Predicting the Toxicity of Drug Molecules with Selecting Effective Descriptors Using a Binary Ant Colony Optimization (BACO) Feature Selection Approach by Yuanyuan Dan, Junhao Ruan, Zhenghua Zhu, Hualong Yu

“…Only those high-frequency features are used to train a support vector machine (SVM) and construct the structure–activity relationship (SAR) prediction model. …”
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Channel equalization in ultraviolet communication based on LSTM-DNN hybrid model by Liwei Zhang

“…These methods include Least Mean Squares (LMS), Recursive Least Squares (RLS), Particle Swarm Optimization (PSO), Support Vector Machine (SVM), and Minimum Mean Squared Error (MMSE). …”
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Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen by Mark Hutchinson, Jeffrey A. Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J. Gray, Gilad Kaplan

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Optimal design of a hybrid system composed of coagulation process and multi-stage filtration unit for on-site treatment of greywater in rural area by Zahra Akbari, Fereshteh Nourmohammadi Dehbalaei, Zahra Mohammad Hosseini, Seyed Taghi Omid Naeeni

“…Multilayer nonlinear machine learning model was utilized to evaluate the performance of this hybrid system and adaptive heuristic search algorithm was used to achieve the optimal configuration of multilayer sand filter using optimization technique. …”
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A Review of Integrated Optimization Method of Batch Planning and Scheduling for Steelmaking-Continuous Casting-Hot Rolling Production Under Uncertain Environment Based on Lagrangia... by Liangliang Sun, Xirang Hao, Yunpeng Li, Jinghan Xue, Natalia M. Matsveichuk, Yuri N. Sotskov, Qichun Zhang

“…Considering the uncertain environment and the integration of SM-CC-HR, this paper summarizes the research status of previous scholars from three aspects: mathematical modeling, model optimization, and algorithm optimization based on the Lagrangian framework. …”
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Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China by Tiezhu Li, Qidi Huang, Qigang Chen

“…In this study, debris flow susceptibility models were developed using three machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). …”
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Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

“…The fusion of Artificial intelligence (AI) with computational chemistry has revolutionized drug discovery by enhancing compound optimization, predictive analytics, and molecular modeling. …”
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Development of Refined Electromagnetic Models of Reciprocating Electric Generators with Permanent Magnets by A. B. Menzhinski, A. N. Malashin, P. B. Menzhinski

“…For this purpose, a mathematical model based on field theory can be used; however, its main drawback is the complexity of its application for solving optimization problems. …”
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Optimizing Outdoor Micro-Space Design for Prolonged Activity Duration: A Study Integrating Rough Set Theory and the PSO-SVR Algorithm by Jingwen Tian, Zimo Chen, Lingling Yuan, Hongtao Zhou

“…The innovative contribution of this study lies in the proposed data-driven optimization method that integrates machine learning and KE. …”
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Research on Credit Default Prediction Model Based on TabNet-Stacking by Shijie Wang, Xueyong Zhang

“…With the development of financial technology, the traditional experience-based and single-network credit default prediction model can no longer meet the current needs. This manuscript proposes a credit default prediction model based on TabNeT-Stacking. …”
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Critical raw material-free multi-principal alloy design for a net-zero future by Swati Singh, Mingwen Bai, Allan Matthews, Saurav Goel, Shrikrishna N. Joshi

“…Among various ML models, the Extra Trees Regressor (ETR) exhibited superior performance and was integrated with metaheuristic optimization techniques to identify novel MPEA compositions. …”
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Modular Robotics Configurator: A MATLAB Model-Based Development Approach by Ernest Andrei Grosz, Marian Borzan

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Detecting sand gradation based on the two-dimensional sand particle features in sand images by Chuanyun Xu, Heng Wang, Yang Zhang, Song Sun, Gang Li

“…Images of sand particles with a single gradation are captured to extract five sand particle feature parameters, which are then used to train a mature network model. The equivalent volume of the sand particles is calculated by multiplying the equivalent projected area by the equivalent elliptical Feret short diameter, and the Feret short diameter is optimized using an optimization algorithm to enhance the volume calculation. …”
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Intelligent Stroke Disease Prediction Model Using Deep Learning Approaches by Chunhua Gao, Hui Wang

“…Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. …”
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Fusion of Visible and Infrared Images Using a Reinforcement Learning System Based on Fuzzy Logic and Convolution Optimized with Wild Horse Algorithm by Mahvash Zarimeidani, Amir Amirabadi, Nasrin Amiri, Iman Ahanian, Siavash Es’haghi

“…This hybrid reinforcement learning system was optimized using algorithms including wild horse optimization (WHO), genetic algorithm (GA), and particle swarm optimization (PSO) to improve specific fusion metrics such as image correlation, similarity coefficient, image entropy, and signal-to-noise ratio. …”
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A hybrid fog-edge computing architecture for real-time health monitoring in IoMT systems with optimized latency and threat resilience by Umar Islam, Mohammed Naif Alatawi, Ali Alqazzaz, Sulaiman Alamro, Babar Shah, Fernando Moreira

“…Simultaneously, edge computing nodes handle data preprocessing and transmit only valuable information—defined as abnormal or high-risk health signals such as irregular heart rate or oxygen levels—using rule-based filtering, statistical thresholds, and lightweight machine learning models like Decision Trees and One-Class SVMs. …”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…The dataset was preprocessed using the Standard Scaler to standardize it, ensuring each feature has a mean of zero and a standard deviation of one, followed by outlier detection with Cook’s distance. Hyperparameter optimization made using the Differential Evolution (DE) method improved the performance of models. …”
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Fatigue strength prediction of Cobalt alloys using material composition and monotonic properties: ML-based approach by Subraya Krishna Bhat, Amritanshu Ranjan, Y S Upadhyaya, Vishwanath Managuli

“…The reliability of the ANN model was validated using an external dataset from the literature, where it maintained reasonable accuracy with prediction errors of 15.5 ± 10.2%. …”
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Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review by Juan Ochoa-Aldeán, Carlos Silva-Cárdenas, Renato Torres, Jorge Ivan Gonzalez, Sergio Fortes

“…Background: Machine learning methods are increasingly being used in mobile network optimization systems, especially next-generation mobile networks. …”
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Advanced smart assistance with enhancing social interaction and daily activities for visually impaired individuals using deep learning with modified seagull optimization by Sana Alazwari, Hussah Nasser AlEisa, Mohammed Rizwanullah, Radwa Marzouk

“…Next, the features are extracted using the Dilated Convolution Block Attention Module with EfficientNet (DCBAM-EfficientNet) module, which derives the intrinsic and complex features. Moreover, the MSGO model is employed to choose the optimal parameter for the DCBAM-EfficientNet module. …”
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