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

“…However, these systems also face challenges, including financial constraints, privacy concerns, and difficulties in scalability. …”
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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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Computer-assisted diagnosis to improve diagnostic pathology: A review by Alessandro Caputo, Elisabetta Maffei, Nalini Gupta, Luca Cima, Francesco Merolla, Giorgio Cazzaniga, Pietro Pepe, Paolo Verze, Filippo Fraggetta

“…It emphasizes the strides made in digital pathology, the integration of AI, and the promising prospects for prognostic biomarker discovery using computational methods. Additionally, ethical considerations regarding data privacy, equity, and trust in AI deployment are examined. …”
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Cybercrime Regulation through Laws and Strategies: a Glimpse into the Indian Experience by Annappa Nagarathna

“…This Act underwent amendment in 2008 to accommodate provisions essential to regulate cybercrimes as well as protect data and privacy on cyber space. In addition to the law, other strategies were designed and adopted to better regulate cyber offences including announcing cyber security policies, constituting institutions to take care of certain concerns including those relating to critical infrastructure information, etc. …”
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Quantifying participant distress: Validity and applicability of a distress measure to evaluate harm in quantitative assessments. by Jess MacArthur, Ratan Budhathoki, Min Prasad Basnet, Ambika Yadav, Sabitra Dhakal, Juliet Willetts

“…To support ethical considerations in quantitative survey deployment, we introduce a four-item formative measure to analyze interview ease, stress, privacy, and comprehension. We present the measure's conceptual and empirical development and examine the validity of the measure through data from Cambodia and Nepal (n = 4,674) using Partial Least Squares Structural Equation Modeling (PLS-SEM) for formative measurement model assessment. …”
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Advancing Biomedical Engineering With Artificial Intelligence and Machine Learning: A Systematic Review by Abebe Belay Adege

“…Also, it reflects on how ethics in AI and ML for biomedical challenges address important issues such as bias, privacy, and accountability. It also underlines how different opportunities and challenges can be opened or addressed by the integration of AI-driven systems in biomedical workflows: engineering, clinicians, and data scientists have to cooperate. …”
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A scaling law to model the effectiveness of identification techniques by Luc Rocher, Julien M. Hendrickx, Yves-Alexandre de Montjoye

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Extending capabilities of artificial intelligence for decision-making and healthcare education by Mohd Javaid, Abid Haleem, Ibrahim Haleem Khan, Raju Vaishya, Abhishek Vaish

“…Medical profession requires extensive knowledge and accuracy from the existing data for improved decision-making. Artificial intelligence (AI) is an appropriate technology used to improve the knowledge, skill, quality of treatment, capability, confidence, and effective decision-making process. …”
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The Future of Artificial Intelligence in Interactive Learning: Trends, Challenges, Opportunities by Teguh Hidayat Iskandar Alam, Ika Safitri Windiarti

“…In addition, the study identifies the social, economic, and cultural impacts of AI adoption, and provides recommendations to mitigate challenges, such as data privacy and technology access. By providing new insights into future trends in AI in education, this study offers a basis for developing innovative and sustainable education policies and practices to meet global needs.…”
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Overreporting and Investigation in the New York City Child Welfare System: A Child’s Perspective by Daniella Rohr, Melissa Friedman

“…This piece argues that ACS’ investigative apparatus not only harms more children than it protects, but the tactics it employs violate the state and federal constitutional rights of children and their families. Using ACS’ own statistics, this piece demonstrates that New York unnecessarily investigates far too many, primarily Black and brown families; examines the harmful, and often unlawful reporting and investigation process in New York City; and enumerates reforms critical to protect both the safety and privacy rights of New York City’s children and families. …”
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Web 3.0: The future of the decentralized Internet by Božić Tamara

“…Decentralization plays an important role in transferring power from providers to users, protecting privacy and regaining control over their information and data. …”
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Perspectives of stakeholders on enhancing access to self-injectable contraception in rural South Sudan by Grace W. Kimemia, Mamothena Carol Mothupi, Lual Agok Luka, Teresia Macharia, Sidonia Akon, Alom Atak Ayom, Lual James Baak, Amijong Deng, Kadra Noor, Taban John Mark, Naoko Kozuki

“…Abstract Background Although self-injectable subcutaneous depot medroxyprogesterone acetate (DMPA-SC) has demonstrated promise, there are significant barriers in promoting initiation and continued use in low-resource settings. This study sought to understand the perspectives of stakeholders on promotion of self-injectable contraception use in rural South Sudan. …”
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Hybrid biometric template protection: Resolving the agony of choice between bloom filters and homomorphic encryption by Amina Bassit, Florian Hahn, Raymond Veldhuis, Andreas Peter

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Transforming higher education with robotic process automation: enhancing efficiency, innovation, and student-centered learning by Vivek Bhardwaj, Mukesh Kumar

“…Inclusion criteria focused on studies published between 2020 and 2024 that addressed RPA applications in education. Data analysis was conducted using thematic synthesis to identify key trends across the selected studies.…”
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A Netnographic Analysis of ChatGPT Usage in Technology Forums in The Context of Society 5.0 by Serdar Pirtini, Songül Bilgili Sülük

“…However, users also expressed notable concerns about issues such as data security, privacy, and ethical considerations, underlining the dual nature of its societal impact. …”
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ChatGPT in orthodontics: limitations and possibilities by Lima Nara Grazieli Martins, Costa Lavínia, Santos Patrícia Bittencourt

“…ChatGPT sometimes produces nonsensical responses and poses privacy risks associated with patient data. Generated medical advice might not therefore match professional expertise. …”
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FedDyH: A Multi-Policy with GA Optimization Framework for Dynamic Heterogeneous Federated Learning by Xuhua Zhao, Yongming Zheng, Jiaxiang Wan, Yehong Li, Donglin Zhu, Zhenyu Xu, Huijuan Lu

“…Federated learning (FL) is a distributed learning technique that ensures data privacy and has shown significant potential in cross-institutional image analysis. …”
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