Machine learning assisted design of reactor steels with high long-term strength and toughness by Ivan Trofimov, Pavel Korotaev, Ivan Ivanov, Anton Malginov, Allen Tokhtamyshev, Alexey Yanilkin, Ivan Kruglov

“…In order to find steels with optimal properties, we collected the database of steels and their properties, trained machine learning models and developed optimization algorithm. …”
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An Optimization Method for the Remanufacturing Dynamic Facility Layout Problem with Uncertainties by Lingling Li, Congbo Li, Huijie Ma, Ying Tang

“…A dynamic multirow layout model is presented for layout optimization and a modified simulated annealing heuristic is proposed toward the determination of optimal layout schemes. …”
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Optimal Collision Energy for Higgs Precision Measurements at the ILC250 by Maria Andrea Siddharta, Tian Junping

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Rolling Bearing Fault Diagnosis Based on Optimized VMD and SSAE by Baoxian Chang, Xing Zhao, Dawei Guo, Siyu Zhao, Jiyou Fei

“…The feature set is then inputted into the deep machine learning model SSAE for training and testing. …”
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Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…By integrating wavelet-based feature extraction with machine learning-driven analysis, this approach enhances the ability to detect complex wave propagation patterns, leading to more precise subsurface modeling. …”
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Dynamic Data Updates and Weight Optimization for Predicting Vulnerability Exploitability by K. Bennouk, D. Mahouachi, N. Ait Aali, Y. El Bouzekri El Idrissi, B. Sebai, A. Z. Faroukhi

“…The methodology incorporates machine learning and deep learning models to compute and predict exploitability scores, following the initial data processing and scoring calculation steps. …”
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ML-Augmented Optimization of LoRa Antennas for Drone Telemetry by Pothala Chaya Devi, Ramarakula Madhu

“…This ANN model is then coupled with a Simulated Annealing (SA) optimizer to generate antennas with the desired characteristics, reducing the total design time to 57% compared to traditional techniques. …”
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Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios by Wacław Kuś, Waldemar Mucha, Iyasu Tafese Jiregna

“…It demonstrates that prediction models perform better when sensor networks are optimized. …”
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Predicting Days on Market to Optimize Real Estate Sales Strategy by Mauro Castelli, Maria Dobreva, Roberto Henriques, Leonardo Vanneschi

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(IoT) Network intrusion detection system using optimization algorithms by Luo Shan

“…Compared with traditional models like the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Support Vector Machine (SVM), the proposed framework significantly improves the sensitivity and generalization ability for detecting various types of attacks through dynamic feature selection and parameter optimization. …”
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Optimal Transport Based Graph Kernels for Drug Property Prediction by Mohammed Aburidi, Roummel Marcia

“…To overcome these obstacles, there has been a growing reliance on computational and predictive tools, leveraging recent advancements in machine learning and graph-based methodologies. This study presents an innovative approach that harnesses the power of optimal transport (OT) theory to construct three graph kernels for predicting drug ADMET properties. …”
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Multi-Decision Vector Fusion Model for Enhanced Mapping of Aboveground Biomass in Subtropical Forests Integrating Sentinel-1, Sentinel-2, and Airborne LiDAR Data by Wenhao Jiang, Linjing Zhang, Xiaoxue Zhang, Si Gao, Huimin Gao, Lin Sun, Guangjian Yan

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Classification and spectrum optimization method of grease based on infrared spectrum by Xin Feng, Yanqiu Xia, Peiyuan Xie, Xiaohe Li

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Effective Gene Expression Prediction and Optimization from Protein Sequences by Tuoyu Liu, Yiyang Zhang, Yanjun Li, Guoshun Xu, Han Gao, Pengtao Wang, Tao Tu, Huiying Luo, Ningfeng Wu, Bin Yao, Bo Liu, Feifei Guan, Huoqing Huang, Jian Tian

“…These findings highlight the efficacy of the developed model in predicting and optimizing gene expression based on protein sequences.…”
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Prediction of sewage pipeline construction duration by introducing machine learning and deep learning approaches by Sang-Jun Park, Norhane Nour, Kang Young Lee, Ju-Hyung Kim

“…The study implemented state-of-the-art frameworks, including hyperparameter optimization and k-fold cross-validation, to evaluate statistic, machine learning and deep learning based regression models using R2 score, RMSE, MAE, and MSE. …”
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Toward Reliable Post-Disaster Assessment: Advancing Building Damage Detection Using You Only Look Once Convolutional Neural Network and Satellite Imagery by César Luis Moreno González, Germán A. Montoya, Carlos Lozano Garzón

“…These findings contribute to machine learning-based disaster response, offering an efficient, cost-effective framework for large-scale damage assessment and reinforcing the importance of model selection, hyperparameter tuning, and optimization functions in critical real-world applications.…”
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Rate distortion optimization for adaptive gradient quantization in federated learning by Guojun Chen, Kaixuan Xie, Wenqiang Luo, Yinfei Xu, Lun Xin, Tiecheng Song, Jing Hu

“…Federated Learning (FL) is an emerging machine learning framework designed to preserve privacy. …”
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Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics by Huiling Qin, Atef F. Hashem, Muhammad Farhan Hanif, Osman Abubakar Fiidow

“…Moreover, SHAP (SHapley Additive exPlanations) analysis was used to explain the contribution of each descriptor toward the models’ predictions. The findings validate the promise of the combination of graph-theoretic descriptors with the tools of machine learning to achieve solid and interpretable models of molecular property prediction, which hold the potential for drug discovery and optimization in oncologic applications.…”
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Application of Machine Learning in Predicting Quality Parameters in Metal Material Extrusion (MEX/M) by Karim Asami, Maxim Kuehne, Tim Röver, Claus Emmelmann

“…The various ML models demonstrate an accuracy of up to 97% after training. …”
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Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation by Nisha Purohit, Chandi Prasad Bhatt

“…Achieved high segmentation accuracy in brain tumor detection, outperforming traditional methods with improved Dice scores, precision, and computational efficiency. Different machine learning and deep learning-based architectures and optimized models yielded superior performance, with Dice scores up to 0.91 and validation accuracy of 98%. …”
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