Source-Free Domain Adaptation for Cross-Modality Abdominal Multi-Organ Segmentation Challenges by Xiyu Zhang, Xu Chen, Yang Wang, Dongliang Liu, Yifeng Hong

“…While unsupervised domain adaptation (UDA) has shown promise in addressing these issues by transferring knowledge from a different modality (source domain), its reliance on both source and target data during training presents a practical challenge in many clinical settings due to data privacy concerns. …”
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SensorsConnect Framework: World-Wide Web for Internet of Things by Abdelrahman Elewah, Khalid Elgazzar

“…A motivating scenario illustrates its potential impact in real-life situations, such as finding a drive-thru coffee shop or crossing a country border. Using real-time road status and service occupancy data, SensorsConnect enhances Google Maps service recommendations, not only minimizing customer wait times but also distributing workload more evenly across service points. …”
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5G-MobiCare: A Secure 5G Assisted Remote Mobile Health Monitoring Platform by Hemangi Goswami, Tarun Pandey, Hiten Choudhury

“…This setup generates vast amounts of real-time data, making secure device authentication essential to ensure data privacy and security. …”
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A Lightweight Intrusion Detection System with Dynamic Feature Fusion Federated Learning for Vehicular Network Security by Junjun Li, Yanyan Ma, Jiahui Bai, Congming Chen, Tingting Xu, Chi Ding

“…Experimental evaluation on the CAN-Hacking dataset shows that the proposed intrusion detection system achieves more than 99% F1 score with only 1.11 MB of memory and 81,863 trainable parameters, while maintaining low computational overheads and ensuring data privacy, which is very suitable for edge device deployment.…”
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TwinFedPot: Honeypot Intelligence Distillation into Digital Twin for Persistent Smart Traffic Security by Yesin Sahraoui, Abdessalam Mohammed Hadjkouider, Chaker Abdelaziz Kerrache, Carlos T. Calafate

“…This inversion allows the system to learn generalized attack patterns using only limited data, while preserving privacy and enhancing robustness. …”
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Advanced Applications of Artificial Intelligence in Pharmacovigilance: Current Trends and Future Perspectives by Yogesh S Ahire, Jayesh H Patil, Harshita N Chordiya, Rashmi A Deore, Vinod A Bairagi

“…The ethical and legal implications of patient privacy, data security, and regulatory compliance underscore the necessity of cautious artificial intelligence deployment in order to maintain public trust and protect patient rights. …”
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Artificial Intelligence in Bronchopulmonary Dysplasia: A Review of the Literature by Tony Jha, Sana Suhail, Janet Northcote, Alvaro G. Moreira

“…Addressing ethical considerations, such as data privacy and demographic representation, as well as other practical considerations will be essential to ensure the proper implementation of AI clinical tools. …”
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Hierarchical Federated Learning-Based Intrusion Detection for In-Vehicle Networks by Muzun Althunayyan, Amir Javed, Omer Rana, Theodoros Spyridopoulos

“…To enhance IDS robustness and preserve user data privacy, researchers are increasingly adopting federated learning (FL). …”
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Quality of Breast Cancer Information on the Internet by African Organizations: An Appraisal by Cynthia Pomaa Akuoko

“…The identified websites were assessed using European Commission (EC) quality criteria for health-related websites, which comprises different assessment areas including, completeness, transparency and honesty, authority, privacy and data protection, updating of information, accountability, and accessibility. …”
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Detecting Learning Behavior in Programming Assignments by Analyzing Versioned Repositories by John Chen, Serena Lau, Juho Leinonen, Valerio Terragni, Nasser Giacaman

“…The Polivr ecosystem comprises three key modules: Polivr Anonymiser, which ensures data privacy by anonymising student identities; Polivr Core, which mines learning metrics from Git repositories; and Polivr Web Viewer, which transforms the raw metrics into insightful visualisations for educators. …”
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The Emergence of AI-Driven Virtual Hospitals: Redefining Patient Care Beyond Physical Boundaries by Ifrah Hameed, Hafiz Muhammad Haseeb Khaliq

“…Because rigorous monitoring is essential for the management of chronic diseases and post-surgery care. With the use of Internet of Things (IoT) technology, portable healthcare devices, and AI-based data analytics, rigorous monitoring of patient’s vitals and detection of anomalies in real-time has become easier. …”
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A qualitative study on the unmet sexual needs of older women by Farzaneh Saadatmand, Mitra Savabi-Esfahani, Zahra Heidari, Marjan Beigi

“…Data were analyzed using the Graneheim and Lundman approach. …”
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Federated learning based reference evapotranspiration estimation for distributed crop fields. by Muhammad Tausif, Muhammad Waseem Iqbal, Rab Nawaz Bashir, Bayan AlGhofaily, Alex Elyassih, Amjad Rehman Khan

“…The study intends to propose ETo estimation of multiple locations with distinct weather conditions using a federated learning approach. Traditional centralized approaches require aggregating all data in one place, which can be problematic due to privacy concerns and data transfer limitations. …”
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Propagation routing for hybrid bandwidth allocation with reinforcement learning in VANET infrastructure analysis by G. Michael, A. Selvakumar, Ramesh Sundar, S. Manikandan, R. Gnanajeyaraman, U. Arul

“…The data are sent using reinforcement Q-learning techniques in conjunction with propagation routeing. …”
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A STUDY OF PEOPLE’S PERCEPTION OF ARTIFICIAL INTELLIGENCE IN FINANCE AND SOCIETY by Dan MITRA, Ioana-Florina COITA

“…This paper investigates how people perceive artificial intelligence (AI), using both original survey data and insights from recent academic and policy literature. …”
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AI in humanitarian healthcare: a game changer for crisis response by Diala Haykal, Mohamad Goldust, Hugues Cartier, Patrick Treacy

“…Ethical concerns such as data privacy, bias, and access inequities remain critical to responsible deployment. …”
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Cooperative inference analysis based on DNN convolutional kernel partitioning by Jialin ZHI, Yinglei TENG, Xinyang ZHANG, Tao NIU, Mei SONG

“…With the popularity of intelligent chip in the application of edge terminal devices, a large number of AI applications will be deployed on the edge of networks closer to data sources in the future.The method based on DNN partition can realize deep learning model training and deployment on resource-constrained terminal devices, and solve the bottleneck problem of edge AI computing ability.Thekernel based partition method (KPM) was proposed as a new scheme on the basis of traditional workload based partition method (WPM).The quantitative analysis of inference performance was carried out from three aspects of computation FLOPS, memory consumption and communication cost respectively, and the qualitative analysis of the above two schemes was carried out from the perspective of flexibility, robustness and privacy of inference process.Finally, a software and hardware experimental platform was built, and AlexNet and VGG11 networks were implemented using PyTorch to further verify the performance advantages of the proposed scheme in terms of delay and energy consumption.It was concluded that, compared with the WPM scheme, the KPM scheme had better DNN reasoning acceleration effect in large-scale computing scenarios.And it has lower memory usage and energy consumption.…”
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Student attendance and evaluation system : A review by Sura Falih, Yaseen Ismael

“…Previous studies have explored a variety of approaches and techniques to achieve these goals, such as using RFID for automatic attendance recording, Arduino systems for automated testing, and machine learning algorithms for analyzing academic performance. …”
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Cooperative inference analysis based on DNN convolutional kernel partitioning by Jialin ZHI, Yinglei TENG, Xinyang ZHANG, Tao NIU, Mei SONG

“…With the popularity of intelligent chip in the application of edge terminal devices, a large number of AI applications will be deployed on the edge of networks closer to data sources in the future.The method based on DNN partition can realize deep learning model training and deployment on resource-constrained terminal devices, and solve the bottleneck problem of edge AI computing ability.Thekernel based partition method (KPM) was proposed as a new scheme on the basis of traditional workload based partition method (WPM).The quantitative analysis of inference performance was carried out from three aspects of computation FLOPS, memory consumption and communication cost respectively, and the qualitative analysis of the above two schemes was carried out from the perspective of flexibility, robustness and privacy of inference process.Finally, a software and hardware experimental platform was built, and AlexNet and VGG11 networks were implemented using PyTorch to further verify the performance advantages of the proposed scheme in terms of delay and energy consumption.It was concluded that, compared with the WPM scheme, the KPM scheme had better DNN reasoning acceleration effect in large-scale computing scenarios.And it has lower memory usage and energy consumption.…”
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The Impact of Artificial Intelligence on Human Resource Management in the Era of Digitalization by PhD. Mugur TOLICI1, PhD. Daniela NICULESCU

“…This study examines AI-driven HR functions, including recruitment, performance evaluation, and learning and development, while addressing ethical and operational challenges such as bias, data privacy, and job displacement. Using a qualitative methodology, including a narrative literature review and case study analysis, the research explores AI’s integration within HRM through theoretical frameworks such as the Technology Acceptance Model (TAM), Resource-Based View (RBV), and Socio-Technical Systems Theory. …”
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