医疗人工智能 · 全部实体（第 42 / 100 页）

本页收录第 4101–4200 条，共 10000 条 · 返回 医疗人工智能 首页

• COSFAMM Baseline Dataset: Psychosocial and Clinical Predictors of Depression in Older Mexican Adults (2014–2019)
  This dataset accompanies the article “Depressive Symptom Predictors in Older Mexican Adults: Interaction Structures and Non‑Linear Effects from Machine Learning Explainability.” It contains baseline data from 1,252 adults aged ≥ 60 years participating in the C…
  datacite Efrén, Murillo-Zamora 2026 置信度 0.66
• From Symptom Snapshots to Symptom Trajectories: Verified Analysis Code and Reproducibility Materials
  Verified analysis code and reproducibility materials accompanying the manuscript “From Symptom Snapshots to Symptom Trajectories: Machine Learning Prediction of Near-Term Suicidal Ideation From Intensive Longitudinal Depression Assessments.” This package repro…
  datacite woo, sungbum 2026 置信度 0.66 suicidal ideationecological momentary assessment machine learningdepressiontemporal dynamicsXGBoost
• From Symptom Snapshots to Symptom Trajectories: Verified Analysis Code and Reproducibility Materials
  Verified analysis code and reproducibility materials accompanying the manuscript “From Symptom Snapshots to Symptom Trajectories: Machine Learning Prediction of Near-Term Suicidal Ideation From Intensive Longitudinal Depression Assessments.” This package repro…
  datacite woo, sungbum 2026 置信度 0.66 suicidal ideationecological momentary assessment machine learningdepressiontemporal dynamicsXGBoost
• ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В НЕЙРОХИРУРГИИ ОТ ДИАГНОСТИКИ ДО ПРОГНОЗИРОВАНИЯ ИСХОДОВ ОПЕРАЦИЙ
  Современная нейрохирургия требует прецизионной точности, минимизации интраоперационных рисков и объективного прогнозирования результатов лечения. Стремительное увеличение объемов диагностических данных и необходимость персонализации хирургических подходов обус…
  datacite Құрбанова Лола Қанағатқызы, Каримбердиев Нурбол Мухамедалиулы, Жолдасов Элдар Бекзатұлы, Ниязов Аскер Зарафулы 等 2026 置信度 0.66 Искусственный интеллектмашинное обучениенейрохирургиярадиомиканейроонкология
• Integrative Bioinformatics Approach to Identify Prognostic Gene Signatures for Risk Stratification in Thyroid Carcinoma
  Title:THCA Prognostic Biomarker Sets – Multiple independent gene expression signatures for risk stratification in thyroid carcinoma Description: Project: THCA Prognostic Biomarker – Integrative bioinformatics approach to identify prognostic gene signatures for…
  datacite Malik, Shivani, Raghava, Gajendra 2026 置信度 0.66
• Integrative Bioinformatics Approach to Identify Prognostic Gene Signatures for Risk Stratification in Thyroid Carcinoma
  Title:THCA Prognostic Biomarker Sets – Multiple independent gene expression signatures for risk stratification in thyroid carcinoma Description: Project: THCA Prognostic Biomarker – Integrative bioinformatics approach to identify prognostic gene signatures for…
  datacite Malik, Shivani, Raghava, Gajendra 2026 置信度 0.66
• From Symptom Snapshots to Symptom Trajectories: Verified Analysis Code and Reproducibility Materials
  Verified analysis code and reproducibility materials accompanying the manuscript “From Symptom Snapshots to Symptom Trajectories: Machine Learning Prediction of Near-Term Suicidal Ideation From Intensive Longitudinal Depression Assessments.” This package repro…
  datacite woo, sungbum 2026 置信度 0.66 suicidal ideationecological momentary assessment machine learningdepressiontemporal dynamicsXGBoost
• Artificial Intelligence in MOLECULAR AUTOPSY and FORENSIC CARDIO-GENOMICS
  Artificial Intelligence in Molecular Autopsy and Forensic Cardio-Genomics: Current Evidence, Clinical Applications, and Future Directions provides a concise, evidence-based overview of the emerging role of artificial intelligence in postmortem genomic investig…
  datacite Saboowala, Hakim K. 2026 置信度 0.66 Molecular Autopsy Forensic Cardio-Genomics Artificial Intelligence Explainable Artificial Intelligence Sudden Cardiac Death Forensic Genomics Genomic Variant Interpretation Precision Forensic Medicine Cardiomyopathy Channelopathies Postmortem Genetic Testing Machine Learning Clinical Genomics Digital Pathology Precision Medicine Cardiovascular Genetics Forensic Pathology Inherited Cardiac Disorders AI-Assisted Diagnostics Computational Genomics
• Artificial Intelligence in MOLECULAR AUTOPSY and FORENSIC CARDIO-GENOMICS
  Artificial Intelligence in Molecular Autopsy and Forensic Cardio-Genomics: Current Evidence, Clinical Applications, and Future Directions provides a concise, evidence-based overview of the emerging role of artificial intelligence in postmortem genomic investig…
  datacite Saboowala, Hakim K. 2026 置信度 0.66 Molecular Autopsy Forensic Cardio-Genomics Artificial Intelligence Explainable Artificial Intelligence Sudden Cardiac Death Forensic Genomics Genomic Variant Interpretation Precision Forensic Medicine Cardiomyopathy Channelopathies Postmortem Genetic Testing Machine Learning Clinical Genomics Digital Pathology Precision Medicine Cardiovascular Genetics Forensic Pathology Inherited Cardiac Disorders AI-Assisted Diagnostics Computational Genomics
• A Revolutionary Paradigm: AI-Driven Genetic Fingerprint-Based Drug Synthesis for Precise Solid Tumor Penetration and Eradication
  Solid tumors represent a formidable challenge in oncology due to their heterogeneous nature, dense extracellular matrix, and evolved resistance mechanisms. This manuscript introduces a groundbreaking, first-of-its-kind technology that leverages artificial inte…
  datacite Shibah, Sami Rashid Mohammed 2026 置信度 0.66
• A Revolutionary Paradigm: AI-Driven Genetic Fingerprint-Based Drug Synthesis for Precise Solid Tumor Penetration and Eradication
  Solid tumors represent a formidable challenge in oncology due to their heterogeneous nature, dense extracellular matrix, and evolved resistance mechanisms. This manuscript introduces a groundbreaking, first-of-its-kind technology that leverages artificial inte…
  datacite Shibah, Sami Rashid Mohammed 2026 置信度 0.66
• Mingzheng — Reproducibility Data Package
  This Zenodo record provides the reproducibility package for the Mingzheng study: a taxonomy-based multimodal artificial intelligence system for interpretable Traditional Chinese Medicine syndrome differentiation in cancer patients with comorbid sleep disorders…
  datacite Zheng, Xueer, Xie, Ying, Luo, Shijun, Yan, Yici 等 2026 置信度 0.66 multimodal fusionTCMsyndrome differentiationlabel shiftdecision support
• Machine Learning Generalizability in Cervical Cancer Diagnosis: A Study Protocol Comparing Standard Benchmark and African-Collected Datasets
  This protocol describes a two-arm computational study examining whether machine learning models trained on standard, non-African cervical cancer cytology benchmark datasets (SIPaKMeD, Herlev) generalize to an independently collected African cervical imaging da…
  datacite Kamau, Nyambura 2026 置信度 0.66 machine learning; cervical cancer; oncology; Africa; health equity; algorithmic bias; generalizability; global health; medical imaging; diagnostic AI
• Machine Learning Generalizability in Cervical Cancer Diagnosis: A Study Protocol Comparing Standard Benchmark and African-Collected Datasets
  This protocol describes a two-arm computational study examining whether machine learning models trained on standard, non-African cervical cancer cytology benchmark datasets (SIPaKMeD, Herlev) generalize to an independently collected African cervical imaging da…
  datacite Kamau, Nyambura 2026 置信度 0.66 machine learning; cervical cancer; oncology; Africa; health equity; algorithmic bias; generalizability; global health; medical imaging; diagnostic AI
• A Review on Artificial Intelligence in Modern Cardiac Therapeutics: Advancements, Applications, and Future Perspectives
  Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, placing immense pressure on healthcare infrastructure worldwide. Traditional paradigms of cardiac care heavily depend on manual interpretation of complex diagnostic data…
  datacite Yash Parkhi*, Manoj Kumar Goyal, Rani Dhurete 2026 置信度 0.66 Cardiovascular diseases (CVDs), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), sophisticated predictive analytics
• A Review on Artificial Intelligence in Modern Cardiac Therapeutics: Advancements, Applications, and Future Perspectives
  Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, placing immense pressure on healthcare infrastructure worldwide. Traditional paradigms of cardiac care heavily depend on manual interpretation of complex diagnostic data…
  datacite Yash Parkhi*, Manoj Kumar Goyal, Rani Dhurete 2026 置信度 0.66 Cardiovascular diseases (CVDs), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), sophisticated predictive analytics
• A New Survival and Prognosis Predictive Model for Combined-Small Cell Lung Cancer (C-SCLC): A Machine Learning Approach
  Combined Small Cell Lung Cancer (C-SCLC), a rare variant subtype of lung cancer, has its distinct characteristics and prognosis challenges. Despite its clinical significance, there exists a knowledge gap in the diagnosis, treatment, and prognosis of C-SCLC. Ut…
  datacite Faridani, Parzon Eyzadpur, Yu, Kaijie 2026 置信度 0.66 Combined Small Cell Lung CancerC-SCLCLung CancerMachine LearningSurvival Prediction
• A New Survival and Prognosis Predictive Model for Combined-Small Cell Lung Cancer (C-SCLC): A Machine Learning Approach
  Combined Small Cell Lung Cancer (C-SCLC), a rare variant subtype of lung cancer, has its distinct characteristics and prognosis challenges. Despite its clinical significance, there exists a knowledge gap in the diagnosis, treatment, and prognosis of C-SCLC. Ut…
  datacite Faridani, Parzon Eyzadpur, Yu, Kaijie 2026 置信度 0.66 Combined Small Cell Lung CancerC-SCLCLung CancerMachine LearningSurvival Prediction
• Mingzheng — Reproducibility Data Package
  Zenodo v2 v2 (2026-05-25): Added a public-cohort transfer stress test on TCM-SD (Supplementary Part I). This release adds a subdirectory, tcmsd_external_validation/ (approximately 270 MB), containing reproducibility artifacts for the TCM-SD external-transfer s…
  datacite Zheng, Xueer, Xie, Ying, Luo, Shijun, Yan, Yici 等 2026 置信度 0.66 multimodal fusionTCMsyndrome differentiationlabel shiftdecision support
• Mingzheng — Reproducibility Data Package
  Zenodo v2 v2 (2026-05-25): Added a public-cohort transfer stress test on TCM-SD (Supplementary Part I). This release adds a subdirectory, tcmsd_external_validation/ (approximately 270 MB), containing reproducibility artifacts for the TCM-SD external-transfer s…
  datacite Zheng, Xueer, Xie, Ying, Luo, Shijun, Yan, Yici 等 2026 置信度 0.66 multimodal fusionTCMsyndrome differentiationlabel shiftdecision support
• Ensemble-Based Robust Framework for Disease Prediction and Treatment Recommendation
  Abstract: Early identification of diseases based on symptoms plays a vital role in enabling timely medical consultations and enhancing healthcare accessibility. With advancements in artificial intelligence, machine learning techniques effectively analyze sympt…
  datacite Boragalli, Vidyashri, Pawar, Digvijay. J., Sagavkar, Sandhya V., Huddar, Mahesh G. 2026 置信度 0.66 Keywords: Ensemble model, Disease Prediction, Treatment Recommendation, Tabular clinical data
• Ensemble-Based Robust Framework for Disease Prediction and Treatment Recommendation
  Abstract: Early identification of diseases based on symptoms plays a vital role in enabling timely medical consultations and enhancing healthcare accessibility. With advancements in artificial intelligence, machine learning techniques effectively analyze sympt…
  datacite Boragalli, Vidyashri, Pawar, Digvijay. J., Sagavkar, Sandhya V., Huddar, Mahesh G. 2026 置信度 0.66 Keywords: Ensemble model, Disease Prediction, Treatment Recommendation, Tabular clinical data
• Artificial Intelligence in Dentistry: Current Applications, Future Perspectives, and Challenges
  This narrative review examines the role of artificial intelligence (AI) in modern dentistry, covering its applications across diagnosis, radiology, orthodontics, implantology, and restorative/prosthetic care. It highlights how deep learning models — particular…
  datacite Bandaliyev 2026 置信度 0.66 Artificial Intelligence; Dentistry; Machine Learning; Deep Learning; Dental Radiology; Digital Dentistry; Convolutional Neural Networks; Clinical Decision Support; Dental Diagnosis; Orthodontics; Implant Dentistry; Restorative Dentistry
• AI-Driven Framework for Smart Healthcare
  This project presents an AI-Driven Framework for Smart Healthcare Diagnosis that combines deep learning and machine learning for automated pneumonia risk assessment. DenseNet121 is used for chest X-ray analysis, while XGBoost, LightGBM, and CatBoost evaluate c…
  datacite Ande Vishnuvardhan, Bonagiri Abhishekvardhan, Bipin Yadav, Ch. Prabhavathi 2026 置信度 0.66
• AI-Driven Framework for Smart Healthcare
  This project presents an AI-Driven Framework for Smart Healthcare Diagnosis that combines deep learning and machine learning for automated pneumonia risk assessment. DenseNet121 is used for chest X-ray analysis, while XGBoost, LightGBM, and CatBoost evaluate c…
  datacite Ande Vishnuvardhan, Bonagiri Abhishekvardhan, Bipin Yadav, Ch. Prabhavathi 2026 置信度 0.66
• Development of a Cyclic Gradient Boosting Model for Early Prediction of Gestational Diabetes Risk
  Gradient Boosting Model is a notable public health challenge in Nigeria, especially in the low resource care facilities lacking adequate facilities for standard GDM diagnosis. This study attempts to develop a machine learning technique capable of accurately in…
  datacite Obianozie, Ifunanya Mirian 2026 置信度 0.66 Gestational Diabetes MellitusExplainable Machine LearningCyclic Gradient BoostingSHapley Additive exPlanationsRisk Prediction
• Development of a Cyclic Gradient Boosting Model for Early Prediction of Gestational Diabetes Risk
  Gradient Boosting Model is a notable public health challenge in Nigeria, especially in the low resource care facilities lacking adequate facilities for standard GDM diagnosis. This study attempts to develop a machine learning technique capable of accurately in…
  datacite Obianozie, Ifunanya Mirian 2026 置信度 0.66 Gestational Diabetes MellitusExplainable Machine LearningCyclic Gradient BoostingSHapley Additive exPlanationsRisk Prediction
• Quantifying Anthropometric Leakage in Obesity Risk Classification and It's Implications for Threshold-Defined Labels in Clinical Machine Learning
  datacite Frank Anokye 2026 置信度 0.66
• AIMS Proof-of-Concept: Reproducible companion code and audit log for Section 5.5 of "Legitimacy Conversion as Organizational Infrastructure: A Three-Stage Feedback Mechanism for AI Governance"
  Deterministic Python implementation (seed 42) of three AIMS governance controls (Pillar 2 equity evaluation and proxy removal; Pillar 4 SHAP-based local audit; Pillar 5 threshold-recalibration sweep) operationalized on the UCI Heart Failure Clinical Records da…
  datacite JEMAI, Marouen 2026 置信度 0.66
• AIMS Proof-of-Concept: Reproducible companion code and audit log for Section 5.5 of "Legitimacy Conversion as Organizational Infrastructure: A Three-Stage Feedback Mechanism for AI Governance"
  Deterministic Python implementation (seed 42) of three AIMS governance controls (Pillar 2 equity evaluation and proxy removal; Pillar 4 SHAP-based local audit; Pillar 5 threshold-recalibration sweep) operationalized on the UCI Heart Failure Clinical Records da…
  datacite JEMAI, Marouen 2026 置信度 0.66
• Early Detection for Cervical Cancer Using XG Boost Algorithm through ML
  This scholarly investigation presented an innovative web-based framework specifically engineered for cervical cancer detection utilizing the XG Boost algorithm, a sophisticated machine learning methodology. Through strategic utilization of a multifaceted datas…
  datacite Pulaganti, Omprakash, C.Yamini 2025 置信度 0.66 XGBoost; Cervical Cancer Detection; Machine Learning; Healthcare; Predictive Modeling
• Early Detection for Cervical Cancer Using XG Boost Algorithm through ML
  This scholarly investigation presented an innovative web-based framework specifically engineered for cervical cancer detection utilizing the XG Boost algorithm, a sophisticated machine learning methodology. Through strategic utilization of a multifaceted datas…
  datacite Pulaganti, Omprakash, C.Yamini 2025 置信度 0.66 XGBoost; Cervical Cancer Detection; Machine Learning; Healthcare; Predictive Modeling
• Temporal Modeling of Maternal Health Indicators Using Sequence-Based ML Models
  The temporal variation of the maternal health indicators, such as blood pressure, fetal heart rate, glucose level, and gestational weight change, necessitates the use of predictive methodologies that can identify the temporal dependencies of the same. Static m…
  datacite FNU Sudhakar Abhijeet 2026 置信度 0.66
• Temporal Modeling of Maternal Health Indicators Using Sequence-Based ML Models
  The temporal variation of the maternal health indicators, such as blood pressure, fetal heart rate, glucose level, and gestational weight change, necessitates the use of predictive methodologies that can identify the temporal dependencies of the same. Static m…
  datacite FNU Sudhakar Abhijeet 2026 置信度 0.66
• The integration of artificial intelligence in medical-surgical nursing: Transforming patient care and clinical decision-making
  Abstract: Artificial Intelligence (AI) is increasingly reshaping healthcare delivery, particularly in the field of medical-surgical nursing, by enhancing patient care, improving clinical efficiency, and supporting evidence-based decision-making. The integratio…
  datacite Manmeet Kaur, Anitha KC, Anshu 2026 置信度 0.66
• The integration of artificial intelligence in medical-surgical nursing: Transforming patient care and clinical decision-making
  Abstract: Artificial Intelligence (AI) is increasingly reshaping healthcare delivery, particularly in the field of medical-surgical nursing, by enhancing patient care, improving clinical efficiency, and supporting evidence-based decision-making. The integratio…
  datacite Manmeet Kaur, Anitha KC, Anshu 2026 置信度 0.66
• Artificial Intelligence in Dentistry: Current Applications, Future Perspectives, and Challenges
  This narrative review examines the role of artificial intelligence (AI) in modern dentistry, covering its applications across diagnosis, radiology, orthodontics, implantology, and restorative/prosthetic care. It highlights how deep learning models — particular…
  datacite Nihad Bandaliyev 2026 置信度 0.66 Artificial Intelligence; Dentistry; Machine Learning; Deep Learning; Dental Radiology; Digital Dentistry; Convolutional Neural Networks; Clinical Decision Support; Dental Diagnosis; Orthodontics; Implant Dentistry; Restorative Dentistry
• Artificial Intelligence in Dentistry: Current Applications, Future Perspectives, and Challenges
  This narrative review examines the role of artificial intelligence (AI) in modern dentistry, covering its applications across diagnosis, radiology, orthodontics, implantology, and restorative/prosthetic care. It highlights how deep learning models — particular…
  datacite Nihad Bandaliyev 2026 置信度 0.66 Artificial Intelligence; Dentistry; Machine Learning; Deep Learning; Dental Radiology; Digital Dentistry; Convolutional Neural Networks; Clinical Decision Support; Dental Diagnosis; Orthodontics; Implant Dentistry; Restorative Dentistry
• Role of Artificial Intelligence in Cancer Research Education
  Cancer remains a leading cause of mortality worldwide, driving urgent demand for advanced research and education frameworks capable of keeping pace with rapidly evolving scientific knowledge. Traditional cancer research education has increasingly demonstrated …
  datacite Mariam Fatima, Mariam Fatima 2026 置信度 0.66 Artificial IntelligenceCancer researchMachine LearningBiomedical EducationDigital Learning in Medicine
• Role of Artificial Intelligence in Cancer Research Education
  Cancer remains a leading cause of mortality worldwide, driving urgent demand for advanced research and education frameworks capable of keeping pace with rapidly evolving scientific knowledge. Traditional cancer research education has increasingly demonstrated …
  datacite Mariam Fatima, Mariam Fatima 2026 置信度 0.66 Artificial IntelligenceCancer researchMachine LearningBiomedical EducationDigital Learning in Medicine
• Connections Between Inductive Inference and Machine Learning
  crossref John Case, Sanjay Jain 2021-04-29T21:32:11Z 置信度 0.70
• Boolean Factor Analysis for Data Preprocessing in Machine Learning
  We present two input data preprocessing methods for machine learning (ML). The first one consists in extending the set of attributes describing objects in input data table by new attributes and the second one consists in replacing the attributes by new attribu…
  crossref Jan Outrata 2011-02-03T16:55:42Z 置信度 0.70
• Techniques of Machine Learning for Modifying the Search Strategy
  This chapter presents the integration of machine learning techniques into metaheuristic algorithm by developing adaptive search strategies to improve the optimization process. The Self-Organization Maps (SOMs) are used as an unsupervised learning mechanism to …
  crossref 2024-11-08T21:32:12Z 置信度 0.70
• Machine Learning for Flare Forecasting
  crossref Anna M. Massone, Michele Piana, FLARECAST Consortium 2018-06-08T18:34:55Z 置信度 0.70
• Machine Learning Approaches to Multi-Agent Inverse Learning Problems
  The problem to infer the goals of an agent on the basis of the observation of its actions has been framed in the context of inverse reinforcement learning (IRL) and has been extensively studied in recent decades. However, this model is valid only when no other…
  crossref Xiaomin Lin 2017-12-11T13:35:07Z 置信度 0.70
• Evolutionary Learning
  In this chapter we are going to start by treating evolution the same way that we treated neuroscience earlier in the book-by cherry-picking a few useful concepts, and then filling in the gaps with computer science in order to make an effective learning method.…
  crossref 2020-12-22T22:34:49Z 置信度 0.70
• In-Vehicle Speech Text Classification based on Multiple Machine Learning Algorithms
  With the rapid development of modern society, especially the popularity of the Internet and the rapid development of computer technology, people's lifestyles are undergoing fundamental changes.The rapid development of the Internet has led to an explosion of da…
  crossref 2023-02-06T01:19:47Z 置信度 0.70
• Creative Machine Learning
  crossref Parag Kulkarni 2017-03-30T07:10:26Z 置信度 0.70
• F154. Machine learning for the analysis of single pulse stimulation in electrocorticography
  crossref Emile d’Angremont, Geertjan J. Huiskamp, Frans S. Leijten, Christoph Brune 等 2018-05-10T19:15:42Z 置信度 0.70
• Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation
  Artificial intelligence (AI) and data science are rapidly developing in healthcare, as is their translation into laboratory medicine. Our review article presents an overview of the data science domain while discussing the reasons for its emergence. We also pre…
  crossref Damien Gruson, Thibault Helleputte, Patrick Rousseau, David Gruson 2019-04-22T20:00:20Z 置信度 0.70
• Perfusion heterogeneity of cerebral small vessel disease revealed via arterial spin labeling MRI and machine learning
  Cerebral small vessel disease (CSVD) is associated with altered cerebral perfusion. However, global and regional cerebral blood flow (CBF) are highly heterogeneous across CSVD patients. The aim of this study was to identify subtypes of CSVD with different CBF …
  crossref Weizhao Lu, Chunyan Yu, Liru Wang, Feng Wang 等 2022-08-26T10:11:44Z 置信度 0.70
• Applying machine learning algorithms to electronic health records to predict pneumonia after respiratory tract infection
  Objectives To predict community acquired pneumonia after respiratory tract infection (RTI) consultations in primary care by applying machine learning to electronic health records. Study design and setting A population-based cohort study was conducted using pri…
  crossref Xiaohui Sun, Abdel Douiri, Martin Gulliford 2022-01-16T09:22:11Z 置信度 0.70
• Unsupervised Learning
  This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consiste…
  crossref 2023-01-24T19:05:52Z 置信度 0.70
• The Magical World of Matrices: Building Blocks for Machine Learning
  crossref Weisheng Jiang 2026-07-31T10:55:03Z 置信度 0.70
• Data Analytics, Machine Learning and Cloud Together
  The integration of data analytics, machine learning (ML) and cloud computing changed the face of the healthcare and education industries into more efficient, personalized and accessible platforms. Predictive analytics offers substantial benefits to the health …
  crossref Neelu Jyothi Ahuja 2025-07-29T15:15:48Z 置信度 0.70
• Quantum Optimization for Machine Learning
  The convergence of machine learning (ML) and quantum computing is quickly becoming a game-changing new frontier in computational science. The most intriguing area of overlap may be quantum optimization, which seeks to reduce the exponentially increasing comple…
  crossref Asha Sohal, Ramesh Kait, Houbing Herbert Song 2026-02-24T17:57:20Z 置信度 0.70
• Regression and optimization
  This chapter returns to the more theoretical embedding of machine learning in regression. Prior chapters have shown that writing machine learning programs is easy using high-level computer languages and with the help of good machine learning libraries. However…
  crossref Thomas P. Trappenberg 2020-01-23T10:55:28Z 置信度 0.70
• Fundamentals of machine learning
  crossref Kiran Mustafa, Mashallah Rezakazemi, Rao Muhammad Mahtab Mahboob 2025-09-27T07:16:40Z 置信度 0.70
• Wind Power Prediction Based on Machine Learning
  Wind power prediction is an important routine task for wind farms and grid operators to deal with the serious risks associated with high wind power penetration. To meet the high requirement of the relevant enterprises on wind power prediction accuracy, a novel…
  crossref Song Liu, Jing Lv 2024-03-19T18:07:03Z 置信度 0.70
• Introduction to Gibbs Measures and Their Relevance in Machine Learning
  crossref 2025-12-08T02:04:12Z 置信度 0.70
• Electricity Load Forecasting Using Machine Learning Techniques
  Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of…
  crossref Manuel Martín-Merino Acera 2011-10-04T09:46:18Z 置信度 0.70
• "IoT and machine learning for enabling sustainable development goals"
  The United Nations created an agenda for 2030 that includes 17 clearly defined objectives for sustainable development. These goals are a pressing call to action that needs cooperation and creativity among nations and organizations. The suggested agenda will be…
  crossref Ranjan Kumar Mondal 2026-06-18T11:06:12Z 置信度 0.70
• InfoGram and admissible machine learning
  crossref Subhadeep Mukhopadhyay 2022-01-10T17:02:36Z 置信度 0.70
• The CN2 Induction Algorithm
  crossref Peter Clark, Tim Niblett 2003-04-04T16:55:36Z 置信度 0.70
• Do Not Forget Me: The Human Side of Machine Learning
  This chapter is the master of all the chapters but it does not discuss machine learning or related issues. How does a chapter in a book of machine learning that does not discuss the technology become the master of all the chapters? The plain and simple answer …
  crossref Patanjali Kashyap 2018-01-05T03:53:10Z 置信度 0.70
• 1 Introduction to machine learning
  crossref 2024-05-07T04:33:40Z 置信度 0.70
• Bias Reduction Techniques in Machine Learning Models: Investigating Strategies to Detect and Minimize Bias in AI and Machine Learning Algorithms
  The increasing deployment of machine learning models across diverse applications necessitates a critical assessment of fairness and bias minimization. Machine learning algorithms, often trained on historical data, can inadvertently perpetuate or even exacerbat…
  crossref Arimondo Scrivano 2025-07-26T19:15:58Z 置信度 0.70
• Solution to real time civil engineering tasks via machine learning
  crossref Kundan Meshram 2024-01-21T07:46:47Z 置信度 0.70
• Machine learning as an experimental science
  crossref Pat Langley 2004-10-31T18:55:14Z 置信度 0.70
• Basics of machine learning
  crossref Julhash U. Kazi 2025-04-06T15:30:37Z 置信度 0.70
• AI/Machine Learning for Finance
  This chapter addresses model robustness, focusing on avoiding overfitting through rolling-window cross-validation[cite: 37]. It introduces ensemble methods and performance metrics like AUC to evaluate trading signals.
  crossref Jason Guevara, Ričards Bulavs, Oskars Linares 2026-04-16T21:06:12Z 置信度 0.70
• Machine Learning (ML) as a Diagnostic Task
  This chapter discusses a revised definition of classification (diagnostic) test. This definition allows considering the problem of inferring classification tests as the task of searching for the best approximations of a given classification on a given set of d…
  crossref Xenia Naidenova 2010-05-25T08:40:40Z 置信度 0.70
• Machine Learning Meets Control Theory
  In this video, we provide a high level overview of reinforcement learning, along with leading algorithms and impressive applications.
  crossref Steven L Brunton 2023-03-28T12:47:51Z 置信度 0.70
• Semi-supervised learning (machine learning)
  crossref Candace Moore, Daniel Bell 2021-10-25T02:40:41Z 置信度 0.70
• Machine‐Learning Forensics: Incorporating Machine‐Learning (ML) Techniques for Implementing Digital Forensic Readiness Model
  This study implements the proposed integration of machine learning (ML) techniques into the ISO/IEC 27043:2015 international standard processes using a hypothetical case scenario for a smart building. ISO/IEC 27043:2015 does not currently incorporate ML techni…
  crossref Laila Tajeldin, Hein Venter 2024-12-24T02:43:31Z 置信度 0.70
• Public Perceptions of AI in Dentistry: Use, Trust, and Willingness in the Madinah Region
  Abstract Background Artificial intelligence (AI), particularly large language models, has transformed health information seeking and patient education. Although AI is increasingly used to obtain dental information, evidence regarding public use, trust in AI-ge…
  preprints 2026 置信度 0.74
• Standardised evaluation and monitoring of site-specific AI performance with physical CT phantoms
  Clinical deployment of medical imaging artificial intelligence (AI) requires objective and continuous quality assurance, yet standardised methods for this purpose have not been established. Here, we present a framework using physical phantoms for standardised …
  preprints 2026 置信度 0.74
• Automated Feedback Generation in Paediatric Simulation Training: A Prospective Validation of AI-Generated Structured Feedback Reports
  Abstract Artificial intelligence (AI) is transforming medical education, but evidence on how to integrate it into competency-oriented training remains limited. We prospectively evaluated whether an AI-assisted feedback system could generate structured written …
  preprints 2026 置信度 0.74
• Toward Clinically Translatable Nanorobots for Cancer: Advances in Design Principles, Navigation Control, AI-Enabled Autonomy, and Post-Treatment Removal Strategies
  Conventional systemic drug delivery often yields low efficacy and significant side effects due to off-target accumulation and rapid clearance. While nanoscale carriers leverage the enhanced permeability and retention (EPR) effect, this passive targeting is unr…
  preprints 2026 置信度 0.74
• Representation Paradigms in AI-based 3D Radiological Image Reconstruction: A Literature Review
  Abstract The growing demand for high-quality medical imaging in clinical practice and computer-assisted diagnosis has made 3D image reconstruction in radiological imaging a key research focus. Artificial intelligence (AI) methods are increasingly being used to…
  preprints 2026 置信度 0.74
• Human In the Loop Challenges for Quality Annotation of Pre-Cancer Lesions in Clinical Oral Images
  Smartphone-based Artificial Intelligence (AI) enabled screening of the hyperplasia and dysplasia stages (pre-cancer, OPMD) offers a viable opportunity to reduce the incidence and mortality of oral cancer through early prevention. However, developing accurate s…
  preprints 2026 置信度 0.74
• Diagnostic Reasoning With and Without AI: Automation Bias in Pre-Clerkship Medical Students
  Abstract Background As large language models become increasingly integrated into clinical workflows, medical students need structured opportunities to learn how to engage critically with artificial intelligence (AI) during clinical reasoning. Empirical evaluat…
  preprints 2026 置信度 0.74
• Bridging the Gap: Explainability Metrics for AI Image-Based Clinical Diagnostics
  Abstract Artificial intelligence (AI) has shown considerable promise in enhancing diagnostic accuracy in clinical practice. However, its integration into healthcare workflows remains hindered by the opaque nature of many AI models, often referred to as “black-…
  preprints 2026 置信度 0.74
• An ECG foundation model for generalizable cardiac function prediction across the lifespan
  Background Artificial intelligence-enhanced electrocardiography (AI-ECG) enables scalable, low-cost cardiac dysfunction screening, but existing models are annotation-intensive and predominantly adult-derived, leaving paediatric generalizability uncertain. Paed…
  preprints 2026 置信度 0.74
• Association of AI Literacy and AI Trust With Subjective and Objective Healthcare-Seeking Delay Among Patients With Gynecologic Malignancies
  Abstract Background: Artificial intelligence (AI) literacy and AI trust may influence how patients process health information and make healthcare-seeking decisions. However, their associations with subjective and objective healthcare-seeking delay among patien…
  preprints 2026 置信度 0.74
• AI Factory: The Future of Scalable Artificial Intelligence
  crossref Ahmed Banafa 2026-07-16T23:41:36Z 置信度 0.70
• Artificial Intelligence in Healthcare and Medical Ethics
  crossref Perihan Elif Ekmekci, Berna Arda 2020-07-30T19:07:52Z 置信度 0.70
• Artificial Intelligence in Agriculture: Technology-Powered Strategies for a Sustainable Farming Future
  crossref Shallu Duggal, Shivani Sood, Shilpa, Kamal Nain Sharma 等 2026-01-29T10:52:07Z 置信度 0.70
• Proceedings of the 2026 International Conference on Artificial Intelligence and Fintech
  crossref 2026-05-01T03:21:14Z 置信度 0.70
• Harmony of Artificial Intelligence and Emotional Intelligence in the Education Sector
  An innovative strategy to improve teaching and learning is presented by the fusion of artificial intelligence (AI) and emotional intelligence (EI) in the educational field. AI is gaining huge importance in every sector. AI is expanding at a pace that is unpara…
  crossref Jagneet Kour, Raino Bhatia 2026-04-16T19:01:11Z 置信度 0.70
• Innovation of cross-cultural music teaching methods based on artificial intelligence
  This research is concerned with the innovative use of AI in cross-cultural music learning, more particularly in the creation of a multimodal learning model that combines audio identification, image analysis, and text understanding. The research is conducted wi…
  crossref Feng Liu 2026-03-23T03:14:05Z 置信度 0.70
• Artificial Intelligence as Strategic Infrastructure
  This chapter examines how Globo, the dominant media conglomerate in Brazil and a major force in the broader Latin American context, is embracing artificial intelligence (AI) as a strategic infrastructure to transform its operations, from content production and…
  crossref Catarina Duff Burnay, Paulo Nuno Vicente 2026-06-25T10:46:35Z 置信度 0.70
• Artificial Intelligence-Driven Evidence in Arbitral Proceedings
  Man and machine in the present are slowly and steadily becoming intertwined in a manner that it is often extremely difficult to separate one from the other. The luxuries of the past have come up to become the necessities of the present. The newly enacted Bhara…
  crossref Atish Chakraborty, Dhruv Maheshwari 2026-07-14T15:51:18Z 置信度 0.70
• Index
  crossref 2025-11-14T15:07:42Z 置信度 0.70
• Copyright Page
  crossref 2026-08-26T19:11:22Z 置信度 0.70
• Front Matter
  The prelims comprise: Half-Title Page Publisher Page Title Page Copyright Page Table of Contents Brief Contents of Volume 2 Preface
  crossref 2026-06-05T21:30:42Z 置信度 0.70
• Algorithmic selection
  crossref Tshilidzi Marwala 2026-02-20T10:57:30Z 置信度 0.70
• Plant disease diagnosis and forecasting in the era of artificial intelligence, machine learning, and deep learning
  crossref Nidhi Kumari, Nainika Nagar 2026-05-29T12:13:00Z 置信度 0.70
• Authentic news detection technology based on artificial intelligence technology
  In today’s increasingly rich digital information, how to effectively identify and prevent the spread of fake news has become an urgent problem that needs to be solved. Therefore, artificial intelligence technology has been introduced to detect genuine and fake…
  crossref Hui Wang, Feng Nan 2026-02-05T10:35:54Z 置信度 0.70
• Teaching Practice of Marketing Major Empowered by Generative Artificial Intelligence
  The pain point of traditional education lies in its single form of interaction, which easily causes students ‘visual fatigue’, reducing learning efficiency and motivation. The emotional expression of digital teachers empowered by generative artificial intellig…
  crossref Qinxian Chen, Saipeng Xing, Xian Qin, Fen Huo 2026-08-28T08:41:13Z 置信度 0.70