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• Performance Analysis of Secure Multiparty Computation Protocol
  In this paper we address the issue related to privacy, security, complexity and Implementation, various adversaries exist which hamper the secure multiparty computation. In the secure multiparty computation, a set of parties wishes to jointly compute some func…
  crossref Samiksha Shukla, D. K. Mishra 2019-06-17T15:22:23Z 置信度 0.70
• Key Technologies and Applications of Secure Multiparty Computation
  crossref Xiaoqiang Guo, Shuai Zhang, Ying Li 2013-05-22T21:56:07Z 置信度 0.70
• Efficient Asynchronous Secure Multiparty Distributed Computation
  crossref K. Srinathan, C. Pandu Rangan 2007-10-28T05:24:06Z 置信度 0.70
• Privacy-Preserving Analytics Using Zero-Knowledge Proofs and Secure Multiparty Computation
  crossref Nelson Lungu, Bibhuti Bhusan Dash, Satyendr Singh, Manoj Ranjan Mishra 等 2025-05-23T17:02:43Z 置信度 0.70
• Secure Multiparty Computation during Privacy Preserving Data Mining: Inscrutability Aided Protocol for Indian Healthcare Sector
  crossref Zulfa Shaikh 2012-11-07T05:22:50Z 置信度 0.70
• Secure Groups for Threshold Cryptography and Number-Theoretic Multiparty Computation
  In this paper, we introduce secure groups as a cryptographic scheme representing finite groups together with a range of operations, including the group operation, inversion, random sampling, and encoding/decoding maps. We construct secure groups from oblivious…
  crossref Berry Schoenmakers, Toon Segers 2023-11-09T10:19:27Z 置信度 0.70
• On unconditionally secure multiparty computation for realizing correlated equilibria in games
  crossref Ye Wang, Shantanu Rane, Prakash Ishwar 2014-02-13T22:45:12Z 置信度 0.70
• Cooking Cryptographers: Secure Multiparty Computation Based on Balls and Bags
  crossref Daiki Miyahara, Yuichi Komano, Takaaki Mizuki, Hideaki Sone 2021-08-10T20:47:26Z 置信度 0.70
• Efficient and Secure Multiparty Computation from Fixed-Key Block Ciphers
  crossref Chun Guo, Jonathan Katz, Xiao Wang, Yu Yu 2020-07-30T20:48:34Z 置信度 0.70
• Secure Multiparty Computations Using a Dial Lock
  crossref Takaaki Mizuki, Yoshinori Kugimoto, Hideaki Sone 2007-07-22T07:36:39Z 置信度 0.70
• A secure group communication using non-interactive key computation in multiparty key agreement
  crossref S. Kalaiselvi, S. Jabeen Begum 2009-02-26T13:19:55Z 置信度 0.70
• Anonymous Bitcoin transaction: A solution using secure multiparty computation
  Abstract Bitcoin is a popular form of cryptocurrencies. Bitcoin provides users' anonymity through cryptographic pseudonyms. Bitcoin operates on a peer‐to‐peer network that maintains a public ledger, called blockchain , to log all transactions from one pseudony…
  crossref Dhaneshwar Mardi, Jaydeep Howlader 2022-04-08T10:46:44Z 置信度 0.70
• How Many Oblivious Transfers Are Needed for Secure Multiparty Computation?
  crossref Danny Harnik, Yuval Ishai, Eyal Kushilevitz 2007-08-09T13:51:33Z 置信度 0.70
• Enhanced Private Decision Trees using Secure Multiparty Computation and Differential Privacy
  crossref Arisa Tajima, Wei Jiang, Virendra Marathe, Hamid Mozaffari 2025-02-19T18:39:49Z 置信度 0.70
• Secure Multiparty Quantum Computation for Summation and Multiplication
  Abstract As a fundamental primitive, Secure Multiparty Summation and Multiplication can be used to build complex secure protocols for other multiparty computations, specially, numerical computations. However, there is still lack of systematical and efficient q…
  crossref Run-hua Shi, Yi Mu, Hong Zhong, Jie Cui 等 2016-01-21T11:29:00Z 置信度 0.70
• Fairness Versus Guaranteed Output Delivery in Secure Multiparty Computation
  crossref Ran Cohen, Yehuda Lindell 2016-09-23T19:39:23Z 置信度 0.70
• Secure genome-wide association analysis using multiparty computation
  crossref Hyunghoon Cho, David J Wu, Bonnie Berger 2018-05-07T12:30:35Z 置信度 0.70
• Optimized Lattice-Based Homomorphic Encryption for Secure Multiparty Computation in Group Communication
  crossref Renisha P.S., Bhawana Rudra 2025-07-17T17:54:59Z 置信度 0.70
• A note on secure multiparty computation via higher residue symbols
  Abstract We generalize a protocol by Yu for comparing two integers with relatively small difference in a secure multiparty computation setting. Yu's protocol is based on the Legendre symbol. A prime number p is found for which the Legendre symbol (· | p ) agre…
  crossref Ignacio Cascudo, Reto Schnyder 2021-02-20T18:42:12Z 置信度 0.70
• Secure Multiparty Computation of Threshold Signatures Made More Efficient
  crossref Harry W. H. Wong, Jack P. K. Ma, Sherman S. M. Chow 2024-02-10T11:35:08Z 置信度 0.70
• Analysis of Secure Multiparty Computation Protocols in Blockchain
  The integration of Blockchain technology with Secure Multiparty Computation (SMC) protocols has gained significant attention due to its potential to enable secure, decentralized collaboration among multiple parties without compromising the privacy of their dat…
  crossref Dr. Helena Rocha – 2026-03-30T04:47:53Z 置信度 0.70
• A Scripting Language for Automating Secure Multiparty Computation
  crossref Kung Chen, Tsan-Sheng Hsu, Churn-Jung Liau, Da-Wei Wang 2013-10-12T02:59:01Z 置信度 0.70
• A Permissioned Blockchain System for Secure Multiparty Computation
  Abstract Permissioned blockchain is the blockchain network that requires access to be part of the network. Participant’s actions are governed by the control layer that runs on top of the blockchain. This type of blockchains is preferred by individuals who need…
  crossref S Garg, R Vashisht 2021-08-24T07:45:30Z 置信度 0.70
• Secure multiparty computation of solid geometric problems and their applications
  crossref Li Shundong, Wu Chunying, Wang Daoshun, Dai Yiqi 2014-04-19T11:47:28Z 置信度 0.70
• Efficiently secure multiparty computation based on homomorphic encryption
  crossref Yuangang Yao, Jinxia Wei, Jianyi Liu, Ru Zhang 2016-12-19T21:56:42Z 置信度 0.70
• Enabling Efficient Secure Multiparty Computation Development in ANSI C
  Secure Multi-Party Computation (SMPC) enables parties to compute a pub- lic function over private inputs. A classical example is the millionaires problem, where two millionaires want to figure out who is wealthier without revealing their actual wealth to each …
  crossref Ahmad Musleh, Soha Hussein, Khaled M. Khan, Qutaibah M. Malluhi 2019-06-30T09:09:27Z 置信度 0.70
• Secure Multiparty Computation for Cooperative Cyber Risk Assessment
  crossref Kyle Hogan, Noah Luther, Nabil Schear, Emily Shen 等 2017-02-07T15:57:22Z 置信度 0.70
• Brief announcement
  crossref Shailesh Vaya 2010-07-27T14:10:11Z 置信度 0.70
• Secure Multiparty Computation with Lazy Sharing
  crossref Shuaishuai Li, Cong Zhang, Dongdai Lin 2024-12-09T12:19:20Z 置信度 0.70
• Knowledge-oriented secure multiparty computation
  crossref Piotr Mardziel, Michael Hicks, Jonathan Katz, Mudhakar Srivatsa 2012-07-26T14:41:14Z 置信度 0.70
• Assessment of Two Privacy Preserving Authentication Methods Using Secure Multiparty Computation Based on Secret Sharing
  Secure authentication is an essential mechanism required by the vast majority of computer systems and various applications in order to establish user identity. Credentials such as passwords and biometric data should be protected against theft, as user imperson…
  crossref Diana-Elena Fălămaş, Kinga Marton, Alin Suciu 2021-05-18T12:17:16Z 置信度 0.70
• A dynamic, zero-message broadcast encryption scheme based on secure multiparty computation
  crossref Mahdi Soodkhah Mohammadi, Abbas Ghaemi Bafghi 2013-01-17T15:30:27Z 置信度 0.70
• Validation Services for Confidential Data
  crossref Gary Benedetto, Rolando A. Rodríguez, Jordan Stanley, Evan Totty 2024-08-23T14:36:41Z 置信度 0.70
• Adapting Secure MultiParty Computation to Support Machine Learning in Radio Frequency Sensor Networks
  crossref Jonathan Berry, Anand Ganti, Kenneth Goss, Carolyn Mayer 等 2022-05-26T04:38:09Z 置信度 0.70
• Privacy-Preserving Data Sharing in Healthcare: Advances in Secure Multiparty Computation
  Secure Multi-Party Computation (SMC) is a thriving strategy for privacy-preserving data sharing in the healthcare domain. This research examined the role of SMC in the healthcare context and its alignment with regulations such as HIPAA and GDPR. The study high…
  crossref Md Fahim Ahammed, Md Rasheduzzaman Labu 2024-04-07T09:17:11Z 置信度 0.70
• A secure multiparty computation privacy preserving OLAP framework over distributed XML data
  crossref Alfredo Cuzzocrea, Elisa Bertino 2010-04-27T12:45:48Z 置信度 0.70
• Privacy preserving Double Auction using Secure Multiparty Computation for Local Electricity Markets
  crossref Bevin K C, Ashu Verma 2023-11-03T17:51:19Z 置信度 0.70
• Characterization of Secure Multiparty Computation Without Broadcast
  crossref Ran Cohen, Iftach Haitner, Eran Omri, Lior Rotem 2017-09-22T19:39:04Z 置信度 0.70
• Collaborative network outage troubleshooting with secure multiparty computation
  crossref Mentari Djatmiko, Dominik Schatzmann, Xenofontas Dimitropoulos, Arik Friedman 等 2013-11-11T19:31:10Z 置信度 0.70
• Network-Efficient Pipelining-Based Secure Multiparty Computation for Machine Learning Applications
  crossref Oscar G. Bautista, Kemal Akkaya 2022-08-26T19:43:27Z 置信度 0.70
• 21st Century Statistical Disclosure Limitation: Motivations and Challenges
  crossref John M. Abowd, Michael B. Hawes 2024-08-23T14:36:41Z 置信度 0.70
• Secure Multiparty Computation and Privacy Preserving scheme using Homomorphic Elliptic Curve Cryptography
  crossref Ankit chouhan, Anupam kumari, Makhduma Saiyad 2020-04-17T05:20:07Z 置信度 0.70
• Using Secure Multiparty Computation for Collaborative Information Exchange
  crossref Dennis Titze, Hans Hofinger, Peter Schoo 2013-12-17T19:46:29Z 置信度 0.70
• Towards Secure Virtual Elections: Multiparty Computation of Order Based Voting Rules
  crossref Tamir Tassa, Lihi Dery 2024-07-25T12:35:50Z 置信度 0.70
• SPGC: Integration of Secure Multiparty Computation and Differential Privacy for Gradient Computation on Collaborative Learning
  crossref Kazuki Iwahana, Naoto Yanai, Jason Paul Cruz, Toru Fujiwara 2022-03-14T22:10:41Z 置信度 0.70
• Enhancing Privacy and Security in IoT Environments through Secure Multiparty Computation
  With the increasing influence of IoT devices in our daily lives, secure data-sharing is becoming ever more important. Sensors and other devices are communicating vast amounts of possibly unencrypted data, which poses a significant privacy concern. To tackle th…
  crossref Rik van de Haterd, Mohammed Elhajj 2024-05-31T12:41:35Z 置信度 0.70
• On achieving the "best of both worlds" in secure multiparty computation
  crossref Jonathan Katz 2007-09-14T12:07:37Z 置信度 0.70
• Tolerating Generalized Mobile Adversaries in Secure Multiparty Computation
  crossref K. Srinathan, C. Pandu Rangan 2007-10-28T05:24:06Z 置信度 0.70
• Reputation-Aware Routing in Payment Channel Networks via Secure Multiparty Computation
  crossref Arun Shrestha, Kyle Downing, Hsiang-Jen Hong 2026-08-27T19:08:40Z 置信度 0.70
• Secure Multiparty Computation
  The Secure Multiparty computation is characterized by computation by a set of multiple parties each participating using the private input they have. There are different types of models for Secure Multiparty computation based on assumption about the type of adv…
  crossref Kannan Balasubramanian, M. Rajakani 2017-08-16T09:05:46Z 置信度 0.70
• An efficient method for privacy preserving data mining in secure multiparty computation
  crossref First A. Neha Pathak, Second B. Shweta Pandey 2014-04-15T21:15:32Z 置信度 0.70
• Increasing the Resilience of Secure Multiparty Computation Using Security Modules
  crossref Lamya Abdullah, Felix C. Freiling, Dominique Schröder 2025-03-18T13:47:55Z 置信度 0.70
• Secure MultiParty Protocol for Differentially-Private Data Release
  crossref Anthony Harris 2018-05-30T14:35:53Z 置信度 0.70
• Learning Method of Fuzzy Inference Systems for Secure Multiparty Computation
  crossref Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Yohtaro Miyanishi 等 2018-02-09T13:55:13Z 置信度 0.70
• Cloud assisted secure multiparty computation on blockchain - CaFGUGChain framework
  crossref Anasuya Threse Innocent Aloysius, Prakash Gopalakrishnan 2024-03-25T13:00:29Z 置信度 0.70
• A Full Proof of the BGW Protocol for Perfectly Secure Multiparty Computation
  crossref Gilad Asharov, Yehuda Lindell 2015-09-28T15:29:59Z 置信度 0.70
• New privacy preserving clustering methods for secure multiparty computation
  crossref Hirofumi Miyajima, Noritaka Shigei, Hiromi Miyajima, Yohtaro Miyanishi 等 2016-08-29T08:24:59Z 置信度 0.70
• Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation
  crossref Du Nguyen Duy, Michael Affenzeller, Ramin Nikzad-Langerodi 2023-07-24T23:30:33Z 置信度 0.70
• Application-Scale Secure Multiparty Computation
  crossref John Launchbury, Dave Archer, Thomas DuBuisson, Eric Mertens 2014-03-21T09:37:17Z 置信度 0.70
• Efficient threshold multiparty computation schemes
  crossref Schuyler Rosefield 2025-03-05T15:46:51Z 置信度 0.70
• Secure Multiparty Computation
  crossref Keith Frikken 2010-05-06T15:51:30Z 置信度 0.70
• Programming Applications Suitable for Secure Multiparty Computation Based on Trusted Execution Environments
  crossref Maja Vukasovic, Danko Miladinovic, Adrian Milakovic, Pavle Vuletic 等 2022-12-22T18:41:08Z 置信度 0.70
• Dynamic Decentralized Reputation System from Blockchain and Secure Multiparty Computation
  In decentralized environments, such as mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs), traditional reputation management systems are not viable due to their dependence on a central authority that is both accessible and trustworthy for all …
  crossref Khalid Mrabet, Faissal El Bouanani, Hussain Ben-Azza 2023-02-08T01:32:05Z 置信度 0.70
• On the (in)efficiency of non-interactive secure multiparty computation
  crossref Maki Yoshida, Satoshi Obana 2018-03-26T00:55:08Z 置信度 0.70
• Secure Multiparty Computation with Minimal Interaction
  crossref Yuval Ishai, Eyal Kushilevitz, Anat Paskin 2010-08-10T04:15:26Z 置信度 0.70
• On 2-Round Secure Multiparty Computation
  crossref Rosario Gennaro, Yuval Ishai, Eyal Kushilevitz, Tal Rabin 2007-10-19T04:48:16Z 置信度 0.70
• Zero-knowledge from secure multiparty computation
  crossref Yuval Ishai, Eyal Kushilevitz, Rafail Ostrovsky, Amit Sahai 2007-09-14T12:07:37Z 置信度 0.70
• Privacy-Preserving Framework for Multi-Institutional Medical Time-Series Analysis via Homomorphic Encryption: Design and Development Study.
  europepmc 2026 置信度 0.80
• Secure Multiplicative Aggregation and Key-Reuse Optimization: Achieving Dropout Resilience with Amortized Efficiency.
  europepmc 2026 置信度 0.80
• A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan–Meier Survival Analysis
  Abstract The proliferation of real-world healthcare data has substantially expanded opportunities for collaborative research, yet stringent privacy regulations hinder the pooling of sensitive patient records in a single location. To address this dilemma, we pr…
  europepmc Narasimha Raghavan Veeraragavan, Svetlana Boudko, Jan Franz Nygård 2025 置信度 0.80
• Information security � Secure multiparty computation
  crossref 2023-08-03T20:30:33Z 置信度 0.70
• Information security � Secure multiparty computation
  crossref 2023-08-03T20:30:33Z 置信度 0.70
• Challenges and Future Directions in Breast Cancer Segmentation: A Research Perspective
  Breast cancer is one of the most aggressive and widespread illnesses afflicting women across the globe. Fast and precise segmentation techniques are essential for early detection, diagnosis, and treatment planning. This paper reviews comprehensively segmentati…
  datacite Iyer, Swathi, Tudilkar, Salwa, R, Srivaramangai 2026 置信度 0.66 Breast cancer; Medical Imaging Segmentation; Segmentation Techniques; CNN; U-Net; GANs; Transformers; Deep Learning; Hybrid Segmentation; Attention Mechanism; Precision Mapping; Multimodal Imaging; Federated Learning; Automated Diagnosis; Computational Complexity; Tumour Detection; Medical Imaging; Feature Extraction; Transfer Learning; Image Processing; Clinical Applications
• Challenges and Future Directions in Breast Cancer Segmentation: A Research Perspective
  Breast cancer is one of the most aggressive and widespread illnesses afflicting women across the globe. Fast and precise segmentation techniques are essential for early detection, diagnosis, and treatment planning. This paper reviews comprehensively segmentati…
  datacite Iyer, Swathi, Tudilkar, Salwa, R, Srivaramangai 2026 置信度 0.66 Breast cancer; Medical Imaging Segmentation; Segmentation Techniques; CNN; U-Net; GANs; Transformers; Deep Learning; Hybrid Segmentation; Attention Mechanism; Precision Mapping; Multimodal Imaging; Federated Learning; Automated Diagnosis; Computational Complexity; Tumour Detection; Medical Imaging; Feature Extraction; Transfer Learning; Image Processing; Clinical Applications
• Interpreting Federated Learning (FL) Models on Edge Devices by Enhancing Model Explainability with Computational Geometry and Advanced Database Architectures
  Federated learning (FL) on edge devices has emerged as a promising approach for decentralized model training, enabling data privacy and efficiency in distributed networks. However, the complexity of these models presents significant challenges in terms of tran…
  datacite Enyejo, Lawrence Anebi, Adewoye, Michael Babatunde, Ugochukwu, Uchenna Nneka 2025 置信度 0.66 Federated Learning; Explainable AI; Edge Computing; Computational Geometry; Data Privacy; Model Transparency
• Interpreting Federated Learning (FL) Models on Edge Devices by Enhancing Model Explainability with Computational Geometry and Advanced Database Architectures
  Federated learning (FL) on edge devices has emerged as a promising approach for decentralized model training, enabling data privacy and efficiency in distributed networks. However, the complexity of these models presents significant challenges in terms of tran…
  datacite Enyejo, Lawrence Anebi, Adewoye, Michael Babatunde, Ugochukwu, Uchenna Nneka 2025 置信度 0.66 Federated Learning; Explainable AI; Edge Computing; Computational Geometry; Data Privacy; Model Transparency
• Deep Learning Models for Predicting and Mitigating Environmental Impact of Industrial Processes in Real-Time
  Industrial processes contribute significantly to environmental degradation through emissions, waste, and resource depletion. The need for real-time monitoring and mitigation strategies has led to the adoption of deep learning (DL) models for predictive analyti…
  datacite Ojadi, Jessica Obianuju, Owulade, Olumide Akindele, Odionu, Chinekwu Somtochukwu, Onukwulu, Ekene Cynthia 2025 置信度 0.66 Deep Learning; Environmental Monitoring; Industrial Processes; Predictive Analytics; Real-Time Mitigation; Sustainability; Anomaly Detection; Edge AI; Explainable AI; Reinforcement Learning
• Deep Learning Models for Predicting and Mitigating Environmental Impact of Industrial Processes in Real-Time
  Industrial processes contribute significantly to environmental degradation through emissions, waste, and resource depletion. The need for real-time monitoring and mitigation strategies has led to the adoption of deep learning (DL) models for predictive analyti…
  datacite Ojadi, Jessica Obianuju, Owulade, Olumide Akindele, Odionu, Chinekwu Somtochukwu, Onukwulu, Ekene Cynthia 2025 置信度 0.66 Deep Learning; Environmental Monitoring; Industrial Processes; Predictive Analytics; Real-Time Mitigation; Sustainability; Anomaly Detection; Edge AI; Explainable AI; Reinforcement Learning
• An Overview of Extreme Learning Machine-Based Intelligent Fault Localization Approaches
  Abstract The reliable operation of modern power systems depends heavily on the rapid detection and accurate localization of faults occurring in transmission and distribution networks. With the increasing integration of renewable energy resources, smart grid te…
  datacite Priyanka V. Raut, Kiran A. Dongre, Amol P. Bhagat 2026 置信度 0.66 Fault LocalizationExtreme Learning MachineArtificial IntelligenceSmart GridDistribution Networks
• An Overview of Extreme Learning Machine-Based Intelligent Fault Localization Approaches
  Abstract The reliable operation of modern power systems depends heavily on the rapid detection and accurate localization of faults occurring in transmission and distribution networks. With the increasing integration of renewable energy resources, smart grid te…
  datacite Priyanka V. Raut, Kiran A. Dongre, Amol P. Bhagat 2026 置信度 0.66 Fault LocalizationExtreme Learning MachineArtificial IntelligenceSmart GridDistribution Networks
• Federated Meta-Learning for Few-Shot Image Classification with Personalized Model Adaptation: A Comprehensive Review
  Federated meta-learning represents a paradigm shift in machine learning that combines the privacy-preserving benefits of federated learning with the rapid adaptation capabilities of meta-learning for few-shot image classification tasks. This comprehensive lite…
  datacite Thenmozhi, R., Santhalakshmi, M., Shanthakumar, M. 2025 置信度 0.66 Federated Learning; Meta-Learning; Few-Shot Learning; Image Classification; Personalized Models; Privacy-Preserving Machine Learning
• Federated Meta-Learning for Few-Shot Image Classification with Personalized Model Adaptation: A Comprehensive Review
  Federated meta-learning represents a paradigm shift in machine learning that combines the privacy-preserving benefits of federated learning with the rapid adaptation capabilities of meta-learning for few-shot image classification tasks. This comprehensive lite…
  datacite Thenmozhi, R., Santhalakshmi, M., Shanthakumar, M. 2025 置信度 0.66 Federated Learning; Meta-Learning; Few-Shot Learning; Image Classification; Personalized Models; Privacy-Preserving Machine Learning
• AI-Driven Risk Analytics for Real-Time Fraud Detection in Modernized U.S. Payment Ecosystems
  This study investigates the application of artificial intelligence (AI)-driven risk analytics for real-time fraud detection within modernized United States payment ecosystems. As digital payment infrastructures transition toward instant, high-volume transactio…
  datacite Fatomilola, Ezekiel 2026 置信度 0.66 Artificial Intelligence; Financial Fraud Detection; Risk Analytics; Machine Learning; Digital Payments; Regulatory Compliance; Explainable AI
• AI-Driven Risk Analytics for Real-Time Fraud Detection in Modernized U.S. Payment Ecosystems
  This study investigates the application of artificial intelligence (AI)-driven risk analytics for real-time fraud detection within modernized United States payment ecosystems. As digital payment infrastructures transition toward instant, high-volume transactio…
  datacite Fatomilola, Ezekiel 2026 置信度 0.66 Artificial Intelligence; Financial Fraud Detection; Risk Analytics; Machine Learning; Digital Payments; Regulatory Compliance; Explainable AI
• Integrating Artificial Intelligence and Natural Language Processing into Automated Adverse Event Signal Detection: A Comprehensive Review of Frameworks, Real-World Data, and Regulatory Challenges
  Pharmacovigilance, the science of detecting, assessing, and preventing adverse effects of medicines, has traditionally relied on spontaneous reporting systems and manually applied disproportionality statistics to generate safety signals. The explosive growth i…
  datacite Sakthikumar P 2026 置信度 0.66 pharmacovigilance; artificial intelligence; natural language processing; adverse event signal detection; disproportionality analysis; real-world data; regulatory science; machine learning.
• Integrating Artificial Intelligence and Natural Language Processing into Automated Adverse Event Signal Detection: A Comprehensive Review of Frameworks, Real-World Data, and Regulatory Challenges
  Pharmacovigilance, the science of detecting, assessing, and preventing adverse effects of medicines, has traditionally relied on spontaneous reporting systems and manually applied disproportionality statistics to generate safety signals. The explosive growth i…
  datacite Sakthikumar P 2026 置信度 0.66 pharmacovigilance; artificial intelligence; natural language processing; adverse event signal detection; disproportionality analysis; real-world data; regulatory science; machine learning.
• Ontology-Based Recommender Systems for Online Learning Platforms: A Review
  The rapid growth of massive open online courses, learning management systems, and open educational repositories has produced an abundance of learning resources that overwhelms learners and complicates the selection of suitable content. Recommender systems have…
  datacite Saritha E, Dr. B Kalpana 2026 置信度 0.66
• Ontology-Based Recommender Systems for Online Learning Platforms: A Review
  The rapid growth of massive open online courses, learning management systems, and open educational repositories has produced an abundance of learning resources that overwhelms learners and complicates the selection of suitable content. Recommender systems have…
  datacite Saritha E, Dr. B Kalpana 2026 置信度 0.66
• Coded evidence base for a PRISMA-guided systematic review of reporting fragmentation in federated-learning-based intrusion detection systems
  The complete coded evidence base underlying a PRISMA-guided systematic review of reporting fragmentation in federated-learning-based network intrusion detection systems (FL-NIDS): the per-study extraction table for all 105 included primary studies, the coding …
  datacite Alzoubi, Mohamad, Serrão, Carlos, Pavia, João Pedro 2026 置信度 0.66 Federated learningNetwork intrusion detectionSystematic literature reviewReporting completenessExplainable artificial intelligence
• Coded evidence base for a PRISMA-guided systematic review of reporting fragmentation in federated-learning-based intrusion detection systems
  The complete coded evidence base underlying a PRISMA-guided systematic review of reporting fragmentation in federated-learning-based network intrusion detection systems (FL-NIDS): the per-study extraction table for all 105 included primary studies, the coding …
  datacite Alzoubi, Mohamad, Serrão, Carlos, Pavia, João Pedro 2026 置信度 0.66 Federated learningNetwork intrusion detectionSystematic literature reviewReporting completenessExplainable artificial intelligence
• Comparative Study On Smart Buy
  The rapid growth of e-commerce has provided consumers with a wide range of products and purchasing options, but comparing prices, discounts, product quality, reviews, and availability across multiple platforms remains time – consuming and difficult. This study…
  datacite Prof. Bharati Rathod, Nayana S, Sinchana G K, Umadevi, Varshini P 2026 置信度 0.66
• Comparative Study On Smart Buy
  The rapid growth of e-commerce has provided consumers with a wide range of products and purchasing options, but comparing prices, discounts, product quality, reviews, and availability across multiple platforms remains time – consuming and difficult. This study…
  datacite Prof. Bharati Rathod, Nayana S, Sinchana G K, Umadevi, Varshini P 2026 置信度 0.66
• "AI-Assisted Multi-Parameter Optimization in Medicinal Chemistry: Integrating Potency, ADMET, Selectivity and Synthetic Accessibility for Rational Drug Design"
  AbstractThe increasing complexity of modern drug discovery has highlighted the limitations ofconventional medicinal-chemistry strategies that optimize molecular potency as a primaryobjective while considering pharmacokinetic, safety and synthetic properties at…
  datacite Nidhi Devrani 2026 置信度 0.66 Artificial intelligence; Multi-parameter optimization; Medicinal chemistry; Machine learning; Deep learning; Generative AI; Drug design; Potency optimization; ADMET
• "AI-Assisted Multi-Parameter Optimization in Medicinal Chemistry: Integrating Potency, ADMET, Selectivity and Synthetic Accessibility for Rational Drug Design"
  AbstractThe increasing complexity of modern drug discovery has highlighted the limitations ofconventional medicinal-chemistry strategies that optimize molecular potency as a primaryobjective while considering pharmacokinetic, safety and synthetic properties at…
  datacite Nidhi Devrani 2026 置信度 0.66 Artificial intelligence; Multi-parameter optimization; Medicinal chemistry; Machine learning; Deep learning; Generative AI; Drug design; Potency optimization; ADMET
• Federated Learning Aggregation Strategies and Compressive Sensing in Massive MIMO OTA-FL Systems
  This report synthesises findings from 7 peer-reviewed papers addressing the following research question: What is the impact of different federated learning aggregation strategies (FedAvg, FedProx, SCAFFOLD) on model alignment and robustness to non-IID data dis…
  datacite Assignee Research 2026 置信度 0.66 impactdifferentfederatedlearningaggregation
• Federated Learning Aggregation Strategies and Compressive Sensing in Massive MIMO OTA-FL Systems
  This report synthesises findings from 7 peer-reviewed papers addressing the following research question: What is the impact of different federated learning aggregation strategies (FedAvg, FedProx, SCAFFOLD) on model alignment and robustness to non-IID data dis…
  datacite Assignee Research 2026 置信度 0.66 impactdifferentfederatedlearningaggregation
• ENSEMBLE - Newsletter April-May 2026
  Official newsletter of the Horizon Europe ENSEMBLE project (April and May 2026 edition) reporting the project review meeting in Brussels before the European Commission alongside analysis by María Hernandez from Byron Labs on pre-attack economy in ransomware pr…
  datacite ENSEMBLE 2026 置信度 0.66
• ENSEMBLE - Newsletter April-May 2026
  Official newsletter of the Horizon Europe ENSEMBLE project (April and May 2026 edition) reporting the project review meeting in Brussels before the European Commission alongside analysis by María Hernandez from Byron Labs on pre-attack economy in ransomware pr…
  datacite ENSEMBLE 2026 置信度 0.66
• Artificial Intelligence in Resource-Limited Healthcare Settings: Applications, Implementation Challenges, and Future Directions in Cardiology and Oncology
  Abstract Artificial intelligence (AI) holds transformative potential for health systems in low- and middle-income countries (LMICs), where clinician shortages, infrastructure deficits, and rising chronic disease burdens converge. This review synthesises 2019–2…
  datacite Oluwakemi Jumoke Bello, Raphael Igbarumah Ayo Daniel, Olabanke Florence Olawuyi, Babajide David Makanjuola and Claret Chinenyenwa Analikwu 2026 置信度 0.66 Artificial intelligence, resource-limited settings, low- and middle-income countries, cardiology, oncology, digital twin, precision medicine, human-in-the-loop, explainable AI, sustainable health technology
• Artificial Intelligence in Resource-Limited Healthcare Settings: Applications, Implementation Challenges, and Future Directions in Cardiology and Oncology
  Abstract Artificial intelligence (AI) holds transformative potential for health systems in low- and middle-income countries (LMICs), where clinician shortages, infrastructure deficits, and rising chronic disease burdens converge. This review synthesises 2019–2…
  datacite Oluwakemi Jumoke Bello, Raphael Igbarumah Ayo Daniel, Olabanke Florence Olawuyi, Babajide David Makanjuola and Claret Chinenyenwa Analikwu 2026 置信度 0.66 Artificial intelligence, resource-limited settings, low- and middle-income countries, cardiology, oncology, digital twin, precision medicine, human-in-the-loop, explainable AI, sustainable health technology