Assessment of breastfeeding self-efficacy in nursing mothers following earthquakes: A case study of the Kahramanmaraş earthquakes in Turkey by Sinem Güven Dinmez, Ozen Karaman, Emine Bakir

“…During the study period, all eligible mothers visiting the polyclinic who met the inclusion criteria were evaluated. Data were collected using a "Descriptive Data Form" and the "Breastfeeding Self-Efficacy Short Form Scale." …”
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Dimensions related to telemedicine and telehealth competencies of health professionals: A scoping review by Jacqueline Ibarra-Peso, Marcela Hechenleitner-Carvallo, Carlos Zúñiga-San Martín, Angelica Avendaño-Veloso, Eileen Sepúlveda-Valenzuela

“…Introduction Telehealth and telemedicine have proven to be useful in complementing face-to-face care, especially when long distances exist or when it is difficult to access specialists. …”
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Implementing a chatbot to promote hereditary breast & ovarian cancer genetic screening in women’s health: identifying barriers and facilitators to screening adoption by Easton N. Wollney, Shireen Madani Sims, Luisel J. Ricks-Santi, Elizabeth Eddy, Daniel Wiesman, Carla L. Fisher

“…Findings illustrate how each factor functioned as a facilitator and/or barrier in women’s uptake (e.g., technology being trusted for accuracy versus concerns for data privacy and security). Conclusions While chatbots can extend women’s cancer risk screening access, particularly in rural regions and with college-educated women, systems-level (cost) and individual-level factors (perceived risk, misconceptions about repeating genetic testing) should be addressed to promote adoption. …”
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Large language models aided patient progression documentation according to the ICD standard by Nuria Lebeña, Arantza Casillas, Alicia Pérez

“…In previous works, this task has been carried out using structured data, such as, ICD-coded diagnoses, overlooking unstructured textual information in EHRs. …”
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A hybrid explainable federated-based vision transformer framework for breast cancer prediction via risk factors by Aymen M. Al-Hejri, Archana Harsing Sable, Riyadh M. Al-Tam, Mugahed A. Al-antari, Sultan S. Alshamrani, Kaled M. Alshmrany, Wedad Alatebi

“…These models are assessed on binary, multi-class, and Breast Imaging Reporting and Data System (BI-RADS) classification tasks using a unique dataset encompassing real-world risk factors. …”
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HIV pre-exposure prophylaxis in Latin America: Public policies, healthcare systems and human rights by Herta de Oliveira Alexandre, Sofia Gruskin, Adriano Massuda, Maria Rita Bertolozzi, Aluisio Cotrim Segurado

“…Achieving universal healthcare and adhering to human rights norms can improve access/use of this essential HIV prevention tool.…”
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Foreground-Driven Contrastive Learning for Unsupervised Human Keypoint Detection by Shuxian Li, Hui Luo, Zhengwei Miao, Zhixing Wang, Qiliang Bao, Jianlin Zhang

“…Recently, unsupervised human keypoint detection has become prevalent due to concerns about data privacy. Most existing methods are based on a reconstruction process that extracts appearance and pose information from transformed image pairs and spatially aligns them to obtain a reconstructed image for detection. …”
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B5G Applications and Emerging Services in Smart IoT Environments by Hani Attar, Mohammed Alghanim, Jafar Ababneh, Khosro Rezaee, Ayat Alrosan, Mohanad A. Deif

“…Furthermore, the proposed study examines the techniques employed for data transmission in B5G applications, such as Vehicle-to-Vehicle (V2V), Device-to-Device (D2D), and Machine to Machine (M2M) transmissions. …”
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Smart cities and electrical and electronic waste management: a review of challenges and opportunities by Deividson Sá Fernandes de Souza, Simone Sehnem, Patricia Guarnieri, Thyago Celso Cavalcante Nepomuceno, Fernando Henrique Taques, Lucio Camara e Silva

“…Key challenges include information security, privacy, interoperability, costs and sustainability. …”
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Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning by Won June Choi, Jin HwangBo, Quan Anh Duong, Jae-Hyeok Lee, Jin Kyu Gahm

“…However, the models typically require extensively labeled training datasets, which are costly and pose patient privacy risks. To address the issue of limited training datasets, we propose a novel few-shot learning framework for classifying multiple system atrophy parkinsonian (MSA-P) and progressive supranuclear palsy (PSP) within the APS category using fewer data items. …”
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Vision-Based Activity Recognition for Unobtrusive Monitoring of the Elderly in Care Settings by Rahmat Ullah, Ikram Asghar, Saeed Akbar, Gareth Evans, Justus Vermaak, Abdulaziz Alblwi, Amna Bamaqa

“…The system integrates a frame differencing algorithm with adjustable sensitivity parameters and an anomaly detection model tailored to identify deviations from individual behavior patterns without relying on large volumes of labeled data. The system was validated through real-world deployments across multiple care home rooms, demonstrating significant improvements in emergency response times and ensuring resident privacy through anonymized frame differencing views. …”
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A Multi-Criteria Decision-Making Framework for Blockchain Technology Adoption in Smart Healthcare by Deepika Gaur, Shivani Bali, Angappa Gunasekaran, Nitish Joshi, Kuldeep Chaudhary, Vikram Bali

“…Among the three blockchain platforms evaluated, the results indicate that permissioned blockchain technology is the most suitable for the healthcare sector, primarily due to its strengths in regulatory compliance, data privacy, and system integration. The study’s findings would help practitioners identify and choose the best blockchain platform, thus contributing to a transition in the healthcare industry called “Smart Healthcare 5.0”.…”
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Optimized Identity Authentication via Channel State Information for Two-Factor User Verification in Information Systems by Chuangeng Tian, Fanjia Li, Xiaomeng Liu, Juanjuan Li

“…For classification, a kernel support vector machine (SVM) model is trained using a randomized hyperparameter search algorithm. …”
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Intrusion Detection Framework for Internet of Things with Rule Induction for Model Explanation by Kayode S. Adewole, Andreas Jacobsson, Paul Davidsson

“…As the proliferation of Internet of Things (IoT) devices grows, challenges in security, privacy, and interoperability become increasingly significant. …”
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A "Quıet" need ın the dısaster of the century: a qualıtatıve study on menstrual hygıene management by Ayşe Aydın, Meltem Şirin Gok, Bahar Çiftçi

“…The interviews were analyzed using content analysis, and themes and sub-themes reflecting the women's experiences were identified. …”
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The Impact of AI-Personalized Recommendations on Clicking Intentions: Evidence from Chinese E-Commerce by Jiwang Yin, Xiaodong Qiu, Ya Wang

“…AI-personalized recommendation technology offers more accurate and diverse choices to consumers and increases click-through rates and sales on e-commerce platforms. Yet, data on consumers’ experiences of AI-personalized recommendations and their impact path on clicking intention are scarce. …”
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The Role of Evidence-Based Design in Enhancing Architectural Design Process of Healthcare Buildings: A Case Study of General Hospitals in Sulaimani City by Zhekaf Sulaiman Mustafa Hassan 1, a, *, Dr. Fouad Jalal Mahmood 2, a

“…The investigation includes correlations between participants' prioritization of hospital design aspects such as circulation, accessibility, wayfinding, visibility, and privacy and their professional background, experience, and considerations in EBD. …”
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Transformations in academic work and faculty perceptions of artificial intelligence in higher education by Jorge Buele, Leonel Llerena-Aguirre

“…Additionally, ethical debates are emerging around algorithmic transparency, data privacy, and institutional responsibility. Effective integration in higher education demands not only technical proficiency but also ethical grounding, regulatory support, and critical pedagogical development. …”
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FedHSP: A Robust Federated Learning Framework Coherently Addressing Heterogeneity, Security, and Performance Challenges by Divya G. Nair, C. V. Aswartha Narayana, K. Jaideep Reddy, Jyothisha J. Nair

“…Federated Learning (FL) is a machine learning training method that leverages local model gradients instead of accessing private data from individual clients, ensuring privacy. However, the practical implementation of FL faces significant challenges. …”
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Class-Hidden Client-Side Watermarking in Federated Learning by Weitong Chen, Chi Zhang, Wei Zhang, Jie Cai

“…Federated learning consists of a central aggregator and multiple clients, forming a distributed structure that effectively protects data privacy. However, since all participants can access the global model, the risk of model leakage increases, especially when unreliable participants are involved. …”
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