An Integrated Lateral and Longitudinal Decision-Making Model for Autonomous Driving Based on Deep Reinforcement Learning by Jianxun Cui, Boyuan Zhao, Mingcheng Qu

“…By setting a reasonable state, action, and reward function, this paper has carried out a large number of simulation experiments on the proposed autonomous driving decision-making model based on deep reinforcement learning in a three-lane road environment. …”
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Restoration of Out-of-Focus Fluorescence Microscopy Images Using Learning-Based Depth-Variant Deconvolution by Da He, De Cai, Jiasheng Zhou, Jiajia Luo, Sung-Liang Chen

“…Image quality is degraded in the out-of-focus region because of the depth-variant (DV) point spread function (DV-PSF) of a fluorescence microscope. Either non-blind or blind deconvolution for restoration results in limited improvement. …”
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Automated Artery Detection and Stenosis Classification in CTA Using Deep Learning for Peripheral Arterial Disease by Ali M. O. A. Anwer, Hacer Karacan, Muhammed Rabee, Levent Enver, Gonca Cabuk

“…We use Faster R-CNN with a ResNet-101 backbone driven by a custom loss function to achieve good artery localization and reduce false positives. …”
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Federated Learning for Multimodal Sentiment Analysis: Advancing Global Models With an Enhanced LinkNet Architecture by P. Vasanthi, V. Madhu Viswanatham

“…In this article, the Enhanced LinkNet (EnLNet)-based Federated Learning (FL) approach is proposed for MSA. This approach utilizes an EnLNet model within an FL framework to manage and process multimodal data, including text, signals, and images. …”
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Effect of modified mobilization with movement and motor learning on volleyball females players with shoulder impingement syndrome by Atef M. Doweir, Ahmed Mashaal, Sami A. Z. Basha, Dina M. M. Abdelhamid, Sarah M. M. Kamel, Ali M. Hassan, Moataz A. El-Aal Mohamed, Sabah M. Elkady

“…BACKGROUND: Shoulder impingement syndrome (SIS) is a common issue among athletes, especially volleyball players, leading to pain and limited function. To evaluate effectiveness of combining modified mobilization with movement (MWM) and motor learning principles with traditional physical therapy for treating shoulder impingement syndrome. …”
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A Self-Supervised Specific Emitter Identification Method Based on Contrastive Asymmetric Masked Learning by Dong Wang, Yonghui Huang, Tianshu Cui, Yan Zhu

“…This design promotes the learning of fine-grained local feature representations. …”
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Urban Thermal Archetype Classification in the Context of Urban Development Transformation Using Machine Learning Techniques by Yan Deng, Huimin Liu

“…This study collected 21 indicators of urban composition, urban form, urban function, and socio-economy to provide a more complete and detailed portrayal of real-world built environments in Wuhan. …”
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Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving by Ambati Pravallika, Mohammad Farukh Hashmi, Aditya Gupta

“…This analysis explores the pivotal function of three-dimensional object detection in improving AV safety and performance, underscoring its importance within the larger framework of self-driving vehicle systems. …”
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Deciphering the cellular and molecular landscape of pulmonary fibrosis through single-cell sequencing and machine learning by Yong Zhou, Zhongkai Tong, Xiaoxiao Zhu, Chunli Wu, Ying Zhou, Zhaoxing Dong

“…Abstract Pulmonary fibrosis is characterized by progressive lung scarring, leading to a decline in lung function and an increase in morbidity and mortality. …”
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ISPIL: Interactive Sub-Goal-Planning Imitation Learning for Long-Horizon Tasks With Diverse Goals by Cynthia Ochoa, Hanbit Oh, Yuhwan Kwon, Yukiyasu Domae, Takamitsu Matsubara

“…To address this situation, we propose Interactive Sub-Goal-Planning Imitation Learning (ISPIL), an IL framework that learns hierarchical, goal-conditioned policies. …”
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Machine learning with hyperparameter optimization applied in facies-supported permeability modeling in carbonate oil reservoirs by Watheq J. Al-Mudhafar, Alqassim A. Hasan, Mohammed A. Abbas, David A. Wood

“…On the other hand, highly permeable fractures function as the main flow conduits within such reservoirs. …”
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Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification by Tikam Chand Dakal, Caiming Xu, Caiming Xu, Abhishek Kumar, Abhishek Kumar

“…This review hunts through the cutting-edge computational methodologies that integrate multi-omics data—such as metagenomics, metaproteomics, and metabolomics—providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. …”
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Enhanced Defect Detection in Additive Manufacturing via Virtual Polarization Filtering and Deep Learning Optimization by Xu Su, Xing Peng, Xingyu Zhou, Hongbing Cao, Chong Shan, Shiqing Li, Shuo Qiao, Feng Shi

“…The YOLO V5-W model, integrated with CBAM attention, DenseNet connections, and an EIoU loss function, demonstrates superior performance in defect identification across various lighting conditions. …”
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Hybrid Machine-Learning Model for Accurate Prediction of Filtration Volume in Water-Based Drilling Fluids by Shadfar Davoodi, Mohammed Al-Rubaii, David A. Wood, Mohammed Al-Shargabi, Mohammad Mehrad, Valeriy S. Rukavishnikov

“…Therefore, employing radial basis function neural network (RBFNN) and multilayer extreme learning machine (MELM) algorithms integrated with the growth optimizer (GO), predictive hybrid ML (HML) models are developed to reliably predict the FV using only two easy-to-measure input variables: drilling fluid density (FD) and Marsh funnel viscosity (MFV). …”
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Advanced Optimization of Flowshop Scheduling with Maintenance, Learning and Deteriorating Effects Leveraging Surrogate Modeling Approaches by Nesrine Touafek, Fatima Benbouzid-Si Tayeb, Asma Ladj, Riyadh Baghdadi

“…Surrogate modeling has recently emerged as an effective solution to reduce these computational demands by approximating the true, time-intensive fitness function. While surrogate-assisted metaheuristics have gained attention in recent years, their application to complex scheduling problems such as the Permutation Flowshop Scheduling Problem (PFSP) under learning, deterioration, and maintenance effects remains largely unexplored. …”
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Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction by Samir Shamma, Mohamed Ali Hussein, Eslam M. A. El-Nahrery, Ahmed Shahat, Tamer Shoeib, Anwar Abdelnaser

“…Our findings highlighted specific OCPs, such as Methoxychlor, p,p-DDT, Heptachlor, Endrin, and various HCH isomers, could impact thyroid function. The study supports a strong correlation between OCP exposure and thyroid dysfunction, demonstrating high accuracy in classifying thyroid status using ML models. …”
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Machine learning models for clinical and structural knee osteoarthritis prediction: Recent advancements and future directions by Gabby B. Joseph, Charles E. McCulloch, Michael C. Nevitt, Nancy E. Lane, Sharmila Majumdar, Thomas M. Link

“…This review highlights recent advances in ML model development across four OA outcome domains: clinical, structural (radiographic and MRI-based), and surgical endpoints, each addressing different but interrelated aspects of the disease.For clinical outcomes, ML studies have focused on predicting changes in patient-reported clinical measures (e.g., pain and function). Radiographic OA has been characterized using deep learning (DL) models, and ML approaches have also been used to predict progression of Kellgren Lawrence grades and joint space narrowing. …”
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Integrated network toxicology, machine learning and molecular docking reveal the mechanism of benzopyrene-induced periodontitis by Wen Wenjie, Li Rui, Zhuo Pengpeng, Deng Chao, Zhang Donglin

“…Machine learning analysis identified three core targets: CXCL12, CYP24A1, and HMGCR. …”
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Online Fault Tolerant RUL Prediction Strategy for Lithium-Ion Batteries Using Machine Learning by Brahim Zraibi, Mohamed Mansouri, Chafik Okar, Hicham Chaoui

“…The framework utilizes an Improved Convolutional Long Short-Term Memory Deep Network (Imp-CLD), a hybrid machine learning algorithm integrating Deep Neural Networks, Convolutional Neural Networks, and Long Short-Term Memory networks. …”
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Toward Spatio‐Temporally Consistent Multi‐Site Fire Danger Downscaling With Explainable Deep Learning by Óscar Mirones, Jorge Baño‐Medina, Swen Brands, Joaquín Bedia

“…The model's temporal performance aligns closely with observed data as measured by the autocorrelation function, making it a promising tool for multi‐site downscaling. …”
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