Microstrip Patch Antenna Design Using a Four-Layer Feed Forward Artificial Neural Network Trained by Levenberg-Marquardt Algorithm by Jitu Prakash Dhar, Maodudul Hasan, Eisuke Nishiyama, Ichihiko Toyoda

“…The ANN contains a multi-layered network architecture that learns and generalizes complex patterns through the LM algorithm and weight optimization based on the datasets without any feature extraction like Deep Neural Network (DNN). …”
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Enhancing MANET Security Through Federated Learning and Multiobjective Optimization: A Trust-Aware Routing Framework by Saad M. Hassan, Mohd Murtadha Mohamad, Farkhana Binti Muchtar, Firoz Bin Yusuf Patel Dawoodi

“…MOO, particularly nondominated sorting genetic algorithm III, effectively balances conflicting network objectives, offering a 15% improvement in overall network performance compared with single-objective approaches. …”
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Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems by Benedetta Fasciolo, Nicolò Grasso, Giulia Bruno, Paolo Chiabert

“…Despite these challenges, the hybrid model offers a promising solution for optimizing controlled environment agriculture, addressing critical challenges in modern agriculture by improving efficiency and sustainability.…”
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DAMAGE IDENTIFICATION METHOD BASED KRIGING MODEL by MA JingJing, YIN Hong, PENG ZhenRui, DONG KangLi

“…To improve damage identification efficiency,a damage identification method based on Kriging model was proposed. …”
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Sentence Embedding Generation Framework Based on Kullback–Leibler Divergence Optimization and RoBERTa Knowledge Distillation by Jin Han, Liang Yang

“…First, a sentence embedding generation method based on Kullback–Leibler Divergence (KLD) optimization is proposed, which enhances semantic differentiation between sentence vectors, thereby improving the accuracy of textual similarity computation. …”
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Advanced Machine Learning Methodology for Earthquake Magnitude Forecasting Using Comprehensive Seismic Data by Subhieh El-Salhi, Bashar Igried, Sari Awwad

“…Feature selection was performed using Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing, while ten machine learning models were implemented — ranging from Linear Regression and Decision Trees to Gradient Boosting, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) networks. …”
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Machine Learning Application for Improving Customer and Postal Logistics Operator Satisfaction in Urban Areas – A Review by Adisa MEDIĆ, Amel KOSOVAC, Ermin MUHAREMOVIĆ, Muhamed BEGOVIĆ

“…The significance of this research is highlighted through the identification of shortcomings in existing literature, offering guidelines for future research in developing new machine learning model for optimal operator selection. This model aims to achieve improvements in both customer and operator satisfaction simultaneously.…”
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Online Estimation and Control Method of Optimal Slip Ratio for Vehicle With an Electric Power-Assisted Brake System by Lei Chen, Yuchao He, Tangtang Ma, Shengshi Zhong

“…Current algorithms for estimating the optimal slip ratio is still room for improving the accuracy and efficiency, especially under the changing road surface, introducing significant lag. …”
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Local Structure Optimization Design of Floating Offshore Wind Turbine Platform Based on Response Surface Analysis by Yajun Ren, Mingxuan Huang, Jungang Hao, Jiazhi Wang, Shuai Li, Ling Zhu, Haisheng Zhao, Wei Shi

“…The response surface model was then optimized using a gradient descent algorithm. …”
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Multiperiod Location–Allocation Optimization of Construction Logistics Centers for Large-Scale Projects in Complex Environmental Regions by Hao Shen, Jin Zhang, Wenjie Sun, Wenguang Yang, Guoqi Li

“…We have designed an NSGA-II based on endocrine hormone regulation (EHR-NSGA-II) to solve the model. Finally, a large-scale railroad construction project in complex environmental regions is used as an example to prove the effectiveness of the model and algorithm. …”
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A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing by Yuchen Liang, Yuqi Wang, Anping Li, Chengyi Gu, Jie Tang, Xianjuan Pang

“…The fruit fly optimization (FFO) algorithm was employed to optimize the remanufacturing parameters; a 29.33% improvement was achieved in fitness compared to the baseline. (3) The DNN model predictions were validated against Finite Element Analysis (FEA) simulations, with a low relative error of 2.5% to 5.8%; the model showed good accuracy in capturing the effects of optimized LSP parameters on bearing life extension.…”
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Optimizing precision farming: enhancing machine learning efficiency with robust regression techniques in high-dimensional data by Nour Hamad Abu Afouna, Majid Khan Majahar Ali

“…To overcome these obstacles, reducing retraining time and resolving the complexity issue is essential for improving the machine learning algorithm’s performance, scalability, and efficiency, especially when dealing with large or high-dimensional datasets. …”
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Optimizing Random Forest Parameters with Hyperparameter Tuning for Classifying School-Age KIP Eligibility in West Java by Silfiana Lis Setyowati, Asyifah Qalbi, Rafika Aristawidya, Bagus Sartono, Aulia Rizki Firdawanti

“…Random Forest is an ensemble learning algorithm that combines multiple decision trees to generate a more stable and accurate classification model. …”
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Optimization of Hand Gesture Object Detection Using Fine-Tuning Techniques on an Integrated Service of Smart Robot by Faikul Umam, Hanifudin Sukri, Ach Dafid, Firman Maolana, Mycel Natalis Stopper Ndruru

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Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN by Juan Lv, Zhaowei Wang, Jianxiao Ma

“…To improve intersection traffic flow and reduce vehicle energy consumption and emissions at intersections, a signal optimization method based on deep reinforcement learning (DRL) is proposed. …”
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Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes by Zhaoyang Wang, Shuo Wang, Damien Ernst, Chenguang Xiao

“…Then, these surrogate models with prior optimization knowledge are combined with the Expected Hypervolume Improvement (EHVI) acquisition function in a Bayesian optimization framework to efficiently discover Pareto-optimal configurations for the target task, which enables AutoCIL-FMOBO to jointly optimize key components, such as resampling methods, classifiers, and their hyperparameters, under a multi-objective setting. …”
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Improved U-Net for Precise Gauge Dial Segmentation in Substation Inspection Systems: A Study on Enhancing Accuracy and Robustness by Wan Zou, Yiping Jiang, Wenlong Liao, Songhai Fan, Yueping Yang, Jin Hou, Hao Tang

“…To address these challenges, we propose an improved U-Net segmentation algorithm. The key innovation of our approach is the insertion of a layer-hopping connection module between the Encoder and Decoder to capture feature information across multiple scales, enhancing semantic expressiveness and optimizing feature fusion. …”
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Artificial Intelligence-Based Tools for Process Optimization: Case Study—Bromocresol Green Decolorization with Active Carbon by Gabriel Dan Suditu, Elena Niculina Drăgoi, Alexandra Georgiana Apostică, Andra Maria Mănăilă, Veronica Mădălina Radu, Adrian Cătălin Puițel, Mircea Teodor Nechita

“…In addition, a modified bacterial foraging optimization algorithm was applied as an alternative optimizer in combination with artificial neural networks in order to determine multiple combinations of parameters that can lead to maximum process efficiency. …”
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Multi-objective optimization of the heating oil circuit inside the calendering roller for the lithium battery pole piece by Shuangqiu Sun, Junqiang Wang, Rongying Zhang, Zhenchao Si, Zhijie Yang

“…Then, the improved NSGA-II algorithm is selected to execute the optimization loop. …”
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Automatic Detection and Classification of Dental Anomalies and Tooth Types Using Transformer-Based Yolo With GA Optimization by Sanabam Bineshwor Singh, Anuradha Laishram, Khelchandra Thongam, Khumanthem Manglem Singh

“…In addition, the system employs Genetic Algorithm (GA) optimization for hyperparameter tuning, which fine-tunes model parameters to maximize performance metrics and enhance accuracy and robustness in dental diagnostics. …”
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