User experience with pregnancy tracker mobile apps: Findings from comment-based qualitative study. by Md Alamgir Hossain, Janet Perkins, Md Tanvir Hasan, Sharon Low Bee Lian, Ribica Chowdhury, Md Tanzirul Alam, Anisuddin Ahmed, Mohammad Sohel Shomik, Shams El Arifeen, Ahmed Ehsanur Rahman, Aniqa Tasnim Hossain, M Shafiqur Rahman

“…This study aimed to explore the user experience (UX) of pregnancy tracker mobile apps used by pregnant women.<h4>Methods</h4>This study explored the dynamics between users' experiences and multifaceted dimensions of advanced features, high-quality materials and information, strict privacy policies, problem-solving abilities, and the usefulness of app features and contents. …”
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Implications of Public Disclosure of Personal Information in a Mobile Alert App for People Living With Dementia Who Go Missing: Qualitative Descriptive Study by Adebusola Adekoya, Christine Daum, Noelannah Neubauer, Antonio Miguel-Cruz, Lili Liu

“…NVivo (version 12) was used to manage data, and conventional content analysis was conducted to identify key themes of the implications of public disclosure of personal information in Community ASAP. …”
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From Surveillance to Sentencing: Evaluating AI's Role in Indian Criminal Justice by Kumar Navin

“…However, the integration of AI into criminal investigations raises critical concerns regarding privacy, surveillance, data protection, and algorithmic bias. …”
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Artificial intelligence in academic research: Benefits, challenges, and ethical dimensions in doctoral studies by Zaki EL HANI, Jaouad KARROUM, Abdelmajid ELOUADI, Aniss MOUMEN

“…However, this transformation is accompanied by challenges, including ethical concerns such as algorithmic bias, risks to data privacy, and over-reliance on automation, which may erode critical thinking and human judgment. …”
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Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management by Ashkan Safari, Mohammadreza Daneshvar, Amjad Anvari-Moghaddam

“…Despite these advances, there are still additional challenges that remain, such as concerns regarding the privacy of data, challenges with integrating different systems, and issues related to scalability. …”
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Machine Learning in E-Commerce: Trends, Applications, and Future Challenges by Elias Dritsas, Maria Trigka

“…Key challenges related to scalability, interpretability, cold-start problems, data sparsity, and privacy are critically analyzed. …”
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The Process for the Formulation of the International Telehealth Position Statement for Occupational Therapy by Karen Jacobs, Jana Cason, Ann McCullough

“…In 2014, WFOT published the WFOT Telehealth Position Statement on the use of telehealth in occupational therapy. The process for the formulation of the official document involved reviewing WFOT member organizations’ telehealth position statements and data collected from a survey sent to member organizations’ delegates in April 2014.  …”
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Advanced Hydro-Informatic Modeling Through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction by Shahariar Hossain Mahir, Md Tanjum An Tashrif, Md Ahsan Karim, Dipanjali Kundu, Anichur Rahman, Md. Amir Hamza, Fahmid Al Farid, Abu Saleh Musa Miah, Sarina Mansor

“…But those models faced significant issues related to scalability, data privacy concerns and limitations of cross-border data sharing including the inaccuracies in prediction models due to changing climate patterns. …”
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Smart governance for smart cities and nations by Z.R.M. Abdullah Kaiser

“…Among these, stakeholders particularly emphasize the need for administrative reform, robust smart infrastructure, finance, uninterrupted electricity, strong data privacy and security, and effective big data management as crucial to the success of the country's vision. …”
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Robust hierarchical federated learning with dual-layer filtering mechanism by Chaoyi Yang, Wei Liang, Yuxiang Chen, Jiahong Xiao, Dacheng He, Tianxiong Liu, Jin Wang, Amr Tolba

“…Hierarchical federated learning (HFL) is an effective “cloud-edge-device” distributed model training framework that protects data privacy. During HFL training, poisoning attacks on local data and transmitted models can affect the accuracy of the global model. …”
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Federated Reinforcement Learning-Based Dynamic Resource Allocation and Task Scheduling in Edge for IoT Applications by Saroj Mali, Feng Zeng, Deepak Adhikari, Inam Ullah, Mahmoud Ahmad Al-Khasawneh, Osama Alfarraj, Fahad Alblehai

“…The system’s design includes collecting data, fusing and preparing it for use, training models, and performing simulations with EdgeSimPy. …”
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A hybrid encryption framework leveraging quantum and classical cryptography for secure transmission of medical images in IoT-based telemedicine networks by Arslan Shafique, Syed Ali Atif Naqvi, Ali Raza, Masoud Ghalaii, Panagiotis Papanastasiou, Julie McCann, Qammer H. Abbasi, Muhammad Ali Imran

“…In this context, ensuring secure transmission and communication is necessary to protect medical data to maintain privacy. To address such privacy concerns and secure medical images against cyberattacks, this research presents a robust hybrid encryption framework that integrates quantum, and classical cryptographic methods. …”
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Joint Class and Domain Continual Learning for Decentralized Federated Processes by Chiara Lanza, Tinne Tuytelaars, Marco Miozzo, Eduard Angelats, Paolo Dini

“…So far, learning in IoT scenarios has almost exclusively been studied in a centralized fashion, but CL can be used in decentralized settings involving a multitude of nodes to learn, adapt, and generalize from their streams of data, even with limited communication and computation capabilities. …”
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BERT ensemble based MBR framework for android malware detection by Faisal S. Alsubaei, Abdulwahab Ali Almazroi, Walid Said Atwa, Abdulaleem Ali Almazroi, Nasir Ayub, Noor Zaman Jhanjhi

“…By leveraging their strengths, the MBR model introduces significant innovation. By using ensemble methods on static data, the MBR framework not only provides a reliable malware detection solution but also presents a novel strategy. …”
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Telemedicine and information technology in health care management: Perspectives and barriers among the nursing students by N Sheeliya White, S Sathya Franklin, N Juliet, Selvia Arokiya Mary Amalanathan, Puvaneswari Kanagaraj, Anasani Somasekhara Babu

“…Data collection was facilitated through a structured questionnaire, and the gathered data were analyzed using SPSS version 22. …”
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Large Language Models in Medicine: Clinical Applications, Technical Challenges, and Ethical Considerations by Kyu-Hwan Jung

“…Ethical concerns involve algorithmic bias risking health inequities, threats to patient safety from inaccuracies, unclear accountability, data privacy, and impacts on clinician-patient interactions. …”
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Evaluating the Ethical Practices in Developing AI and Ml Systems in Tanzania by Lazaro Inon Kumbo, Victor Simon Nkwera, Rodrick Frank Mero

“…Artificial Intelligence (AI) and Machine Learning (ML) present transformative opportunities for sectors in developing countries like Tanzania that were previously hindered by manual processes and data inefficiencies. Despite these advancements, the ethical challenges of bias, fairness, transparency, privacy, and accountability are critical during AI and ML system design and deployment. …”
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Geodesign in the era of artificial intelligence by Xinyue Ye, Tianchen Huang, Yang Song, Xin Li, Galen Newman, Zhongjie Lin, Dayong Jason Wu

“…AI enhances existing Geodesign methods by automating spatial data analysis, improving land use classification, refining heat island effect assessment, optimizing energy use, facilitating green infrastructure planning, and generating design scenarios. …”
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AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis by Esther Ugo Alum

“…Existing gaps include data quality, algorithmic transparency, and ethical concerns around privacy, among others. …”
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Blockchain and Its Application in the Peer Review of Scientific Works: A Systematic Review by Cristian Hugo Morales-Alarcón, Elba Bodero-Poveda, Henry Mauricio Villa-Yánez, Pamela Alexandra Buñay-Guisñan

“…However, studies addressing this topic focus on its potential to improve transparency and trust in the process, offer incentives and rewards to reviewers and authors, enhance the quality and fairness of evaluations, and strengthen the security and privacy of the data involved.…”
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