A Survey on Video Compression Optimization Techniques for Accuracy Enhancement in Video Analytics Applications (VAPs) by Kholidiyah Masykuroh, Hendrawan, Eueung Mulyana, Farhan Krishna

“…We examine traditional compression algorithms, machine learning-based approaches, dynamic parameter adjustment strategies, and hybrid models, each offering unique strengths and limitations. …”
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Enhancing Smoothness via Redundancy in 3D Laser Cutting Manufacturing: A Collision-Free, Minimized Jerk Trajectory Optimization Approach by Zhipeng Ding, Marina Indri, Alessandro Rizzo

“…This study presents an innovative trajectory optimization approach for laser cutting machines equipped with a redundant standoff axis. …”
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A NEURAL NETWORK APPROACH TO BEAM SELECTION AND POWER OPTIMIZATION IN MM WAVE MASSIVE MIMO by Shahnaz Fatima, M. P. Subba Raju, R. Anil Kumar, Vatsala Anand, Yarrapragada K. S. S. Rao, D. Kishore, U. S. B. K. Mahalaxmi, K. Kalyani

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Applications of artificial intelligence and computational intelligence in hydraulic optimization of centrifugal pumps: a comprehensive review by Yuanhui Xu, Xingcheng Gan, Ji Pei, Wenjie Wang, Jia Chen, Shouqi Yuan

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Augmented robustness in home demand prediction: Integrating statistical loss function with enhanced cross-validation in machine learning hyperparameter optimisation by Banafshe Parizad, Ali Jamali, Hamid Khayyam

“…However, conventional hyperparameter tuning methods often rely solely on minimizing average prediction errors, typically through fixed k-fold cross-validation, which overlooks error variability and limits model robustness. To address this limitation, we propose the Optimized Robust Hyperparameter Tuning for Machine Learning with Enhanced Multi-fold Cross-Validation (ORHT-ML-EMCV) framework. …”
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Advanced hybrid intelligence evaluation of nanomedicine delivery to various organs using machine learning and adaptive tree structured Parzen estimator by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…Abstract We have examined the efficiency of drug delivery for targeted therapy by theoretical models. Machine learning strategy was tested to analyze the drug delivery of nanomedicines to the desired sites for efficient treatment. …”
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Sequential Parts Analysis Using Local Optimization Method for Hybrid Excitation Flux Switching Generator by Nur Afiqah Mostaman, Erwan Sulaiman, Mahyuzie Jenal

“…The Hybrid Excitation Flux Switching Generator (HEFSG) has gained significant popularity in recent times owing to its relatively simple remarkably efficient topology. To optimize the performance of the generator, recent advancements and emerging patterns in mathematical modeling and software simulation, along with the utilization of optimization techniques, have facilitated the development of a novel methodology for electrical machine design. …”
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FedOcw: optimized federated learning for cross-lingual speech-based Parkinson’s disease detection by Changqin Quan, Zhonglue Chen, Kang Ren, Zhiwei Luo

“…However, developing robust machine learning models is challenging due to the decentralized nature of medical data and the substantial heterogeneity in multilingual PD speech datasets. …”
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An Accurate and Reliable Behavioral Modeling Technique for Fully Printed Vanadium Dioxide RF Switches Using Model Ensembling Approach by Saddam Husain, Bagylan Kadirbay, Mohammad Vaseem, Atif Shamim, Mohammad Hashmi

“…Subsequently, weighted ensemble models are constructed by leveraging the optimally trained XGBoost, CatBoost, and LightGBM based models. …”
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Improved Optimized Minimum Generalized L_p/L_q Deconvolution and Application to Bearing Fault Detection by Na Yang, Zhigang Pan, Yuanbo Xu

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Exploration of the Ignition Delay Time of RP-3 Fuel Using the Artificial Bee Colony Algorithm in a Machine Learning Framework by Wenbo Liu, Zhirui Liu, Hongan Ma

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Optimizing drug design by merging generative AI with a physics-based active learning framework by Isaac Filella-Merce, Alexis Molina, Lucía Díaz, Marek Orzechowski, Yamina A. Berchiche, Yang Ming Zhu, Júlia Vilalta-Mor, Laura Malo, Ajay S. Yekkirala, Soumya Ray, Victor Guallar

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Integrating AI and Multi-objective Optimization for Enhanced Microgrid Energy Management Using Quadratic Programming by Agrawal Priyanka, Thethi H. Pal, Mohammad Q., Gupta Navya, Asha V., Reddy K. Jyothsna

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Enhanced Underwater Image Restoration and Clarity Optimization Using Advanced Digital Image Processing Techniques by Gopirajan Punniyakotti Varadharajan, Pradeep T., Charan R.

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Optimal graph representations and neural networks for multichannel time series data in seizure phase classification by Alan A. Díaz-Montiel, Richard Zhang, Milad Lankarany

“…We evaluated four GNN models, with the highest performance achieving a seizure phase classification accuracy of 97%, demonstrating its potential for clinical application. …”
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Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique by Punita Panwar, Sandeep Chaurasia, Jayesh Gangrade, Ashwani Bilandi, Dayananda Pruthviraja

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Multi-objective optimization for smart cities: a systematic review of algorithms, challenges, and future directions by YiFan Chen, Weng Howe Chan, Eileen Lee Ming Su, Qi Diao

“…With the growing complexity and interdependence of urban systems, multi-objective optimization (MOO) has become a critical tool for smart-city planning, sustainability, and real-time decision-making. …”
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Integrated evaluation and optimization of acid fracturing effectiveness in carbonate reservoirs: Experimental insights and field validation. by Peng Lu, Songlei Li, Jinsong Liu, Nan Yuan, Heng Liu

“…The study's key innovation lies in identifying conductivity-governing factors through an integrated experimental-modeling approach, establishing criteria for optimal etching pattern development. …”
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Research on Mechanical Properties of Steel Tube Concrete Columns Reinforced with Steel–Basalt Hybrid Fibers Based on Experiment and Machine Learning by Bohao Zhang, Xiao Xu, Wenxiu Hao

“…On the basis of the experiments, a parametric expansion analysis of several structural parameters of the specimen was carried out by using ABAQUS finite element software, and a combined model NRBO-XGBoost, based on the Newton-Raphson optimization algorithm (NRBO), and the advanced machine learning model XGBoost was proposed for the prediction of the BSFCFST’s ultimate carrying capacity. …”
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Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data by Nur Hani Zainal, Hui Han Tan, Ryan Yee Shiun Hong, Michelle Gayle Newman

“…They completed self-reports of symptoms, risk factors, treatment, and sociodemographics at baseline, posttreatment, and 1-month follow-up (1MFU). Machine learning (ML) models with 17 predictors of optimization to MEMI over SM, defined as a higher probability of SAD remission from MEMI at posttreatment and 1MFU, were evaluated. …”
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