Intelligent Diagnosis and Research of Epileptic Diseases Based on EEG Signals by LIU Chang-yuan, ZHANG Fu-hao, WEI Qi

“…Aiming at the problem of low accuracy and classification of epileptic EEG in medical diagnosis,a signal classification and detection technique based on particle swarm optimization (PSO) was proposed to optimize the support vector machine (SVM) based on the theory of particle swarm optimization and support vector machine (SVM).Firstly, the EEG signals were decomposed and reconstructed by wavelet analysis.Secondly, the coefficients of fluctuation and approximate entropy of the reconstructed signals containing the functional parameters of epilepsy were extracted. …”
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Development of algorithms and software for classification of nucleotide sequences by V. R. Zakirava, D. A. Syrakvash, S. V. Hileuski, P. V. Nazarov, M. M. Yatskou

“…Seven models of vectorization of nucleotide sequences based on mono-, bi-, trigram nucleotide frequencies, parameters of the category-position-frequency model, the lengths of sequences, nucleotide correlation factors, statistical features of coding and non-coding regions of DNA molecules were developed. …”
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AUTOMATED WATER MANAGEMENT SYSTEM WITH AI-BASED DE-MAND PREDICTION by Arman Mohammad Nakib

“…Since this aspect is increasing, this paper introduces a system to estimate the households’ water needs in a day taking into consideration, the size of the family, zone, temperature, season, working status, location, and religion. By applying machine learning, the system determines the balance of water distribution between the families by these parameters. …”
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Deep learning-based applications in metal additive manufacturing processes: Challenges and opportunities–A review by Tuğrul Özel

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Classification of edible vegetable oils based on three-dimensional fluorescence spectroscopy and ISSA-SVM by ZHANG Jing, QI Guohong, CHEN Jingzhao, CAO Xiaoli, LI Lili

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Real-Time Decision Support System for Dynamic Optimization in Multi-Product Process Manufacturing by Mustapha Belmouadden, Camelia Dadouchi, Robert Pellerin

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Research on TOGI‐EFLL flux observer for IPMSM sensorless control by Xue Hao, Yutao Luo

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Quality Evaluation and Predictive Analysis of Drilled Holes in Jute/Palm/Polyester Hybrid Bio-Composites Using CMM and ANN Techniques by Salah Amroune, Abdelmalek Elhadi, Mohamed Slamani, Mustapha Arslane, Ahmed Belaadi, Mahmood M. S. Abdullah, Hamad A. Al-Lohedan, Tarek Bidi, Herbert Mukalazi, Amar Al-Khawlani

“…In this study, the evaluation of 75 holes drilled in a hybrid bio-composite jute/palm/polyester plate and controlled by a coordinate measuring machine (CMM) is essential to ensure the quality, dimensional precision, and geometric conformity of the plate. …”
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An analytics-driven model for identifying autism spectrum disorder using eye tracking by Deblina Mazumder Setu

“…These properties are utilized in machine learning and deep learning model training with hyperparameter adjusting for optimization. …”
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Development of an IMU-Based Post-Stroke Gait Data Acquisition and Analysis System for the Gait Assessment and Intervention Tool by Yu-Chi Wu, Yu-Jung Huang, Chin-Chuan Han, Yuan-Yang Cheng, Chao-Shu Chang

“…In this paper, we developed a gait data acquisition and analysis system based on IMU wearable devices, proposed a simple yet accurate calibration process to reduce the IMU drifting errors, designed a machine learning algorithm to obtain real-time coordinates from IMU data, computed gait parameters, and derived a formula for G.A.I.T. scores with significant correlation with the physician’s observational scores.…”
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Prescribed‐time resilient current‐sensorless DC power converter control by Maryam Homayounzadeh, Mohamadreza Homayounzade, Mohammad‐Hassan Khooban

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Evolutionary Polynomial Regression Algorithm with Uncertain Variables: Two Case-Studies in the Field of Civil Engineering by Alessandra Fiore, Sebastiano Marasco, Rita Greco

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Wear Prediction and Visual Decision Support of Drop Forged Rivetless Chain for Conveyors by Liu Manxian, Xu Zijia, Shen Xujia, Li Xiaodong, Zhang Zhijun, Yang Yi

“…The wear data are acquired by wear detection device based on machine vision. The obtained data are cleaned by defining and analyzing key parameters of wear condition. …”
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Physics-informed neural networks for a highly nonlinear dynamic system by Ruxandra Barbulescu, Gabriela Ciuprina, Anton Duca, Paulo Machado, L. Miguel Silveira

“…The pull-in voltage and the response time are some of the most important parameters of this system. With physics-based approaches, the challenge of modelling and producing simplified representations comes from the strong nonlinearities involved and the interaction of more than one physical field. …”
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An AutoML-Powered Analysis Framework for Forest Fire Forecasting: Adapting to Climate Change Dynamics by Shuo Zhang, Mengya Pan

“…Wildfires pose a serious threat to ecosystems and human safety, and with the backdrop of global climate change, the prediction of forest fires has become increasingly important. Traditional machine learning methods face challenges in forest fire prediction, such as difficulty identifying feature parameters, manual intervention in model selection, and hyperparameter tuning, which affect prediction accuracy and efficiency. …”
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Learning Constitutive Relations From Soil Moisture Data via Physically Constrained Neural Networks by Toshiyuki Bandai, Teamrat A. Ghezzehei, Peishi Jiang, Patrick Kidger, Xingyuan Chen, Carl I. Steefel

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Investigation of the Features Influencing the Accuracy of Wind Turbine Power Calculation at Short-Term Intervals by Pavel V. Matrenin, Dmitry A. Harlashkin, Marina V. Mazunina, Alexandra I. Khalyasmaa

“…It is discovered that using ensemble machine learning models and additional features, including the actual power from the previous time step, enhances the accuracy of the wind power calculation. …”
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A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization by Dongjie Pang, Cristina Moliner, Tao Wang, Jin Sun, Xinyan Zhang, Yingping Pang, Xiqiang Zhao, Zhanlong Song, Ziliang Wang, Yanpeng Mao, Wenlong Wang

“…This review examines the deployment of AI-optimized control algorithms in processes including pyrolysis, incineration, and gasification. The application of machine learning models, including linear regression (LR), genetic algorithm (GA), support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and Extreme Gradient Boosting (XGBoost), enables real-time monitoring of performance and dynamic adjustment of parameters to enhance energy recovery and minimize pollution. …”
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Artificial Intelligence and Internet of Things Integration in Pharmaceutical Manufacturing: A Smart Synergy by Reshma Kodumuru, Soumavo Sarkar, Varun Parepally, Jignesh Chandarana

“…This integration facilitates enabling machine learning and deep learning for real-time analysis, predictive maintenance, and automation—continuously monitoring key manufacturing parameters. …”
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Predictive Models with Applicable Graphical User Interface (GUI) for the Compressive Performance of Quaternary Blended Plastic-Derived Sustainable Mortar by Aïssa Rezzoug, Ahmed A. Abdou Elabbasy, Muwaffaq Alqurashi, Ali H. AlAteah

“…Machine learning (ML) models in material science and construction engineering have significantly improved predictive accuracy and decision making. …”
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