Frost Resistance Prediction of Concrete Based on Dynamic Multi-Stage Optimisation Algorithm by Xuwei Dong, Jiashuo Yuan, Jinpeng Dai

“…To predict the frost resistance of concrete more accurately, based on the four ensemble learning models of random forest (RF), adaptive boosting (AdaBoost), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost), this paper optimises the ensemble learning models by using a dynamic multi-stage optimisation algorithm (DMSOA). These models are trained using 7090 datasets, which use nine features as input variables; relative dynamic elastic modulus (RDEM) and mass loss rate (MLR) as prediction indices; and six indices of the coefficient of determination (R²), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and standard deviation ratio (SDR) are selected to evaluate the models. …”
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Sharpbelly Fish Optimization Algorithm: A Bio-Inspired Metaheuristic for Complex Engineering by Jian Liu, Rong Wang, Yonghong Deng, Xiaona Huang, Zhibin Li

“…The experimental results demonstrate that SFO consistently achieves competitive or superior optimization accuracy and convergence speed compared to seven state-of-the-art metaheuristic algorithms. Furthermore, the algorithm is applied to three classical constrained engineering design problems: pressure vessel, speed reducer, and gear train design. …”
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Modeling and Optimization of Concrete Mixtures Using Machine Learning Estimators and Genetic Algorithms by Ana I. Oviedo, Jorge M. Londoño, John F. Vargas, Carolina Zuluaga, Ana Gómez

“…Using a dataset of over 19,000 samples from a local ready-mix concrete producer, various predictive ML models were trained and evaluated regarding cost-effective solutions. …”
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Inconsistency evaluation of the curriculum logical structure by Yu. D. Ageev, S. V. Fedoseev, Yu. A. Kavin, S. G. Vorona, I. S. Pavlovskiy

“…Violation of this logic becomes apparent only directly during the training sessions.A large variety of quantitative methods uses indicators that do not reveal structural deficiencies in the curriculum. …”
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Voice pathology detection using machine learning algorithms based on different voice databases by Nurul Mu'azzah Abdul Latiff, Fahad Taha Al-Dhief, Nurul Fariesya Suhaila Md Sazihan, Marina Mat Baki, Nik Noordini Nik Abd. Malik, Musatafa Abbas Abbood Albadr, Ali Hashim Abbas

“…Unlike traditional approaches that focus solely on single-database training and testing, this study presents a cross-database evaluation strategy to assess the robustness and generalizability of machine learning algorithms for voice pathology detection. …”
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Classification of Service Sentiments on the by.U Application using the Support Vector Machine Algorithm by Zulkarnain Zulkarnain, Rice Novita, Angraini Angraini, Zarnelly Zarnelly

“…The evaluation results showed that the SVM algorithm achieved an accuracy of 83% in classifying sentiments. …”
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Application of Deep Learning Algorithm for Web Shell Detection in Web Application Security System by Rezky Yuranda, Edi Surya Negara

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Machine Learning for Chinese Corporate Fraud Prediction: Segmented Models Based on Optimal Training Windows by Chang Chuan Goh, Yue Yang, Anthony Bellotti, Xiuping Hua

“…Using the best machine learning model and optimal training window, we build general model and segmented models to compare fraud types and industries based on their respective predictive performance via four evaluation metrics and top features using SHAP. …”
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The use of machine translation algorithm based on residual and LSTM neural network in translation teaching. by Beibei Ren

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Genetic Artificial Hummingbird Algorithm-Support Vector Machine for Timely Power Theft Detection by Emmanuel Gbafore, Davies Rene Segera, Cosmas Raymond Mutugi Kiruki

“…The methodology entailed data preprocessing, data split into training, validation, and testing sets in an 80-10-10 ratio, z-score normalization, optimization, training, validation, testing, and computation of six evaluation metrics. …”
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Optimization of Business English Teaching Based on the Integration of Interactive Virtual Reality Genetic Algorithm by Xiao Ma

“…The results of the simulation experiment indicate that the improved algorithm designed in this article can reduce the computational overhead of the meta-algorithm to a great extent, and the improvement strategy is designed based on the evaluation results of practical examples.…”
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Efficient Deep Learning Models Revolutionize Doctor’s Training for Point-of-Care Ultrasound by Hsin-Hung Chou, Yi-Chung Chang, Wan-Ching Lien, Lung-Chun Lin, Xing-Zheng Lin, Ting-En Hsu, Yueh-Ping Liu, Li Liu, Yen-Ting Chan, Feng-Sen Kuan

“…The overall sensitivity increased by 4% in the classification task after training with the UNet++ model, although there were no statistically significant differences in all evaluation metrics. …”
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An algorithm for variational inclusion problems including quasi-nonexpansive mappings with applications in osteoporosis prediction by Raweerote Suparatulatorn, Wongthawat Liawrungrueang, Thanasak Mouktonglang, Watcharaporn Cholamjiak

“…From the experimental results, our proposed algorithm consistently outperforms existing algorithms across multiple evaluation metrics. …”
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A multi objective collaborative reinforcement learning algorithm for flexible job shop scheduling by Jian Li, Shifa Li, Pengbo He, Huankun Li

“…Reward parameters are computed based on temporal differences at various moments, constructing a multi-objective Markov decision-process training model. Using hypervolume, set coverage and inverted generational distance as evaluation metrics, the algorithm is compared with those proposed in other studies on standard instances. …”
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Financial Fraud Detection Approach Based on Firefly Optimization Algorithm and Support Vector Machine by Ajeet Singh, Anurag Jain, Seblewongel Esseynew Biable

“…In the first level, the firefly algorithm (FFA) and the CfsSubsetEval feature section method have been applied to optimize the subset of features, while in the second level, the support vector machine classifier has been used to build the training model for the detection of credit card fraud cases. …”
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Research on the Construction of Event Evolutionary Graph for Urban Rail Transit Train Operation and Maintenance Safety by FAN Qianqi, ZUO Jianyong, GONG Ming, JIA Bo, LI Zhengjiang

“…［Method］ Focusing on the construction of event evolutionary graph for urban rail train operation and maintenance safety, three key steps for constructing the event evolutionary graph are optimized. …”
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Slope stability prediction under seismic loading based on the EO-LightGBM algorithm by Ning Ma, Ning Ma, Yuqi Zhang, Zaizhen Yao

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Predictive PID Control for Automated Guided Vehicles Using Genetic Algorithm and Machine Learning by Kinza Nazir, Yong-Woon Kim, Yung-Cheol Byun

“…A custom simulator, designed with Webots, facilitated realistic testing environments, and comprehensive dataset with systematically varied track geometries was curated for training and evaluation. Results demonstrate the proposed framework’s ability to maintain high tracking accuracy while significantly reducing computational costs, with SVR predictions closely approximating the GA-derived benchmarks. …”
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Assessing generalizability of Deep Reinforcement Learning algorithms for Automated Vulnerability Assessment and Penetration Testing by Andrea Venturi, Mauro Andreolini, Mirco Marchetti, Michele Colajanni

“…The main contribution of this paper is to fill this gap by investigating the generalization capabilities of existing DRL agents to extend their VAPT operations to hosts that were not used in the training phase. To this purpose, we define a novel VAPT environment through which we devise multiple evaluation scenarios. …”
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Identification of Alcoholism Based on Wavelet Renyi Entropy and Three-Segment Encoded Jaya Algorithm by Shui-Hua Wang, Khan Muhammad, Yiding Lv, Yuxiu Sui, Liangxiu Han, Yu-Dong Zhang

“…The classifier was constructed based on feedforward neural network and a three-segment encoded (TSE) Jaya algorithm providing parameter-free training of the weights, biases, and number of hidden neurons. …”
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