Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…The study explores the potential of machine learning for improved diabetes prediction using microarray gene expression data and PIMA data set. …”
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Charging Station Management Strategy for Returns Maximization via Improved TD3 Deep Reinforcement Learning by Hengjie Li, Jianghao Zhu, Yun Zhou, Qi Feng, Donghan Feng

“…Also, we use marginalised importance sampling to calculate the Q-return function for TD3, which ensures that the constructed strategies are more likely to learn high-value experiences while having higher robustness. …”
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DLPLSR: Dual Label Propagation-Driven Least Squares Regression with Feature Selection for Semi-Supervised Learning by Shuanghao Zhang, Zhengtong Yang, Zhaoyin Shi

“…Benefiting from these two designs, DLPLSR boosts learning performance without pseudo-labeling. Finally, the objective function admits an efficient closed-form solution solvable via an alternating optimization strategy. …”
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Rolling Bearing Dynamics Simulation Information-Assisted Fault Diagnosis with Multi-Adversarial Domain Transfer Learning by Zhe Li, Zhidan Zhong, Zhihui Zhang, Wentao Mao, Weiqi Zhang

“…To address the issues of negative transfer and reduced stability in transfer learning models for rolling bearing fault diagnosis under variable working conditions, an unsupervised multi-adversarial transfer learning fault diagnosis algorithm based on bearing dynamics simulation data is proposed. …”
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Deep Learning-Enhanced Motor Training: A Hybrid VR and Exoskeleton System for Cognitive–Motor Rehabilitation by Kathya P. Acuña Luna, Edgar Rafael Hernandez-Rios, Victor Valencia, Carlos Trenado, Christian Peñaloza

“…The project showcased an optimized system designed to assess and train cognitive–motor functions in elderly individuals. Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. …”
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A Novel Multi-Objective Fuzzy Deep Learning Framework for Predictive Maintenance in Industrial Internet of Things by Jiangang Feng, Jicheng Kan

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Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data by Nazir Jan, Nasru Minallah, Madiha Sher, Muhammad Wasim, Shahid Khan, Amal Al‐Rasheed, Hazrat Ali

“…ABSTRACT Automating mineral delineation and rock type analysis using remote sensing imaging data is a critical application of machine learning. Traditional machine learning methods often struggle with accuracy and precise map generation. …”
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Detection and classification of hypertensive retinopathy based on retinal image analysis using a deep learning approach by Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri

“…Second, the architectural specifications of the CNN model are determined, and the input size, depth, and number of nodes for each layer, as well as the transfer function, learning rate, and number of epochs, are set by adjusting hyperparameters.…”
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Intelligent Priority-Aware Spectrum Access in 5G Vehicular IoT: A Reinforcement Learning Approach by Adeel Iqbal, Tahir Khurshaid, Yazdan Ahmad Qadri

“…This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). …”
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Developing an explainable machine learning and fog computing-based visual rating scale for the prediction of dementia progression by Zainab H. Ali, Esraa Hassan, Shimaa Elgamal, Nora El-Rashidy

“…Abstract Recently, dementia research has primarily concentrated on using Magnetic Resonance Imaging (MRI) to develop learning models in processing and analyzing brain data. …”
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Deep Learning Empowered Service Selection in FANETs: A 5G-Enabled UAV-Cloud Architecture by Mohamed Ben Bezziane, Messaoud Abbas, Hessa Alfraihi, Bouziane Brik, Yacine Khaldi, Oussama Aiadi, Hasan Siham

“…In this context, a UAV can function as a cloud service provider, offering services such as memory, computing, and network connectivity to consumer UAVs. …”
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A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities by Xu Pengru, Zhou Junhui, Kausar Nasreen, Lin Chunlei, Lu Qianqian, Ghaderpour Ebrahim, Pamucar Dragan, Zadeh Ardashir M.

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Distributed Decision Making for Electromagnetic Radiation Source Localization Using Multi-Agent Deep Reinforcement Learning by Jiteng Chen, Zehui Zhang, Dan Fan, Chaoqun Hou, Yue Zhang, Teng Hou, Xiangni Zou, Jun Zhao

“…We adopt a dual-branch actor structure for independent decisions in UAV control, which reduces decision complexity and improves learning efficiency. The algorithm incorporates task-specific knowledge into the reward function design to guide UAVs in exploring abnormal radiation sources. …”
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Entropy-driven multi agent deep reinforcement learning for resilient distribution networks: coordinating MESS and microgrids by Changchun Cai, Fenglu Gan, Yong Cui, Bo Li, Shixi Hou

“…First, with critical load restoration as the objective function, coordinated optimization model for MESS dispatch and network reconfiguration is constructed, which comprehensively considers security constraints of both distribution networks and microgrids. …”
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Optimizing Screw Fixation in Total Hip Arthroplasty: A Deep Learning and Finite Element Analysis Approach by Jacob Stroud, Emily T. Yan, Jacob Anthony, Kyle Walker, Chung-Hyun Goh

“…Total hip arthroplasty (THA) is a widely performed procedure to restore hip function in patients with degenerative joint diseases. …”
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Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method by Ali Khosrozadeh, Seyed Ali Niknam, Fatemeh Hajizadeh

“…Therefore, burr size cannot be formulated simply as a function of direct parameters. This study proposes an ensemble learning regression model to accurately predict burr size and surface roughness during the slot milling of aluminum alloy (AA) 6061. …”
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What Is My Plaza for? Implementing a Machine Learning Strategy for Public Events Prediction in the Urban Square by Jumana Hamdani, Pablo Antuña Molina, Lucía Leva Fuentes, Hesham Shawqy, Gabriella Rossi, David Andrés León

“…As a result, plazas have lost their function and value. In recent years, awareness has risen of the need to re-activate these public spaces to strive for social inclusion and urban resilience. …”
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Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study by Natthanaphop Isaradech, Wachiranun Sirikul, Nida Buawangpong, Penprapa Siviroj, Amornphat Kitro

“… Abstract BackgroundFrailty is defined as a clinical state of increased vulnerability due to the age-associated decline of an individual’s physical function resulting in increased morbidity and mortality when exposed to acute stressors. …”
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A Collaborative Multi-Agent Reinforcement Learning Approach for Non-Stationary Environments with Unknown Change Points by Suyu Wang, Quan Yue, Zhenlei Xu, Peihong Qiao, Zhentao Lyu, Feng Gao

“…We propose a novel cooperative Multi-Agent Reinforcement Learning (MARL) algorithm based on MADDPG, termed MACPH, which innovatively incorporates three mechanisms: a Composite Experience Replay Buffer (CERB) mechanism that balances recent and important historical experiences through a dual-buffer structure and mixed sampling; an Adaptive Parameter Space Noise (APSN) mechanism that perturbs actor network parameters and dynamically adjusts the perturbation intensity to achieve coherent and state-dependent exploration; and a Huber loss function mechanism to mitigate the impact of outliers in Temporal Difference errors and enhance training stability. …”
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Role of Artificial Intelligence and Deep Learning in Easier Skin Cancer Detection through Antioxidants Present in Food by Sreevidya R. C., Jalaja G, Sajitha N, D. Lakshmi Padmaja, S. Nagaprasad, Kumud Pant, Yekula Prasanna Kumar

“…The study’s purpose is to comprehend the vital function performed by DL methods in supporting healthcare professionals in easier skin cancer detection using big data networks. …”
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