Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adeeb Noor, Sufyan Ali Memon

“…The K-Nearest Neighbors (KNN) algorithm is employed as the base model, with hyperparameter optimization performed using GridSearchCV, RandomizedSearchCV, Optuna, and Particle Swarm Optimization (PSO). …”
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An Intelligent Optimization System Using Neural Networks and Soft Computing for the FMM Etching Process by Wen-Chin Chen, An-Xuan Ngo, Jun-Fu Zhong

“…Experimental results demonstrate a marked improvement in product yield and process stability while reducing manufacturing costs. …”
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A Data-Driven Approach for Energy Consumption Modeling and Optimization of Welding Robot Systems by Minling Pan, Bingqi Jia, Lei Zhang, Haihong Pan, Lin Chen

“…To solve the optimization problem, an improved whale optimization algorithm (IWOA) was employed. …”
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Multi-Objective Optimization of Machinability and Energy Consumption of Cast Iron Depending on Cooling Rate by Burak Öztürk, Fuat Kara

“…Energy efficiency has become a critical focus in sustainable production techniques and cost-effective machining processes. This variation directly influences machinability, with higher hardness generally improving surface quality. …”
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Enhancing Quality Control of Packaging Product: A Six Sigma and Data Mining Approach by Resty Ayu Ramadhani, Rina Fitriana, Anik Nur Habyba, Yun-Chia Liang

“… Six Sigma is of paramount importance to organizations as it provides a structured and data-driven approach, fostering continuous improvement, minimizing defects, and optimizing processes to meet and exceed customer expectations. …”
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Developing an Equitable Machine Learning–Based Music Intervention for Older Adults At Risk for Alzheimer Disease: Protocol for Algorithm Development and Validation by Chelsea S Brown, Luna Dziewietin, Virginia Partridge, Jennifer Rae Myers

“…The recommendation accuracy of the ML algorithm will be assessed using multiple performance metrics, including root-mean-square error and normalized discounted cumulative gain as well as the mean acceptability score with a goal of 85% user acceptability. …”
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An adaptive k-means clustering algorithm based on grid and domain centroid weights for digital twins in the context of digital transformation by Wei Cai, Fei Yang, Bo Yao, Chuanxian Li, Guangyu Sun

“…The algorithm automatically determines the optimal number of clusters k and initial centroids. …”
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Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations by Panagiotis Michailidis, Iakovos Michailidis, Elias Kosmatopoulos

“…The current review systematically examines RL-based control strategies applied in BEMS frameworks integrating RES technologies between 2015 and 2025, classifying them by algorithmic approach and evaluating the role of multi-agent and hybrid methods in improving real-time adaptability and occupant comfort. …”
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Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management by Vikram Pasupuleti, Bharadwaj Thuraka, Chandra Shikhi Kodete, Saiteja Malisetty

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Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network by Zhiqiang Liu, Wenbo Zhu, Hongzhou Zhang, Shengjin Wang, Lu Fang, Weijun Hong, Hua Shao, Guopeng Wang

“…In this article, we propose a novel evaluation method with True Positive Identification Rate in dynamic and M:N mode and create a novel evaluation model of system reliability with the improved Fuzzy Dynamic Bayesian Network. Subsequently, we infer to solve the fuzzy reliability state probabilities of the six systems with Netica and get two most important factors with the improved fuzzy C-means algorithm. …”
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Multi-Agent Deep Reinforcement Learning for Scheduling of Energy Storage System in Microgrids by Sang-Woo Jung, Yoon-Young An, BeomKyu Suh, YongBeom Park, Jian Kim, Ki-Il Kim

“…To defeat the above issues, in this paper, we propose a new DRL-based scheduling algorithm using a multi-agent proximal policy optimization (MAPPO) framework that is combined with Pareto optimization. …”
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Vibration Analysis and Optimization of Iron-Core Reactors Based on Fe-Based Soft Magnetic Composite Materials by Yangyang Ma, Wenle Song, Jie Gao, Yang Liu, Yilei Shang, Weimei Zhao, Fuyao Yang

“…The characteristic parameters of the improved model are identified using the particle swarm optimization–simulated annealing (PSO-SA) algorithm, with the identified root mean square error not exceeding 3.5, verifying the model’s accuracy. …”
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Data-Driven Robust Optimization for Solving the Heterogeneous Vehicle Routing Problem with Customer Demand Uncertainty by Jingling Zhang, Mengfan Yu, Qinbing Feng, Longlong Leng, Yanwei Zhao

“…Through test experiments, it is proved that the robust optimization model can effectively reduce the number of customers affected by the uncertainty, greatly improve customer satisfaction, and effectively reduce total cost and demonstrate that the improved algorithm also exhibits good performance.…”
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Data Mining Techniques for Early Detection and Classification of Plant Diseases: An Optimization-Based Approach by Wagh Swapnil, Sharma Ruchi

“…Furthermore, low-level optimization techniques like genetic algorithms as well as particle swarm optimization are used to fine tune the specific model parameters and to reduce the computational overhead for improving the detection efficacy still more. …”
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Toward Robust GNSS Real-Time Orbit Determination for Microsatellites Using Factor Graph Optimization by Cong Hou, Xiaojun Jin, Xiaopeng Yang, Tong Xiao

“…However, the performance of EKF-RTOD is markedly degraded when the microsatellite deviates from a stable Earth-pointing attitude and employs a low-cost receiver. Factor graph optimization (FGO), which addresses nonlinear problems through multiple iterations and re-linearization, has demonstrated superior accuracy and robustness compared to EKF in challenging environments such as urban canyons. …”
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Research on Multi-Objective Coordinated Planning of Distribution Networks Based on Improved Generative Adversarial Networks by Li Zhu

“…Next, a bi-level distribution network planning model considering carbon footprints is established: the upper level minimizes the annual comprehensive cost by optimizing the planning schemes of distributed generation (DG), energy storage systems (ESS), and capacitor banks (CB); the lower level minimizes operating costs, voltage deviations, and carbon emissions by formulating operation strategies under typical scenarios, considering on-load tap changers (OLTC), controllable loads, capacitor banks, energy storage, and distributed generation. …”
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UV-Vis spectroscopy coupled with firefly algorithm-enhanced artificial neural networks for the determination of propranolol, rosuvastatin, and valsartan in ternary mixtures by Ahmed Serag, Maram H. Abduljabbar, Yusuf S. Althobaiti, Farooq M. Almutairi, Shaker T. Alsharif, Rami M. Alzhrani, Marwa F. Ahmed, Atiah H. Almalki

“…An experimental design of 25 samples was employed as a calibration set, and a central composite design of 20 samples was used as a validation set. The firefly algorithm (FA) was evaluated as a variable selection procedure to optimize the developed ANN models resulting in simpler models with improved predictive performance as evident by lower relative root mean square error of prediction (RRMSEP) values compared to the full spectrum ANN models. …”
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Optimization Model of Express–Local Train Schedules Under Cross-Line Operation of Suburban Railway by Jingyi Zhu, Xin Guo, Jianju Pan

“…Comparative analysis shows that the proposed hybrid operation mode reduces total passenger travel cost by 6% and improves the cost efficiency ratio by 13% compared to independent operations. …”
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Action Recognition, Tracking, and Optimization Analysis of Training Process Based on SVR Model and Multimedia Technology by Xuejiao Zhong

“…Experimental results show the proposed algorithm has a significant performance improvement compared to before the improvement; at the same time, it is better than most current mainstream algorithms, which proves the feasibility and effectiveness of the algorithm. …”
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DGCA3QM: DESIGN OF A DUAL GENETIC ALGORITHM BASED AUTOREGRESSION MODEL FOR CORRELATIVE PREDICTION OF AIR QUALITY METRICS by Harna M. Bodele, G. M. Asutkar, Kiran G. Asutkar

“…The predicted values are further optimized via another bioinspired layer that assists in identification of high correlation value changes, thereby improving prediction performance under large data samples. …”
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