A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns by Rajamanickam Yuvaraj, Shivam Chadha, A. Amalin Prince, M. Murugappan, Md. Sakib Bin Islam, Md. Shaheenur Islam Sumon, Muhammad E. H. Chowdhury

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Analysis of signals from air conditioner compressors with ordinal patterns and machine learning by Keila Barbosa, Alejandro C Frery, George DC Cavalcanti

“…Furthermore, we incorporate machine learning algorithms, such as Artificial Neural Networks, Support Vector Machines, and Decision Trees, to evaluate and validate the effectiveness of Ordinal Patterns as discriminative features. …”
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Improvement of classification accuracy of functional near-infrared spectroscopy signals for hand motion and motor imagery using a common spatial pattern algorithm by Omid Asadi, Mahsan Hajihosseini, Sima Shirzadi, Zahra Einalou, Mehrdad Dadgostar

“…This study aimed to address this challenge by employing the common spatial pattern (CSP) algorithm to reduce input dimensions for support vector machine (SVM) and linear discriminant analysis (LDA) classifiers. …”
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A hybrid Hadoop-based sentiment analysis classifier for tweets associated with COVID-19 utilizing two machine learning algorithms: CNN, and fuzzy C4.5 by Fatima Es-sabery, Ibrahim Es-sabery, Junaid Qadir, Beatriz Sainz-de-Abajo, Begonya Garcia-Zapirain

“…Many researchers prefer using machine and deep learning techniques for this analysis. …”
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Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 by Morteza Abbasi, Amir Hossein Namadchi, Mehdi Abbasi, Mohsen Abbasi

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Image Reconstruction Algorithm Based on Extreme Learning Machine for Electrical Capacitance Tomography by SU Ziheng, CHEN Deyun, WANG Lili

“…Aiming at the problem that the traditional ECT is not accurate in complex situations, this paper proposes a depth learning based inversion method Through the improvement and optimization of the traditional extreme learning machine, the image feature information obtained by the reconstructed image method is used as the training data, and the result obtained by inputting the data into the predictive model is used as the prior information The cost function is used to encapsulate the prior knowledge and domain expertise, and spatial regularizers and time regularizers are introduced to enhance sparsity The separated Bregman (SB) algorithm and the iterative shrinkage threshold (FIST) method are used to solve the specified cost function The final imaging result is obtained The simulation results show that the image reconstructed by this method has less than 10% error compared with the original flow pattern, and reduces artifacts and distortion, which improves the reconstructed image quality…”
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Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm by Kuan-Cheng Lin, Sih-Yang Chen, Jason C. Hung

“…The proposed method is a classified model in which an artificial fish swarm algorithm and a support vector machine are combined. …”
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Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map by Satoshi Suzuki, Fumio Harashima

“…We present an intention estimator algorithm that can deal with dynamic change of the environment in a man-machine system and will be able to be utilized for an autarkical human-assisting system. …”
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Method for recognizing abnormal operation patterns in hydraulic support machine-following and shifting control by MA Zhitao, FU Xiang, LI Haojie, NIU Penghao, JIA Yifan

“…To address this issue, a method was proposed for identifying abnormal condition patterns in machine-following and shifting control of hydraulic supports. …”
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Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning by Mi Liu, Xingxing Gao, Hongfa Wang, Yiping Zhang, Xiaojun Li, Renlai Zhu, Yunru Sheng

“…Results A total of 289 PCD-related differentially expressed genes were identified between sepsis patients and healthy individuals. The machine learning algorithm screened three PCD-related genes, NLRC4, TXN and S100A9, as potential biomarkers for sepsis. …”
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Machine learning algorithms to detect patient–ventilator asynchrony: a feasibility study by Guillermo Gutierrez, Kendrew Wong, Arun Jose, Jeffrey Williams

“…We explored the feasibility of using machine learning algorithms to replicate the assessment of breathing patterns by experienced clinicians, based on airway flow and pressure signals. …”
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Spatial Clusters of Gambling Outlet: A Machine Learning Tree-Based Algorithm by Salvador Martínez-Cava, Fernando A. López, MLuz Maté Sánchez-del-Val

“…While public gambling establishments do not exhibit spatial clustering, private gambling establishments show a growth in spatial clustering with dynamic behavior, seeking locations with specific sociodemographic characteristics. A machine learning tree-based algorithm is used to confirm that decisions on where to put new gambling establishments are based on targeting customers with a gambling profile.…”
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A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems by Asli Kaya Karakutuk, Ozer Ozdemir

“…On the JAFFE dataset, the algorithm achieved an average classification accuracy of 94.55% when supported with local binary patterns and 94.27% with a histogram of oriented gradients. …”
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Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia by Cheng-Wen Lee, Mao-Wen Fu, Chin-Chuan Wang, Muh. Irfandy Azis

“…These findings emphasize the critical need for enhanced fraud detection frameworks, leveraging machine learning algorithms like Random Forest to identify fraud patterns effectively. …”
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Evaluating the Effectiveness of Dimensionality Reduction on Machine Learning Algorithms in Time Series Forecasting by Rida Zaheer, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Ramzan Talib

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Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia by Aditya Eaturu, Krishna Prasad Vadrevu

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Prediction and Optimization of Civil Aviation Flight Delays Based on Machine Learning Algorithms by Qingwei Zhong, Yingxue Yu, Yiru Huang, Tianhang Zhang

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Multivariate machine learning algorithms for energy demand forecasting and load behavior analysis by Farhan Hussain, M. Hasanuzzaman, Nasrudin Abd Rahim

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Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction by Shuo Zhang, Biao Chen, Chaoyang Chen, Maximillian Hovorka, Jin Qi, Jie Hu, Gui Yin, Marie Acosta, Ruby Bautista, Hussein F. Darwiche, Bryan E. Little, Carlos Palacio, John Hovorka

“…This study aimed to determine the feasibility of using lower limb myoelectrical signals (electromyographic signals, EMG) for gait pattern recognition and to identify the optimal machine learning (ML) algorithms for EMG signal processing. …”
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Deciphering algorithmic collusion: Insights from bandit algorithms and implications for antitrust enforcement by Frédéric Marty, Thierry Warin

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