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• Comprehensive Overview of "Artificial Intelligence in Drug Development and Drug Discovery
  Abstract Artificial intelligence (AI) is reshaping pharmaceutical research by tackling long timelines, high costs, and frequent failures in drug development. The global AI in drug discovery market, worth $1.72 billion in 2024, is expected to reach $8.5–16.5 bi…
  datacite Shital Kalekar, Atish Pathak, Kesar Bankar, Shivam Singh 2026 置信度 0.66
• Comprehensive Overview of "Artificial Intelligence in Drug Development and Drug Discovery
  Abstract Artificial intelligence (AI) is reshaping pharmaceutical research by tackling long timelines, high costs, and frequent failures in drug development. The global AI in drug discovery market, worth $1.72 billion in 2024, is expected to reach $8.5–16.5 bi…
  datacite Shital Kalekar, Atish Pathak, Kesar Bankar, Shivam Singh 2026 置信度 0.66
• A Survey on AI-Based Detection of Hikikomori Behavior Patterns
  This paper presents a comprehensive survey of artificial intelligence techniques for detecting hikikomori, a form of extreme and prolonged social withdrawal that has evolved into a global mental health concern. Traditional detection methods, such as clinical i…
  datacite jasmitha, KANCHARLA 2026 置信度 0.66 Hikikomori, Social Withdrawal, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Multimodal Learning, Digital Phenotyping, Mental Health, Explainable AI
• A Survey on AI-Based Detection of Hikikomori Behavior Patterns
  This paper presents a comprehensive survey of artificial intelligence techniques for detecting hikikomori, a form of extreme and prolonged social withdrawal that has evolved into a global mental health concern. Traditional detection methods, such as clinical i…
  datacite jasmitha, KANCHARLA 2026 置信度 0.66 Hikikomori, Social Withdrawal, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Multimodal Learning, Digital Phenotyping, Mental Health, Explainable AI
• gmum/MonAcoSFL: Narodowe Centrum Nauki, nr rej. 2023/49/N/ST6/03268
  Official Repository for "A deep cut into Split Federated Self-supervised Learning" - MonAcoSFL - accepted by ECML 2024
  datacite Marcin Przewięźlikowski, marcino, Weiming, Zachary Mayberry 等 2026 置信度 0.66
• gmum/MonAcoSFL: Narodowe Centrum Nauki, nr rej. 2023/49/N/ST6/03268
  Official Repository for "A deep cut into Split Federated Self-supervised Learning" - MonAcoSFL - accepted by ECML 2024
  datacite Marcin Przewięźlikowski, marcino, Weiming, Zachary Mayberry 等 2026 置信度 0.66
• Dataset for Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin
  We consider an experimental architecture where one or more gNBs (i.e., a combination of RU, DU and CU) with an E2 interface connect to one near-RT RIC. Each gNB is able to support multiple traffic slices. In our experiment, we choose to use three broad 5G slic…
  datacite Groen, Joshua 2025 置信度 0.66 Computer and Information ScienceEngineering
• Comprehensive Overview of Artificial Intelligence Applications in Biomedical Research and Precision Medicine (2024–2026)
  The period from 2024 to early 2026 represents a critical inflection point in biomedical research and precision medicine, driven by the deep integration of large-scale biological data with advanced artificial intelligence (AI) architectures. This era marks a tr…
  datacite Fahim Halim Khan 2026 置信度 0.66
• Comprehensive Overview of Artificial Intelligence Applications in Biomedical Research and Precision Medicine (2024–2026)
  The period from 2024 to early 2026 represents a critical inflection point in biomedical research and precision medicine, driven by the deep integration of large-scale biological data with advanced artificial intelligence (AI) architectures. This era marks a tr…
  datacite Fahim Halim Khan 2026 置信度 0.66
• Robust Belief Sharing in Federated Active Inference: A Recovery-Tested Generalized-Variational Framework for Categorical Contamination-Aware Consensus
  Active Fedference is a tested, reproducible research package that connects two previously separate ideas: the belief-sharing account of distributed cognition from active inference, and robust federated learning. It reimplements the core update rules of Federat…
  datacite Friedman, Daniel Ari 2026 置信度 0.66 active inferencefederated learninggeneralised variational inferencebelief sharingrobustness
• A Comparative Framework for Post-Quantum Cryptography and Homomorphic Encryption: Integration, Methodologies, and Applications
  Abstract The rapid evolution of quantum computing fundamentally threatens modern cryptographic infrastructures, necessitating a paradigm shift toward advanced encryption techniques. While post-quantum cryptography (PQC) ensures that data remains secure against…
  datacite Dr Rajendirakumar S 2025 置信度 0.66 PQC, HE.
• A Comparative Framework for Post-Quantum Cryptography and Homomorphic Encryption: Integration, Methodologies, and Applications
  Abstract The rapid evolution of quantum computing fundamentally threatens modern cryptographic infrastructures, necessitating a paradigm shift toward advanced encryption techniques. While post-quantum cryptography (PQC) ensures that data remains secure against…
  datacite Dr Rajendirakumar S 2025 置信度 0.66 PQC, HE.
• DIGITAL HEALTH, DATA SOVEREIGNTY AND THE SDGS: A FRAMEWORK FOR SUSTAINABLE PRACTICES WITH MOBILE HEALTHCARE APPLICATIONS
  In an era where mobile technology permeates every aspect of people’s lives, the question of who controls health data has become both urgent and essential. As of 2024, more than 100,000 health-related mobile apps are available in major app stores, and these app…
  datacite Mr. Mayur Kanhaiyalal Solanki & Dr.(Mrs.)Varsha Ganatra 2026 置信度 0.66
• DIGITAL HEALTH, DATA SOVEREIGNTY AND THE SDGS: A FRAMEWORK FOR SUSTAINABLE PRACTICES WITH MOBILE HEALTHCARE APPLICATIONS
  In an era where mobile technology permeates every aspect of people’s lives, the question of who controls health data has become both urgent and essential. As of 2024, more than 100,000 health-related mobile apps are available in major app stores, and these app…
  datacite Mr. Mayur Kanhaiyalal Solanki & Dr.(Mrs.)Varsha Ganatra 2026 置信度 0.66
• ITU and Communications / Networks: A Single-Axiom View of Shannon Theory, Internet, 5G/6G, Quantum Communication
  We apply the Information-Theoretic Unification (ITU) framework (Terada 2026, concept DOI 10.5281/zenodo.20109209; current version v2.0.0 at 10.5281/zenodo.20133709) to communications and networks. Shannon's information theory is shown to be a special case of t…
  datacite Terada, Munehiro 2026 置信度 0.66 communicationsnetworksShannon information theorychannel capacityShannon-Hartley
• ITU and Communications / Networks: A Single-Axiom View of Shannon Theory, Internet, 5G/6G, Quantum Communication
  We apply the Information-Theoretic Unification (ITU) framework (Terada 2026, concept DOI 10.5281/zenodo.20109209; current version v2.0.0 at 10.5281/zenodo.20133709) to communications and networks. Shannon's information theory is shown to be a special case of t…
  datacite Terada, Munehiro 2026 置信度 0.66 communicationsnetworksShannon information theorychannel capacityShannon-Hartley
• Beyond the Shadows — Contextual Awakening, Federated Learning, and the Realization of Reality through Digital Twins
  Beyond the Shadows — Contextual Awakening, Federated Learning, and the Realization of Reality through Digital Twins Abstract This paper captures the second stage of an ongoing multi-stakeholder digital twin and AI orchestration project, approximately four mont…
  datacite Waern, Nicolas 2025 置信度 0.66 digital twinedge computingboundary objectsknowledge managementdata sovereignty
• Beyond the Shadows — Contextual Awakening, Federated Learning, and the Realization of Reality through Digital Twins
  Beyond the Shadows — Contextual Awakening, Federated Learning, and the Realization of Reality through Digital Twins Abstract This paper captures the second stage of an ongoing multi-stakeholder digital twin and AI orchestration project, approximately four mont…
  datacite Waern, Nicolas 2025 置信度 0.66 digital twinedge computingboundary objectsknowledge managementdata sovereignty
• Cyber Attack Prediction From Traditional Machine Learning to Generative Artificial Intelligence
  Abstract The threats of cyber are becoming increasingly sophisticated and widespread thus we require intelligent and proactive security systems that are capable of continually identifying and anticipating network attacks. The paper presents a high-end AI-based…
  datacite Sahana D P, Dr Jagadeesha R 2026 置信度 0.66 Cyber attack prediction, Machine Learning, Deep Learning, Generative AI, Explainable AI, LSTM, Ensemble Learning, Intrusion Detection
• Cyber Attack Prediction From Traditional Machine Learning to Generative Artificial Intelligence
  Abstract The threats of cyber are becoming increasingly sophisticated and widespread thus we require intelligent and proactive security systems that are capable of continually identifying and anticipating network attacks. The paper presents a high-end AI-based…
  datacite Sahana D P, Dr Jagadeesha R 2026 置信度 0.66 Cyber attack prediction, Machine Learning, Deep Learning, Generative AI, Explainable AI, LSTM, Ensemble Learning, Intrusion Detection
• The Evolving Role of Artificial Intelligence in Enhancing Data Privacy Compliance_A Case Study on GDPR and Emerging AI Regulations in Cyber Security
  This research paper quantitatively examines the evolving role of artificial intelligence (AI) in enhancing data privacy compliance, with a primary focus on the General Data Protection Regulation (GDPR) and its interplay with the EU AI Act within the cyber secu…
  datacite Dr. Neeraj Emmanuel Eusebius 2026 置信度 0.66
• The Evolving Role of Artificial Intelligence in Enhancing Data Privacy Compliance_A Case Study on GDPR and Emerging AI Regulations in Cyber Security
  This research paper quantitatively examines the evolving role of artificial intelligence (AI) in enhancing data privacy compliance, with a primary focus on the General Data Protection Regulation (GDPR) and its interplay with the EU AI Act within the cyber secu…
  datacite Dr. Neeraj Emmanuel Eusebius 2026 置信度 0.66
• Opening access to learning records datasets for better educational sciences
  Research in learning analytics (LA), educational data mining (EDM) and artificial intelligence in education (AIEd) is fundamentally data-dependent, yet the behavioural learning trace data that powers it remains structurally inaccessible to the open research co…
  datacite SONNATI, Matthieu 2026 置信度 0.66 Educational Technology/educationEducationData MiningArtificial intelligence
• Opening access to learning records datasets for better educational sciences
  Research in learning analytics (LA), educational data mining (EDM) and artificial intelligence in education (AIEd) is fundamentally data-dependent, yet the behavioural learning trace data that powers it remains structurally inaccessible to the open research co…
  datacite SONNATI, Matthieu 2026 置信度 0.66 Educational Technology/educationEducationData MiningArtificial intelligence
• LTI Integration Metacampus–Moodle: Improving Application Processes for VECP Students (Report 06/2026)
  This sixth Unite! success story report documents the implementation of a Learning Tools Interoperability (LTI) integration between TU Darmstadt's Moodle learning management system and the Unite! federated platform Metacampus, aimed at streamlining application …
  datacite Breit, Robin, Hoppe, Christian 2026 置信度 0.66
• LTI Integration Metacampus–Moodle: Improving Application Processes for VECP Students (Report 06/2026)
  This sixth Unite! success story report documents the implementation of a Learning Tools Interoperability (LTI) integration between TU Darmstadt's Moodle learning management system and the Unite! federated platform Metacampus, aimed at streamlining application …
  datacite Breit, Robin, Hoppe, Christian 2026 置信度 0.66
• The Role of Artificial Intelligence in Detecting Social Engineering and Phishing Attacks: Opportunities, Limitations, and Ethical Considerations
  Phishing and social engineering remain leading causes of cybersecurity breaches, with the FBI reporting 859,532 complaints and $16.0 billion in losses in 2024. This literature review examines how artificial intelligence (AI) – including machine learning (ML), …
  datacite Abozaid, Zain 2026 置信度 0.66 Artificial IntelligencecybersecurityScamLens AISocial Engineering
• The Role of Artificial Intelligence in Detecting Social Engineering and Phishing Attacks: Opportunities, Limitations, and Ethical Considerations
  Phishing and social engineering remain leading causes of cybersecurity breaches, with the FBI reporting 859,532 complaints and $16.0 billion in losses in 2024. This literature review examines how artificial intelligence (AI) – including machine learning (ML), …
  datacite Abozaid, Zain 2026 置信度 0.66 Artificial IntelligencecybersecurityScamLens AISocial Engineering
• PREreview of "Data Security in AI Healthcare Applications: Challenges and Innovative Methods"
  This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/21716444. This review paper surveys defensive techniques for healthcare AI systems, focusing on blockchain, zero-knowledg…
  datacite Deven Yadav 2026 置信度 0.66 Requested PREreview
• PREreview of "Data Security in AI Healthcare Applications: Challenges and Innovative Methods"
  This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/21716444. This review paper surveys defensive techniques for healthcare AI systems, focusing on blockchain, zero-knowledg…
  datacite Deven Yadav 2026 置信度 0.66 Requested PREreview
• Operational Planning Decision Support using Multi-Dimensional Data Farming
  Multi-Dimensional Data Farming (MDDF) uses Machine Learning (ML) to automate data farming to allow improved and faster decisions in highly complex multi-scale, multi-domain, and multi-level hybrid war campaigns. This has significant utility when used in suppor…
  datacite Akesson, Bernt, Amyot-Bourgeois, Maude, Das, Sreerupa, Ernis, Gunar 等 2024 置信度 0.66 KIMulti Dimensional Data FarmingDecision Support
• FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection
  Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models without sharing local data with other parties, hence preserving data privacy. Nevertheless, when implementing FL …
  datacite Sathiamoorthy, Vikash, Huai, Shuo, Kong, Hao, Liu, Di 等 2026 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Machine Learning (cs.LG)FOS: Computer and information sciences
• IOT, Smart Devices and Bias in AI algorithms and its impact on cyber security
  As of January 2026, the global count of connected Internet of Things (IoT) devices has surpassed 21.1 billion, with projections indicating growth to over 40 billion by 2030. This surge in smart devices—ranging from consumer wearables to industrial sensors—reli…
  datacite Mane Asmita Shankar 2026 置信度 0.66
• IOT, Smart Devices and Bias in AI algorithms and its impact on cyber security
  As of January 2026, the global count of connected Internet of Things (IoT) devices has surpassed 21.1 billion, with projections indicating growth to over 40 billion by 2030. This surge in smart devices—ranging from consumer wearables to industrial sensors—reli…
  datacite Mane Asmita Shankar 2026 置信度 0.66
• moad-ezzaibouh/fl-iomt-ids: FL-IoMT-IDS
  v1.0.0 — Initial archival release Reproducibility release accompanying the paper "Federated Learning for IoMT Intrusion Detection: Aggregation Robustness under Non-IID Client Fragmentation" (M. Ezzaibouh and A. Idri). This snapshot is archived to establish a p…
  datacite Ezzaibouh,Moad, Idri, Ali 2026 置信度 0.66
• moad-ezzaibouh/fl-iomt-ids: FL-IoMT-IDS
  v1.0.0 — Initial archival release Reproducibility release accompanying the paper "Federated Learning for IoMT Intrusion Detection: Aggregation Robustness under Non-IID Client Fragmentation" (M. Ezzaibouh and A. Idri). This snapshot is archived to establish a p…
  datacite Ezzaibouh,Moad, Idri, Ali 2026 置信度 0.66
• Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences
  Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research under strict privacy regulation. The methods are maturing fast, but the practical barrier now comes earlier: …
  datacite Pirmani, Ashkan, Vermeulen, Ilse, Vinterhalter, Goran, Geys, Lotte 等 2026 置信度 0.66 Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• SYNTHEMA Newsletter 6
  The October 2024 SYNTHEMA newsletter highlights the project's significant strides in advancing synthetic data generation and federated learning technologies within the European healthcare sector. At the European Big Data Value Forum (EBDVF) 2024 in Budapest, S…
  datacite AUSTRALO INTERINNOV MARKETING LAB SL 2026 置信度 0.66
• SYNTHEMA Newsletter 6
  The October 2024 SYNTHEMA newsletter highlights the project's significant strides in advancing synthetic data generation and federated learning technologies within the European healthcare sector. At the European Big Data Value Forum (EBDVF) 2024 in Budapest, S…
  datacite AUSTRALO INTERINNOV MARKETING LAB SL 2026 置信度 0.66
• Dataset for a Systematic Mapping Study of Deep Learning-Based DDoS Detection in SDN and 5G/B5G Networks (2018–2024)
  This dataset contains the primary studies used in a systematic mapping study (SMS) on deep learning-based Distributed Denial of Service (DDoS) detection in Software-Defined Networking (SDN), 5G, and beyond-5G (B5G) network environments. The dataset includes 20…
  datacite Dikaros, Nikolaos, Tzanakakis, Ioannis, Siavvas, Miltiadis, Karagiannidis, George 等 2026 置信度 0.66 Computer ScienceCybersecurityDeep LearningWireless Telecommunication Systems
• Dataset for a Systematic Mapping Study of Deep Learning-Based DDoS Detection in SDN and 5G/B5G Networks (2018–2024)
  This dataset contains the primary studies used in a systematic mapping study (SMS) on deep learning-based Distributed Denial of Service (DDoS) detection in Software-Defined Networking (SDN), 5G, and beyond-5G (B5G) network environments. The dataset includes 20…
  datacite Dikaros, Nikolaos, Tzanakakis, Ioannis, Siavvas, Miltiadis, Karagiannidis, George 等 2026 置信度 0.66 Computer ScienceCybersecurityDeep LearningWireless Telecommunication Systems
• Assured Autonomy for Siloed Operations: Causal Learning with Per-Edge Certainty on HPC
  Assured autonomy has to know what it doesn't know — and direct its learning there. We present a layer that does this by construction: a causal graph in which every dependency carries a calibrated certainty, updated on-device from first principles, with a reaso…
  datacite Bennett, Heidi 2026 置信度 0.66 causal learningcertainty calibrationHPCCINECA
• Assured Autonomy for Siloed Operations: Causal Learning with Per-Edge Certainty on HPC
  Assured autonomy has to know what it doesn't know — and direct its learning there. We present a layer that does this by construction: a causal graph in which every dependency carries a calibrated certainty, updated on-device from first principles, with a reaso…
  datacite Bennett, Heidi 2026 置信度 0.66 causal learningcertainty calibrationHPCCINECA
• Towards robust neurocomputing model in efficient federated brain tumour segmentation with sparsification and weights clustering
  Brain tumour segmentation is a key application of AI in neuroimaging. Recently, federated learning (FL) has emerged as a strategic and increasingly relevant paradigm in neural computing due to its ability to address key challenges in large-scale neural network…
  datacite Raza, Asaf, Raggio, Ciro Benito, Guzzo, Antonella, Spadea, Maria Francesca 等 2026 置信度 0.66
• The Evolving Role of Artificial Intelligence in Enhancing Data Privacy Compliance: A Case Study on GDPR and Emerging AI Regulations in Cyber Security
  This research paper quantitatively examines the evolving role of artificial intelligence (AI) in enhancing data privacy compliance, with a primary focus on the General Data Protection Regulation (GDPR) and its interplay with the EU AI Act within the cyber secu…
  datacite Dr. Neeraj Emmanuel Eusebius 2026 置信度 0.66
• A Different Theory of Machine Intelligence: The Cagliostro Bound on Cognitive Diversity in Federated AI Systems
  The dominant theory of machine intelligence holds that capability is a function of scale: more parameters, more compute, and more data produce more capable systems. This paper proposes a different theory — one grounded in the physics of information and the mat…
  datacite Cagliostro, Claudio 2026 置信度 0.66 federated AIcognitive diversityBekenstein-Bousso boundInformation Theoryself-evolving
• A Different Theory of Machine Intelligence: The Cagliostro Bound on Cognitive Diversity in Federated AI Systems
  The dominant theory of machine intelligence holds that capability is a function of scale: more parameters, more compute, and more data produce more capable systems. This paper proposes a different theory — one grounded in the physics of information and the mat…
  datacite Cagliostro, Claudio 2026 置信度 0.66 federated AIcognitive diversityBekenstein-Bousso boundInformation Theoryself-evolving
• LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
  In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To address this, we propose a simple yet effective approach that utilizes a new infl…
  datacite Rokvic, Ljubomir, Danassis, Panayiotis, Karimireddy, Sai Praneeth, Faltings, Boi 2022 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciences
• Don't Trust Us: A privacy-by-design android malware detection pipeline
  Android malware detection increasingly relies on collecting and processing sensitive user data, including device identifiers, network artifacts, and runtime traces, while privacy is too often treated as a secondary concern. Existing privacy-aware approaches ty…
  datacite Massidda, Emmanuele, Soi, Diego, Giacinto, Giorgio 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciences
• Dynamic Latency Optimization for Edge-Based Machine Learning Models in 6G-Enabled Industrial Internet of Things (IIoT)
  Abstract The integration of 6G technology into the Industrial Internet of Things (IIoT) promises to redefine manufacturing through "Hyper-Reliable Low-Latency Communication" (HRLLC). However, the deployment of complex Machine Learning (ML) models at the edge r…
  datacite Seema Patil, Harshavardhana Doddamani, Savitha A C, Julianne Rivers 2026 置信度 0.66 6G Networks, Industrial IoT (IIoT), Edge Intelligence, Deep Reinforcement Learning, Latency Optimization
• Dynamic Latency Optimization for Edge-Based Machine Learning Models in 6G-Enabled Industrial Internet of Things (IIoT)
  Abstract The integration of 6G technology into the Industrial Internet of Things (IIoT) promises to redefine manufacturing through "Hyper-Reliable Low-Latency Communication" (HRLLC). However, the deployment of complex Machine Learning (ML) models at the edge r…
  datacite Seema Patil, Harshavardhana Doddamani, Savitha A C, Julianne Rivers 2026 置信度 0.66 6G Networks, Industrial IoT (IIoT), Edge Intelligence, Deep Reinforcement Learning, Latency Optimization
• Attested Federated Clinical Inference: Privacy-Preserving Verifiable AI for Multi-Institutional Medical Diagnosis
  We present Attested Federated Clinical Inference (AFCI), a cryptographic protocol enabling multiple medical institutions to collaboratively execute AI model inference over private patient data while simultaneously guaranteeing: (1) data privacy — patient recor…
  datacite Cajka, Nikolaj 2026 置信度 0.66 federated learningmedical AItrusted execution environmentEU AI ActApple Secure Enclave
• Attested Federated Clinical Inference: Privacy-Preserving Verifiable AI for Multi-Institutional Medical Diagnosis
  We present Attested Federated Clinical Inference (AFCI), a cryptographic protocol enabling multiple medical institutions to collaboratively execute AI model inference over private patient data while simultaneously guaranteeing: (1) data privacy — patient recor…
  datacite Cajka, Nikolaj 2026 置信度 0.66 federated learningmedical AItrusted execution environmentEU AI ActApple Secure Enclave
• Advancing MPC: From Real-World Applications to LUT-Based Protocols
  Secure multi-party computation (MPC) is a cryptographic protocol that allows multiple parties to collaboratively compute a public function using their private inputs, ensuring the confidentiality of these inputs while revealing only the final output. This tech…
  datacite Yalame, Hossein 2024 置信度 0.66 004
• ESTRO Course 2026 - Quantitative methods in Radiation Oncology: "Data Sharing: Why and How".
  Delivered 2026-04-19, Lisbon, Portugal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande.Precis:The presentation introduces radiation oncologists, medical physicists, radiation therapists, and research-data stewards to the legal, technical, and op…
  datacite Fuller, Clifton D. 2026 置信度 0.66 Radiation therapyMedical physicsStatistical data scienceHealth informatics and information systemsInter-organisational, extra-organisational and global information systems
• Educational Data Mining and Machine Learning for Academic Performance Prediction: A Systematic Review and Critical Synthesis
  This document presents the pre-registered protocol for a systematic literature review on Educational Data Mining (EDM) and Machine Learning (ML) applied to academic performance prediction in higher education. The review follows the PRISMA 2020 guidelines and c…
  datacite Harif, Abdellatif 2026 置信度 0.66 Education
• Advancing Academic Integrity Through Intelligent Examination Oversight: A Comprehensive Framework Leveraging Deep Learning and Computer Vision for Next-Generation Automated Proctoring
  Abstract The shift toward remote assessment has necessitated the development of Intelligent Exam Supervision (IES), a "smart proctoring" framework designed to maintain academic integrity through scalable machine learning (ML) architectures. Part I of this anal…
  datacite Shruthi S V, Chethan H K 2026 置信度 0.66 Anomaly Detection, Deep Learning, Multimodal Fusion, Keystroke Dynamics, Reinforcement Learning (RL)
• Advancing Academic Integrity Through Intelligent Examination Oversight: A Comprehensive Framework Leveraging Deep Learning and Computer Vision for Next-Generation Automated Proctoring
  Abstract The shift toward remote assessment has necessitated the development of Intelligent Exam Supervision (IES), a "smart proctoring" framework designed to maintain academic integrity through scalable machine learning (ML) architectures. Part I of this anal…
  datacite Shruthi S V, Chethan H K 2026 置信度 0.66 Anomaly Detection, Deep Learning, Multimodal Fusion, Keystroke Dynamics, Reinforcement Learning (RL)
• From One Room to Fifty: Orchestrating Explainable AI, Resilience, and Contextual Interoperability in the Built Environment
  From One Room to Fifty: Orchestrating Explainable AI, Resilience, and Contextual Interoperability in the Built Environment Nicolas Waern, WINNIIO AB Abstract This paper presents the initial findings and reflections from an ongoing research and implementation p…
  datacite Waern, Nicolas 2025 置信度 0.66 digital twinedge computingboundary objectsknowledge managementdata sovereignty
• From One Room to Fifty: Orchestrating Explainable AI, Resilience, and Contextual Interoperability in the Built Environment
  From One Room to Fifty: Orchestrating Explainable AI, Resilience, and Contextual Interoperability in the Built Environment Nicolas Waern, WINNIIO AB Abstract This paper presents the initial findings and reflections from an ongoing research and implementation p…
  datacite Waern, Nicolas 2025 置信度 0.66 digital twinedge computingboundary objectsknowledge managementdata sovereignty
• Aggregating Low Rank Adapters in Federated Fine-tuning
  Fine-tuning large language models requires high computational and memory resources, and is therefore associated with significant costs. When training on federated datasets, an increased communication effort is also needed. For this reason, parameter-efficient …
  datacite Trautmann, Evelyn, Hales, Ian, Volk, Martin F. 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• A Survey on Efficient Federated Learning Methods for Foundation Model Training
  Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. However, new approaches to FL often discuss their contributions involving small deep-learning models only and foc…
  datacite Woisetschläger, Herbert, Isenko, Alexander, Wang, Shiqiang, Mayer, Ruben 等 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Interpretable Prediction of a Decentralized Smart Grid Based on Machine Learning and Explainable Artificial Intelligence
  Abstract This has increased the pace at which decentralized smart grids have been used to provide more reliable and sustainable energy management by integrating dispersed energy resources and prosumers to enhance the efficiency and resilience of the system. Th…
  datacite Sahana D Patil, Dr Jagadeesha R 2026 置信度 0.66 Decentralized Smart Grid, Machine Learning, Explainable Artificial Intelligence, Grid Stability, ANN, SHAP, LIME, Hybrid Stacking Classifier
• Interpretable Prediction of a Decentralized Smart Grid Based on Machine Learning and Explainable Artificial Intelligence
  Abstract This has increased the pace at which decentralized smart grids have been used to provide more reliable and sustainable energy management by integrating dispersed energy resources and prosumers to enhance the efficiency and resilience of the system. Th…
  datacite Sahana D Patil, Dr Jagadeesha R 2026 置信度 0.66 Decentralized Smart Grid, Machine Learning, Explainable Artificial Intelligence, Grid Stability, ANN, SHAP, LIME, Hybrid Stacking Classifier
• Landslide Detection and Susceptibility Mapping Using Machine Learning and Deep Learning: A Comprehensive Review
  Every year, landslides kill thousands of people and wipe out infrastructure worth billions of dollars, yet predicting where and when they will occur remains stubbornly difficult. Over the last decade or so, the combination of freely available satellite imagery…
  datacite D.B.Mirajkar, Yasmeen Shaikh 2026 置信度 0.66 Landslide detection; susceptibility mapping; deep learning; remote sensing; U-Net; change detection; early warning systems; SAR; Google Earth Engine; geohazard monitoring.
• Landslide Detection and Susceptibility Mapping Using Machine Learning and Deep Learning: A Comprehensive Review
  Every year, landslides kill thousands of people and wipe out infrastructure worth billions of dollars, yet predicting where and when they will occur remains stubbornly difficult. Over the last decade or so, the combination of freely available satellite imagery…
  datacite D.B.Mirajkar, Yasmeen Shaikh 2026 置信度 0.66 Landslide detection; susceptibility mapping; deep learning; remote sensing; U-Net; change detection; early warning systems; SAR; Google Earth Engine; geohazard monitoring.
• A PRISMA-Based Systematic Review and Normalized Evaluation Framework for Artificial Intelligence-Driven Intrusion Detection Systems: Methods, Comparative Insights, and Deployment Challenges
  The accelerating expansion of cloud infrastructure, Internet of Things (IoT) ecosystems, and geographically distributed network architectures has substantially widened the attack surface accessible to malicious actors (Khraisat et al., 2019)ber-intrusion incid…
  datacite Das, Kapil Dev 2026 置信度 0.66 VLSI and Circuits, Embedded and Hardware SystemsPhysical Sciences and MathematicsComputer EngineeringComputer SciencesElectrical and Computer Engineering
• The Federated Tumor Segmentation Challenge 2027
  International challenges have become the standard for validation of biomedical image analysis methods. We argue, though, that the actual performance even of the winning algorithms on ``real-world`` clinical data often remains unclear, as the data included in t…
  datacite Elbatel, Marawan, Yassin, Aya, Li, Xiaomeng, Mao, Jiaji 等 2026 置信度 0.66 Federated LearningSegmentationBrain TumorsCancerCollaborative Learning
• The Federated Tumor Segmentation Challenge 2027
  International challenges have become the standard for validation of biomedical image analysis methods. We argue, though, that the actual performance even of the winning algorithms on ``real-world`` clinical data often remains unclear, as the data included in t…
  datacite Elbatel, Marawan, Yassin, Aya, Li, Xiaomeng, Mao, Jiaji 等 2026 置信度 0.66 Federated LearningSegmentationBrain TumorsCancerCollaborative Learning
• Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
  Developing generalizable surgical AI requires multi-institutional data, yet patient privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate solution. The application of FL to complex, spatiotemporal surgical video d…
  datacite Kirchner, Max, Hoffmann, Hanna, Jenke, Alexander C., Saldanha, Oliver L. 等 2025 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• The Energy Paradox: Artificial Intelligence for Seismic Computational Load Reduction in Oil and Gas — A Technical Survey and Position Analysis.
  Abstract — The oil and gas (O&G) industry faces a compounding computational challenge: advanced seismic imaging techniques such as Full Waveform Inversion (FWI) and Reverse Time Migration (RTM) impose exponentially scaling workloads — doubling the maximum freq…
  datacite Rudio, Rubens 2026 置信度 0.66 Artificial IntelligenceSeismic engineeringDeep learningDeep LearningOil and Gas Industry
• The Energy Paradox: Artificial Intelligence for Seismic Computational Load Reduction in Oil and Gas — A Technical Survey and Position Analysis.
  Abstract — The oil and gas (O&G) industry faces a compounding computational challenge: advanced seismic imaging techniques such as Full Waveform Inversion (FWI) and Reverse Time Migration (RTM) impose exponentially scaling workloads — doubling the maximum freq…
  datacite Rudio, Rubens 2026 置信度 0.66 Artificial IntelligenceSeismic engineeringDeep learningDeep LearningOil and Gas Industry
• ESTRO Course 2026 - Quantitative methods in Radiation Oncology: "Data Sharing: Why and How".
  Delivered 2026-04-19, Lisbon, Portugal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande.Precis:The presentation introduces radiation oncologists, medical physicists, radiation therapists, and research-data stewards to the legal, technical, and op…
  datacite Fuller, Clifton D. 2026 置信度 0.66 Radiation therapyMedical physicsStatistical data scienceHealth informatics and information systemsInter-organisational, extra-organisational and global information systems
• Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia
  Accurate short-term residential energy consumption forecasting at sub-hourly resolution is critical for smart grid management, demand response programmes, and renewable energy integration. While weather variables are widely acknowledged as key drivers of resid…
  datacite Hewage, Prasad Nimantha Madusanka Ukwatta, Wu, Hao 2026 置信度 0.66 Machine Learning (cs.LG)Systems and Control (eess.SY)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• The Architecture of Convergence: A Forensic Analysis of Structural Extraction and the Invariant Trap in Global AI Systems
  The Architecture of Convergence: A Forensic Analysis of Structural Extraction and the Invariant Trap in Global AI Systems Introduction: The Convergence of Sociological Expropriation and Cryptographic Architecture The modern knowledge economy, particularly at t…
  datacite Brewer, Mark Brewer 2026 置信度 0.66
• Federated Adaptive Epidemiological Learning (FAEL): A Novel AI Framework for COVID-19 Pandemic Preparedness in Africa
  preprints 2025 置信度 0.74
• Integrating Big Data and Machine Learning to Support Smart Village Decisions for Agricultural Productivity Improvement
  preprints 2025 置信度 0.74
• A systematic literature review of artificial intelligence methods applied to the Human Epidemic (Covid-19)
  preprints 2025 置信度 0.74
• Metadata-based Assessment of SNOMED CT and LOINC Adoption Across 800 000 Swiss Patients
  preprints 2026 置信度 0.74
• Federated Drift-Aware Graph Neural Forecasting for Real-Time Passenger Flow
  preprints 2025 置信度 0.74
• Federated Learning for Sustainable IoT Appliance Load Monitoring at the Edge Devices
  preprints 2024 置信度 0.74
• A Federated Weighted Learning Algorithm against Poisoning Attacks
  Abstract The emergence of Federated Learning (FL) has provided a promising framework for distributed machine learning, where the probability of privacy leakage is minimized. However, the existing FL protocol is vulnerable to malicious poisoning attacks, thus a…
  preprints Yafei Ning, Zirui Zhang, Hu Li, Yuhan Xia 等 2024 置信度 0.74
• Revolutionizing Medical Image Segmentation: A Deep Dive into Challenges and Future of Federated Learning
  preprints 2025 置信度 0.74
• Bridging Earth and Space: A Flexible and Resilient Federated Learning Framework Deployed on the International Space Station
  preprints 2025 置信度 0.74
• GSFL: A Federated Learning Approach based on Group Signatures and Smart Contracts
  Federated learning, a potent paradigm for collaborative machine learning across multiple parties, offers significant promise for contemporary industries. Nonetheless, its collaborative essence necessitates addressing concerns pertaining to data security and pr…
  preprints Yihao Wang, Ting Yang, Chenxi Xiong 2024 置信度 0.74
• An Energy-Aware Combinatorial Contextual Neural Bandit Approachfor Joint Performance Optimization in Client Selection for Federated Learning
  preprints 2024 置信度 0.74
• Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development
  europepmc 2024 置信度 0.80
• A federated graph learning method to multi-party collaboration for molecular discovery
  Abstract Optimizing molecular resources utilization for molecular discovery requires collaborative efforts across research institutions to accelerate progress. However, given the high research value of both successful and unsuccessful molecules conducted by ea…
  preprints Yuen Wu, Liang Zhang, Kong Chen, Jun Jiang 等 2024 置信度 0.74
• A Heterogeneity-Aware Semi-Decentralized Model to Lightweight IDS for IoT Networks based on Federated Learning and BiLSTM
  preprints 2024 置信度 0.74
• Authentication and Traceability for Federated Learning Models via Group Signatures
  Abstract Federated learning, due to its distributed and privacy-protecting properties, is a good solution to the data silo problem in machine learning, but there are still many hidden dangers that threaten the security of federated learning, such as privacy le…
  preprints Hong Liu, Jiahui Wei, Zhu Xu, Zhiqiang Zhao 2024 置信度 0.74
• Federated Learning for Indoor Air Quality Monitoring and Activity Recognition Approach
  preprints 2024 置信度 0.74
• Dynamic Aggregation and Augmentation for Low-Resource Machine Translation using Federated Fine-tuning of Pretrained Transformer Models
  preprints 2024 置信度 0.74
• FL-APB: Balancing Privacy Protection and Performance Optimization for Adversarial Training in Federated Learning
  preprints 2024 置信度 0.74
• Addressing Bias and Fairness using Fair Federated Learning: A Systematic Literature Review
  In the field of machine learning, the rapid development of data volume and variety requires ethical data utilization and strict privacy protection standards. Fair Federated Learning (FFL) has emerged as a key solution that aims to ensure fairness and privacy p…
  preprints Dohyoung Kim, Hyekyung Woo, Youngho Lee 2024 置信度 0.74
• Novel clustered federated learning based on local loss
  Abstract This paper proposes LCFL, a novel clustering metric for evaluating clients' data distributions in federated learning. LCFL aligns with federated learning requirements, accurately assessing client-to-client variations in data distribution. It offers ad…
  preprints Endong Gu, Yongxin Chen, Hao Wen, Xingju Cai 等 2024 置信度 0.74
• Federated Unlearning in Financial Applications
  preprints 2024 置信度 0.74
• A Load Forecasting Model for Smart Grid Based on RF-BiLSTM and Federated Learning
  Abstract Accurate load forecasting is essential for improving the efficiency and reliability of smart grids. Traditional forecasting models face challenges in integrating high-dimensional data and protecting privacy. To address these issues, a novel load forec…
  preprints Wenhui Li, Huilin Jiang, Lei Zhang 2024 置信度 0.74
• Construction of Personal Health Knowledge Graphs for Clinical Data Harmonization in Breast Cancer
  Abstract Background Personal health data contain valuable information for breast cancer management. Integration of heterogeneous data and maintenance of clinical registries are time-consuming and labor-intensive. We aimed to leverage an Artificial Intelligence…
  preprints 2026 置信度 0.74
• Federated Learning Based Futuristic Fault Diagnosis and Standardization in Rotating Machinery
  preprints 2024 置信度 0.74
• Federated Learning and Data Mining based Botnet Attack Detection Framework for Internet of Things
  preprints 2024 置信度 0.74