Dimensional Accuracy Analysis of Splined Shafts and Hubs Obtained by Fused-Deposition Modeling 3D Printing Using a Genetic Algorithm and Artificial Neural Network by Alin-Daniel Rizea, Cristina-Florena Banică, Tatiana Georgescu, Alexandru Sover, Daniel-Constantin Anghel

“…Three-dimensional printing, especially FDM, enables fast production of customized components with complex geometries, reducing material waste and costs. Optimized printing parameters improve dimensional accuracy and performance. …”
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Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier by C. V. Subbulakshmi, S. N. Deepa

“…This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. …”
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Optimized Design and Deep Vision-Based Operation Control of a Multi-Functional Robotic Gripper for an Automatic Loading System by Yaohui Wang, Sheng Guo, Jinliang Zhang, Hongbo Ding, Bo Zhang, Ao Cao, Xiaohu Sun, Guangxin Zhang, Shihe Tian, Yongxu Chen, Jixuan Ma, Guangrong Chen

“…A deep vision-based recognition system, enhanced by an improved YOLOv5 algorithm and multi-feature fusion, was employed for real-time object detection and pose estimation. …”
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Hybrid Darknet53-SVM model with random grid search optimization for enhanced colorectal cancer histological image classification by Pragati Patharia, Prabira Kumar Sethy, K. Lakshmipathi Raju, Anita Khanna, Ashoka Kumar Ratha, Santi Kumari Behera, Aziz Nanthaamornphong

“…To enhance the classification performance, Darknet53 was hybridized with a SVM by replacing the dense layer, and hyperparameters were optimized using a Random Grid Search algorithm. The optimized hybrid model exhibited a remarkable improvement, with an Acc. of 99.7%, Sen. of 99.7%, Spec. of 99.91%, Prec. of 99.98%, and F1-score of 99.98%, alongside significant improvements in other metrics. …”
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Dung beetle optimizer based on mean fitness distance balance and multi-strategy fusion for solving practical engineering problems by Wanru Tang, Haoze Qin, Shuang Kang

“…These results indicate that MMDBO consistently outperforms most algorithms and provides accurate and reliable optimizer solutions. …”
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Detection of Phosphorus in Water by Laser-Induced Breakdown Spectroscopy Based on Liquid-Solid Transformation of Graphite Substrate Combined with PLS-SVR Fusion Quantitative Analys... by Huijie Zhang, Yao Chen, Zongjie Bi, Xiaohua Che, Zhaoshuo Tian

“…To enhance sensitivity in detecting phosphorus in water via laser-induced breakdown spectroscopy (LIBS), this study integrates liquid–solid conversion on graphite substrates with a PLS-SVR fusion algorithm. Optimized laser parameters (500 mJ, 13 pulses) improved plasma excitation and signal-to-noise ratios. …”
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Outage reduction in 5G uplink cooperative NOMA using PSO-optimized relay selection and successive interference cancelation by David S. Bhatti, M. Asif Haroon, Haeung Choi, Seungmin Kim, Heung-No Lee

“…The inclusion of PSO-based relay selection demonstrates a substantial improvement in network performance. We compared the performance of particle swarm optimization (PSO) with that of the genetic algorithm (GA) and deep reinforcement learning (DRL). …”
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Transfer Learning based Image Classification of Diseased Tomato Leaves with Optimal Fine-Tuning combined with Heat Map Visualization by Sivakumar Palanıswamy, Vijayakumar Vaıthyam Rengarajan, Sandhya Devi Ramıah Subburaj

“…Geometrical data augmentation is used as a regularization technique to expand the size of the dataset. Significant improvement in the performance metrics is observed when the finetuning is optimum. …”
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Predicting Endpoint Temperature of Molten Steel in VD Furnace Refining Process Using Metallurgical Mechanism and Bayesian Optimization XGBoost by Ji XU, Zicheng XIN, Mo LAN, Wenhui LIN, Bo ZHANG, Qing LIU

“…The results indicated that BO hyperparameter optimization is the most effective, providing the model with the best performance and higher prediction accuracy than the other two optimization algorithms. …”
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Optimizing the Joint Replenishment and Delivery Scheduling Problem under Fuzzy Environment Using Inverse Weight Fuzzy Nonlinear Programming Method by Yu-Rong Zeng, Lin Wang, Xian-Hao Xu, Qing-Liang Fu

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Sparse Array Mutual Coupling Reduction by C. Larmour, N. Buchanan, V. Fusco, M. Ali Babar Abbasi

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Transforming Manufacturing Quality Management with Cognitive Twins: A Data-Driven, Predictive Approach to Real-Time Optimization of Quality by Asif Ullah, Muhammad Younas, Mohd Shahneel Saharudin

“…The cognitive twin achieved a 2% improvement in the total quality scores. A 60% decrease in defects per unit (DPU) is observed as well as a sharp 40% decrease in scrap rate. …”
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Distributed Robust Low-Carbon Economic Dispatch of Power Systems Considering Extreme Scenarios by HU Heng, QIN Jianru, LI Haibo, LIU Jiefeng

“…[Results] Case studies on an improved IEEE 39-node system using the column-and-constraint generation （C&CG） algorithm demonstrate that， compared with traditional deterministic and DRO models based on typical scenarios， the proposed approach increases scheduling costs by 7.11% and 14.37% respectively， but reduces renewable curtailment rates by 8.28% and 34.65%， and load shedding rates by 8.19% and 33.32%. …”
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Current Status, Challenges and Future Perspectives of Operation Optimization, Power Prediction and Virtual Synchronous Generator of Microgrids: A Comprehensive Review by Ling Miao, Ning Zhou, Jianwei Ma, Hao Liu, Jian Zhao, Xiaozhao Wei, Jingyuan Yin

“…Then, the microgrid optimization operation technologies are analyzed in detail, including energy management optimization algorithms for efficient use of energy and cost reduction. …”
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Inter-Object Navigation of Unmanned Aerial Vehicles to Increase the Efficiency and Accuracy of Inspection of Power Lines by Tymochko O., Fustii V., Kolesnyk G., Olizarenko S., Kalashnyk G., Kulish R., Tymoschuk O., Galinskji D.

“…Solving the problem of multi-criteria optimization of the drone team flight route planning made it possible to reduce the cost of monitoring critical infrastructure facilities while improving its efficiency and accuracy. …”
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Multi-strategy hybrid adaptive dung beetle optimization for UAV photogrammetric 3D path planning under complex constraints by Muyang Wu, Guo Li, Jinwei Liao, Hang Wang, Wen Liu, Xiang Yan, Mengdie Yang, Shun Li

“…Through systematic validation encompassing 21 benchmark functions, Wilcoxon tests, and CEC2021, MSDBO exhibits superior convergence accuracy and robustness when compared to seven other metaheuristic algorithms. Urban flight experiments further demonstrate MSDBO’s ability to generate smoother paths with a 7.5% lower optimal cost and a 31% reduced standard deviation than DBO. …”
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Research on unmanned mining truck path planning based on grey wolf optimization adaptive hybrid A* and artificial potential field by HAN Yanling, LYU Jinke, ZHOU Guofeng, ZHANG Like, ZHANG Yun

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Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson’s Disease Using Vocal Characteristics: A Comparative Analysis by Mehmet Meral, Ferdi Ozbilgin, Fatih Durmus

“…This study seeks to assess the effectiveness of machine learning algorithms optimized to classify PD based on vocal characteristics to serve as a non-invasive and easily accessible diagnostic tool. …”
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A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…These findings offer a valuable reference for optimizing PSC process parameters and improving performance, thereby saving time and labor costs.…”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…Based on the performance evaluation, the Ensemble Classifier is further optimized and demonstrates improved accuracy of 88.80%, specificity of 94.41%, recall of 88.82%, precision of 88.46%, and F1 Score of 88.84%. …”
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