation", "they", "large spikes", "the lstm architecture", "the other hand", "such movements", "its forecast", "forecasting accuracy", "different horizons mape", "mae metrics", "–mariano tests", "the forecast", "the superiority", "deep learning methodologies", "this study", "deep learning methodologies", "a promising tool", "bitcoin returns", "therefore volatility", "short-term horizons", "2753", "08", "01", "mae metrics" ]

[ "Bahman Jafari Tabaghsar", "Reza Tavoli", "Mohammad Mahdi Alizadeh Toosi" ]

[ "the most common types", "cancer", "the world", "skin cancer", "the different types", "skin diseases", "different shapes", "the classification", "skin diseases", "a very difficult task", "a result", "such a problem", "a combination model", "deep learning algorithms", "machine", "a three-layer architecture", "ensemble learning", "the first layer", "the training input", "convolutional neural network", "efficientnet", "the output", "the first layer", "the classifiers", "the second layer", "machine learning classifiers", "the output", "the best decision", "these classifiers", "the third layer classifier", "the final prediction", "made.the reason", "the three-layer architecture", "group learning", "the lack", "correct recognition", "some classes", "simple classifications", "the other hand", "some diseases", "different classes", "the same class", "this model", "input samples", "the correct combination", "classifications", "different layers.ham10000 data set", "the proposed method", "the mentioned dataset", "10,015 images", "skin lesions", "seven different classes", "different types", "skin diseases", "the accuracy", "the testing set", "which", "the previous heavy models", "one", "three", "first", "first", "second", "third", "three", "10,015", "seven", "99.97" ]

[ "Minh Hoang Nguyen", "Thuat Thien Nguyen", "Minh Nhat Ta", "Tien Minh Nguyen", "Kiet Van Nguyen" ]

[ "tourism", "which", "sightseeing", "relaxation", "discovery", "a fundamental aspect", "human life", "the most critical considerations", "accommodation", ", mainly hotels", "the travel experience", "we", "a solution", "a recommendation model", "user data", "travelers", "the ideal hotel", "our data", "two well-known websites", "traveloka", "ivivu", "information", "hotels", "vietnam", "users’ feedback history", "comments", "ratings", "the names", "users", "hotels", "we", "the inter-annotator agreement", "various aspects", "service", "infrastructure", "location", "attitude", "our recommendation model", "collaborative filtering", "deep learning techniques", "we", "context vectors", "tourists’ vietnamese comments", "the recommendation process", "the context model", "deep learning techniques", "topics", "sentiments", "the words", "the results", "our proposed model", "the mse", "which", "a context-free model", "the same parameters", "which", "an mse", "our deep learning", "phobert", "an accuracy", "81%", "topic classification", "82%", "sentiment classification", "the fasttext based model", "82%", "81% accuracy", "topic", "sentiment", "our research", "our approach", "the accuracy", "the recommendation model", "the potential", "further development", "the future", "this idea", "a new recommendation system", "that", "existing limitations", "other areas", "one", "vietnamese", "two", "vietnam", "0.89", "0.84", "0.83", "0.89", "0.83", "vietnamese", "0.027", "0.061", "81%", "82%", "82% and", "81%" ]

[ "Akanksha Maurya", "R. Joe Stanley", "Hemanth Y. Aradhyula", "Norsang Lama", "Anand K. Nambisan", "Gehana Patel", "Daniyal Saeed", "Samantha Swinfard", "Colin Smith", "Sadhika Jagannathan", "Jason R. Hagerty", "William V. Stoecker" ]

[ "recent years", "deep learning", "dl", "different cancers", "dermoscopic images", "most approaches", "clinical inputs", "dermatologists", "that", "higher accuracy", "explainability", "dermatologists", "the presence", "telangiectasia", "narrow blood vessels", "that", "a critical indicator", "basal cell carcinoma", "(bcc", "the feature information", "telangiectasia", "a combination", "dl-based techniques", "a pathway", "both", "dl results", "dermatologists", "bcc diagnosis", "this study", "a novel “fusion” technique", "bcc", "non-bcc classification", "ensemble learning", "a combination", "(a) handcrafted features", "semantically segmented telangiectasia", "(b) deep learning features", "whole lesion images", "this fusion method", "a binary classification accuracy", "97.2%", "a 1.3% improvement", "the corresponding dl-only model", "a holdout test", "395 images", "an increase", "3.7%", "sensitivity", "1.5%", "specificity", "1.5%", "precision", "an auc", "metric improvements", "three stages", "the addition", "deep learning features", "areas", "telangiectasia", "surround areas", "the noisy lower-importance features", "feature importance", "another novel approach", "weak annotations", "the examination", "the surrounding areas", "telangiectasia", "this study", "the experimental results", "the-art", "precision", "the diagnosis", "bcc", "three benchmark techniques", "further exploration", "deep learning techniques", "individual dermoscopy feature detection", "recent years", "telangiectasia", "bcc", "telangiectasia", "telangiectasia", "97.2%", "1.3%", "395", "3.7%", "1.5%", "1.5%", "0.99", "three", "1", "telangiectasia", "2", "telangiectasia", "3", "telangiectasia", "bcc", "three" ]

[ "Suryakant Tyagi", "Sándor Szénási" ]

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

[ "Sunen Chakraborty", "Kingshuk Chatterjee", "Paramita Dey" ]

[ "images", "a reliable source", "information", "photo-editing software", "it", "rise", "illegal activities", "which", "image tampering", "we", "innumerable tampered images", "the internet", "software", "photoshop", "gnu image manipulation program", "tampered images", "real ones", "just a few minutes", "hidden signs", "an image", "deep learning models", "an effective tool", "any other methods", "models", "deep learning", "intricate features", "an image", "we", "a combination", "traditional handcrafted features", "a deep learning model", "authentic and tampered images", "we", "a dual-branch convolutional neural network", "conjunction", "error level analysis", "noise residuals", "spatial rich model", "our experiment", "we", "the freely accessible casia dataset", "the dual-branch network", "16 epochs", "it", "an accuracy", "98.55%", "we", "a comparative analysis", "other previously proposed work", "the field", "image forgery detection", "this hybrid approach", "deep learning models", "some well-known traditional approaches", "better results", "tampered images", "these days", "just a few minutes", "16", "98.55%" ]

[ "Kaushlendra Kumar Sinha", "Somaraju Suvvari" ]

[ "next basket recommendation system", "users", "interactions", "personalized recommendations", "a better understanding", "the complex relations", "users", "objects", "large datasets", "most recommendation system researchers", "deep learning-based models", "these deep learning based models", "users", "better performance", "these existing deep learning-based models", "the repetition behavior", "the user", "some researchers", "the importance", "repetition behavior", "the literature", "this objective", "we", "a deep learning model", "that", "the user’s repetition behavior", "our model", "the correlation dynamics", "the embedding level", "this", "the generated embeddings", "a combination", "a user’s repetition behavior", "the correlation-based dynamics", "items", "the short-term preferences", "the user", "we", "these embeddings", "an lstm architecture", "it", "suitable personalized recommendations", "the effectiveness", "the proposed model", "we", "our model", "different real-world datasets", "extensive performance metrics", "the results", "the-art", "considerable improvement", "recall", "42 to 99%", "various values", "k", "ta feng", "dunnhumby", "precision", "an improvement", "221–400%", "42 to 99%", "feng", "221–400%" ]

[ "Kaiqiang Wang", "Li Song", "Chutian Wang", "Zhenbo Ren", "Guangyuan Zhao", "Jiazhen Dou", "Jianglei Di", "George Barbastathis", "Renjie Zhou", "Jianlin Zhao", "Edmund Y. Lam" ]

[ "phase recovery", "(pr", "the phase", "the light field", "its intensity measurements", "quantitative phase imaging", "coherent diffraction", "adaptive optics", "the refractive index distribution", "topography", "an object", "the aberration", "an imaging system", "recent years", "deep learning", "dl", "deep neural networks", "unprecedented support", "computational imaging", "more efficient solutions", "various pr problems", "this review", "we", "conventional methods", "we", "dl", "support", "pr", "the following three stages", "-processing", "processing", "we", "dl", "phase image processing", "we", "the work", "dl", "pr", "an outlook", "dl", "the reliability", "efficiency", "pr", "we", "a live-updating resource", "https://github.com/kqwang/phase-recovery", "readers", "pr", "recent years", "first", "three" ]

[ "Xinge Yang", "Qiang Fu", "Wolfgang Heidrich" ]

[ "deep optical optimization", "a new paradigm", "computational imaging systems", "only the output image", "the objective", "it", "either simple optical systems", "a single element", "a diffractive optical element", "metalens", "the fine-tuning", "compound lenses", "good initial designs", "we", "a deeplens design method", "curriculum learning", "which", "optical designs", "compound lenses", "ab initio", "randomly initialized surfaces", "human intervention", "the need", "a good initial design", "we", "the effectiveness", "our approach", "both classical imaging lenses", "view", "field", "a cellphone-style form factor", "highly aspheric surfaces", "a short back focal length", "deeplens" ]

[ "Rukhshanda Ihsan", "Syed Khaldoon Khurshid", "Muhammad Shoaib", "Sadia Ali", "Sana Mahnoor", "Syed Muhammad Hamza" ]

[ "future events", "a challenging task", "that", "a significant impact", "decision-making", "policy-making", "this research", "we", "news", "pakistan", "the importance", "accurate predictions", "this field", "no dataset", "pakistani news", "regards", "politics", "numerical time series data", "textual data", "information", "the event's potential causes", "addition", "its impact", "better forecasts", "a result", "this greater information", "order", "this gap", "our research", "a first pakistani news dataset", "forecasting", "pakistan news", "that", "politics", "pakistan", "this dataset", "various sources", "pakistani news websites", "social media platforms", "questions", "pakistani politics", "we", "a forecasting model", "this dataset", "the effectiveness", "cutting-edge deep hybrid learning techniques", "neural networks", "random forest", "word2vec", "natural language processing", "nlp", "naive bayes", "our understanding", "no research", "the application", "a deep hybrid learning model", "a blend", "deep learning", "machine learning", "news forecasting", "the accuracy", "forecasting model", "97%", "our findings", "the model's performance", "that", "other forecasting models", "our research", "the gap", "the current literature", "a new challenge", "large language models", "the potential", "significant practical advantages", "the field", "forecasting", "the unique contribution", "this study", "the intelligent modeling", "the prediction challenge", "the utilization", "text", "content", "forecasting objectives", "pakistan", "pakistani", "first", "pakistani", "pakistan", "pakistan", "pakistani", "pakistani", "97%" ]

[ "Jichi Chen", "Hong Wang", "Enqiu He" ]

[ "the detection", "unfavorable driving states", "uds", "drivers", "electroencephalogram (eeg) measures", "continuous attention", "extensive scholars", "account", "brain neural activity", "high temporal resolution", "low risk", "the existing eeg-based driver uds detection methods", "limited exploration", "the functional connectivity patterns", "interaction relationships", "the brain network", "room", "improvement", "the accuracy", "detection", "this project", "we", "three pretrained convolutional neural network", "cnn)-based automatic detection frameworks", "uds", "drivers", "30-channel eeg signals", "the frameworks", "the learning rate", "the optimization", "two different conditions", "driving experiments", "eeg signals", "sixteen subjects", "the acquired 1-dimensional 30-channel eeg signals", "2-dimensional matrices", "the granger causality", "(gc) method", "the functional connectivity graphs", "the brain", "fcgb", "the fcgb", "pretrained deep learning models", "that", "transfer learning strategy", "feature extraction", "judgment", "different eeg signal types", "we", "two visualization interpretability techniques", "grad-cam", "better visualizing", "the predictions", "the pretrained models", "fine-tuning", "the experimental outcomes", "resnet", "18 model", "the highest average recognition accuracy", "90%", "the rmsprop optimizer", "a learning rate", "the overall outcomes", "biologically inspired functional connectivity graphs", "the brain", "pretrained transfer learning algorithms", "a prospective approach", "the rate", "major traffic accidents", "driver unfavorable driving states", "three", "30", "two", "1", "30", "2", "fed", "two", "18", "90%", "1e −", "3" ]

[ "Hui Tang", "Ming Hong", "Lu Yu", "Yang Song", "Mengqiu Cao", "Lei Xiang", "Yan Zhou", "Shiteng Suo" ]

[ "backgroundwe", "mri", "a deep learning technique", "tse-dl", "standard turbo spin-echo (tse-sd) images", "the lumbar spine", "image quality and detection performance", "common degenerative pathologies.methodsthis prospective, single-center study", "31 patients", "15 males", "16 females", "51 ±", "± standard deviation", "who", "lumbar spine exams", "both tse-sd and tse-dl acquisitions", "degenerative spine diseases", "images", "two radiologists", "qualitative image quality", "a 4-point likert scale", "noise", "snr", "anatomic landmarks", "detection", "common pathologies", "paired-sample t", "wilcoxon", "mcnemar tests", "cohen κ statistics", "correlation coefficients", "used.resultsscan time", "tse-dl and tse-sd protocols", "min", ":", "s", "the overall image quality", "tse-dl", "tse-sd and tse-dl. tse-dl", "higher snr and subject noise scores", "tse-sd", "pathology detection", "the interreader agreement", "tse-dl", "κ values", "the interprotocol agreement", "both readers", "κ values", "no significant difference", "the diagnostic confidence or detection rate", "common pathologies", "the two sequences", "p ≥", "0.081).conclusionstse-dl", "a 45% reduction", "scan time", "tse-sd", "lumbar spine mri", "the overall image quality", "comparable detection performance", "common pathologies", "the evaluation", "degenerative lumbar spine changes.relevance", "learning-reconstructed lumbar spine mri protocol", "a 45% reduction", "scan time", "conventional reconstruction", "comparable image quality and detection performance", "lumbar spine mri", "deep learning reconstruction", "broad application prospects.•", "reconstruction", "lumbar spine mri", "45% scan time", "overall image", "standard sequences", "deep learning reconstruction", "similar detection performance", "common degenerative lumbar spine", "31", "15", "16", "51 ± 16 years", "two", "4", "mcnemar", "used.resultsscan", "2:55", "5:17", "0.61", "1.00", "0.84", "1.00", "two", "45%", "45%", "45%" ]

[ "Munetoshi Akazawa", "Kazunori Hashimoto" ]

[ "placenta previa", "life-threatening bleeding", "accurate prediction", "severe hemorrhage", "risk stratification", "optimum allocation", "interventions", "we", "a multimodal deep learning model", "severe hemorrhage", "mri t2-weighted image", "the placenta and tabular data", "patient demographics", "preoperative blood examination data", "a multimodal deep learning model", "cases", "intraoperative blood loss", "we", "the prediction performance", "the model", "it", "that", "two machine learning methods", "only tabular data and mri images", "that", "two human expert obstetricians", "the enrolled 48 patients", "54.2%", "2000 ml", "blood", "22 (45.8%", "2000 ml", "blood", "multimodal deep learning model", "the best accuracy", "auc", "the machine learning model", "tabular data and mri images", "a class accuracy", "the human experts", "median accuracies", "multimodal deep learning models", "the two types", "information", "severe hemorrhage cases", "the model", "human expert", "the prediction", "intraoperative hemorrhage", "the case", "placenta previa", "placenta previa", "2000 ml", "two", "two", "48", "26", "54.2%", "2000 ml", "22", "45.8%", "2000 ml", "0.68", "0.74", "0.61", "0.53", "0.61", "two" ]

[ "Vandana Shakya", "Jaytrilok Choudhary", "Dhirendra Pratap Singh" ]

[ "wireless sensor networks", "wsns", "a vital role", "various applications", "robust network security", "sensitive data", "intrusion detection systems", "idss", "the integrity", "availability", "confidentiality", "wsns", "potential attacks", "significant research efforts", "the existing ids solutions", "challenges", "detection accuracy", "false alarms", "these challenges", "this paper", "we", "a bayesian optimization-based deep learning (dl) model", "the proposed optimized dl model", "promising results", "security", "challenges", "data dependency", "computational complexity", "the potential", "the literature", "researchers", "reinforcement learning", "these issues", "it", "its own concerns", "exploration", "reward design", "prolonged training times", "these challenges", "this paper", "an innovative integrated rl-based advanced dl algorithm", "irada", "attack detection", "wsns", "irada", "the convergence", "dl and rl models", "superior intrusion detection performance", "the performance analysis", "irada", "impressive results", "accuracy", "99.50%", "specificity", "99.94%", "sensitivity", "99.48%", "f1-score", "98.26%", "kappa statistics", "99.42%", "area", "the curve", "99.38%", "we", "irada’s robustness", "adversarial attacks", "its applicability", "real-world security scenarios", "advanced dl algorithm", "irada", "irada", "irada", "99.50%", "99.94%", "99.48%", "98.26%", "99.42%", "99.38%", "irada" ]

[ "Jamal Alasadi", "Ghassan F. Bati", "Ahmed Al Hilli" ]

[ "deep learning", "5g applications", "computer vision", "the internet", "things", "iot", "intermedia classification", "the deep learning approach", "limited-resource mobile devices", "the most challenging issues", "the same time", "users’ experience", "terms", "quality", "service", "qos", "machine learning applications", "mobile edge computing", "(mec", "a cooperative approach", "computation resources", "proximity", "end-user devices", "these limitations", "this article", "a novel image reiterative extraction algorithm", "convolution neural network", "(cnn) learning", "computational task", "machine learning-based mobile applications", "resource-limited and uncertain environments", "we", "the framework", "image retrieval extraction", "three approaches", "privacy preservation", "personal data", "feature matching time", "our simulation results", "real-time experiments", "a small-scale mec server", "the effectiveness", "the proposed deep learning-based approach", "existing schemes", "the source code", "https://github.com/jamalalasadi/cnn_image_retrieval", "5", "cnn", "three", "first", "second", "third" ]

[ "Jing Hao", "Feng He", "Feng Ma", "Shibo Zhang", "Xiaotao Zhang" ]

[ "machine learning", "deep learning", "powerful tools", "quantitative investment", "the effectiveness", "the models", "different markets", "this paper", "random forest", "dnn models", "stock prices", "statistical arbitrage strategies", "five stock markets", "mainland china", "the united states", "the united kingdom", "canada", "japan", "each model", "the price", "major stock indices", "stocks", "these markets", "a long-short portfolio", "20 selected stocks", "the model", "the results", "the a particular model", "significantly different profits", "different markets", "which", "dnn", "the best performance", "the chinese stock market", "we", "dnn models", "other machine learning models", "all markets", "five", "china", "the united states", "the united kingdom", "canada", "japan", "2005", "2020", "20", "chinese" ]

[ "Won-Jun Noh", "Mu Sook Lee", "Byoung-Dai Lee" ]

[ "this study", "a deep learning-based system", "the automatic measurement", "angles", "(specifically, meary’s angle", "calcaneal pitch", "weight-bearing lateral radiographs", "the foot", "flatfoot diagnosis", "we", "3960 lateral radiographs", "the left or right foot", "a pool", "4000 patients", "a deep learning-based model", "these radiographs", "june", "november", "patients", "who", "total ankle replacement surgery", "ankle arthrodesis surgery", "various methods", "correlation analysis", "bland–altman plots", "t-tests", "the concordance", "the angles", "the system", "those", "clinical experts", "the evaluation dataset", "150 weight-bearing", "150 patients", "all test cases", "the angles", "the deep learning-based system", "good agreement", "the reference standards", "meary’s angle", "pcc", "intraclass correlation coefficient", "icc", "concordance correlation coefficient", "ccc", "absolute error", "mae", "1.59°", "calcaneal pitch", "the average time", "angle measurement", "only the cpu", "the deep learning-based system", "11 ±", "the deep learning-based automatic angle measurement system", "a tool", "comparable accuracy", "reliability", "the results", "medical professionals", "patients", "internal fixation devices", "3960", "4000", "between june and november 2021", "150", "150", "0.964", "0.963", "0.963", "0.632", "1.59", "0.988", "0.987", "0.987", "0.055", "0.63", "11", "1" ]

[ "Rui Yu", "Yue Cao", "Jennifer Martin", "Otto Chiang", "YuMing Zhang" ]

[ "double-electrode gas metal arc welding", "de", "-", "gmaw", "gmaw", "a second electrode", "a portion", "the current", "the wire", "this", "the heat input", "the workpiece", "successful bypassing", "the relative position", "the bypass", "the continuously varying wire tip", "proper operation", "we", "the system", "a follower robot", "the bypass", "the primary information", "this process", "the arc image", "which", "desired and undesired modes", "a robust algorithm", "the complex arc image", "a deep learning approach", "numerous arc images", "the corresponding de-gmaw modes", "which", "alternative labels", "we", "arc phenomena", "various de-gmaw modes", "them", "distinct arc systems", "varying voltages", "these voltages", "automatically derived labels", "the deep-learning network", "the results", "reliable process monitoring", "second" ]

[ "Zan Zhou", "Thomas Man-Hoi Lok", "Wan-Huan Zhou" ]

[ "surface wave inversion", "a key step", "the application", "surface waves", "velocity profiling", "a common practice", "the process", "inversion", "the number", "soil layers", "heuristic search algorithms", "the shear wave velocity profile", "the number", "soil layers", "an optimization variable", "an improper selection", "the number", "layers", "an incorrect shear wave velocity profile", "this study", "a deep learning and genetic algorithm hybrid learning procedure", "the surface wave inversion", "the need", "the number", "soil layers", "a deep neural network", "a large number", "synthetic dispersion curves", "the layer number", "the shear-wave velocity profile", "a genetic algorithm", "the known layer number", "this procedure", "both simulated and real-world cases", "the results", "the proposed method", "surface wave inversion", "first" ]

[ "Bahman Jafari Tabaghsar", "Reza Tavoli", "Mohammad Mahdi Alizadeh Toosi" ]

[ "the most common types", "cancer", "the world", "skin cancer", "the different types", "skin diseases", "different shapes", "the classification", "skin diseases", "a very difficult task", "a result", "such a problem", "a combination model", "deep learning algorithms", "machine", "a three-layer architecture", "ensemble learning", "the first layer", "the training input", "convolutional neural network", "efficientnet", "the output", "the first layer", "the classifiers", "the second layer", "machine learning classifiers", "the output", "the best decision", "these classifiers", "the third layer classifier", "the final prediction", "made.the reason", "the three-layer architecture", "group learning", "the lack", "correct recognition", "some classes", "simple classifications", "the other hand", "some diseases", "different classes", "the same class", "this model", "input samples", "the correct combination", "classifications", "different layers.ham10000 data set", "the proposed method", "the mentioned dataset", "10,015 images", "skin lesions", "seven different classes", "different types", "skin diseases", "the accuracy", "the testing set", "which", "the previous heavy models", "one", "three", "first", "first", "second", "third", "three", "10,015", "seven", "99.97" ]

[ "Minh Hoang Nguyen", "Thuat Thien Nguyen", "Minh Nhat Ta", "Tien Minh Nguyen", "Kiet Van Nguyen" ]

[ "tourism", "which", "sightseeing", "relaxation", "discovery", "a fundamental aspect", "human life", "the most critical considerations", "accommodation", ", mainly hotels", "the travel experience", "we", "a solution", "a recommendation model", "user data", "travelers", "the ideal hotel", "our data", "two well-known websites", "traveloka", "ivivu", "information", "hotels", "vietnam", "users’ feedback history", "comments", "ratings", "the names", "users", "hotels", "we", "the inter-annotator agreement", "various aspects", "service", "infrastructure", "location", "attitude", "our recommendation model", "collaborative filtering", "deep learning techniques", "we", "context vectors", "tourists’ vietnamese comments", "the recommendation process", "the context model", "deep learning techniques", "topics", "sentiments", "the words", "the results", "our proposed model", "the mse", "which", "a context-free model", "the same parameters", "which", "an mse", "our deep learning", "phobert", "an accuracy", "81%", "topic classification", "82%", "sentiment classification", "the fasttext based model", "82%", "81% accuracy", "topic", "sentiment", "our research", "our approach", "the accuracy", "the recommendation model", "the potential", "further development", "the future", "this idea", "a new recommendation system", "that", "existing limitations", "other areas", "one", "vietnamese", "two", "vietnam", "0.89", "0.84", "0.83", "0.89", "0.83", "vietnamese", "0.027", "0.061", "81%", "82%", "82% and", "81%" ]

[ "Renugadevi Ammapalayam Sinnaswamy", "Natesan Palanisamy", "Kavitha Subramaniam", "Suresh Muthusamy", "Ravita Lamba", "Sreejith Sekaran" ]

[ "aneurysm", "rupture", "blood vessels", "the cerebrum", "abdominal aorta", "aorta", "humans", "which", "a high fatal rate", "the advancement", "the artificial technologies", "specifically machine learning algorithms", "deep learning models", "the aneurysm", "which", "the death rate", "the main objective", "this paper", "the review", "various algorithms", "models", "the early prediction", "the various types", "aneurysms", "the focused literature review", "the preferred journals", "various parameters", "way", "images", "the techniques", "images", "data set", "performance metrics", "future work", "the summarized overview", "advances", "prediction", "aneurysms", "the machine learning algorithms", "non linear kernel support regression algorithm", "3d unet architecture", "deep learning models", "ct scan images", "final performance analysis", "prediction", "the range", "sensitivity", "specificity", "area", "operating characteristic", "the abdominal aortic aneurysm detection", "intracranial aneurysm detection", "the thoracic aortic aneurysm", "the literature review", "the prediction", "thoracic aortic aneurysm", "machine learning", "deep learning model", "2007", "2022", "3d", "scan", "0", "7 to 1", "intracranial aneurysm detection", "the thoracic aortic aneurysm" ]

[ "Mehran Radak", "Haider Yabr Lafta", "Hossein Fallahi" ]

[ "backgroundbreast cancer", "a major public health concern", "early diagnosis", "classification", "effective treatment", "machine learning", "deep learning techniques", "great promise", "the classification", "diagnosis", "breast cancer.purposein", "this review", "we", "studies", "that", "these techniques", "breast cancer classification", "diagnosis", "five groups", "medical images", "mammography", "ultrasound", "mri", "histology", "thermography", "we", "the use", "five popular machine learning techniques", "neighbor", "svm", "naive bayesian network", "dt", "ann", "deep learning architectures", "convolutional neural networks.conclusionour review", "machine learning", "deep learning techniques", "high accuracy rates", "breast cancer classification", "diagnosis", "various medical imaging modalities", "these techniques", "the potential", "clinical decision-making", "better patient outcomes", "five", "five" ]

[ "Shijie Tang", "Yong Ding", "Meng Zhao", "Huiyong Wang" ]

[ "the advent", "industry", "industrial control systems", "ics", "the internet", "a rapid increase", "the types", "quantities", "security threats", "that", "ics", "anomaly detection", "an effective defense measure", "attacks", "present", "it", "the main trend", "hybrid deep learning methods", "ics anomaly detection", "we", "that many ics anomaly detection methods", "hybrid deep learning adopt phased learning", "which", "each phase", "optimization goals", "the overall goal", "view", "this issue", "we", "end", "hybrid deep learning", "our method", "radial basis function network", "rbfn", "k", "it", "stacked auto-encoder", "sae", "which", "reconstruction error", "error", "an objective function", "joint optimization", "feature extraction", "classification", "experiments", "the commonly used kddcup99", "swat datasets", "the results", "sakmr", "abnormal industrial control data", "the baseline methods", "multiple performance indicators", "f1-measure", "4.0", "anomaly detection", "anomaly detection method", "kddcup99" ]

[ "Soolmaz Abbasi", "Haoyu Lan", "Jeiran Choupan", "Nasim Sheikh-Bahaei", "Gaurav Pandey", "Bino Varghese" ]

[ "medical imaging datasets", "research", "multiple imaging centers", "different scanners", "protocols", "settings", "these variations", "data consistency", "compatibility", "different sources", "image harmonization", "a critical step", "the effects", "factors", "inherent differences", "various vendors", "hardware upgrades", "protocol changes", "scanner calibration drift", "consistent data", "medical image processing techniques", "the critical importance", "widespread relevance", "this issue", "a vast array", "image harmonization methodologies", "deep learning-based approaches", "substantial advancements", "recent times", "the goal", "this review paper", "the latest deep learning techniques", "image harmonization", "cutting-edge architectural approaches", "the field", "medical image harmonization", "both their strengths", "limitations", "this paper", "a comprehensive fundamental overview", "image harmonization strategies", "three critical aspects", "imaging datasets", "commonly used evaluation metrics", "characteristics", "different scanners", "this paper", "recent structural mri", "magnetic resonance imaging", "harmonization techniques", "network architecture", "network learning algorithm", "network supervision strategy", "network output", "the underlying architectures", "u", "-", "net", "generative adversarial networks", "gans", "variational autoencoders", "flow-based generative models", "transformer-based approaches", "custom-designed network architectures", "this paper", "the effectiveness", "drl", "a pivotal learning algorithm", "harmonization", "the review", "the primary limitations", "harmonization techniques", "specifically the lack", "comprehensive quantitative comparisons", "different methods", "the overall aim", "this review", "a guide", "researchers", "practitioners", "appropriate architectures", "their specific conditions", "requirements", "it", "discussions", "ongoing challenges", "the field", "light", "future research directions", "the potential", "significant advancements", "three" ]

[ "Abdelrahman I. Saad", "Fahima A. Maghraby", "Osama Badawy" ]

[ "a deep convolution neural network image segmentation model", "a cost-effective active learning mechanism", "polyseg", "it", "polyp segmentation", "a lack", "labeled data", "a high false-positive rate", "polyp discovery", "addition", "active learning", "which", "more image samples", "a comprehensive polyp dataset", "five benchmark datasets", "the number", "images", "the captured image features", "the locally shared feature method", "which", "the power", "neighboring features", "the quality", "image features", "the drawbacks", "the conditional random features method", "medical image segmentation", "resunet++", "resunet", "unet++", "unet models", "gaussian noise", "the images", "a gaussian filter", "the images", "the models", "addition", "model performance", "hyperparameter tuning", "grid search", "the optimum parameters", "model performance", "the results", "a significant improvement", "applicability", "the proposed method", "polyp segmentation", "the-art", "the datasets cvc-clinicdb", "cvc-colondb", "etis larib polyp", "kvasir-seg", "kvasir-sessile", "dice coefficients", "the suggested method", "the dice coefficients", "the individual datasets", "it", "better results", "the comprehensive dataset", "which", "the development", "computer-aided diagnosis systems", "polyseg", "five", "gaussian noise", "0.9558", "0.8947", "0.7547", "0.9476", "0.6023" ]

[ "Min Yu", "Weiming Li", "Yunru Yu", "Yu Zhao", "Lizhi Xiao", "Volker M. Lauschke", "Yiyu Cheng", "Xingcai Zhang", "Yi Wang" ]

[ "large-scale drug discovery", "repurposing", "the mechanism", "action", "(moa", "current approaches", "we", "an approach", "moa identification", "changes", "mitochondrial phenotypes", "mitochondrial morphology", "membrane potential", "we", "a pipeline", "time-resolved mitochondrial images", "a dataset", "570,096 single-cell images", "cells", "1,068 united states food and drug administration-approved drugs", "a deep learning model", "mitoreid", "a re", "-", "identification (reid) framework", "an inflated 3d resnet backbone", "it", "76.32%", "rank-1", "65.92%", "the moas", "six untrained drugs", "the basis", "mitochondrial phenotype", "mitoreid", "inhibition", "the moa", "the natural compound epicatechin", "tea", "which", "our approach", "an automated and cost-effective alternative", "target identification", "that", "large-scale drug discovery", "repurposing", "570,096", "1,068", "united states", "food and drug administration", "mitoreid", "3d", "76.32%", "65.92%", "six", "mitoreid" ]

[ "Sagar Deep Deb", "Rajib Kumar Jha", "Rajnish Kumar", "Prem S. Tripathi", "Yash Talera", "Manish Kumar" ]

[ "purposecovid-19", "a part", "our life", "the world health organization", "it", "it", "all", "us", "many ways", "several deep learning techniques", "covid-19", "chest x-ray images", "covid-19 infection severity scoring", "the optimum course", "treatment", "care", "a positive patient", "all covid-19 positive patients", "special medical attention", "very few works", "the severity", "the disease", "the chest x-ray images", "the unavailability", "the large-scale dataset", "a reason.methodswe aim", "covseverity-net", "a deep learning-based architecture", "the severity", "covid-19", "chest x-ray images", "covseverity-net", "a public covid-19 dataset", "experienced radiologists", "severity estimation", "that", "a large publicly available dataset", "three levels", "severity", "namely mild, moderate, and severe.resultsan accuracy", "85.71%", "5-fold cross", "-", "validation", "we", "an accuracy", "87.82 ±", "6.25%", "cross", "validation", "we", "accuracy", "91.26 ±", "the results", "the-art", "this study", "a high chance", "the workload", "overworked front-line radiologists", "patient diagnosis", "treatment", "pandemic control", "future work", "a novel deep learning-based architecture", "a larger dataset", "severity estimation", "purposecovid-19", "the world health organization", "2020", "covid-19", "covid-19", "covid-19", "covid-19", "covid-19", "three", "85.71%", "5-fold", "87.82", "6.25%", "10-fold", "91.26", "3.42" ]

[ "Domenico Santoro", "Tiziana Ciano", "Massimiliano Ferrara" ]

[ "advances", "sensor", "computing", "communication technologies", "big data analytics", "time series data", "conventional models", "sequence features", "forecast accuracy", "this paper investigates", "some machine learning algorithms", "deep learning models", "the problem", "the number", "vehicles", "an italian tollbooth", "the dataset", "8766 rows", "6 columns", "additional tollbooths", "high stationarity", "machine learning methods", "support vector machine", "random forest", "deep learning", "recurrent neural networks", "long short-term memory", "rnn-lstm) cells", "the comparison", "these models", "the prediction", "the xgboost algorithm", "competing algorithms", "terms", "mae", "mse", "the result", "a shallower algorithm", "a neural network", "this case", "a better adaptation", "the time series", "a much deeper model", "that", "a smoother prediction", "italian", "2021", "8766", "6", "mae" ]

[ "Gregory Holste", "Evangelos K. Oikonomou", "Bobak J. Mortazavi", "Zhangyang Wang", "Rohan Khera" ]

[ "backgroundadvances", "self-supervised learning", "ssl", "the-art", "small, labeled datasets", "this label efficiency", "the difficulty", "expert labels", "medical image recognition tasks", "most efforts", "ssl", "medical imaging", "video-based modalities", "echocardiography.methodswe", "a self-supervised contrastive learning approach", "echocardiogram videos", "the goal", "strong representations", "efficient fine-tuning", "downstream cardiac disease diagnosis", "echoclr pretraining", "(i) contrastive learning", "the model", "distinct videos", "the same patient", "(ii) frame", "the model", "the correct", "video frames", "small portions", "labeled data", "echoclr pretraining", "classification performance", "left ventricular hypertrophy", "lvh", "aortic stenosis", "other transfer learning", "approaches", "internal and external test sets", "10%", "available training data", "519 studies", "an echoclr-pretrained model", "0.72 auroc", "95% ci", "lvh classification", "0.61 auroc", "95% ci", "a standard transfer learning approach", "1%", "available training data", "53 studies", "0.82 auroc", "95% ci", "classification", "0.61 auroc", "95% ci", "transfer learning.conclusionsechoclr", "its ability", "representations", "echocardiogram videos", "ssl", "label-efficient disease classification", "small amounts", "labeled data", "10%", "519", "0.72", "95%", "0.69", "0.75", "0.61", "95%", "0.57", "0.64", "1%", "53", "0.82", "95%", "0.79", "0.84", "0.61", "95%", "0.58", "0.65" ]

[ "Weifeng LI" ]

[ "defects", "automated inspection systems", "printed circuit board (pcb) manufacturing", "a critical endeavor", "product quality", "numerous methodologies", "the literature", "recent advancements", "the superiority", "deep learning techniques", "defect identification", "their remarkable accuracy", "conventional methods", "however, the persistent challenge", "higher accuracy rates", "real-time processing capabilities persists", "the need", "innovative solutions", "response", "this study", "a novel approach", "the yolov8 architecture", "pcb defect detection", "the aforementioned research challenge", "a custom dataset", "this purpose", "our method", "remarkable performance", "yolov8x", "the top-performing model", "a notable f1 score", "98%", "a high mean average precision", "(map", "98.9%", "this study’s novelty", "its comprehensive evaluation", "deep learning models", "pcb defect detection", "promising prospects", "precision", "real-time capabilities", "manufacturing processes", "yolov8", "98%", "98.9%" ]

[ "Hujun Yang", "Zhongyang Wang", "Xinyao Liu", "Chuangang Li", "Junchang Xin", "Zhiqiong Wang" ]

[ "-", "resolution (sr) reconstruction", "a hot topic", "medical image processing", "sr", "hr", "observed low-resolution (lr) images", "image sequences", "recent years", "significant breakthroughs", "sr", "deep learning", "many advanced results", "a lack", "review literature", "that", "the field’s current state", "an outlook", "future developments", "we", "a comprehensive summary", "the literature", "medical image sr", "medsr", "deep learning", "five aspects", "the sr problem", "medical images", "the methods", "image degradation", "we", "the existing studies", "three categories", "two-dimensional image sr", "three-dimensional image sr", "video sr", "vsr", "each category", "we", "the network structure", "method", "characteristics", "typical methods", "existing sr reconstruction quality evaluation metrics", "detail", "the application", "medsr methods", "deep learning", "we", "the challenges", "this phase", "valuable research directions", "recent years", "2018", "five", "1", "2", "three", "two", "2disr", "three", "vsr", "3", "4", "5" ]

[ "Srividya Kotagiri", "A. Mary Sowjanya", "B. Anilkumar", "N Lakshmi Devi" ]

[ "aspect-oriented extraction", "the identification", "extraction", "specific aspects", "features", "entities", "a piece", "text", "traditional methods", "the complexity", "variability", "language", "the exploration", "advanced deep learning approaches", "the realm", "sentiment analysis", "the conventional approaches", "it", "a nuanced understanding", "sentiments", "textual data", "traditional sentiment analysis models", "the specific aspects", "entities", "the text", "that", "the overall sentiment", "this limitation", "a significant challenge", "businesses", "organizations", "detailed insights", "customer opinions", "product reviews", "other forms", "we", "an innovative approach", "aspect-oriented extraction", "sentiment analysis", "optimized hybrid deep learning techniques", "our methodology", "the powerful capabilities", "deep learning models", "the efficiency", "reptile search optimization", "we", "an advanced sentiment analysis framework", "the-art", "algorithm", "the fusion", "these techniques", "the precision", "interpretability", "aspect-oriented sentiment analysis", "the proposed approach", "deep learning architectures", "diverse aspects", "textual data", "the incorporation", "reptile search optimization", "we", "the learning process", "adaptability", "improved model generalization", "various datasets", "the sentiment analysis phase", "the robust extreme gradient", "algorithm", "its effectiveness", "complex relationships", "patterns", "data", "our experiments", "diverse datasets", "the superior performance", "the proposed methodology", "comparison", "traditional approaches", "the optimized hybrid deep learning approach", "the reptile search optimization", "extreme gradient", "algorithm", "promising results", "nuanced sentiments", "different aspects", "this research", "the advancement", "aspect-oriented sentiment analysis techniques", "a comprehensive and efficient solution", "sentiment nuances", "textual data", "various domains", "the resnet 50 and efficientnet b7 architecture", "the modified pre-trained model", "the aspect extraction function", "the reptile search optimization", "based extreme gradient", "algorithm", "rso", "egba", "customer sentiments", "the execution", "this study", "python software", "it", "the overall accuracy", "our proposed method", "99.8%", "the other state", "the-art", "the overall accuracy", "our proposed method", "an increment", "9–16%", "that", "the-art", "first", "50", "99.8%", "9–16%" ]

[ "Fan Zhang", "Lin Chai" ]

[ "micro", "-", "expression", "a special kind", "human emotion", "its characteristics", "short time", "low intensity", "local region", "micro-expression recognition", "a difficult task", "the same time", "it", "a natural, spontaneous, and unconcealable emotion", "that", "a person's actual psychological state", "certain research value", "practical significance", "this paper", "micro-expression recognition", "the field", "deep learning", "the survey", "understanding", "existing micro-expression recognition research", "the research trend", "the previous literature", "micro-expression review", "the handcrafted features", "an important part", "the micro-expression recognition framework", "the same time", "the analysis", "the various enhancement processing", "a new micro-expression recognition framework", "deep learning", "the model", "the perspective", "modularity and streaming data", "the other hand", "the previous process", "the data", "the network", "training", "recognition", "the handcrafted features", "the initial encoding", "the micro-expression recognition data", "the training", "learning", "the deep model", "the same time", "the modular embedding approach", "the feature enhancement module", "finally the classification", "recognition", "the article", "a detailed summary", "analysis", "each part", "the whole framework", "a comprehensive introduction", "the current problems", "experimental protocols", "evaluation metrics", "application areas", "it", "possible future research directions", "this paper", "a comprehensive summary", "analysis", "micro-expression recognition", "deep learning", "the related personnel", "a new understanding", "the development", "this field", "the other hand", "it", "a new recognition framework", "that", "a reference", "the researchers' later research" ]

[ "Kaiqiang Wang", "Li Song", "Chutian Wang", "Zhenbo Ren", "Guangyuan Zhao", "Jiazhen Dou", "Jianglei Di", "George Barbastathis", "Renjie Zhou", "Jianlin Zhao", "Edmund Y. Lam" ]

[ "phase recovery", "(pr", "the phase", "the light field", "its intensity measurements", "quantitative phase imaging", "coherent diffraction", "adaptive optics", "the refractive index distribution", "topography", "an object", "the aberration", "an imaging system", "recent years", "deep learning", "dl", "deep neural networks", "unprecedented support", "computational imaging", "more efficient solutions", "various pr problems", "this review", "we", "conventional methods", "we", "dl", "support", "pr", "the following three stages", "-processing", "processing", "we", "dl", "phase image processing", "we", "the work", "dl", "pr", "an outlook", "dl", "the reliability", "efficiency", "pr", "we", "a live-updating resource", "https://github.com/kqwang/phase-recovery", "readers", "pr", "recent years", "first", "three" ]

[ "Munetoshi Akazawa", "Kazunori Hashimoto" ]

[ "placenta previa", "life-threatening bleeding", "accurate prediction", "severe hemorrhage", "risk stratification", "optimum allocation", "interventions", "we", "a multimodal deep learning model", "severe hemorrhage", "mri t2-weighted image", "the placenta and tabular data", "patient demographics", "preoperative blood examination data", "a multimodal deep learning model", "cases", "intraoperative blood loss", "we", "the prediction performance", "the model", "it", "that", "two machine learning methods", "only tabular data and mri images", "that", "two human expert obstetricians", "the enrolled 48 patients", "54.2%", "2000 ml", "blood", "22 (45.8%", "2000 ml", "blood", "multimodal deep learning model", "the best accuracy", "auc", "the machine learning model", "tabular data and mri images", "a class accuracy", "the human experts", "median accuracies", "multimodal deep learning models", "the two types", "information", "severe hemorrhage cases", "the model", "human expert", "the prediction", "intraoperative hemorrhage", "the case", "placenta previa", "placenta previa", "2000 ml", "two", "two", "48", "26", "54.2%", "2000 ml", "22", "45.8%", "2000 ml", "0.68", "0.74", "0.61", "0.53", "0.61", "two" ]

[ "Xinge Yang", "Qiang Fu", "Wolfgang Heidrich" ]

[ "deep optical optimization", "a new paradigm", "computational imaging systems", "only the output image", "the objective", "it", "either simple optical systems", "a single element", "a diffractive optical element", "metalens", "the fine-tuning", "compound lenses", "good initial designs", "we", "a deeplens design method", "curriculum learning", "which", "optical designs", "compound lenses", "ab initio", "randomly initialized surfaces", "human intervention", "the need", "a good initial design", "we", "the effectiveness", "our approach", "both classical imaging lenses", "view", "field", "a cellphone-style form factor", "highly aspheric surfaces", "a short back focal length", "deeplens" ]

[ "Elmenawy Osman", "C. Banerjee", "Ajeet Singh Poonia" ]

[ "air pollution", "cities", "an important tool", "its negative impacts", "citizens", "air quality level", "individuals", "diseases", "air pollutants", "collective efforts", "researchers", "environmental institutions", "governments", "industrial companies", "policy makers", "the future", "the air quality index", "aqi", "severe air pollution", "urban areas", "many air quality prediction models", "the literature", "modern advances", "deep learning techniques", "more precise prediction results", "data integration", "the aim", "this paper", "methods", "contributions", "findings", "limitations", "gaps", "air quality index", "pm2.5 concentrations", "a hybrid deep learning approach", "a literature review", "researchers", "a hybrid deep learning model", "particle swarm optimization", "hdlp", "which", "cnn", "lstm", "algorithm", "first discrete wavelet transform", "dwt", "the air pollution signal", "it", "cnn-lstm neural network", "pso optimization", "the final prediction result", "optimized parameters input models", "the original data", "addition", "the beneficial assessment cycle", "current models", "cnn", "first", "fed", "cnn" ]

[ "Wenhao Li", "Yin Zhao", "Yining Zhu", "Zhongtian Dong", "Fenghe Wang", "Fengliang Huang" ]

[ "water", "an invaluable and non-renewable resource", "an indispensable role", "human survival", "societal development", "accurate forecasting", "water quality", "early identification", "future pollutant concentrations", "water quality indices", "evidence-based decision-making and targeted environmental interventions", "the emergence", "advanced computational technologies", "particularly deep learning", "considerable interest", "researchers", "applications", "water quality prediction", "its robust data analytics capabilities", "this article", "the deployment", "deep learning methodologies", "water quality forecasting", "single-model and mixed-model approaches", "we", "optimization strategies", "data fusion techniques", "other factors", "the efficacy", "deep learning-based water quality prediction models", "understanding", "these factors", "accurate water quality prediction", "challenges", "data scarcity", "long-term prediction accuracy", "limited deployments", "large-scale models", "future research", "these limitations", "refining prediction algorithms", "high-dimensional datasets", "model performance", "large-scale model application", "these efforts", "precise water resource management", "environmental conservation" ]

[ "Igor Sadoune", "Marcelin Joanis", "Andrea Lodi" ]

[ "we", "a deep learning solution", "the challenges", "realistic synthetic first-price sealed-bid auction data", "the complexities", "this type", "auction data", "high-cardinality discrete feature spaces", "a multilevel structure", "multiple bids", "a single auction instance", "our methodology", "deep generative modeling", "(dgm", "an artificial learner", "that", "the conditional bid distribution", "auction characteristics", "advancements", "simulation-based research", "this approach", "the groundwork", "realistic auction environments", "agent-based learning and modeling applications", "our contribution", "we", "a comprehensive methodology", "multilevel discrete auction data", "we", "the potential", "dgm", "a powerful instrument", "simulation techniques", "the development", "economic models", "generative ai", "first" ]

[ "Jamal Alasadi", "Ghassan F. Bati", "Ahmed Al Hilli" ]

[ "deep learning", "5g applications", "computer vision", "the internet", "things", "iot", "intermedia classification", "the deep learning approach", "limited-resource mobile devices", "the most challenging issues", "the same time", "users’ experience", "terms", "quality", "service", "qos", "machine learning applications", "mobile edge computing", "(mec", "a cooperative approach", "computation resources", "proximity", "end-user devices", "these limitations", "this article", "a novel image reiterative extraction algorithm", "convolution neural network", "(cnn) learning", "computational task", "machine learning-based mobile applications", "resource-limited and uncertain environments", "we", "the framework", "image retrieval extraction", "three approaches", "privacy preservation", "personal data", "feature matching time", "our simulation results", "real-time experiments", "a small-scale mec server", "the effectiveness", "the proposed deep learning-based approach", "existing schemes", "the source code", "https://github.com/jamalalasadi/cnn_image_retrieval", "5", "cnn", "three", "first", "second", "third" ]

[ "Won-Jun Noh", "Mu Sook Lee", "Byoung-Dai Lee" ]

[ "this study", "a deep learning-based system", "the automatic measurement", "angles", "(specifically, meary’s angle", "calcaneal pitch", "weight-bearing lateral radiographs", "the foot", "flatfoot diagnosis", "we", "3960 lateral radiographs", "the left or right foot", "a pool", "4000 patients", "a deep learning-based model", "these radiographs", "june", "november", "patients", "who", "total ankle replacement surgery", "ankle arthrodesis surgery", "various methods", "correlation analysis", "bland–altman plots", "t-tests", "the concordance", "the angles", "the system", "those", "clinical experts", "the evaluation dataset", "150 weight-bearing", "150 patients", "all test cases", "the angles", "the deep learning-based system", "good agreement", "the reference standards", "meary’s angle", "pcc", "intraclass correlation coefficient", "icc", "concordance correlation coefficient", "ccc", "absolute error", "mae", "1.59°", "calcaneal pitch", "the average time", "angle measurement", "only the cpu", "the deep learning-based system", "11 ±", "the deep learning-based automatic angle measurement system", "a tool", "comparable accuracy", "reliability", "the results", "medical professionals", "patients", "internal fixation devices", "3960", "4000", "between june and november 2021", "150", "150", "0.964", "0.963", "0.963", "0.632", "1.59", "0.988", "0.987", "0.987", "0.055", "0.63", "11", "1" ]

[ "Bahman Jafari Tabaghsar", "Reza Tavoli", "Mohammad Mahdi Alizadeh Toosi" ]

[ "the most common types", "cancer", "the world", "skin cancer", "the different types", "skin diseases", "different shapes", "the classification", "skin diseases", "a very difficult task", "a result", "such a problem", "a combination model", "deep learning algorithms", "machine", "a three-layer architecture", "ensemble learning", "the first layer", "the training input", "convolutional neural network", "efficientnet", "the output", "the first layer", "the classifiers", "the second layer", "machine learning classifiers", "the output", "the best decision", "these classifiers", "the third layer classifier", "the final prediction", "made.the reason", "the three-layer architecture", "group learning", "the lack", "correct recognition", "some classes", "simple classifications", "the other hand", "some diseases", "different classes", "the same class", "this model", "input samples", "the correct combination", "classifications", "different layers.ham10000 data set", "the proposed method", "the mentioned dataset", "10,015 images", "skin lesions", "seven different classes", "different types", "skin diseases", "the accuracy", "the testing set", "which", "the previous heavy models", "one", "three", "first", "first", "second", "third", "three", "10,015", "seven", "99.97" ]

[ "Jan Vosshenrich", "Jan Fritz" ]

[ "klinisches/methodisches problemdie magnetresonanztomographie", "(mrt", "lange akquisitionszeiten können jedoch zu einschränkungen", "der", "standardverfahrendie mrt hat", "des hohen auflösungsvermögens", "signal-zu-rausch-verhältnisses", "snr", "als modalität der", "der hard- und softwaretechnologie", "haben eine", "und genauigkeitsneutrale beschleunigung von 2d-turbo-spin-echo(tse)-sequenzen um den faktor 4 ermöglicht", "kürzlich vorgestellte", "auf", "deep learning", "dl", "bildrekonstruktionsalgorithmen", "abhängigkeit zwischen snr", "räumlicher auflösung und akquisitionszeit weiter zu minimieren und erlauben die anwendung höherer beschleunigungsfaktoren.leistungsfähigkeitdie kombinierte anwendung fortschrittlicher", "und dl-basierter bildrekonstruktion birgt", "die effizienz, den patientenkomfort und die zugänglichkeit der", "mrt bei", "hoher diagnostischer genauigkeit", "zu maximieren.bewertungdl-rekonstruierte beschleunigte mrt-untersuchungen", "ihre praxisreife", "ihren mehrwert", "kürzester zeit unter beweis gestellt", "dass das potenzial dieser technologie noch nicht", "praxisbeschleunigte mrt-untersuchungen mit dl-gestützter bildrekonstruktion können zuverlässig", "der primärdiagnostik und verlaufskontrolle muskuloskeletaler fragestellungen eingesetzt werden", "klinisches", "problemdie magnetresonanztomographie", "ein zentraler", "bestandteil der muskuloskeletalen diagnostik", "führen.radiologische standardverfahrendie mrt", "von verletzungen und erkrankungen des muskuloskeletalen", "beschleunigung von", "2d", "4", "kürzlich vorgestellte", "bildrekonstruktionsalgorithmen helfen", "zwischen snr", "akquisitionszeit", "zu minimieren", "anwendung höherer", "anwendung fortschrittlicher beschleunigungstechniken", "zugänglichkeit der muskuloskeletalen mrt", "ihren mehrwert", "kürzester zeit", "aktuelle wissenschaftliche erkenntnisse legen", "das", "verlaufskontrolle", "fragestellungen" ]

[ "Rui Yu", "Yue Cao", "Jennifer Martin", "Otto Chiang", "YuMing Zhang" ]

[ "double-electrode gas metal arc welding", "de", "-", "gmaw", "gmaw", "a second electrode", "a portion", "the current", "the wire", "this", "the heat input", "the workpiece", "successful bypassing", "the relative position", "the bypass", "the continuously varying wire tip", "proper operation", "we", "the system", "a follower robot", "the bypass", "the primary information", "this process", "the arc image", "which", "desired and undesired modes", "a robust algorithm", "the complex arc image", "a deep learning approach", "numerous arc images", "the corresponding de-gmaw modes", "which", "alternative labels", "we", "arc phenomena", "various de-gmaw modes", "them", "distinct arc systems", "varying voltages", "these voltages", "automatically derived labels", "the deep-learning network", "the results", "reliable process monitoring", "second" ]

[ "Innyoung Kim", "Donghyun You" ]

[ "this paper", "a review", "recent research", "deep reinforcement learning", "fluid dynamics", "reinforcement learning", "a technique", "which", "the agent", "optimal action strategies", "the environment", "human learning mechanisms", "artificial intelligence technology", "it", "a new direction", "fluid dynamic control", "optimization", "which", "the nonlinear and high-dimensional characteristics", "the fluid", "the section", "fluid dynamic control", "control strategies", "drag reduction", "research", "biological motion", "the optimization section", "shape optimization", "automation", "computational fluid dynamics", "current challenges", "possible future developments" ]

[ "Amra Peles", "Vincent C. Paquit", "Ryan R. Dehoff" ]

[ "accelerating fabrication", "additively manufactured components", "precise microstructures", "quality", "qualification", "built parts", "a fundamental understanding", "process improvement", "this", "fast and robust characterization", "melt pool geometries", "structural defects", "images", "this paper", "a pragmatic approach", "implementation", "deep learning models", "self-consistent workflow", "that", "systematic segmentation", "defects", "pools", "optical images", "deep learning", "image", "conditional generative adversarial neural network architecture", "an artificial intelligence", "(ai) tool", "this deep learning model", "fast and incrementally more accurate predictions", "the prevalent geometric features", "pool boundaries", "printing-induced structural defects", "we", "statistical analysis", "geometric features", "that", "the ai tool", "strong spatial correlation", "defects", "the melt pool boundaries", "the correlations", "widths", "heights", "melt pools", "dataset processing parameters", "the highest sensitivity", "thermal influences", "laser passes", "adjacent and subsequent layer passes", "the presented models", "tools", "the aluminum alloy", "datasets", "different sets", "processing parameters", "they", "universal quality", "different material compositions", "the method", "microstructural characterizations", "optical microscopy" ]

[ "Kevin Geevers", "Lotte van Hezewijk", "Martijn R. K. Mes" ]

[ "this paper", "the applicability", "a deep reinforcement learning approach", "three different multi-echelon inventory systems", "the objective", "the holding and backorder costs", "we", "an extensive literature review", "the current applications", "reinforcement learning", "multi-echelon inventory systems", "we", "our deep reinforcement learning method", "three cases", "different network structures", "general structures", "the linear and divergent cases", "literature", "the general case", "a real-life manufacturer", "we", "the proximal policy optimization", "ppo", "algorithm", "a continuous action space", "it", "the benchmark solution", "it", "an average improvement", "16.4%", "the linear case", "11.3%", "the divergent case", "6.6%", "the general case", "we", "the limitations", "our approach", "avenues", "future research", "three", "first", "three", "linear", "linear", "16.4%", "11.3%", "6.6%" ]

[ "P. K. Midhunraj", "K. S. Thivya", "M. Anand" ]

[ "plants", "they", "the main source", "human energy generation", "their nutritional, therapeutic, and other benefits", "it", "crop productivity", "these significant factors", "reduced agricultural yields", "the prevalence", "bacterial, fungal, and viral illnesses", "techniques", "plant disease identification", "these diseases", "numerous machine learning", "ml", "deep learning (dl) methods", "researchers", "plant diseases", "this study", "a detailed discussion", "the various research studies", "plant disease detection", "ml", "dl-based techniques", "this review", "research advancements", "plant disease recognition", "ml", "dl techniques", "many datasets", "plant diseases", "it", "the difficulties", "issues", "the current systems", "one" ]

[ "Daniele Venturi", "Xiantao Li" ]

[ "we", "a new formulation", "deep learning", "the mori", "zwanzig (mz) formalism", "irreversible statistical mechanics", "the new formulation", "the well-known duality", "deep neural networks", "discrete dynamical systems", "it", "us", "quantities", "interest", "conditional expectations", "probability density functions", "the network", "means", "exact linear operator equations", "such new equations", "a starting point", "new effective parameterizations", "deep neural networks", "a new framework", "deep learning", "operator-theoretic methods", "the proposed mz formulation", "deep learning", "a new concept", "the neural network", "which", "a fundamental role", "low-dimensional modeling", "parameterization", "the theory", "contraction mappings", "we", "sufficient conditions", "the memory", "the neural network", "the number", "layers", "this", "us", "deep networks", "shallow ones", "the number", "neurons", "layer", "projection operators", "the total number", "layers", "the decay property", "the memory operator", "linear" ]

[ "Rajashree Chaurasia", "Udayan Ghose" ]

[ "dna patterns", "knowledge", "illnesses", "cell function", "gene control", "motifs", "which transcription factor", "a protein", "a better unraveling", "gene expression", "advancements", "the fields", "deep learning", "high-throughput sequencing", "the exploration", "motif discovery", "greater accuracy", "performance", "methodology", "this paper", "a novel deep learning framework", "xdemo – transformer-based deep motifs", "dna motif mining", "transformer models", "a hybrid encoding scheme", "‘blended’ encoding", "use", "deep learning transformer models", "that", "dna sequences", "results", "our proposed transformer-based framework", "dna motif discovery", "blended encoding outperforms", "the-art", "many baseline performance metrics", "the standard datasets", "our models", "robust performance", "motifs", "high discriminative power", "precision", "recall", "f1 score", "conclusion", "the model’s ability", "intricate sequence patterns", "long-range dependencies", "the discovery", "biologically meaningful motifs", "that", "known transcription factor binding motif databases", "this", "our novel framework", "dna motifs", ", aid", "further downstream analyses", "biomedical and biotechnological applications" ]

[ "Lingbo Jin", "Yubo Tang", "Jackson B. Coole", "Melody T. Tan", "Xuan Zhao", "Hawraa Badaoui", "Jacob T. Robinson", "Michelle D. Williams", "Nadarajah Vigneswaran", "Ann M. Gillenwater", "Rebecca R. Richards-Kortum", "Ashok Veeraraghavan" ]

[ "histopathology", "a critical role", "the diagnosis", "surgical management", "cancer", "access", "histopathology services", "especially frozen section pathology", "surgery", "resource-constrained settings", "slides", "resected tissue", "expensive infrastructure", "we", "a deep-learning-enabled microscope", "deepdof-se", "intact tissue", "cellular resolution", "the need", "physical sectioning", "three key features", "deepdof-se", "tissue specimens", "inexpensive vital fluorescent dyes", "ultra-violet excitation", "that", "fluorescent emission", "a thin surface layer", "a deep-learning algorithm", "the depth", "field", "rapid acquisition", "focus", "large areas", "tissue", "the tissue surface", "a semi-supervised generative adversarial network", "deepdof-se fluorescence images", "hematoxylin-and-eosin appearance", "image interpretation", "pathologists", "significant additional training", "we", "the deepdof-se platform", "a data-driven approach", "its performance", "surgical resections", "suspected oral tumors", "our results", "deepdof-se", "histological information", "diagnostic importance", "a rapid and affordable slide-free histology platform", "intraoperative tumor margin assessment", "low-resource settings", "three", "first", "second", "hematoxylin" ]

[ "Maojun Wang", "Wenyu Xu", "Guangzhong Cao", "Tao Liu" ]

[ "the issue", "unoccupied or abandoned homesteads", "courtyards", "china", "the increasing aging population", "rapid urbanization", "massive rural-urban migration", "the aspect", "rural vitalization", "land-use planning", "policy making", "the number", "unoccupied courtyards", "field", "questionnaire-based surveys", "the main approaches", "these traditional methods", "a new workflow", "deep learning and machine learning algorithms", "unmanned aerial vehicle", "(uav) images", "features", "the built environment", "deep learning", "the courtyard management", "complete or collapsed farmhouses", "alexnet", "solar water heaters", "yolov5s", "green looking ratio", "glr", "fcn", "their precisions", "seven machine learning algorithms", "adaboost, binomial logistic regression", "neural network", "random forest", "support vector machine", "decision trees", "xgboost algorithms", "the rural courtyards’ utilization status", "the adaboost algorithm", "the best performance", "the comprehensive consideration", "most metrics", "accuracy", "precision", "recall", "f1-score", "results", "the courtyards’ utilization statuses", "the courtyard built environment", "it", "large-scale village surveys", "the intensive and sustainable approach", "rural land use", "china", "98%", "seven", "0.933", "0.932", "0.984", "0.957" ]

[ "Jichi Chen", "Hong Wang", "Enqiu He" ]

[ "the detection", "unfavorable driving states", "uds", "drivers", "electroencephalogram (eeg) measures", "continuous attention", "extensive scholars", "account", "brain neural activity", "high temporal resolution", "low risk", "the existing eeg-based driver uds detection methods", "limited exploration", "the functional connectivity patterns", "interaction relationships", "the brain network", "room", "improvement", "the accuracy", "detection", "this project", "we", "three pretrained convolutional neural network", "cnn)-based automatic detection frameworks", "uds", "drivers", "30-channel eeg signals", "the frameworks", "the learning rate", "the optimization", "two different conditions", "driving experiments", "eeg signals", "sixteen subjects", "the acquired 1-dimensional 30-channel eeg signals", "2-dimensional matrices", "the granger causality", "(gc) method", "the functional connectivity graphs", "the brain", "fcgb", "the fcgb", "pretrained deep learning models", "that", "transfer learning strategy", "feature extraction", "judgment", "different eeg signal types", "we", "two visualization interpretability techniques", "grad-cam", "better visualizing", "the predictions", "the pretrained models", "fine-tuning", "the experimental outcomes", "resnet", "18 model", "the highest average recognition accuracy", "90%", "the rmsprop optimizer", "a learning rate", "the overall outcomes", "biologically inspired functional connectivity graphs", "the brain", "pretrained transfer learning algorithms", "a prospective approach", "the rate", "major traffic accidents", "driver unfavorable driving states", "three", "30", "two", "1", "30", "2", "fed", "two", "18", "90%", "1e −", "3" ]

[ "Jiehao Liu", "Zhao Yang", "Liufei Luo", "Mingkai Luo", "Luyu Hu", "Jiahao Li" ]

[ "few-shot learning", "unseen classes", "only a few samples", "each class", "this challenging research endeavors", "the gap", "the computer vision technology", "the human visual system", "mainstream approaches", "fsl", "meta-learning and classification learning", "these two methods", "the fsl model", "local and global classification viewpoints", "our work", "we", "the former method", "transferable knowledge", "generalization capacity", "an episodic training paradigm", "the problem", "slow convergence", "the latter method", "an essential classification ability", "classification capacity", "a mini-batch training paradigm", "an over-fitting problem", "light", "this issue", "we", "a hybrid deep model", "cumulative learning", "the fsl problem", "the advantages", "the both methods", "the proposed hybrid deep model", "meta-learning and classification learning", "imc", "a unified two-branch network framework", "which", "a meta-learning branch", "a classification learning branch", "the different characteristics", "the two branches", "we", "a cumulative learning strategy", "care", "both generalization capacity learning and classification capacity", "our imc model training", "the proposed method", "the model", "the basic classification capability", "the initial stage", "continually mine discriminative class information", "the remaining training", "better generalization", "extensive experiments", "cifar-fs, fc100, mini", "-", "imagenet", "tiered-imagenet datasets", "the promising performance", "our method", "two", "two", "two" ]

[ "Quan Tian", "Ruiyan Cai", "Gongrun Qiu", "Yang Luo" ]

[ "space electromagnetic environments", "the direction", "arrival", "(doa) estimation", "the point source model", "the requirements", "spatial target location", "the characteristics", "the distributed source", "a new doa estimation algorithm", "deep learning", "algorithm", "the distributed source model", "the point source model", "a generative adversarial network", "gan", "the subspace-based method", "central doa estimation", "a deep neural network", "dnn", "the covariance matrix", "the received signals", "the input", "the angular spread", "the distributed source", "the experimental results", "the proposed algorithm", "better performance", "the existing methods", "a distributed source", "first", "second" ]

[ "Hui Tang", "Ming Hong", "Lu Yu", "Yang Song", "Mengqiu Cao", "Lei Xiang", "Yan Zhou", "Shiteng Suo" ]

[ "backgroundwe", "mri", "a deep learning technique", "tse-dl", "standard turbo spin-echo (tse-sd) images", "the lumbar spine", "image quality and detection performance", "common degenerative pathologies.methodsthis prospective, single-center study", "31 patients", "15 males", "16 females", "51 ±", "± standard deviation", "who", "lumbar spine exams", "both tse-sd and tse-dl acquisitions", "degenerative spine diseases", "images", "two radiologists", "qualitative image quality", "a 4-point likert scale", "noise", "snr", "anatomic landmarks", "detection", "common pathologies", "paired-sample t", "wilcoxon", "mcnemar tests", "cohen κ statistics", "correlation coefficients", "used.resultsscan time", "tse-dl and tse-sd protocols", "min", ":", "s", "the overall image quality", "tse-dl", "tse-sd and tse-dl. tse-dl", "higher snr and subject noise scores", "tse-sd", "pathology detection", "the interreader agreement", "tse-dl", "κ values", "the interprotocol agreement", "both readers", "κ values", "no significant difference", "the diagnostic confidence or detection rate", "common pathologies", "the two sequences", "p ≥", "0.081).conclusionstse-dl", "a 45% reduction", "scan time", "tse-sd", "lumbar spine mri", "the overall image quality", "comparable detection performance", "common pathologies", "the evaluation", "degenerative lumbar spine changes.relevance", "learning-reconstructed lumbar spine mri protocol", "a 45% reduction", "scan time", "conventional reconstruction", "comparable image quality and detection performance", "lumbar spine mri", "deep learning reconstruction", "broad application prospects.•", "reconstruction", "lumbar spine mri", "45% scan time", "overall image", "standard sequences", "deep learning reconstruction", "similar detection performance", "common degenerative lumbar spine", "31", "15", "16", "51 ± 16 years", "two", "4", "mcnemar", "used.resultsscan", "2:55", "5:17", "0.61", "1.00", "0.84", "1.00", "two", "45%", "45%", "45%" ]

[ "Jing Hao", "Feng He", "Feng Ma", "Shibo Zhang", "Xiaotao Zhang" ]

[ "machine learning", "deep learning", "powerful tools", "quantitative investment", "the effectiveness", "the models", "different markets", "this paper", "random forest", "dnn models", "stock prices", "statistical arbitrage strategies", "five stock markets", "mainland china", "the united states", "the united kingdom", "canada", "japan", "each model", "the price", "major stock indices", "stocks", "these markets", "a long-short portfolio", "20 selected stocks", "the model", "the results", "the a particular model", "significantly different profits", "different markets", "which", "dnn", "the best performance", "the chinese stock market", "we", "dnn models", "other machine learning models", "all markets", "five", "china", "the united states", "the united kingdom", "canada", "japan", "2005", "2020", "20", "chinese" ]

[ "Faris Azhari", "Charlotte C. Sennersten", "Craig A. Lindley", "Ewan Sellers" ]

[ "deep learning", "a sub", "-", "field", "artificial intelligence", "that", "feature engineering", "classification", "one method", "it", "a data-driven technique", "that", "a predictive model", "a large dataset", "digitisation", "industry", "acquisition", "storage", "a variety", "large datasets", "interpretation", "decision making", "this", "the adoption", "deep learning", "different industries", "transportation", "manufacturing", "medicine", "agriculture", "the mining industry", "new technologies", "deep learning methods", "the same rate", "other industries", "the past 5 years", "applications", "deep learning", "the mining research space", "deep learning", "a variety", "problems", "mine exploration, ore and metal extraction and reclamation processes", "the increased automation adoption", "mining", "an avenue", "wider application", "deep learning", "an element", "a mine automation framework", "this work", "a compact, comprehensive review", "deep learning implementations", "mining-related applications", "the trends", "these implementations", "terms", "years", "venues", "deep learning network types", "tasks", "general implementation", "the value chain operations", "exploration", "extraction", "reclamation", "the review", "shortcomings", "progress", "the research context", "the proprietary nature", "data", "small datasets", "tens to thousands", "data points", "single operations", "unique geology", "mine design", "equipment", "large scale publicly available mining related datasets", "limited sensor types", "the majority", "applications", "image-based analysis", "gaps", "future research", "application", "the usage", "a wider range", "sensor data", "understanding", "the outputs", "mining practitioners", "adversarial testing", "the deep learning models", "development", "public datasets", "the extensive range", "conditions", "mines", "one", "the past 5 years", "tens to thousands" ]

[ "Zan Zhou", "Thomas Man-Hoi Lok", "Wan-Huan Zhou" ]

[ "surface wave inversion", "a key step", "the application", "surface waves", "velocity profiling", "a common practice", "the process", "inversion", "the number", "soil layers", "heuristic search algorithms", "the shear wave velocity profile", "the number", "soil layers", "an optimization variable", "an improper selection", "the number", "layers", "an incorrect shear wave velocity profile", "this study", "a deep learning and genetic algorithm hybrid learning procedure", "the surface wave inversion", "the need", "the number", "soil layers", "a deep neural network", "a large number", "synthetic dispersion curves", "the layer number", "the shear-wave velocity profile", "a genetic algorithm", "the known layer number", "this procedure", "both simulated and real-world cases", "the results", "the proposed method", "surface wave inversion", "first" ]

[ "Renugadevi Ammapalayam Sinnaswamy", "Natesan Palanisamy", "Kavitha Subramaniam", "Suresh Muthusamy", "Ravita Lamba", "Sreejith Sekaran" ]

[ "aneurysm", "rupture", "blood vessels", "the cerebrum", "abdominal aorta", "aorta", "humans", "which", "a high fatal rate", "the advancement", "the artificial technologies", "specifically machine learning algorithms", "deep learning models", "the aneurysm", "which", "the death rate", "the main objective", "this paper", "the review", "various algorithms", "models", "the early prediction", "the various types", "aneurysms", "the focused literature review", "the preferred journals", "various parameters", "way", "images", "the techniques", "images", "data set", "performance metrics", "future work", "the summarized overview", "advances", "prediction", "aneurysms", "the machine learning algorithms", "non linear kernel support regression algorithm", "3d unet architecture", "deep learning models", "ct scan images", "final performance analysis", "prediction", "the range", "sensitivity", "specificity", "area", "operating characteristic", "the abdominal aortic aneurysm detection", "intracranial aneurysm detection", "the thoracic aortic aneurysm", "the literature review", "the prediction", "thoracic aortic aneurysm", "machine learning", "deep learning model", "2007", "2022", "3d", "scan", "0", "7 to 1", "intracranial aneurysm detection", "the thoracic aortic aneurysm" ]

[ "Punam Bedi", "Anuradha Singhal", "Veenu Bhasin" ]

[ "steganalysis", "a vital role", "cybersecurity", "today’s digital era", "exchange", "malicious information", "web pages", "steganography techniques", "data", "an object", "the existence", "hidden information", "steganalysis", "the process", "detection", "steganography", "an object", "active and passive steganalysis", "passive steganalysis", "a given object", "a clean or modified object", "active steganalysis", "more details", "hidden contents", "length", "embedded message", "region", "inserted message", "cybersecurity experts", "comprehensive analysis", "images", "a viable source", "exchange", "information", "the era", "internet", "social media", "the most susceptible source", "such transmission", "many researchers", "techniques", "such counterfeit exchanges", "the internet", "literature", "passive and active image steganalysis techniques", "these issues", "details", "such obscured communication", "this paper", "a systematic and comprehensive review", "work", "active image steganalysis techniques", "deep learning techniques", "this review", "the new researchers", "a strong foundation", "literature", "active image steganalysis", "deep learning techniques", "the paper", "various steganographic algorithms", "dataset", "performance evaluation metrics", "literature", "open research challenges", "possible future research directions", "the paper", "today" ]

[ "S. Sivasubramaniam", "S. P. Balamurugan" ]

[ "this paper", "we", "a multimodal deep learning algorithm", "that", "cnns", "lstm", "early detection", "prediction", "heart disease", "data", "wearable devices", "this combined multi-model deep learning algorithm", "the accurate precision", "accuracy value", "we", "ecg and ppg signals", "which", "the dataset", "the features", "ecg", "ppg", "cnn", "the accelerometer features", "the lstm model", "the combined features", "hybrid cnn-lstm network architecture", "the algorithm", "a publicly available benchmark dataset", "the model", "an accuracy", "99.33%", "heart disease", "the-art", "addition", "the model", "the likelihood", "heart disease", "a precision", "99.33%", "an early warning system", "risk", "the results", "the potential", "a multimodal approach", "early detection", "prediction", "heart disease", "wearable devices", "deep learning algorithms", "first", "cnn", "cnn", "99.33%", "99.33%" ]

[ "Dung Nguyen", "Duc Thanh Nguyen", "Sridha Sridharan", "Mohamed Abdelrazek", "Simon Denman", "Son N. Tran", "Rui Zeng", "Clinton Fookes" ]

[ "deep learning", "significant progress", "emotion recognition", "multimedia data", "such substantial progress", "existing approaches", "insufficient training data", "weak generalisation", "mismatched conditions", "these challenges", "we", "a learning strategy", "which", "emotional knowledge", "rich datasets", "source-poor datasets", "our method", "cross-domain features", "improved recognition performance", "the robustness", "the proposed learning strategy", "we", "extensive experiments", "several benchmark datasets", "enterface", "emodb", "ravdess", "experimental results", "the proposed method", "existing transfer learning schemes", "a significant margin" ]

[ "Shuo Feng", "Lixia Yu", "Fen Liu" ]

[ "the progress", "natural language processing technology", "deep learning", "the subjectivity", "slow feedback", "long grading time", "intelligent english automatic scoring", "scholars", "the limitations", "topic relevance feature extraction methods", "traditional automatic grading methods", "english compositions", "a topic decision model", "the topic relevance score", "the topic richness", "english composition", "the score", "relevance", "topic richness (trsr) calculation method", "an intelligent english composition scoring method", "artificial feature extraction", "deep learning", "the findings", "the topic decision", "(td) model", "the best effect", "it", "the corresponding accuracy", "recall", "f1 value", "the model training loss", "the intelligent english composition", "method", "deep learning (dliecg) method", "the best overall performance", "the best performance", "dataset p.", "the intelligent english composition scoring method", "better effectiveness", "reliability", "english", "english", "english", "english", "english", "80", "0.97", "0.93", "0.95", "0.03", "english", "english" ]

[ "Mehran Radak", "Haider Yabr Lafta", "Hossein Fallahi" ]

[ "backgroundbreast cancer", "a major public health concern", "early diagnosis", "classification", "effective treatment", "machine learning", "deep learning techniques", "great promise", "the classification", "diagnosis", "breast cancer.purposein", "this review", "we", "studies", "that", "these techniques", "breast cancer classification", "diagnosis", "five groups", "medical images", "mammography", "ultrasound", "mri", "histology", "thermography", "we", "the use", "five popular machine learning techniques", "neighbor", "svm", "naive bayesian network", "dt", "ann", "deep learning architectures", "convolutional neural networks.conclusionour review", "machine learning", "deep learning techniques", "high accuracy rates", "breast cancer classification", "diagnosis", "various medical imaging modalities", "these techniques", "the potential", "clinical decision-making", "better patient outcomes", "five", "five" ]

[ "Sara El Mekkaoui", "Ghait Boukachab", "Loubna Benabbou", "Abdelaziz Berrado" ]

[ "this paper", "a methodology", "the vessel arrival process", "a critical factor", "maritime operational efficiency", "this approach", "deep learning sequence models", "statistical process control charts", "the variability", "a vessel arrival process", "the proposed solution", "the predictive deep learning model", "a vessel’s estimated time", "arrival", "quality characteristics", "statistical control charts", "their variability", "the paper", "the results", "the proposed methodology", "vessel arrivals", "a coal terminal", "which", "the effectiveness", "the method", "precise monitoring", "arrival times", "this methodology", "efficient ship and port operations planning", "the timely adoption", "operational adjustments", "this", "operational measures", "shipping emissions", "resource utilization" ]

[ "Shijie Tang", "Yong Ding", "Meng Zhao", "Huiyong Wang" ]

[ "the advent", "industry", "industrial control systems", "ics", "the internet", "a rapid increase", "the types", "quantities", "security threats", "that", "ics", "anomaly detection", "an effective defense measure", "attacks", "present", "it", "the main trend", "hybrid deep learning methods", "ics anomaly detection", "we", "that many ics anomaly detection methods", "hybrid deep learning adopt phased learning", "which", "each phase", "optimization goals", "the overall goal", "view", "this issue", "we", "end", "hybrid deep learning", "our method", "radial basis function network", "rbfn", "k", "it", "stacked auto-encoder", "sae", "which", "reconstruction error", "error", "an objective function", "joint optimization", "feature extraction", "classification", "experiments", "the commonly used kddcup99", "swat datasets", "the results", "sakmr", "abnormal industrial control data", "the baseline methods", "multiple performance indicators", "f1-measure", "4.0", "anomaly detection", "anomaly detection method", "kddcup99" ]

[ "Vivek Tomar", "Nitin Kumar", "Ayush Raj Srivastava" ]

[ "face recognition", "the last few decades", "researchers", "the globe", "its applicability", "several domains", "this problem", "only a single training image", "single sample face recognition (ssfr) problem", "images", "illumination conditions", "different poses", "occlusion", "expression", "deep learning methods", "performance", "par", "humans", "the emergence", "deep learning methods", "the last decade", "it", "faces", "excellent accuracy", "a single sample scenario", "this paper", "we", "a comprehensive survey", "deep learning", "we", "a novel taxonomy", "these methods", "three categories", "virtual sample generation", "feature-based, and hybrid methods", "performance comparison", "these methods", "the literature", "we", "publicly available databases", "the researchers", "some important future research directions", "which", "researchers", "this fascinating area", "the last few decades", "the last decade", "three" ]

[ "Oskar Taubert", "Fabrice von der Lehr", "Alina Bazarova", "Christian Faber", "Philipp Knechtges", "Marie Weiel", "Charlotte Debus", "Daniel Coquelin", "Achim Basermann", "Achim Streit", "Stefan Kesselheim", "Markus Götz", "Alexander Schug" ]

[ "the path", "full understanding", "the structure-function relationship", "even design", "rna", "structure prediction", "an intriguing complement", "experimental efforts", "any deep learning", "rna structure", "the sparsity", "labeled training data", "the limited data", "we", "spatial adjacencies", "(\"contact maps", "a proxy", "3d structure", "our model", "barnacle", "the utilization", "unlabeled data", "self-supervised pre", "-", "training and efficient use", "the sparse", "data", "an xgboost classifier", "barnacle", "a considerable improvement", "both the established classical baseline", "a deep neural network", "order", "our approach", "tasks", "similar data constraints", "we", "our findings", "the related setting", "accessible surface area prediction", "3d" ]

[ "Soolmaz Abbasi", "Haoyu Lan", "Jeiran Choupan", "Nasim Sheikh-Bahaei", "Gaurav Pandey", "Bino Varghese" ]

[ "medical imaging datasets", "research", "multiple imaging centers", "different scanners", "protocols", "settings", "these variations", "data consistency", "compatibility", "different sources", "image harmonization", "a critical step", "the effects", "factors", "inherent differences", "various vendors", "hardware upgrades", "protocol changes", "scanner calibration drift", "consistent data", "medical image processing techniques", "the critical importance", "widespread relevance", "this issue", "a vast array", "image harmonization methodologies", "deep learning-based approaches", "substantial advancements", "recent times", "the goal", "this review paper", "the latest deep learning techniques", "image harmonization", "cutting-edge architectural approaches", "the field", "medical image harmonization", "both their strengths", "limitations", "this paper", "a comprehensive fundamental overview", "image harmonization strategies", "three critical aspects", "imaging datasets", "commonly used evaluation metrics", "characteristics", "different scanners", "this paper", "recent structural mri", "magnetic resonance imaging", "harmonization techniques", "network architecture", "network learning algorithm", "network supervision strategy", "network output", "the underlying architectures", "u", "-", "net", "generative adversarial networks", "gans", "variational autoencoders", "flow-based generative models", "transformer-based approaches", "custom-designed network architectures", "this paper", "the effectiveness", "drl", "a pivotal learning algorithm", "harmonization", "the review", "the primary limitations", "harmonization techniques", "specifically the lack", "comprehensive quantitative comparisons", "different methods", "the overall aim", "this review", "a guide", "researchers", "practitioners", "appropriate architectures", "their specific conditions", "requirements", "it", "discussions", "ongoing challenges", "the field", "light", "future research directions", "the potential", "significant advancements", "three" ]

[ "Zahir Malik", "Ansh Mirani", "Tanneru Gopi", "Mallika Alapati" ]

[ "bolted connections", "steel structures", "detection", "bolt loosening", "the prime concern", "the bolted joints", "sudden failure", "catastrophe", "loosening", "the bolts", "interfacial movement", "the pre", "torque", "vibrations", "dynamic loads", "the advent", "computing capabilities", "sensor technologies", "machine learning model accuracy", "bolt loosening detection", "damage recognition efficiency", "bolted joints", "deep learning", "machine vision", "effective models", "human interventions", "the present paper", "the research review", "bolt loosening detection", "machine vision", "deep learning techniques", "the past decade", "the past decade" ]

[ "Mohammed Abdulla Salim Al Husaini", "Mohamed Hadi Habaebi", "Md Rafiqul Islam" ]

[ "this study", "we", "a framework", "that", "breast cancer classification accuracy", "spatial features", "situ cooling support", "the framework", "real-time thermography video streaming", "early breast cancer detection", "deep learning models", "inception v3", "inception v4", "a modified inception mv4", "matlab", "the thermal camera", "a mobile phone", "images", "the breast area", "classification", "normal and abnormal breast", "this study’s training dataset", "1000 thermal images", "a flir one pro thermal camera", "a mobile device", "the imaging process", "the 1000 images", "700 images", "the normal breast thermography class", "the 300 images", "the abnormal class", "we", "deep convolutional neural network models", "inception v3", "inception v4", "a modified inception mv4", "our results", "inception mv4", "real-time video streaming", "even the slightest temperature contrast", "breast tissue sequences", "a 99.748% accuracy", "comparison", "a 99.712%", "96.8%", "inception v4", "v3", "the use", "situ", "gel", "image acquisition efficiency", "detection accuracy", "the tumor surface temperature", "0.1%", "an average 7% improvement", "detection and classification accuracy", "our findings", "the effectiveness", "inception mv4", "real-time breast cancer detection", "gel and varying tumor temperatures", "conclusion", "future research directions", "thermal video clips", "the thermal images database", "high-quality thermal cameras", "alternative deep learning models", "improved breast cancer detection", "v3", "2019", "1000", "one", "1000", "700", "300", "99.748%", "99.712%", "96.8%", "v3", "0.1%", "an average", "7%" ]

[ "Deepak Kar" ]

[ "atlas collaboration", "machine learning", "ml", "many different ways", "its physics programme", "object reconstruction", "simulation", "calorimeter showers", "background discrimination", "searches", "measurements", "tagging jets", "their origin", "anomaly detection", "(ad) techniques", "popularity", "they", "hidden patterns", "the data", "lesser dependence", "simulated samples", "the case", "supervised learning-based methods", "ml methods", "detector simulation", "jet tagging", "atlas", "four searches", "ml/ad techniques", "four" ]

[ "Muhammad Attique Khan", "Ameer Hamza", "Mohammad Shabaz", "Seifeine Kadry", "Saddaf Rubab", "Muhammad Abdullah Bilal", "Muhammad Naeem Akbar", "Suresh Manic Kesavan" ]

[ "a serious, all-encompassing, and deadly cancer", "that", "every part", "the body", "skin cancer", "the most prevalent causes", "skin lesions", "uv radiation", "which", "human skin", "skin cancer", "it", "order", "skin lesions", "less effort", "dermatologists", "machine learning", "(ml) techniques", "computer-aided diagnostic (cad) systems", "this paper", "a computerized method", "multiclass lesion classification", "a fusion", "optimal deep-learning model features", "the dataset", "this work", "isic2018", "augmentation", "a few mathematical operations", "that", "two pre-trained deep learning models", "darknet-19", "mobilenet-v2", "the selected dataset", "training", "features", "the average pool layer", "a hybrid firefly optimization technique", "the selected features", "two ways", "(i) original serial approach", "(ii) proposed threshold approach", "machine learning classifiers", "the chosen features", "the end", "the isic2018 dataset", "the experimental procedure", "an accuracy", "89.0%", "sensitivity", "precision", "f1 score", "the end", "comparison", "recent techniques", "it", "the proposed method", "improved accuracy", "other performance measures", "isic2018", "two", "darknet-19", "two", "isic2018", "89.0%", "87.34", "87.57", "87.45" ]

[ "Fengyu Wang", "Chuanqi Tao", "Jerry Gao" ]

[ "mobile applications", "daily life", "the demand", "their functionality", "reliability", "traditional mobile application testing methods", "particularly graphical user interface", "gui", "testing", "challenges", "limited automation", "the application", "various machine learning approaches", "gui testing", "the utilization", "limited component samples", "complex mobile application environments", "an overlooked issue", "many automated testing methods", "this study", "a mobile application testing method", "deep reinforcement learning", "performance", "adaptability", "the testing process", "the feature recognition capabilities", "deep learning", "the decision-making mechanisms", "reinforcement learning", "our method", "user operations", "potential application pitfalls", "the study", "the limitations", "traditional mobile application testing methods", "the advantages", "deep reinforcement learning", "complex tasks", "we", "an automated mobile application gui testing method", "a deep reinforcement learning algorithm", "redq", "the utilization", "application information", "the characteristics", "the redq", "a study", "18 open-source android applications", "github", "our method", "promising performance", "terms", "code coverage", "testing", "speed", "daily", "18" ]

[ "Sarmad Dashti Latif", "Ali Najah Ahmed" ]

[ "a result", "global climate change", "sustainable water supply management", "dams", "reservoirs", "key tools", "water resources", "they", "human cultures", "a variety", "ways", "enhanced human health", "increased food production", "water supply", "domestic and industrial use", "economic growth", "irrigation", "hydro-power generation", "flood control", "this study", "the application", "deep learning", "conventional machine", "algorithms", "daily reservoir inflow", "long short-term memory", "lstm", "a deep learning algorithm", "regression tree", "brt", "a machine learning algorithm", "five statistical indices", "the performance", "the proposed models", "the selected statistical measurements", "mean absolute error", "mae", "root mean square error", "rmse", "correlation coefficient", "r", "coefficient", "determination", "r2", "square error", "mse", "nash sutcliffe model efficiency coefficient", "nse", "the rmse-observations standard deviation ratio", "rsr", "the findings", "lstm", "brt", "a significant difference", "terms", "accuracy", "daily", "five", "nash sutcliffe" ]

[ "Vahid Ebrahimi", "Till Stephan", "Jiah Kim", "Pablo Carravilla", "Christian Eggeling", "Stefan Jakobs", "Kyu Young Han" ]

[ "sted microscopy", "subcellular structures", "super", "-", "resolution", "we", "sted images", "deep learning", "photobleaching", "photodamage", "the pixel", "dwell time", "one or two orders", "magnitude", "our method", "efficient and robust restoration", "noisy 2d", "3d sted images", "multiple targets", "long-term imaging", "mitochondrial dynamics", "one", "two", "2d", "3d" ]

[ "Pınar Tüfekci", "Melike Bektaş Kösesoy" ]

[ "the biological gender", "authors", "the content", "their written work", "a crucial task", "natural language processing", "nlp", "accurate biological gender identification", "numerous applications", "fields", "linguistics", "sociology", "marketing", "high accuracy", "the biological gender", "the author", "the quality", "the collected data", "its proper splitting", "the best-performing model necessitates experimental evaluation", "this study", "four learning algorithms", "biological gender identification", "news texts", "this end", "a comprehensive dataset", "iag", "tnku", "a turkish newspaper", "43,292 news articles", "four models", "popular machine learning algorithms", "naive bayes", "random forest", "two deep learning algorithms", "long short term memory", "convolutional neural networks", "the results", "the long short term memory", "lstm", "algorithm", "the other three models", "an exceptional accuracy", "88.51%", "this model's outstanding performance", "the importance", "innovative deep learning algorithms", "biological gender identification tasks", "nlp", "the present study", "extant literature", "a new dataset", "biological gender identification", "news texts", "four machine learning algorithms", "our findings", "the significance", "innovative techniques", "biological gender identification tasks", "the dataset and deep learning algorithm", "many areas", "sociolinguistics", "marketing research", "journalism", "the identification", "biological gender", "written content", "a pivotal role", "four", "turkish", "43,292", "four", "two", "three", "88.51%", "four" ]

[ "Sancho Salcedo-Sanz", "Jorge Pérez-Aracil", "Guido Ascenso", "Javier Del Ser", "David Casillas-Pérez", "Christopher Kadow", "Dušan Fister", "David Barriopedro", "Ricardo García-Herrera", "Matteo Giuliani", "Andrea Castelletti" ]

[ "atmospheric extreme events", "severe damage", "human societies", "ecosystems", "the frequency", "intensity", "extremes", "other associated events", "climate change", "global warming", "the accurate prediction", "characterization", "attribution", "atmospheric extreme events", "a key research field", "which", "many groups", "different methodologies", "computational tools", "machine learning", "deep learning methods", "the last years", "powerful techniques", "the problems", "atmospheric extreme events", "this paper", "machine learning", "deep learning approaches", "the analysis", "characterization", "prediction", "attribution", "the most important atmospheric extremes", "a summary", "the most used machine learning", "deep learning techniques", "this area", "a comprehensive critical review", "literature", "ml", "ees", "the critical literature review", "extreme events", "rainfall", "floods", "heatwaves", "extreme temperatures", "droughts", "severe weather events", "fog", "low-visibility episodes", "a case study", "the analysis", "extreme atmospheric temperature prediction", "ml", "dl techniques", "the paper", "conclusions", "perspectives", "outlooks", "the field", "the last years" ]