Commercial vision sensors and AI-based pose estimation frameworks for markerless motion analysis in sports and exercises: a mini review by Saeid Edriss, Cristian Romagnoli, Lucio Caprioli, Vincenzo Bonaiuto, Elvira Padua, Giuseppe Annino, Giuseppe Annino

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Hypertension a Predictive Risk Factor on Progression to Alzheimer’s Disease Using APOEε4 as a Benchmark by Mingfei Li, Ying Wang, Lewis Kazis, Weiming Xia

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Factors Determining Adherence to Hand Antisepsis by Healthcare Workers during Pandemic Infection Spread (as exemplified by COVID­-19) by S. S. Smirnova, Yu. S. Stagilskaya, I. A. Egorov, N. N. Zhuikov

“…Epidemiological (descriptive-evaluation), bacteriological (conventional culture, AMR determination using a VITEK2 analyzer), molecular biological (RT-PCR, Sanger sequencing) and statistical (a questionnaire survey, building machine learning models) methods were used in the study. …”
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Method for predicting cutter remaining life based on multi-scale cyclic convolutional network by Tao Li, Yinquan Yu, Chaoqiang Huang, Jinwen Yang, Yuanchang Zhong, Yong Hao

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Dynamic monitoring and drivers of ecological environmental quality in the Three-North region, China: Insights based on remote sensing ecological index by Leyi Zhang, Xia Li, Xiuhua Liu, Zhiyang Lian, Guozhuang Zhang, Zuyu Liu, Shuangxian An, Yuexiao Ren, Yile Li, Shangdong Liu

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Ambulatory Smoking Habits Investigation based on Physiology and Context (ASSIST) using wearable sensors and mobile phones: protocol for an observational study by Donghui Zhai, Giuseppina Schiavone, Ilse Van Diest, Elske Vrieze, Walter DeRaedt, Chris Van Hoof

“…Most recent machine learning techniques will also be explored to combine heterogeneous data for classification of smoking events and prediction of craving.Ethics and dissemination The study was designed together by an interdisciplinary group of researchers, including psychologist, psychiatrist, engineer and user involvement coordinator. …”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…For instance, machine learning models can evaluate patient records, lifestyle factors, and genetic information to deliver precise risk assessments and personalized recommendations. …”
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Metabolomic profiling in heart failure as a new tool for diagnosis and phenotyping by Maria V. Kozhevnikova, Yuri N. Belenkov, Ksenia M. Shestakova, Anton A. Ageev, Pavel A. Markin, Anastasiia V. Kakotkina, Ekaterina O. Korobkova, Natalia E. Moskaleva, Ivan V. Kuznetsov, Natalia V. Khabarova, Alexey V. Kukharenko, Svetlana A. Appolonova

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Towards New Strategies for Investing: Insights on Sustainable Exchange-Traded Funds (ETFs) by Nini Johana Marín-Rodríguez, Juan David González-Ruíz, Sergio Botero

“…The findings highlight the conceptual evolution of Green ETFs, from early definitions focused on ESG-aligned investments to more complex instruments incorporating diversified screening criteria and advanced technologies like machine learning and artificial intelligence. Practical challenges such as regulatory inconsistencies, high implementation costs, and limited investor education are underscored as critical barriers to broader adoption. …”
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THE SUCCESS OF SATELLITE GRAVIMETRY IN GEOPHYSICAL INVESTIGATIONS by Ahmad Alhassan, Okoro Ernest chidi, Mohammed Auwal Adamu, Salisu Tata, Muhammad Shettima Nur, Auwal Aliyu, Yusuf Abdulhameed

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On signal encryption at MapReduce and collaborative attribute-based access with ECAs for a preprocessed data set with ML in a privacy-preserving health 4.0 by Arnab Mitra, Anabik Pal

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QSPR analysis of physico-chemical and pharmacological properties of medications for Parkinson’s treatment utilizing neighborhood degree-based topological descriptors by Vignesh Ravi, Natarajan Chidambaram

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Pig Butchering in Cybersecurity: A Modern Social Engineering Threat by Dr. Sharon L. Burton, Dr. Pamela D. (Vickerson) Moore

“…Future research should focus on developing predictive models and integrating AI and machine learning for better detection and prevention. …”
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Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making. by Rawan Abulibdeh, Karen Tu, Debra A Butt, Anthony Train, Noah Crampton, Ervin Sejdić

“…The association between the completeness rates of the sociodemographic data and the various clinics, electronic medical record vendors, and physician characteristics was analyzed. Supervised machine learning models were used to determine the absence or presence of each characteristic for all adult patients over the age of 18 in the database. …”
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Stigma Attitudes Toward HIV/AIDS From 2011 Through 2023 in Japan: Retrospective Study in Japan by Yi Piao, Nao Taguchi, Keisuke Harada, Kunihiro Hirahara, Yosuke Takaku, John Austin, KuanYeh Lee, Yui Shiozawa, Yunfei Cheng, Yoji Inoue

“…Individual tweets were labeled with the messages they conveyed (stigma and corresponding antistigma types included labels, marks, responsibility, peril, insults, and fear; tweets without stigma or antistigma messages were considered general education or neutral) along with demographic characteristics and locations; phase 1 results were used to develop a machine learning model to apply in phase 2. …”
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A NLP analysis of digital demand for healthcare jobs in China by Yirui Chen, Xinrui Zhan, Wencan Yang, Xueying Yan, Yuxin Du, Tieniu Zhao

“…The analysis revealed a strong demand for technical skills such as data analysis, AI and machine learning, and technology integration. Compliance and data privacy skills were also highly demanded, reflecting the healthcare sector’s commitment to regulatory adherence and data security. …”
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Optimal Structure to Maximize Torque per Volume for the Consequent-Pole PMSM and Investigating the Temperature Effect by Alireza Hosseinpour, Ahmed Abbas, Mahmoud Oukati Sadegh, Atif Iqbal, Aymen Flah, Lukas Prokop, Enas Ali, Ramy N. R. Ghaly

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SIDNet: A SQL Injection Detection Network for Enhancing Cybersecurity by Debendra Muduli, Shantanu Shookdeb, Abu Taha Zamani, Surabhi Saxena, Anuradha Shantanu Kanade, Nikhat Parveen, Mohammad Shameem

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Developing a Research Center for Artificial Intelligence in Medicine by Curtis P. Langlotz, MD, PhD, Johanna Kim, MPH, MBA, Nigam Shah, MBBS, PhD, Matthew P. Lungren, MD, MPH, David B. Larson, MD, MBA, Somalee Datta, PhD, Fei Fei Li, PhD, Ruth O’Hara, PhD, Thomas J. Montine, MD, PhD, Robert A. Harrington, MD, Garry E. Gold, MD, MS

“…Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. …”
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Generalizing the Cross Product to <i>N</i> Dimensions: A Novel Approach for Multidimensional Analysis and Applications by Samir Brahim Belhaouari, Yunis Carreon Kahalan, Ilyasse Aksikas, Abdelouahed Hamdi, Ismael Belhaouari, Elias Nabel Haoudi, Halima Bensmail

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