Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…In recent years, machine learning (ML) has demonstrated significant potential in corrosion inhibitor molecule research and has emerged as a powerful tool for scientists to explore new and efficient corrosion inhibitors. …”
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An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT by Bahman Sanjabi, Mahmood Ahmadi

“…They are trained in machine learning and deep neural network learning to detect attack patterns. …”
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Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data by Abiel Aguilar-González, Alejandro Medina Santiago

“…In this work, we propose a Random Forest-based event classification algorithm designed to handle the unique patterns of vibration and acceleration data in road event detection for an urban traffic scenario. …”
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Application of Genetic Algorithms for Finding Edit Distance between Process Models by Anna A. Kalenkova, Danil A. Kolesnikov

“…In particular, we find distances between BPMN (Business Process Model and Notation) models discovered from event logs by using different process discovery algorithms. We show that the genetic algorithm allows us to dramatically reduce the time of comparison and produces results which are close to the optimal solutions (minimal graph edit distances calculated by the exact search algorithm).…”
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Brain activity patterns reflecting security perceptions of female cyclists in virtual reality experiments by Mohammad Arbabpour Bidgoli, Arian Behmanesh, Navid Khademi, Phromphat Thansirichaisree, Zuduo Zheng, Sara Saberi Moghadam Tehrani, Sajjad Mazloum, Sirisilp Kongsilp

“…Subsequently, four supervised machine learning methods, random forest, support vector machine, logistic regression, and multilayer perceptron, are utilized to classify influential factors on security perception using clustered EEG data. …”
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Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting by Abdul Wadood, Hani Albalawi, Aadel Mohammed Alatwi, Hafeez Anwar, Tariq Ali

“…These results highlight the significant improvements of FWOA-SVR in prediction accuracy and efficiency, surpassing benchmark models in capturing complex patterns within the data. The findings highlight the effectiveness of integrating fractional optimization techniques into machine learning frameworks for advancing solar energy forecasting solutions.…”
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Anomaly detection in virtual machine logs against irrelevant attribute interference. by Hao Zhang, Yun Zhou, Huahu Xu, Jiangang Shi, Xinhua Lin, Yiqin Gao

“…The LADSVM approach excels at detecting anomalies in virtual machine logs characterized by strong sequential patterns and noise. …”
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Examining peptide–gold nanoparticle interactions through explainable machine learning by Malak Gamal Abdelmeguid, Jose Isagani B. Janairo, Nishanth G. Chemmangattuvalappil

“…This work develops an explainable binary machine learning classifier using rough sets as the algorithm and amino acid composition as the features. …”
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Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context by Ana Gavrovska, Goran Zajić, Vesna Bogdanović, Irini Reljin, Branimir Reljin

“…The analysis of heart sound patterns is performed using support vector machine classifier showing promising results (above 95% accuracy). …”
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A Comprehensive Monte Carlo-Simulated Dataset of WAXD Patterns of Wood Cellulose Microfibrils by Ricardo Baettig, Ben Ingram

“…It enables the development, validation, and benchmarking of novel algorithms and machine learning models for MFA prediction from diffraction patterns. …”
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Assessing distortion in carbon fiber woven fabrics based on machine vision by Shiyue Li, Quanzhou Yao, Lin Ye

“…This work proposes a machine vision method to locate defective areas, identify defects, and describe fiber tow distribution patterns. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. …”
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Predictive Analytics in Agriculture: Machine Learning Models for Coconut Tree Health by Goswami Anjali, Kirit Dhablia Dharmesh

“…We note that coconut tree health issues have been addressed using advanced ML models for early detection and prediction in this paper. Several ML algorithms are analyzed in the study for data from several sources like satellite imagery, drone based sensors, and field data, including Convolutional Neural Networks (CNNs), Random Forest and Support Vector Machines (SVMs). …”
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Machine Learning Advancements in Urban Traffic Simulation: A Comprehensive Survey by Harshit Maheshwari, Li Yang, Richard W. Pazzi

“…This survey systematically reviews the state-of-the-art Machine Learning techniques applied to urban traffic simulation. …”
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Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics by Saurabh Sharma, Vijay Kumar Gahlawat, Kumar Rahul, Rahul S Mor, Mohit Malik

“…This paper addresses the research concerning AI and big data analytics in the food industry, including machine learning, artificial neural networks (ANNs), and various algorithms. …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…For regression and classification, deep neural network (DNN) from deep learning algorithms and support vector machine (SVM), decision trees (DTs), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) algorithms from machine learning algorithms were used. …”
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THE INVERSE GAUSSIAN PLUME METHOD FOR ESTIMATING THE LEVEL OF AIR POLLUTION by Volodymyr Hura, Liubomyr Monastyrskyi

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Associations between forest vertical structure and habitat preferences of black-and-white snub-nosed monkeys (Rhinopithecus bieti) in high-elevation environments by Zhipang Huang, Yuling Chen, Haitao Yang, Yihao Fang, Kai Cheng, Hongcan Guan, Cyril C. Grueter, Wen Xiao, Qinghua Guo

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Analysis and prediction of infectious diseases based on spatial visualization and machine learning by Yunyun Cheng, Yanping Bai, Jing Yang, Xiuhui Tan, Ting Xu, Rong Cheng

“…Firstly, we used ArcGIS software to analyze the spatial agglomeration pattern of the number of patients in various regions of China through global spatial autocorrelation analysis, local spatial autocorrelation analysis, center of gravity trajectory migration algorithm and other statistical tools; In addition, the areas with serious COVID-19 epidemic and requiring special attention were screened out. …”
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Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning. by Ridhanya Sree Balamurugan, Yusuf Asad, Tommy Gao, Dharmakeerthi Nawarathna, Umamaheswara Rao Tida, Dali Sun

“…Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. …”
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