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• TaxFL: Federated Learning and Federated Graph Intelligence for Cross-Border Tax and AML Compliance
  Cross-border tax non-compliance and money laundering exploit the same structural weakness: the data needed to identify the beneficial owner of a suspicious structure is fragmented across jurisdictions and institutions. Traditional exchange-of-information (EOI)…
  datacite Frantz, Pedro Augusto 2026 置信度 0.66 federated learninggraph neural networkstax compliancetax fraudexchange of information
• TaxFL: Federated Learning and Federated Graph Intelligence for Cross-Border Tax and AML Compliance
  Cross-border tax non-compliance and money laundering exploit the same structural weakness: the data needed to identify the beneficial owner of a suspicious structure is fragmented across jurisdictions and institutions. Traditional exchange-of-information (EOI)…
  datacite Frantz, Pedro Augusto 2026 置信度 0.66 federated learninggraph neural networkstax compliancetax fraudexchange of information
• Silent Failures and Structural Gaps: A Cross-Domain Framework for Evaluation Rigor in Large-Model Systems
  Version 2 — revised in response to an external structural review and an automated critique pass. See "Response to Review" appendix in the PDF for the change log. Large-model systems are evaluated at multiple layers—statistical, algorithmic, systems, and behavi…
  datacite Saluca Agentic AI Research Team 2026 置信度 0.66 AI-drafted synthesisarXivpreprint reviewv2
• Silent Failures and Structural Gaps: A Cross-Domain Framework for Evaluation Rigor in Large-Model Systems
  Version 2 — revised in response to an external structural review and an automated critique pass. See "Response to Review" appendix in the PDF for the change log. Large-model systems are evaluated at multiple layers—statistical, algorithmic, systems, and behavi…
  datacite Saluca Agentic AI Research Team 2026 置信度 0.66 AI-drafted synthesisarXivpreprint reviewv2
• Differential Privacy Trade-offs in Federated Code Generation Model Fine-Tuning
  This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the trade-off between inference latency and model robustness against adversarial attacks when applying differential privacy mechanisms to federated…
  datacite Assignee Research 2026 置信度 0.66 trade-offinferencelatencymodelrobustness
• Differential Privacy Trade-offs in Federated Code Generation Model Fine-Tuning
  This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the trade-off between inference latency and model robustness against adversarial attacks when applying differential privacy mechanisms to federated…
  datacite Assignee Research 2026 置信度 0.66 trade-offinferencelatencymodelrobustness
• Coordination Topology as a Design Variable: How Memory Depth, Communication Sparsity, Censored Feedback, and Governance Boundaries Jointly Determine Multi-Agent System Behavior
  Version 2 — revised in response to an external structural review and an automated critique pass. See "Response to Review" appendix in the PDF for the change log. Multi-agent systems (MAS) are typically designed by fixing a task objective and then selecting age…
  datacite Saluca Agentic AI Research Team 2026 置信度 0.66 AI-drafted synthesisarXivpreprint reviewv2
• Air Quality Index Prediction: A Review of Machine Learning, Deep Learning, and Hybrid Approaches
  Abstract - The increasing evolution of air pollution as a global environmental and public health issue worldwide warrants the timely provision of accurate predictions for the Air Quality Index (AQI). Therefore, this review paper will explore in-depth all the t…
  datacite Abhishek Tiwari, Atesh Kumar 2026 置信度 0.66 Air Quality Index (AQI)Machine LearningDeep LearningTime Series ForecastingEnvironmental Monitoring
• Air Quality Index Prediction: A Review of Machine Learning, Deep Learning, and Hybrid Approaches
  Abstract - The increasing evolution of air pollution as a global environmental and public health issue worldwide warrants the timely provision of accurate predictions for the Air Quality Index (AQI). Therefore, this review paper will explore in-depth all the t…
  datacite Abhishek Tiwari, Atesh Kumar 2026 置信度 0.66 Air Quality Index (AQI)Machine LearningDeep LearningTime Series ForecastingEnvironmental Monitoring
• Diversity-Driven Client Selection in Federated Learning for CodeLlama-7B on HumanEval
  This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does diversity-driven client selection in federated learning affect the pass@1 scores of CodeLlama-7B on the HumanEval benchmark under extreme non-IID…
  datacite Assignee Research 2026 置信度 0.66 diversity-drivenclientselectionfederatedlearning
• Diversity-Driven Client Selection in Federated Learning for CodeLlama-7B on HumanEval
  This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does diversity-driven client selection in federated learning affect the pass@1 scores of CodeLlama-7B on the HumanEval benchmark under extreme non-IID…
  datacite Assignee Research 2026 置信度 0.66 diversity-drivenclientselectionfederatedlearning
• Supporting Data for State-Aware Upload Gating for Personalized Federated Learning in Mobile Edge Environments
  This dataset contains the client-partition files, per-seed raw experimental logs, processed summary data, and analysis scripts supporting the results reported in “State-Aware Upload Gating for Personalized Federated Learning in Mobile Edge Environments.” The p…
  datacite Long, Bangfa, Wang, Xiaohan, Wu, Nianhua 2026 置信度 0.66 personalized federated learningstate-aware upload gatingmobile edge learningCIFAR-10CIFAR-100
• Supporting Data for State-Aware Upload Gating for Personalized Federated Learning in Mobile Edge Environments
  This dataset contains the client-partition files, per-seed raw experimental logs, processed summary data, and analysis scripts supporting the results reported in “State-Aware Upload Gating for Personalized Federated Learning in Mobile Edge Environments.” The p…
  datacite Long, Bangfa, Wang, Xiaohan, Wu, Nianhua 2026 置信度 0.66 personalized federated learningstate-aware upload gatingmobile edge learningCIFAR-10CIFAR-100
• ## Artificial Intelligence for Advanced Materials Science: A Comprehensive Framework for Phase Transformations, Microstructure Evolution, Alloy Design, Additive Manufacturing, and Beyond
  # ALTERNATIVE COMBINED TITLES ## Alternative Title 1**AI-Driven Materials Science: From Phase Transformations to Intelligent Manufacturing – A Unified Framework for Accelerated Discovery and Optimization** ### SubtitleIntegrating Machine Learning, Deep Learnin…
  datacite geruganti, sudhakar 2026 置信度 0.66
• ## Artificial Intelligence for Advanced Materials Science: A Comprehensive Framework for Phase Transformations, Microstructure Evolution, Alloy Design, Additive Manufacturing, and Beyond
  # ALTERNATIVE COMBINED TITLES ## Alternative Title 1**AI-Driven Materials Science: From Phase Transformations to Intelligent Manufacturing – A Unified Framework for Accelerated Discovery and Optimization** ### SubtitleIntegrating Machine Learning, Deep Learnin…
  datacite geruganti, sudhakar 2026 置信度 0.66
• Review of Context-Aware DL-Based Models to Improve Grocery Retail Forecasting
  There a few factors that make grocery retail forecasting particularly challenging: intricate demand patterns are recorded based on many contextual effects such as promotions, seasonality, local events or weather and fickle shopping habits. Nonlinear relationsh…
  datacite Tomar, Ujjawal, Rajput, Ayan 2025 置信度 0.66 Retail Forecasting; Deep Learning; Context-Aware Systems; Demand Prediction; Grocery Analytics; TIME SERIES ANALYSIS & MACHINE LEARNING FOR GROCERY RETAIL 3 Inventory Optimization
• Review of Context-Aware DL-Based Models to Improve Grocery Retail Forecasting
  There a few factors that make grocery retail forecasting particularly challenging: intricate demand patterns are recorded based on many contextual effects such as promotions, seasonality, local events or weather and fickle shopping habits. Nonlinear relationsh…
  datacite Tomar, Ujjawal, Rajput, Ayan 2025 置信度 0.66 Retail Forecasting; Deep Learning; Context-Aware Systems; Demand Prediction; Grocery Analytics; TIME SERIES ANALYSIS & MACHINE LEARNING FOR GROCERY RETAIL 3 Inventory Optimization
• Intelligent Power Infrastructure: A Structured Examination of Artificial Intelligence Techniques, Operational Challenges, and Evolutionary Pathways in Next-Generation Smart Grid Systems
  Abstract Global momentum toward intelligent power infrastructure is accelerating as electricity demand grows, renewable energy penetration deepens, and sustainability imperatives intensify. Artificial intelligence (AI) technologies—encompassing machine learnin…
  datacite Dr Gokula Krishnan B, Dr Senthil Kumar M P, Shanmugam S, K Gopinath 2026 置信度 0.66 Artificial Intelligence, Smart Grid, Demand Response, Renewable Energy Forecasting
• Intelligent Power Infrastructure: A Structured Examination of Artificial Intelligence Techniques, Operational Challenges, and Evolutionary Pathways in Next-Generation Smart Grid Systems
  Abstract Global momentum toward intelligent power infrastructure is accelerating as electricity demand grows, renewable energy penetration deepens, and sustainability imperatives intensify. Artificial intelligence (AI) technologies—encompassing machine learnin…
  datacite Dr Gokula Krishnan B, Dr Senthil Kumar M P, Shanmugam S, K Gopinath 2026 置信度 0.66 Artificial Intelligence, Smart Grid, Demand Response, Renewable Energy Forecasting
• Predictive Analytics Framework for Multi-Generational Health Data: Architecture, Implementation, and Clinical Outcomes at Population Scale
  A substantial share of chronic and hereditary disease risk propagates through family units rather than individuals, yet the majority of clinical analytics frameworks treat patient records as independent observations without reference to family health context. …
  datacite Akash Kamble LNU 2026 置信度 0.66
• Predictive Analytics Framework for Multi-Generational Health Data: Architecture, Implementation, and Clinical Outcomes at Population Scale
  A substantial share of chronic and hereditary disease risk propagates through family units rather than individuals, yet the majority of clinical analytics frameworks treat patient records as independent observations without reference to family health context. …
  datacite Akash Kamble LNU 2026 置信度 0.66
• TextRefs: An open registry for canonical text references
  Slides introducing TextRefs, an open registry for canonical text references. The presentation starts from a familiar scholarly practice: references such as “Plato, Republic 514a” identify the same passage across many editions, translations, languages, layouts,…
  datacite Mähr, Moritz, Seiberth, Luz Christopher 2026 置信度 0.66 TextRefsInformation Science/standardsComputer and information sciencesPublishing/standardsOpen Access Publishing
• TextRefs: An open registry for canonical text references
  Slides introducing TextRefs, an open registry for canonical text references. The presentation starts from a familiar scholarly practice: references such as “Plato, Republic 514a” identify the same passage across many editions, translations, languages, layouts,…
  datacite Mähr, Moritz, Seiberth, Luz Christopher 2026 置信度 0.66 TextRefsInformation Science/standardsComputer and information sciencesPublishing/standardsOpen Access Publishing
• Straggler Mitigation Strategies in Asynchronous Federated Learning for Robust Multimodal IoT Malware Detection
  This report synthesises findings from 8 peer-reviewed papers addressing the following research question: What is the impact of straggler mitigation strategies in asynchronous federated learning on the robustness of multimodal IoT malware detectors against labe…
  datacite Assignee Research 2026 置信度 0.66 impactstragglermitigationstrategiesasynchronous
• Coordination Without Continuous Synchronization: Why Sparse, Event-Triggered, and Budget-Aware Protocols Dominate Dense Communication in Multi-Agent Systems
  Version 2 — revised in response to an external structural review and an automated critique pass. See "Response to Review" appendix in the PDF for the change log. A persistent assumption in multi-agent systems (MAS) design is that more communication produces be…
  datacite Saluca Agentic AI Research Team 2026 置信度 0.66 AI-drafted synthesisarXivpreprint reviewv2
• Coordination Without Continuous Synchronization: Why Sparse, Event-Triggered, and Budget-Aware Protocols Dominate Dense Communication in Multi-Agent Systems
  Version 2 — revised in response to an external structural review and an automated critique pass. See "Response to Review" appendix in the PDF for the change log. A persistent assumption in multi-agent systems (MAS) design is that more communication produces be…
  datacite Saluca Agentic AI Research Team 2026 置信度 0.66 AI-drafted synthesisarXivpreprint reviewv2
• A Comprehensive Survey on AI-Driven Optimization and Dynamic Cluster Head Selection for Energy-Efficient Wireless Sensor Networks
  Abstract— There is no doubt that the Wireless SensorNetworks (WSNs) are indispensable to a variety of applicationswhich include environmental monitoring, healthcare, smartgrids, and industrial IoT systems. However, WSNs still sufferfrom the same problems of li…
  datacite Manoj Bhade, Rachana Kamble, Amar Nayak 2026 置信度 0.66 Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), Machine Learning, Dynamic Cluster Head Selection, Energy Efficiency, Network Lifetime Optimization.
• A Comprehensive Survey on AI-Driven Optimization and Dynamic Cluster Head Selection for Energy-Efficient Wireless Sensor Networks
  Abstract— There is no doubt that the Wireless SensorNetworks (WSNs) are indispensable to a variety of applicationswhich include environmental monitoring, healthcare, smartgrids, and industrial IoT systems. However, WSNs still sufferfrom the same problems of li…
  datacite Manoj Bhade, Rachana Kamble, Amar Nayak 2026 置信度 0.66 Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), Machine Learning, Dynamic Cluster Head Selection, Energy Efficiency, Network Lifetime Optimization.
• AI-Driven Threat Detection In Multi-Cloud Environments
  The rapid adoption of multi-cloud environments has fundamentally transformed enterprise IT infrastructure, offering enhanced scalability, resilience, and vendor flexibility. However, this architectural evolution has also introduced complex security challenges,…
  datacite Sergey Ivanov 2021 置信度 0.66
• AI-Driven Threat Detection In Multi-Cloud Environments
  The rapid adoption of multi-cloud environments has fundamentally transformed enterprise IT infrastructure, offering enhanced scalability, resilience, and vendor flexibility. However, this architectural evolution has also introduced complex security challenges,…
  datacite Sergey Ivanov 2021 置信度 0.66
• Multi-View Graph Anomaly Detection Robustness Under View Dropout and Metattack Perturbations
  This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the impact of view dropout on the robustness of multi-view graph anomaly detection frameworks against Metattack perturbations, measured by the degr…
  datacite Assignee Research 2026 置信度 0.66 impactviewdropoutrobustnessmulti-view
• Multi-View Graph Anomaly Detection Robustness Under View Dropout and Metattack Perturbations
  This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the impact of view dropout on the robustness of multi-view graph anomaly detection frameworks against Metattack perturbations, measured by the degr…
  datacite Assignee Research 2026 置信度 0.66 impactviewdropoutrobustnessmulti-view
• A Comprehensive Review on Leveraging Artificial Intelligence (AI) for the Cutting-edge Discoveries in Computer Aided Drug Design and Pharmaceutical R & D
  The integration of Artificial Intelligence (AI) into Computer-Aided Drug Design (CADD) has transformed pharmaceutical research and development by enhancing efficiency and accuracy in drug discovery. This review discusses the evolution of drug discovery from tr…
  datacite Archana Kongari, Kirtana Gajul, Annasaheb Valgude, Saikrishna Devsani 等 2026 置信度 0.66
• A Comprehensive Review on Leveraging Artificial Intelligence (AI) for the Cutting-edge Discoveries in Computer Aided Drug Design and Pharmaceutical R & D
  The integration of Artificial Intelligence (AI) into Computer-Aided Drug Design (CADD) has transformed pharmaceutical research and development by enhancing efficiency and accuracy in drug discovery. This review discusses the evolution of drug discovery from tr…
  datacite Archana Kongari, Kirtana Gajul, Annasaheb Valgude, Saikrishna Devsani 等 2026 置信度 0.66
• Compressive Sensing Integration in Over-the-Air Federated Learning for Massive MIMO Systems
  This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the integration of compressive sensing techniques with over-the-air federated learning (OTA-FL) in massive MIMO systems compare to traditional FL…
  datacite Assignee Research 2026 置信度 0.66 integrationcompressivesensingtechniquesover-the-air
• Compressive Sensing Integration in Over-the-Air Federated Learning for Massive MIMO Systems
  This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the integration of compressive sensing techniques with over-the-air federated learning (OTA-FL) in massive MIMO systems compare to traditional FL…
  datacite Assignee Research 2026 置信度 0.66 integrationcompressivesensingtechniquesover-the-air
• Structural Similarity Degradation in Multimodal Federated Learning Under Non-IID Data
  This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the structural similarity of feature embeddings in collaborative multimodal federated learning models scale with increasing degrees of non-IID da…
  datacite Assignee Research 2026 置信度 0.66 structuralsimilarityfeatureembeddingscollaborative
• Structural Similarity Degradation in Multimodal Federated Learning Under Non-IID Data
  This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the structural similarity of feature embeddings in collaborative multimodal federated learning models scale with increasing degrees of non-IID da…
  datacite Assignee Research 2026 置信度 0.66 structuralsimilarityfeatureembeddingscollaborative
• The European Moonshot for Education: AI-Powered Learning as Sovereign Infrastructure
  Policy paper proposing education as the twelfth Moonshot project in the EU Multiannual Financial Framework 2028–2034. Argues that Google's deployment of LearnLM to 170 million accounts via existing Workspace infrastructure constitutes cognitive lock-in without…
  datacite Pochmann, Matthias 2026 置信度 0.66 EU MFF 2028-2034moonshoteducation infrastructurecognitive sovereigntyAI in education
• The European Moonshot for Education: AI-Powered Learning as Sovereign Infrastructure
  Policy paper proposing education as the twelfth Moonshot project in the EU Multiannual Financial Framework 2028–2034. Argues that Google's deployment of LearnLM to 170 million accounts via existing Workspace infrastructure constitutes cognitive lock-in without…
  datacite Pochmann, Matthias 2026 置信度 0.66 EU MFF 2028-2034moonshoteducation infrastructurecognitive sovereigntyAI in education
• Deep Learning Driven Threat Detection in Modern Database Management Systems
  Contemporary Database Management Systems (DBMS) support critical data-intensive applications across banking, e-commerce, healthcare, and government services. However, with the increasing adoption of distributed and cloud-based architectures, DBMS have become m…
  datacite Deshraj Bairwa, YADAVA, PRAMOD 2026 置信度 0.66
• Deep Learning Driven Threat Detection in Modern Database Management Systems
  Contemporary Database Management Systems (DBMS) support critical data-intensive applications across banking, e-commerce, healthcare, and government services. However, with the increasing adoption of distributed and cloud-based architectures, DBMS have become m…
  datacite Deshraj Bairwa, YADAVA, PRAMOD 2026 置信度 0.66
• Federated Learning Architectures For Privacy-Preserving Intrusion Detection In IoT Networks
  The rapid proliferation of Internet of Things (IoT) devices and smart infrastructure has led to an exponential surge in network traffic, rendering traditional security perimeters increasingly vulnerable. Intrusion Detection Systems (IDS) serve as a critical fr…
  datacite Mayur Girish Taunk, Jigarkumar Ambalal Patel 2022 置信度 0.66
• Federated Learning Architectures For Privacy-Preserving Intrusion Detection In IoT Networks
  The rapid proliferation of Internet of Things (IoT) devices and smart infrastructure has led to an exponential surge in network traffic, rendering traditional security perimeters increasingly vulnerable. Intrusion Detection Systems (IDS) serve as a critical fr…
  datacite Mayur Girish Taunk, Jigarkumar Ambalal Patel 2022 置信度 0.66
• How AI can be Powerful in Providing Security for IoT
  The proliferation of the Internet of Things (IoT) globally has revolutionized industries such as healthcare, smart cities, agriculture, and manufacturing. However, this widespread integration has brought along critical security concerns, making IoT networks vu…
  datacite Mr. Prashant Tanaji Bagade, Mr. Nayan Vijay Patil 2026 置信度 0.66
• How AI can be Powerful in Providing Security for IoT
  The proliferation of the Internet of Things (IoT) globally has revolutionized industries such as healthcare, smart cities, agriculture, and manufacturing. However, this widespread integration has brought along critical security concerns, making IoT networks vu…
  datacite Mr. Prashant Tanaji Bagade, Mr. Nayan Vijay Patil 2026 置信度 0.66
• A Hybrid Machine Learning Framework for Anomaly Detection and Electricity Theft Identification in Smart Distribution Networks: A Comprehensive Review
  This paper presents a review of hybrid machine learning approaches for electricity theft detection in smart grids using Advanced Metering Infrastructure (AMI) data. The study analyzes supervised, unsupervised, and hybrid anomaly detection techniques including …
  datacite Lakdeswar, Anjali, Shamkule, Devashree, Awachat, Mansi, Urade, Bobby 2026 置信度 0.66 Smart GridMachine LearningElectricity Theft DetectionIsolation ForestHistGBM
• A Hybrid Machine Learning Framework for Anomaly Detection and Electricity Theft Identification in Smart Distribution Networks: A Comprehensive Review
  This paper presents a review of hybrid machine learning approaches for electricity theft detection in smart grids using Advanced Metering Infrastructure (AMI) data. The study analyzes supervised, unsupervised, and hybrid anomaly detection techniques including …
  datacite Lakdeswar, Anjali, Shamkule, Devashree, Awachat, Mansi, Urade, Bobby 2026 置信度 0.66 Smart GridMachine LearningElectricity Theft DetectionIsolation ForestHistGBM
• HantaWatch: Federated Learning for Hantavirus Genomic Surveillance
  Abstract—Hantavirus genomic surveillance is limited by distributed sequence data, non-IID source heterogeneity, and constrained expert-review capacity. We propose HantaWatch, a federated learning framework that enables laboratories and surveillance sites to co…
  datacite Nanayakkara, shanika, Pokhrel, Shiva 2026 置信度 0.66
• HantaWatch: Federated Learning for Hantavirus Genomic Surveillance
  Abstract—Hantavirus genomic surveillance is limited by distributed sequence data, non-IID source heterogeneity, and constrained expert-review capacity. We propose HantaWatch, a federated learning framework that enables laboratories and surveillance sites to co…
  datacite Nanayakkara, shanika, Pokhrel, Shiva 2026 置信度 0.66
• Federated Learning Aggregation Strategies for Non-IID Data in Massive MIMO Systems
  This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How do different federated learning aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) perform in terms of robustness to non-IID data distributions …
  datacite Assignee Research 2026 置信度 0.66 differentfederatedlearningaggregationstrategies
• Federated Learning Aggregation Strategies for Non-IID Data in Massive MIMO Systems
  This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How do different federated learning aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) perform in terms of robustness to non-IID data distributions …
  datacite Assignee Research 2026 置信度 0.66 differentfederatedlearningaggregationstrategies
• Quantized Large Language Model Inference Latency Scaling in Federated Edge Deployments
  This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the inference latency of quantized large language models scale with the number of concurrent edge devices in a federated learning setup for real-…
  datacite Assignee Research 2026 置信度 0.66 inferencelatencyquantizedlargelanguage
• Quantized Large Language Model Inference Latency Scaling in Federated Edge Deployments
  This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the inference latency of quantized large language models scale with the number of concurrent edge devices in a federated learning setup for real-…
  datacite Assignee Research 2026 置信度 0.66 inferencelatencyquantizedlargelanguage
• ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DRUG DESIGN AND DISCOVERY: A COMPREHENSIVE ANALYSIS
  Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and drug development. This review highlights the application of AI across the drug discovery pipeline, including multiomics data analysis, target identification, pr…
  datacite *1Sachin Panth, 3Poonam Kashyap, 2Deepak Baghel, 1Poonam Kaimaiyan, 3Deepsingh Bhadouriya 2026 置信度 0.66
• ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DRUG DESIGN AND DISCOVERY: A COMPREHENSIVE ANALYSIS
  Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and drug development. This review highlights the application of AI across the drug discovery pipeline, including multiomics data analysis, target identification, pr…
  datacite *1Sachin Panth, 3Poonam Kashyap, 2Deepak Baghel, 1Poonam Kaimaiyan, 3Deepsingh Bhadouriya 2026 置信度 0.66
• Federated Learning Scalability in Heterogeneous IoT Malware Detection Networks
  This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the communication efficiency of federated learning-based malware detection models scale with increasing device heterogeneity across IoT networks,…
  datacite Assignee Research 2026 置信度 0.66 communicationefficiencyfederatedlearning-basedmalware
• Federated Learning Scalability in Heterogeneous IoT Malware Detection Networks
  This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the communication efficiency of federated learning-based malware detection models scale with increasing device heterogeneity across IoT networks,…
  datacite Assignee Research 2026 置信度 0.66 communicationefficiencyfederatedlearning-basedmalware
• AI Integration with Network Pharmacology for Poly Herbal Drug Discovery: Current Advances and Future Scope
  The convergence of Artificial Intelligence (AI) and Network Pharmacology (NP) has emerged as a transformative paradigm in the rational discovery of poly-herbal medicines. Traditional herbal formulations, characterized by multi-component and multi-target pharma…
  datacite Sadiya Prabin*1, K. Hamsika Sri2, Edalada Pavan Kumar3, Gurleen Kaur4, Dr. Md Sayeed Anwar5 2026 置信度 0.66 Network Pharmacology; Poly-herbal Drug Discovery; Artificial Intelligence; Machine Learning; Graph Neural Networks; Traditional Chinese Medicine; Ayurveda; Multi-target Pharmacology; Drug-Target Interaction; Phytochemoinformatics
• AI Integration with Network Pharmacology for Poly Herbal Drug Discovery: Current Advances and Future Scope
  The convergence of Artificial Intelligence (AI) and Network Pharmacology (NP) has emerged as a transformative paradigm in the rational discovery of poly-herbal medicines. Traditional herbal formulations, characterized by multi-component and multi-target pharma…
  datacite Sadiya Prabin*1, K. Hamsika Sri2, Edalada Pavan Kumar3, Gurleen Kaur4, Dr. Md Sayeed Anwar5 2026 置信度 0.66 Network Pharmacology; Poly-herbal Drug Discovery; Artificial Intelligence; Machine Learning; Graph Neural Networks; Traditional Chinese Medicine; Ayurveda; Multi-target Pharmacology; Drug-Target Interaction; Phytochemoinformatics
• Machine Learning Efforts That Enhance Personalized Patient Care and Chronic Disease Management
  The growing burden of chronic diseases has underscored the urgent need for personalized, data-driven approaches to healthcare delivery. Machine learning (ML) has emerged as a transformative technology capable of enhancing chronic disease management through pre…
  datacite Adeyinka, Adepeju Ayotunde, Lamina, Yejide, Tawo, Obah Edom, Adeyeye, Yewande Iyimide 等 2022 置信度 0.66 Machine LearningPersonalized Patient CareChronic Disease Management
• Machine Learning Efforts That Enhance Personalized Patient Care and Chronic Disease Management
  The growing burden of chronic diseases has underscored the urgent need for personalized, data-driven approaches to healthcare delivery. Machine learning (ML) has emerged as a transformative technology capable of enhancing chronic disease management through pre…
  datacite Adeyinka, Adepeju Ayotunde, Lamina, Yejide, Tawo, Obah Edom, Adeyeye, Yewande Iyimide 等 2022 置信度 0.66 Machine LearningPersonalized Patient CareChronic Disease Management
• Anomaly Detection in IoT Networks Using Machine Learning and Deep Learning
  The Internet of Things (IoT) has grown rapidly, now connecting over 30 billion heterogeneous devices that generate large volumes of real-time data across domains such as healthcare, smart cities, industrial automation, and transportation. This growth has creat…
  datacite Jain, Hardik 2026 置信度 0.66
• Anomaly Detection in IoT Networks Using Machine Learning and Deep Learning
  The Internet of Things (IoT) has grown rapidly, now connecting over 30 billion heterogeneous devices that generate large volumes of real-time data across domains such as healthcare, smart cities, industrial automation, and transportation. This growth has creat…
  datacite Jain, Hardik 2026 置信度 0.66
• Advances in AI and ML for Cloud Computing: A Review of Algorithms, Challenges, and Innovations
  This review delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing cloud computing. It explores how AI/ML algorithms optimize resource management, enhance system scalability, strengthen security, and reduce operationa…
  datacite Ramamoorthi, Vijay 2025 置信度 0.66 Cloud computing; Artificial Intelligence; Machine Learning; Dynamic resource allocation; Predictive analytics; Sustainability; Federated learning; Explainable AI
• Advances in AI and ML for Cloud Computing: A Review of Algorithms, Challenges, and Innovations
  This review delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing cloud computing. It explores how AI/ML algorithms optimize resource management, enhance system scalability, strengthen security, and reduce operationa…
  datacite Ramamoorthi, Vijay 2025 置信度 0.66 Cloud computing; Artificial Intelligence; Machine Learning; Dynamic resource allocation; Predictive analytics; Sustainability; Federated learning; Explainable AI
• fl-oran-tmc: Federated Learning Benchmark for ColO-RAN Slice SLA Prediction
  Local PyTorch federated-learning pipeline for ColO-RAN O-RAN slice SLA forecasting. Cross-architecture empirical benchmark on the Colosseum/ColO-RAN public dataset: 3-arch core panel (LSTM, Mamba, Spiking-SSM) plus a 2-arch recent-SOTA extension (xLSTM, Mamba-…
  datacite Tsai, Hsiu-Chi 2026 置信度 0.66 federated-learningO-RANslice-SLA-predictionstate-space-modelsspiking-neural-networks
• fl-oran-tmc: Federated Learning Benchmark for ColO-RAN Slice SLA Prediction
  Local PyTorch federated-learning pipeline for ColO-RAN O-RAN slice SLA forecasting. Cross-architecture empirical benchmark on the Colosseum/ColO-RAN public dataset: 3-arch core panel (LSTM, Mamba, Spiking-SSM) plus a 2-arch recent-SOTA extension (xLSTM, Mamba-…
  datacite Tsai, Hsiu-Chi 2026 置信度 0.66 federated-learningO-RANslice-SLA-predictionstate-space-modelsspiking-neural-networks
• A Comprehensive Literature Review on Federated Machine Learning for Privacy-Preserving Cyber Threat Detection in Distributed Network Environments
  Cloud computing and IoT devices are actually growing very fast, and this definitely makes cyber attacks more complex and common. Traditional systems for catching cyber attacks actually have problems with new threats and keeping data safe. These old methods def…
  datacite Research Scholar Sunil Chandolu, Professor Dr.Pankaj Khairnar 2024 置信度 0.66
• A Comprehensive Literature Review on Federated Machine Learning for Privacy-Preserving Cyber Threat Detection in Distributed Network Environments
  Cloud computing and IoT devices are actually growing very fast, and this definitely makes cyber attacks more complex and common. Traditional systems for catching cyber attacks actually have problems with new threats and keeping data safe. These old methods def…
  datacite Research Scholar Sunil Chandolu, Professor Dr.Pankaj Khairnar 2024 置信度 0.66
• Small language models for clinical natural language processing: A systematic review
  This systematic review evaluates small language models (SLMs, ≤7B parameters) for clinical NLP tasks in ambient and agentic healthcare workflows. We propose the 3R evaluation framework (Real-time, Resource-constrained, Regulation-aware) to assess SLM fitness f…
  datacite Dawa Chyophel Lepcha 2026 置信度 0.66 Health Information TechnologyTranslational Medical ResearchComputational EngineeringMedicine and Health SciencesElectrical and Computer Engineering
• Toward next-generation machine learning and deep learning for spatial omics.
  pubmed Zirem Y, Fournier I, Salzet M 2026 Mar 1 置信度 0.82
• Strategies for Class-Imbalanced Learning in Multi-Sensor Medical Imaging.
  pubmed Zhou D, Gao S, Huang X 2026 Mar 23 置信度 0.82
• Clinical AI in Radiology: Foundations, Trends, Applications, and Emerging Directions.
  pubmed Hartsock I, Koutsoubis N, Ahmed S, Parker N 等 2026 Mar 13 置信度 0.82
• The Current Landscape of Artificial Intelligence in Positron Emission Tomography (PET) Imaging Across the Cancer Continuum.
  pubmed Zar WYT, Kim MR, Ghose A, Adeleke S 等 2026 Mar 23 置信度 0.82
• Assessing the Role of Vocal Plasticity in Sociospatial Coordination.
  pubmed Mercado E 3rd, Hyland Bruno J 2026 Mar 12 置信度 0.82
• An embedded deep learning framework for real-time violence detection and alert generation.
  pubmed Salman M, Abbas N, Ur Rahman SI, Al Alshaikh M 等 2026 Mar 28 置信度 0.82
• AI innovations for ovarian and endometrial cancer diagnosis: Methodological challenges and engineering roadmap.
  pubmed Angeline J, Bethanney Janney J, Arvind N 2026 Jun 置信度 0.82
• Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.
  pubmed Dang M, Liu B, Chen Y, Zhang Z 2026 置信度 0.82
• Fundamentals of big data and artificial intelligence in transfusion medicine.
  pubmed Turki AT, Brieske CM, Gurkan UA, Scheidler KM 等 2026 Apr 置信度 0.82
• Innovative Applications and Challenges of Artificial Intelligence in the Whole-Course Management of Chronic Obstructive Pulmonary Disease.
  pubmed Chen S, Xing S, Zhang G, Qiu F 2026 置信度 0.82
• Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.
  pubmed Wang X, Xiong D, Cui S, Duan B 等 2026 Mar 19 置信度 0.82
• Harnessing artificial intelligence in healthcare: Advancing diagnosis, treatment, and patient-centered care.
  pubmed Das A, Arora D, Deswal G, Grewal AS 等 2026 Jun 置信度 0.82
• Application of AI in Cyberattack Detection: A Review.
  pubmed Boateng YJ, Mim NJ, Akhter N, Naha R 等 2026 Feb 28 置信度 0.82
• AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell Manufacturing.
  pubmed Foti R, Storti G, Palmesano M, Calicchia A 等 2026 Mar 4 置信度 0.82
• Artificial intelligence in oncology: Current status and possibilities (Review).
  pubmed Roy A, Bhoyar A, Ahirwar A, Pawade Y 等 2026 Mar-Apr 置信度 0.82
• Machine Learning-Based Risk Prediction Models for Pregnancy-Related Syndromes.
  pubmed Wu Y 2026 Mar 置信度 0.82
• Narrative review: the research advances of artificial intelligence in the prediction of pulmonary nodule growth.
  pubmed Huang W, Ye M, Wang B, Li Q 等 2026 Feb 28 置信度 0.82
• Artificial intelligence-enabled multi-omics biomarkers for immune checkpoint blockade: mechanisms, predictive modeling, and clinical translation.
  pubmed Wang X, Xiong D, Cui S, Duan B 等 2026 置信度 0.82
• AI-powered radiogenomics: imaging-driven diagnosis and discovery of cancer's molecular and cellular landscape.
  pubmed Qiu HF, Zhu JK 2026 Mar 10 置信度 0.82
• Artificial Intelligence and Machine Learning Applications in Fibromuscular Dysplasia: Transforming Diagnosis, Risk Stratification, and Clinical Decision-Making.
  pubmed Hamza A, Faiz M, Iftikhar A, Badal B 等 2026 Mar 6 置信度 0.82
• Artificial Intelligence in Colon Cancer: Advances, Challenges, and Future Perspectives.
  pubmed Bitere OA, Minciuna CE, Andras C, Almarii F 等 2026 Feb 置信度 0.82
• Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.
  pubmed Alshorman J, Mehran MJ, Bahrami Y, Mohammadzadeh S 等 2026 Mar 6 置信度 0.82
• Utility of Artificial Intelligence in Stereotactic Radiosurgery for Vestibular Schwannomas: A Systematic Review.
  pubmed Pandey K, Kumar AG, Girish A, Verma S 等 2026 Mar 置信度 0.82
• Artificial Intelligence-Enabled Multi-Omics for Predicting Immune Checkpoint Inhibitor Response and Resistance.
  pubmed Wang X, He J, Ding G, Tang Y 等 2026 置信度 0.82
• Artificial Intelligence and Big Data in Urological Oncology: From Radiomics to Real-World Evidence.
  pubmed Katsimperis S, Tzelves L, Kyriazis I, Neofytou P 等 2026 Jan 置信度 0.82
• Nature meets machine: the AI renaissance in natural product drug discovery.
  pubmed Muthuraj R, Chandrasekaran J 2026 Mar 2 置信度 0.82
• Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.
  pubmed Mou C, Yang J, Wu Q, Qin L 等 2026 置信度 0.82
• Securing Data in Vehicles: Privacy-Preserving Frameworks for Dynamic CAV Environments.
  pubmed Hammedi R, Brown DJ, Kaiwartya O, Gaur P 2026 置信度 0.82
• A Review of Federated Large Language Models for Industry 4.0.
  pubmed Jing F, Zhang Y, Gao M, Zhang X 等 2026 置信度 0.82