Red-KPLS Feature Reduction with 1D-ResNet50: Deep Learning Approach for Multiclass Alzheimer’s Staging by Syrine Neffati, Ameni Filali, Kawther Mekki, Kais Bouzrara

“…However, the complexity of neuroimaging data presents significant obstacles to accurate classification. …”
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Lightweight deep learning system for automated bone age assessment in Chinese children: enhancing clinical efficiency and diagnostic accuracy by Pang Hai, Zhang Bin, Liu Kesheng, Li Cong, Xu Fei

“…In the first stage, the YOLOv8 algorithm precisely localizes 13 key epiphyses in hand radiographs, achieving a mean Average Precision (mAP) of 99.5% at Intersection over Union (IoU) = 0.5 and 94.0% within IoU 0.5–0.95, demonstrating robust detection performance. …”
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A novel machine learning architecture to improve classification of intermediate cases in health: workflow and case study for public health by Bassel Hammoud, Aline Semaan, Lenka Benova, Imad H. Elhajj

“…Results The results show, for most algorithms, an enhanced detection of data points belonging to intermediate classes (up to 14% absolute increase in accuracy), as well as an overall improvement in the models’ accuracies (up to 5.8% absolute increase). …”
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Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome by Weirong Xue, Bing Xu, Hui Wang, Xiaoxiao Zhu, Jiajia Qin, Guangshuang Zhou, Peilin Yu, Shengli Li, Yingliang Jin

“…This study aims to develop a novel machine learning model based on multiple kernel learning to improve the prediction of 3-year recurrence in BCS patients.MethodsData were collected from BCS patients admitted to the Affiliated Hospital of Xuzhou Medical University between January 2015 and July 2022. …”
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Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study by Rishishankar E. Suresh, M S Zobaer, Matthew J. Triano, Brian F. Saway, Parneet Grewal, Nathan C. Rowland

“…Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. …”
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Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images by Ahmad Alenezi, Ali Mayya, Mahdi Alajmi, Wegdan Almutairi, Dana Alaradah, Hamad Alhamad

“…Methods: In this study, a deep learning (DL) algorithm, U-Net, was employed to enhance segmentation accuracy for image segmentation in MPI. …”
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Enhanced Multi-Criteria DNA Sequence Analysis Using Neutrosophic Logic and Deep Learning: An Integrated Approach for Comparison and Classification by Romany M. Farag, Mahmoud Y. Shams, Dalia A. Aldawody, Hazem M. El-Bakry, Huda E. Khalid, Ahmed K. Essa, A. A. Salama

“…Traditionally, traditional methods such as multiple sequence alignment (MSA) algorithms or the NeedlemanWinch algorithm become extremely cumbersome when faced with large or unclear data. …”
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Monitoring Multiple Behaviors in Beef Calves Raised in Cow–Calf Contact Systems Using a Machine Learning Approach by Seong-Jin Kim, Xue-Cheng Jin, Rajaraman Bharanidharan, Na-Yeon Kim

“…This study demonstrates the potential of machine learning and collar-mounted sensors for monitoring multiple behaviors in calves, providing a valuable tool for optimizing production management and early disease detection in the CCC system…”
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Development and validation of a probability-assessment nomogram for non-suicidal self-injury in hospitalized adolescents and young adults with mental disorders by Qi Liu, Yanjuan Ren, Yalin Huang, Ruyi Xie, Yinyuan Li, Zaiquan Dong, Xiaoxia Zhang

“…Age, history of suicide attempts, gender, and psychiatric diagnosis were identified as key detectors through Boruta and LASSO machine learning algorithms, leading to the construction of a nomogram. …”
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Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study by Yuqi Yan, Yuanzhen Liu, Yao Wang, Tian Jiang, Jiayu Xie, Yahan Zhou, Xin Liu, Meiying Yan, Qiuqing Zheng, Haifei Xu, Jinxiao Chen, Lin Sui, Chen Chen, RongRong Ru, Kai Wang, Anli Zhao, Shiyan Li, Ying Zhu, Yang Zhang, Vicky Yang Wang, Dong Xu

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The role of artificial intelligence in occupational health in radiation exposure: a scoping review of the literature by Zohreh Fazli, Mehran Sadeghi, Mohebat Vali, Parvin Ahmadinejad

“…These include the need for high-quality training data, interpretability of complex AI algorithms, alignment with safety standards, integration with existing systems, and the lack of interdisciplinary expertise. …”
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Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning by Alejandro Casallas-Lagos, Javier M. Antelis, Claudia Moreno, Ramiro Franco-Hernández

“…This type of architecture is ideal for this purpose as it can detect the connections between input and output data. …”
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Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry by Valeria Di Stefano, Martina D’Angelo, Francesco Monaco, Annarita Vignapiano, Vassilis Martiadis, Eugenia Barone, Michele Fornaro, Luca Steardo, Marco Solmi, Mirko Manchia, Luca Steardo

“…Moreover, ethical considerations surrounding data integrity, algorithmic transparency, and patient individuality must guide AI’s integration into psychiatry. …”
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Enhancing Cybersecurity and Privacy Protection for Cloud Computing-Assisted Vehicular Network of Autonomous Electric Vehicles: Applications of Machine Learning by Tiansheng Yang, Ruikai Sun, Rajkumar Singh Rathore, Imran Baig

“…Electric autonomous vehicles will require more data, computing resources, and communication capabilities to support them. …”
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Predictive Archaeological Risk Assessment at Reservoirs with Multitemporal LiDAR and Machine Learning (XGBoost): The Case of Valdecañas Reservoir (Spain) by Enrique Cerrillo-Cuenca, Primitiva Bueno-Ramírez

“…This study assesses the potential of using multitemporal LiDAR data and Machine Learning (ML)—specifically the XGBoost algorithm—to predict erosional and sedimentary processes affecting archaeological sites in the Valdecañas Reservoir (Spain). …”
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The Impact of a Deep Learning Self-Adaptive Colour Restoration Pipeline for Deep Underwater Images in 3D Reconstruction by M. Vlachos, D. Skarlatos, S. Demesticha

“…Evaluation was performed using established feature detection algorithms, such as SIFT and SURF, applied to multiple underwater datasets capturing diverse imaging conditions. …”
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Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum by Mohammed Maray

“…Therefore, the machine learning (ML) model is mostly used for the growth of the HAR system to discover the models of human activity from the sensor data. …”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…This study aims to develop a novel method for BV detection by integrating surface-enhanced Raman scattering (SERS) with machine learning (ML) algorithms. …”
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Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery by Xiaoyu Hu, Xiuyuan Zhao, Wenhe Liu

“…This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. …”
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Machine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications by Caizhi Liao, Huaxing Wu, Luigi G. Occhipinti

“…Machine learning algorithms further enhance the analytical capabilities of FOT sensors by effectively processing complex data, identifying patterns, and predicting diagnostic outcomes with 100% high accuracy. …”
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