Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient by Yanxiang Niu, Xin Chen, Jianqi Fan, Chunli Liu, Menghao Fang, Ziquan Liu, Xiangyan Meng, Yanqing Liu, Lu Lu, Haojun Fan

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Predicting Surface Roughness and Grinding Forces in UNS S34700 Steel Grinding: A Machine Learning and Genetic Algorithm Approach to Coolant Effects by Mohsen Dehghanpour Abyaneh, Parviz Narimani, Mohammad Sadegh Javadi, Marzieh Golabchi, Samareh Attarsharghi, Mohammadjafar Hadad

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A comprehensive review of predictive analytics models for mental illness using machine learning algorithms by Md. Monirul Islam, Shahriar Hassan, Sharmin Akter, Ferdaus Anam Jibon, Md. Sahidullah

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Atrial Cardiomyopathy in Atrial Fibrillation: A Multimodal Diagnostic Framework by Paschalis Karakasis, Panayotis K. Vlachakis, Panagiotis Theofilis, Nikolaos Ktenopoulos, Dimitrios Patoulias, Barbara Fyntanidou, Antonios P. Antoniadis, Nikolaos Fragakis

“…A diagnostic framework informed by atrial substrate assessment may support more tailored therapeutic decision-making in AF. …”
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PRUS: Product Recommender System Based on User Specifications and Customers Reviews by Naveed Hussain, Hamid Turab Mirza, Faiza Iqbal, Ayesha Altaf, Ahtsham Shoukat, Monica Gracia Villar, Emmanuel Soriano Flores, Marco Antonio Rojo Gutierrez, Imran Ashraf

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Automatic collateral quantification in acute ischemic stroke using U2-net by Qingqing Lu, Qingqing Lu, Hongyi Chen, Junyan Fu, Xiaodong Zheng, Yiren Xu, Yuning Pan

“…The qCS aligns well with vCS and demonstrates the feasibility of automated collateral assessment, which may enhance diagnostic accuracy and therapeutic decision-making.…”
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Transforming Manufacturing Quality Management with Cognitive Twins: A Data-Driven, Predictive Approach to Real-Time Optimization of Quality by Asif Ullah, Muhammad Younas, Mohd Shahneel Saharudin

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Justifying the selection of a neural network linguistic classifier by Олеся Барковська, Ксенія Воропаєва, Олександр Руських

“…These aspects include classification accuracy, training and prediction time, model size, data distribution, and overall ease of use. By systematically assessing these attributes, this study aims to provide valuable information about the strengths and weaknesses of each model and enable researchers and practitioners to make informed decisions when selecting a neural network classifier for text classification tasks. …”
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The Role of MDCT Coronary Angiography in the Detection of Benign Varieties and Anomalies of Coronary Blood Vessels—A Narrative Review by Ana Mladenovic Markovic, Ana Tomic, Miodrag Nisevic, Biljana Parapid, Nikola Boskovic, Marina Vitas, Miona Jevtovic, Sandra Grujicic

“…It offers high-resolution imaging that enables precise assessment of congenital coronary variations, aiding in both clinical decision-making and long-term patient management. …”
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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review by Platon S. Papageorgiou, Rafail Christodoulou, Panagiotis Korfiatis, Dimitra P. Papagelopoulos, Olympia Papakonstantinou, Nancy Pham, Amanda Woodward, Panayiotis J. Papagelopoulos

“…Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. …”
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Perceived worries in the adoption of artificial intelligence among nurses in neonatal intensive care units by Ahmad Ayed, Ahmad Batran, Ibrahim Aqtam, Malakeh Z. Malak, Moath Abu Ejheisheh, Mosaab Farajallah, Lamees Farraj, Sanaa Alkhatib

“…Thus, artificial intelligence in healthcare sectors has the potential to enhance nursing care by assisting nurses with tasks like documentation, workflow improvement, and decision-making, while reducing workforce stress. This study, guided by the Technology Acceptance Model (TAM), assesses perceived worries regarding AI adoption among nurses in neonatal intensive care units (NICUs). …”
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Fine-Grained Land Use Remote Sensing Mapping in Karst Mountain Areas Using Deep Learning with Geographical Zoning and Stratified Object Extraction by Bo Li, Zhongfa Zhou, Tianjun Wu, Jiancheng Luo

“…This knowledge gap impedes scientific decision-making and precise policy formulation for regional ecological environment management. …”
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Design Trends and Comparative Analysis of Lightweight Block Ciphers for IoTs by Safia Meteb Al-Nofaie, Sanaa Sharaf, Rania Molla

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Vertebroplasty Increases the Incidence Of New Vertebral Compression Fractures Compared To Conservative Management by Maram Othman, Meshal Alshaalan, Abdulrahman Khawaji, Rawan Benkuddah, Hala Khalil, Khalid Alismail, Waleed Althobaity, Saleh Alreshoodi

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Impact of bridging the gap between Artificial Intelligence and nanomedicine in healthcare by Divyam Mishra, Bhavishya Chaturvedi, Vishal Soni, Dhairya Valecha, Megha Goel, Jamilur R. Ansari

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Health service utilisation cost associated with residential fire incidents in New South Wales, Australia by Fahmida Saadia Rahman, Wadad Kathy Tannous, Kingsley Emwinyore Agho, Gulay Avsar, Lara Ann Harvey

“…Our study will assist the government and stakeholders in making informed decisions that prioritise funding for healthcare service and improve the quality of overall fire safety and public health outcomes.…”
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Sugarcane leaf disease classification using deep neural network approach by Saravanan Srinivasan, S. M. Prabin, Sandeep Kumar Mathivanan, Hariharan Rajadurai, Suresh Kulandaivelu, Mohd Asif Shah

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Does Artificial Intelligence Bring New Insights in Diagnosing Phlebological Diseases?—A Systematic Review by Sergiu-Ciprian Matei, Sorin Olariu, Ana-Maria Ungureanu, Daniel Malita, Flavia Medana Petrașcu

“…In the realm of venous pathologies such as chronic venous disease (CVD), venous reflux, and deep venous thrombosis (DVT), AI has shown tremendous potential to improve diagnostic accuracy, streamline workflows, and enhance clinical decision-making. This study aims to evaluate the efficacy and feasibility of AI algorithms in diagnosing venous diseases and explore their potential impact on clinical practice. …”
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Investigation of a Novel Noninvasive Risk Analytics Algorithm With Laboratory Central Venous Oxygen Saturation Measurements in Critically Ill Pediatric Patients by Sarah A. Teele, MD, MSHPEd, Avihu Z. Gazit, MD, Craig Futterman, MD, William G. La Cava, PhD, David S. Cooper, MD, MPH, MBA, Steven M. Schwartz, MD, MS, Joshua W. Salvin, MD, MPH

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Prediction of generalized anxiety disorder treatment outcomes with neurobehavioral responses to approach-avoidance conflict: a randomized clinical trial by Hannah Berg, Timothy J. McDermott, Rayus Kuplicki, Hung-Wen Yeh, Wesley K. Thompson, Ryan Smith, Elisabeth Akeman, Namik Kirlic, Ashley Clausen, Mallory Cannon, Evan White, Christopher R. Martell, Kate B. Wolitzky-Taylor, Michelle G. Craske, James L. Abelson, Martin P. Paulus, Robin L. Aupperle

“…Participants completed an AAC task during functional magnetic resonance imaging pre-treatment. Computational parameters of task behavior were derived, and neural activity was assessed during decision-making and positive and negative outcomes of decisions. …”
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