Integration of geospatial techniques and machine learning in land parcel prediction by Nekkanti Haripavan, Subhashish Dey, Chimakurthi Harika Mani Chandana

“…The integration of geospatial techniques and machine learning algorithms has revolutionized our ability to analyze and predict changes in land parcels. …”
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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

“…The Gaussian mixture approach is used to cluster brainwave patterns based on security perception from EEG data. …”
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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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Application of Genetic Algorithms for Finding Edit Distance between Process Models by Anna A. Kalenkova, Danil A. Kolesnikov

“…In particular, finding graph-edit distance techniques can be used to reveal patterns (subprocesses), compare discovered process models. …”
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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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A Machine Learning Approach for User Behavior Analysis in Developing Websites by Aeesha Shaheen

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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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Machine learning-based characteristic identification of MSG content in gravy foods by Rosyady Phisca Aditya, Habibah Nurina Umy, Masita, Yudhana Anton

“…Therefore, this research aims to detect the level of MSG content in soupy foods using Machine Learning. This research determines the identification of MSG using the Machine Learning method Naive Bayes classifier algorithm in Python software. …”
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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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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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Machine learning assisted estimation of total solids content of drilling fluids by B.T. Gunel, Y.D. Pak, A.Ö. Herekeli, S. Gül, B. Kulga, E. Artun

“…The relationships among various rheological parameters were analyzed using statistical methods and machine learning algorithms. Several machine learning algorithms of diverse classes, namely linear (linear regression, ridge regression, and ElasticNet regression), kernel-based (support vector machine) and ensemble tree-based (gradient boosting, XGBoost, and random forests) algorithms, were trained and tuned to estimate solids content from other readily available drilling fluid properties. …”
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Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis by Marcos Uceta, Alberto del Cerro‐León, Danylyna Shpakivska‐Bilán, Luis M. García‐Moreno, Fernando Maestú, Luis Fernando Antón‐Toro

“…Recent studies have changed their scope into finding predisposition factors that may lead adolescents into this kind of patterns of consumption. Methods In this article, using unsupervised machine learning (UML) algorithms, we analyze the relationship between electrophysiological activity of healthy teenagers and the levels of consumption they had 2 years later. …”
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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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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

“…Traditional simulation models often struggle to capture the intricacies of urban traffic patterns, leading to unrealistic simulations, which negatively affect traffic management and urban planning. …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. …”
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