A semi-synchronous federated learning framework with chaos-based encryption for enhanced security in medical image sharing by Animesh Roy, Deva Raj Mahanta, Lipi B. Mahanta

“…This paper suggests an innovative framework that integrates healthcare engineering, chaotic encryption, and artificial intelligence (AI) to address the privacy issue of medical data. …”
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Factors Associated With the Accuracy of Large Language Models in Basic Medical Science Examinations: Cross-Sectional Study by Naritsaret Kaewboonlert, Jiraphon Poontananggul, Natthipong Pongsuwan, Gun Bhakdisongkhram

“… Abstract BackgroundArtificial intelligence (AI) has become widely applied across many fields, including medical education. …”
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A decentralized feedback-based consensus model considering the consistency maintenance and readability of probabilistic linguistic preference relations for large-scale group decisi... by Xian-Yong Zhang, Yi-Yang Zhou, Jian-Lan Zhou

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Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process by Mohamed K. Mostafa, Ahmed S. Mahmoud, Mohamed S. Mahmoud, Mahmoud Nasr

“…Predicting the adsorption performance to remove organic pollutants from wastewater is an essential environmental-related topic, requiring knowledge of various statistical tools and artificial intelligence techniques. Hence, this study is the first to develop a quadratic regression model and artificial neural network (ANN) for predicting biochemical oxygen demand (BOD) removal under different adsorption conditions. …”
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Statistical Analysis of Medium‐Scale Traveling Ionospheric Disturbances Over Japan Based on Deep Learning Instance Segmentation by Peng Liu, Tatsuhiro Yokoyama, Weizheng Fu, Mamoru Yamamoto

“…With the development of artificial intelligence in recent years, deep learning approach is expecting to clarify the controversy of MSTID external dependence (season and solar/geomagnetic activity) under debating for decades. …”
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Conditions of construction of the system of digital transport and logistics in the management of competitiveness by D. R. Abdyusheva, A. O. Merenkov, A. A. Stepanov

“…The next stage in the development of technologies involves the development of elements of artificial intelligence (digitalization) in many branches of the real sector of the economy. …”
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Plantonome: A Cross-Platform Application for Precision Agriculture by Deroussi Anass, Ait Madi Abdessalam, Alihamidi Imam, Chabou Zakaria, Addaim Adnane

“…In recent years, there has been growing interest in leveraging the Internet of Things (IoT) and Artificial Intelligence (AI) technologies for agriculture. …”
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Probe‐Based Mechanical Data Storage on Polymers Made by Inverse Vulcanization by Abigail K. Mann, Samuel J. Tonkin, Pankaj Sharma, Christopher T. Gibson, Justin M. Chalker

“…Abstract Big data and artificial intelligence are driving increasing demand for high‐density data storage. …”
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Color fundus photograph-based diabetic retinopathy grading via label relaxed collaborative learning on deep features and radiomics features by Chao Zhang, Guanglei Sheng, Jie Su, Lian Duan

“…With the advancement of artificial intelligence technologies, numerous researchers have conducted studies on DR grading based on deep features and radiomic features extracted from color fundus photographs.MethodWe combine deep features and radiomic features to design a feature fusion algorithm. …”
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AI Can Be a Powerful Social Innovation for Public Health if Community Engagement Is at the Core by Alessandra N Bazzano, Andrea Mantsios, Nicholas Mattei, Michael R Kosorok, Aron Culotta

“… There is a critical need for community engagement in the process of adopting artificial intelligence (AI) technologies in public health. …”
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Privacy leakage risk assessment for reversible neural network by Yifan HE, Jie ZHANG, Weiming ZHANG, Nenghai YU

“…In recent years, deep learning has emerged as a crucial technology in various fields.However, the training process of deep learning models often requires a substantial amount of data, which may contain private and sensitive information such as personal identities and financial or medical details.Consequently, research on the privacy risk associated with artificial intelligence models has garnered significant attention in academia.However, privacy research in deep learning models has mainly focused on traditional neural networks, with limited exploration of emerging networks like reversible networks.Reversible neural networks have a distinct structure where the upper information input can be directly obtained from the lower output.Intuitively, this structure retains more information about the training data, potentially resulting in a higher risk of privacy leakage compared to traditional networks.Therefore, the privacy of reversible networks was discussed from two aspects: data privacy leakage and model function privacy leakage.The risk assessment strategy was applied to reversible networks.Two classical reversible networks were selected, namely RevNet and i-RevNet.And four attack methods were used accordingly, namely membership inference attack, model inversion attack, attribute inference attack, and model extraction attack, to analyze privacy leakage.The experimental results demonstrate that reversible networks exhibit more serious privacy risks than traditional neural networks when subjected to membership inference attacks, model inversion attacks, and attribute inference attacks.And reversible networks have similar privacy risks to traditional neural networks when subjected to model extraction attack.Considering the increasing popularity of reversible neural networks in various tasks, including those involving sensitive data, it becomes imperative to address these privacy risks.Based on the analysis of the experimental results, potential solutions were proposed which can be applied to the development of reversible networks in the future.…”
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Advancing eHealth in Society 5.0: A Fuzzy Logic and Blockchain-Enhanced Framework for Integrating IoMT, Edge, and Cloud With AI by Joy Dutta, Deepak Puthal

“…To advance eHealth within this vision, we present a comprehensive framework that integrates the Internet of Medical Things (IoMT), edge computing, and cloud services with Explainable Artificial Intelligence (XAI) and blockchain technology, customized for the 6G era. …”
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Edge-centric optimization: a novel strategy for minimizing information loss in graph-to-text generation by Zheng Yao, Jingyuan Li, Jianhe Cen, Shiqi Sun, Dahu Yin, Yuanzhuo Wang

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Exploring the typhoon intensity forecasting through integrating AI weather forecasting with regional numerical weather model by Hongxiong Xu, Yang Zhao, Zhao Dajun, Yihong Duan, Xiangde Xu

“…Abstract Recent advancements in artificial intelligence (AI) have notably enhanced global weather forecasting, yet accurately predicting typhoon intensity remains challenging. …”
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A novel RFE-GRU model for diabetes classification using PIMA Indian dataset by Mahmoud Y. Shams, Zahraa Tarek, Ahmed M. Elshewey

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Kisspeptin is elevated in the brain after intracerebral haemorrhagic stroke by Saumya Maheshwari, In Hwa Um, Struan Donachie, Nafeesa Asghar, Karina McDade, Tracey Millar, David J. Harrison, Javier A. Tello

“…Machine learning and artificial intelligence-enabled image analysis of multiplexed immunolabeled brain tissues demonstrated that kisspeptin immunoreactivity was higher in cells of the microvasculature (CD105+), but not in neurons or astrocytes when compared to controls. …”
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An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization by Mojtaba Ghasemi, Mohamed Deriche, Pavel Trojovský, Zulkefli Mansor, Mohsen Zare, Eva Trojovská, Laith Abualigah, Absalom E. Ezugwu, Soleiman kadkhoda Mohammadi

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Printed circuit board recycling: a focus on a novel, efficient and sustainable process for spent critical metals recovery by Charpentier, Nicolas M., Xia, Dong, Gabriel, Jean-Christophe P.

“…These ECs are then subjected to a classification process using advanced sorting techniques, including machine vision and multi-energy X-ray transmission spectroscopy powered by artificial intelligence, which allows ECs with similar elemental composition to be accurately sorted together. …”
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Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature by Abbas Mardani, Mehrbakhsh Nilashi, Jurgita Antucheviciene, Madjid Tavana, Romualdas Bausys, Othman Ibrahim

“…Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. …”
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The present and future of digital health, digital medicine, and digital therapeutics for allergic diseases by He Zhang, Yang Cao, Haibo Jiang, Qilin Zhou, Qintai Yang, Lei Cheng

“…It highlights key advancements, including telehealth, mobile health (mHealth), artificial intelligence, clinical decision support systems (CDSS), and digital biomarkers, with a focus on their relevance to allergic disease management. …”
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