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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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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Feasibility and acceptability of a contextualized brief psychological intervention for people with bipolar disorder in rural Ethiopia by Mekdes Demissie, Charlotte Hanlon, Lauren C. Ng, Rosie Mayston, Abebaw Fekadu

“…We conducted a mixed-method evaluation, including in-depth qualitative interviews, fidelity ratings of a random selection of 25% of the audio recorded intervention sessions, and self-reported change in symptom severity. We used thematic analysis for qualitative data and descriptive analysis for quantitative data. …”
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Opportunities and challenges of social media for oral health literacy improvement: a qualitative investigation based on Iranian dentists’ perspectives by Hossein Ghalavand, Sirous Panahi, Mohsen Nouri

“…Methods In the current qualitative investigation, a semi-structured interview was conducted with 24 dentists utilizing a purposive snowball sampling method. The analysis of the data was performed using thematic analysis in the MAXQDA 10 software. …”
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Engaging Stakeholders in the Development of a National Digital Mental Health Strategy: Reflexive Thematic Analysis by Sarah Kennedy, Robyn Fitzgerald, Ruth Melia

“…Invited stakeholders included experts in DMH research, clinical practice, and mental health advocacy and policy, together with those with lived experience of accessing mental health services. Qualitative data were analyzed using a reflexive thematic analysis approach. …”
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The Impact of Artificial Intelligence (AI) on Students’ Academic Development by Aniella Mihaela Vieriu, Gabriel Petrea

“…However, challenges such as over-reliance on AI, diminished critical thinking skills, data privacy risks, and academic dishonesty were also identified. …”
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Application progress of artificial intelligence in managing thyroid disease by Qing Lu, Yu Wu, Jing Chang, Li Zhang, Qing Lv, Hui Sun

“…Despite the broad prospects of AI applications, challenges related to data privacy, model interpretability, and clinical applicability remain. …”
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An efficient ECC and fuzzy verifier based user authentication protocol for IoT enabled WSNs by T. Sudhakar, R. Praveen, V. Natarajan

“…Ensuring integrity and privacy of data while transmitting it from sensors to the data analytics servers is crucial in open network. …”
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Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study by Shiying Shen, Wenhao Qi, Sixie Li, Jianwen Zeng, Xin Liu, Xiaohong Zhu, Chaoqun Dong, Bin Wang, Qian Xu, Shihua Cao

“…Conclusion ML is rapidly integrating into personalized medicine, real-time monitoring, and multimodal data fusion. However, challenges such as limited collaboration, weak model generalization, and data privacy persist. …”
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