Quality monitoring of hybrid welding processes: A comprehensive review by Solomon Habtamu Tessema, Dariusz Bismor

“…Artificial intelligence/Machine learning enables real-time analysis of welding parameters and defect detection, while digital twins offer virtual representations of physical welding processes, facilitating predictive maintenance and optimization. …”
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AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes by José E. Valerio, Guillermo de Jesús Aguirre Vera, Maria P. Fernandez Gomez, Jorge Zumaeta, Andrés M. Alvarez-Pinzon

“…A significant challenge in optimizing these therapeutic strategies is the early identification and recruitment of suitable candidates for clinical trials. …”
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Wide-Angle Image Distortion Correction and Embedded Stitching System Design Based on Swin Transformer by Shiwen Lai, Zuling Cheng, Wencui Zhang, Maowei Chen

“…Experiments on synthetic and real-world datasets show that the method outperforms mainstream algorithms, with PSNR gains of 3.28 dB and 2.18 dB on wide-angle and fisheye images, respectively, while maintaining real-time performance. …”
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Predictive framework of vegetation resistance in channel flow by Fengcong Jia, Weijie Wang, Yu Han, Jiayu Du, Yue Zhang, Zihan Liu, Hairong Gao

“…To improve predictive performance, optimization algorithms such as PSO, WSO, and RIME were applied. …”
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Numerical analysis of geometrical configuration effects on near-field shockwave propagation in CL-20 based underwater explosives by Ji-ping Chen, Jun Wang, Xian-pi Zhang, Yan-jie Zhao, Xing-xing Wu, Fang-zhou Zhu, Jun Yu

“…These observations underscore the deterministic relationship between the explosive geometric topology and resultant fluidic shock phenomenology, providing critical insights for optimizing blast mitigation strategies in naval architecture and precision demolition engineering.…”
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Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection by Alymzhan Toleu, Gulmira Tolegen, Rustam Mussabayev, Alexander Krassovitskiy, Bagashar Zhumazhanov

“…Further analysis revealed that BIC-GMM exhibits greater stability in managing variations in feature performance, whereas KL-GMM is more sensitive to threshold optimization. Nevertheless, KL-GMM achieved competitive results when paired with specific features, such as MFCC and zero crossing rate. …”
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A triple pronged approach for ulcerative colitis severity classification using multimodal, meta, and transformer based learning by Md. Nasim Ahmed, Dipta Neogi, Muhammad Rafsan Kabir, Shafin Rahman, Sifat Momen, Nabeel Mohammed

“…Abstract Ulcerative colitis (UC) is a chronic inflammatory disorder necessitating precise severity stratification to facilitate optimal therapeutic interventions. This study harnesses a triple-pronged deep learning methodology—including multimodal inference pipelines that eliminate domain-specific training, few-shot meta-learning, and Vision Transformer (ViT)-based ensembling—to classify UC severity within the HyperKvasir dataset. …”
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A CART-Based Model for Analyzing the Shear Behaviors of Frozen–Thawed Silty Clay and Structure Interface by Fengpan Zhu, Bo Wang, Zhiqiang Liu

“…Through cross-validation, the model’s optimal hyperparameters were determined on the training set, and its performance was then verified on the test set. …”
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HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot by Xiaoming Li, Yang Zhou, Yongguang Li, Shiqi Wang, Wenxue Bian, Hongmin Sun

“…However, both conventional field monitoring and machine learning algorithms remain challenged in achieving rapid and accurate detection. …”
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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…Three machine learning algorithms—Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting—were evaluated to determine the optimal implementation. …”
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From Simulation to Implementation: A Systems Model for Electric Bus Fleet Deployment in Metropolitan Areas by Ludger Heide, Shuyao Guo, Dietmar Göhlich

“…This study presents “eflips-X”, a modular, open-source simulation framework that integrates energy consumption modeling, battery-aware block building, depot–block assignment, terminus charger placement, depot operations simulation, and smart charging optimization within a unified workflow. The framework employs empirical energy models, graph-based scheduling algorithms, and integer linear programming for depot assignment and smart charging. …”
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The synergy of neuromarketing and artificial intelligence: A comprehensive literature review in the last decade by Ahmed H. Alsharif, Junhai Wang, Salmi Mohd Isa, Nor Zafir Md Salleh, Husam Azzawi Dawas, Mohammed H. Alsharif

“…Emotion, attention, and memory have become vital in NM and AI studies. AI algorithms analyze vast neural and physiological datasets, offering marketers insights into the emotional impact of campaigns, granular insights into consumer focus, and optimizing content for maximum impact. …”
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GCV-SLAM: Ground Constrained Visual SLAM Through Local Ground Planes by Yu Fan, Peng Zhang, Zhi Wang, Chengbao Liu, Guang Zhang

“…The classical visual Simultaneous Localization and Mapping(SLAM) algorithms assume that the camera moves in a free 3D space, while it is not valid for ground vehicles whose poses are influenced by ground surfaces. …”
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Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko, Elżbieta Wójcik-Gront

“…The accuracy of the models was compared to identify the optimal approach. The strongest correlation coefficients with crop yield were observed for the NDVI at the beginning of March, ranging from 0.454 for rapeseed to 0.503 for rye. …”
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From molecules to data: the emerging impact of chemoinformatics in chemistry by Anup Basnet Chetry, Keisuke Ohto

“…Additionally, it outlines future challenges and opportunities, emphasizing the need for improved algorithms, data standardization, and interdisciplinary collaboration. …”
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Unraveling the Reshaping of Human Resource Management Function: the mediating role of artificial intelligence by Samuel Bangura, Anos Chitamba Chitamba, Lucia Zithobile Ngidi, Sizwe Mbona

“…However, the study also identifies potential challenges associated with AI adoption, including concerns related to algorithmic bias, data privacy, transparency, and employee apprehensions. …”
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Reinforcement learning in artificial intelligence and neurobiology by Tursun Alkam, Andrew H Van Benschoten, Ebrahim Tarshizi

“…Reinforcement learning (RL), a computational framework rooted in behavioral psychology, enables agents to learn optimal actions through trial and error. It now powers intelligent systems across domains such as autonomous driving, robotics, and logistics, solving tasks once thought to require human cognition. …”
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Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection—Precision Screening for Lung Cancer by Hsin-Hung Chen, Yun-Ju Wu, Fu-Zong Wu

“…However, implementation must also address challenges related to health equity, algorithmic bias, and system integration. As precision medicine continues to evolve, it holds the promise of optimizing early detection, minimizing harm, and extending the benefits of lung cancer screening to broader and more diverse populations. …”
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A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence by Abdullah Alabdulatif

“…Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model’s decisions. …”
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An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection by Yexin Tian, Shuo Xu, Yuchen Cao, Zhongyan Wang, Zijing Wei

“…We analyze each model’s optimization framework, decision boundaries, and feature importance mechanisms, highlighting the empirical tradeoffs between representational capacity, generalization, and interpretability. …”
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