Decision Support System for Evaluating Corpus-Based Word Lists for Use in English Language Teaching Contexts by Ruoxi Yin, Chunmei Zhu, Jiuyang Zhu

“…This study proposes a Decision Support System (DSS) that integrates corpus linguistics, Natural Language Processing (NLP), and machine learning to generate optimized vocabulary lists tailored for ELT. …”
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Probabilistic back analysis method for determining surrounding rock parameters of deep hard rock tunnel by WU Zhong-guang, WU Shun-chuan

“…Second, a multi-output support vector machine (MSVM) was optimized by particle swarm optimization (PSO) algorithm, and an intelligent response surface model was established to reflect the nonlinear mapping relationship between back-analyzed parameters and field monitoring data. …”
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Adaptive biases-incorporated latent factorization of tensors for predicting missing data in water quality monitoring networks by Xuke Wu, Xuke Wu, Lan Wang, Lan Wang, Miao Ge, Miao Ge, Jing Jiang, Jing Jiang, Yu Cai, Bing Yang, Bing Yang

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A Comparative Study of Customized Algorithms for Anomaly Detection in Industry-Specific Power Data by Minsung Jung, Hyeonseok Jang, Woohyeon Kwon, Jiyun Seo, Suna Park, Beomdo Park, Junseong Park, Donggeon Yu, Sangkeum Lee

“…This study compares and analyzes statistical, machine learning, and deep learning outlier-detection methods on real power-usage data from the metal, food, and chemical industries to propose the optimal model for improving energy-consumption efficiency. …”
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System Identification of a Servo-Valve Controlled Hydraulic Cylinder Operating Under Variable Load by Daniel Catalin Stroita, Dorin Bordeasu, Florin Dragan

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Construction of Knowledge Graph for Marine Diesel Engine Faults Based on Deep Learning Methods by Xiaohe Tian, Huibing Gan, Yanlin Liu

“…Experiments show that the model significantly outperforms baseline methods such as HMM, CRF, and BiLSTM, and the graph visualization clearly presents the fault causality network, which supports knowledge reasoning and decision optimization. …”
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Comparison of artificial intelligence approaches for estimating wind energy production: A real-world case study by Mohamed Bousla, Mohamed Belfkir, Ali Haddi, Youness El Mourabit, Badre Bossoufi

“…The present work investigates several forecasting methodologies for wind energy by employing sophisticated machine learning algorithms, including Support Vector Machines and Recurrent Neural Networks. …”
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A Quantum-like Approach to Semantic Text Classification by Anastasia S. Gruzdeva, Rodion N. Iurev, Igor A. Bessmertny, Andrei Y. Khrennikov, Alexander P. Alodjants

“…In this work, we conduct a sentiment analysis of English-language reviews using a quantum-like (wave-based) model of text representation. This model is explored as an alternative to machine learning (ML) techniques for text classification and analysis tasks. …”
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Fuzzy deep learning architecture for cucumber plant disease detection and classification by Anas Bilal, Junaid Ali Khan, Abdulkareem Alzahrani, Khalid Almohammadi, Maha Alamri, Xiaowen Liu

“…At the same time, the ReLU transfer function ensures robustness, mainly when dealing with noisy or incomplete image segments. Feature vector optimization is performed using a chaotic particle swarm algorithm, enhancing the model’s overall accuracy, reliability, and ease of implementation. …”
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A combined model integrating deep learning, radiomics, and clinical ultrasound features for predicting BRAF V600E mutation in papillary thyroid carcinoma with Hashimoto’s thyroidit... by Peng-Fei Zhu, Xiao-Feng Zhang, Pu Zhou, Jiang-Yuan Ben, Hao Wang, Shu-E Zeng, Xin-Wu Cui, Ying He

“…Feature selection was performed using Pearson’s correlation coefficient, the Minimum Redundancy Maximum Relevance (mRMR) algorithm, and LASSO regression. The optimal algorithm was selected from nine machine learning algorithms for model construction, including the traditional radiomics model (RAD), the deep learning model (DL), and their fusion model (DL_RAD). …”
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Multilayer perceptron deep learning radiomics model based on Gd-BOPTA MRI to identify vessels encapsulating tumor clusters in hepatocellular carcinoma: a multi-center study by Mengting Gu, Wenjie Zou, Huilin Chen, Ruilin He, Xingyu Zhao, Ningyang Jia, Wanmin Liu, Peijun Wang

“…Least absolute shrinkage and selection operator (LASSO) and univariate logistic regression analysis were used to select the important features. Seven different machine learning classifiers respectively combined the radiomics signatures selected from four ROIs to constitute different models, and compare the performance between them in three sets and then select the optimal combination to become the radiomics model we need. …”
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Accelerating Energy Forecasting with Data Dimensionality Reduction in a Residential Environment by Rafael Gonçalves, Diogo Magalhães, Rafael Teixeira, Mário Antunes, Diogo Gomes, Rui L. Aguiar

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Predicting Index Trend Using Hybrid Neural Networks with a Focus on Multi-Scale Temporal Feature Extraction in the Tehran Stock Exchange by Mohammad Osoolian, Ali Nikmaram, Mahdi Karimi

“…A wide array of predictive modeling techniques have been meticulously investigated, spanning from conventional statistical methodologies to more sophisticated machine learning algorithms. …”
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Inversion of Leaf Chlorophyll Content in Different Growth Periods of Maize Based on Multi-Source Data from “Sky–Space–Ground” by Wu Nile, Su Rina, Na Mula, Cha Ersi, Yulong Bao, Jiquan Zhang, Zhijun Tong, Xingpeng Liu, Chunli Zhao

“…On this basis, we selected three nonlinear machine learning models (XGBoost, RFR, SVR) and one multiple linear regression model (PLSR) to construct the LCC inversion model, and we chose the optimal model to generate spatial distribution maps of maize LCC at the regional scale. …”
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Hybrid Quantum–Classical Deep Neural Networks Based Smart Contract Vulnerability Detection by Sinan Durgut, Ecir Uğur Küçüksille, Mahmut Tokmak

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Development of a relay-vector control system for a multi-phase semiconductor converter of electric energy by S. V. Panteleev, A. N. Malashin

“…To select the optimal control action, the objective function of the minimum deviation of the projections of the base voltage vectors of the semiconductor switch for the j-th combination of the state of the keys from the calculated control action determined by the mathematical model is used. …”
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Stepper Motor Position Control Using PD and MPC Algorithms Embedded in Programmable Logic Controller by Anshul Jaswal, Ma'moun Abu-Ayyad, Yash Lad, Anilchandra Attaluri

“…The MPC algorithm is widely used in industrial plants, particularly in slower processes, such as press machines and heat treatment. However, their application in faster processes, such as servomotors and robotics, is often require faster optimization algorithms or more powerful processors. …”
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Predicting the strengths of basalt fiber reinforced concrete mixed with fly ash using AML and Hoffman and Gardener techniques by Kennedy C. Onyelowe, Viroon Kamchoom, Shadi Hanandeh, Ahmed M. Ebid, José Luis Llamuca Llamuca, Juan Carlos Cayán Martínez, Evlin Rose, Paul Awoyera, Siva Avudaiappan

“…This shows the effectiveness of boosting techniques for predictive modeling in concrete strength estimation. For splitting tensile strength (Fsp), AdaBoost also outperforms most models, achieving an R2 of 0.96 for training and validation phases. …”
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Multi-Disciplinary Investigations on the Best Flying Wing Configuration for Hybrid Unmanned Aerial Vehicles: A New Approach to Design by Janani Priyadharshini Veeraperumal Senthil Nathan, Martin Navamani Chellapandian, Vijayanandh Raja, Parvathy Rajendran, It Ee Lee, Naveen Kumar Kulandaiyappan, Beena Stanislaus Arputharaj, Subhav Singh, Deekshant Varshney

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Performance Analysis and Reliability Prediction of Multi‐State Service Systems With Multiple Failure Modes of Unreliable Server: An Engineering Perspective by Shreekant Varshney, Mohit Bajaj, Kapil Kumar Choudhary, Mukesh Pushkarna, Ievgen Zaitsev

“…The proposed research contributes to the queueing literature by addressing the research gaps between theoretical modeling and real‐life applications, highlighting the insights that are essential for system designers, decision‐makers, and researchers aiming to optimize the reliability and availability of complex machining systems.…”
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