Intelligent Technologies and Robotic Machines for Garden Crops Cultivation by I. G. Smirnov, D. O. Khort, A. I. Kutyrev

“…In particular, the following methods were applied: systems analysis, artificial neural networks theory, pattern recognition, digital signal processing. The development of software, hardware and software was carried out in accordance with the requirements of GOST technical standards. …”
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Machine learning for predicting earthquake magnitudes in the Central Himalaya by Ram Krishna Tiwari, Rudra Prasad Poudel, Harihar Paudyal

“… Human intervention cannot halt natural disasters like earthquakes, but machine learning application expertise can be utilized to detect patterns in data and increase understanding and predictive power. …”
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Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces by Zhaojun Sun, Xin Li, Yiming Wu, Qilin Gu, Shiyou Zheng

“…These interfaces play pivotal roles in phenomena such as dendrite growth and the formation of solid–electrolyte interphases (SEI), which are crucial for the performance, longevity, and safety of batteries. Machine learning (ML), a vital subset of artificial intelligence, offers robust capabilities by autonomously identifying patterns in complex datasets, thereby enhancing the understanding of these intricate interfacial processes. …”
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Novel Multimodal Fusion Algorithm for Non-Intrusive Anxiety Detection by Mahir Shadid, Mushfiqus Salehin Afnan, Rashed Mustafa, M. Jamshed Alam Patwary

“…The study utilizes six advanced machine learning algorithms (Gaussian Naive Bayes, XGB Classifier, K-Neighbors, SVM, Decision-Tree, and RandomForest) for data classification, pattern recognition, and predictive accuracy. …”
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Research on Classification of Rail Defects Based on Image Processing Algorithm by Mengying HUANG, Jiangping LUO, Wenxing WANG, Jingwei CAO

“…Therefore, a classification for rail defects based on image processing algorithm was proposed. Firstly, the Tamura texture feature algorithm was combined with the local binary pattern algorithm to extract the feature values of the defect images, and form feature vectors. …”
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Angle Modulated Artificial Bee Colony Algorithms for Feature Selection by Gürcan Yavuz, Doğan Aydin

“…Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. …”
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Automatic Card Fraud Detection Based on Decision Tree Algorithm by Elena Flondor, Liliana Donath, Mihaela Neamtu

“…The paper examines advanced techniques such as data analysis, automatic learning algorithms, and real-time monitoring systems to detect suspicious patterns, anomalies, and deviations from normal behavior with precision. …”
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ICBoost: An XGBoost-Based Unbiased Transformed Algorithm for Survival Regression by Jingyi Zhang, Shishun Zhao, Yang Xu, Tao Hu

“…This study aims to develop a reliable and efficient prediction algorithm that utilizes unbiased transformation to improve machine learning model performance on interval-censored data. …”
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Modeling an Image Clustering Algorithm For Detecting Overheated Railway Axle by Oleksandr Gertsiy, Serhii Karnatov, Vitaliy Gladish, Valentyna Tkachenko

“…The principles of the proposed algorithm are described in the context of image segmentation and compression, as well as pattern recognition in the transportation domain. …”
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Implementing Anomaly-Based Intrusion Detection for Resource-Constrained Devices in IoMT Networks by Georgios Zachos, Georgios Mantas, Kyriakos Porfyrakis, Jonathan Rodriguez

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Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing by Waqar Shehbaz, Qingjin Peng

“…Initially, experimental data are generated by systematically varying key AM parameters, layer height, infill density, infill pattern, build orientation, and number of shells. …”
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A Machine Learning Approach for the Prediction of Thermostable β-Glucosidases by Diego Mariano

“…I collected 1717 structures from thermophilic (<i>n</i> = 890) and non-thermophilic (<i>n</i> = 827) organisms and divided them into two datasets: training (<i>n</i> = 1134) and test (<i>n</i> = 583). I then used seven machine learning algorithms to classify them. The best model achieved 77.1% accuracy using logistic regression in training with 10-fold cross-validation and 81.6% accuracy in testing using the CatBoost algorithm. …”
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Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

“…After pre-processing, an enhanced Deep Reinforcement Learning (EDRL) model is employed to extract high-level representations and learn complex patterns from data by means of interaction with the environment. …”
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Phyloreferences: Tree-Native, Reproducible, and Machine-Interpretable Taxon Concepts by Hilmar Lapp, Nico Cellinese, Stijn Conix

“…To fully take advantage of this wealth of data to discover and understand large-scale evolutionary and ecological patterns, computational data integration, i.e., the use of machines to link data at large scale, is crucial. …”
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A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting by Iman Ahmadianfar, Aitazaz Ahsan Farooque, Mumtaz Ali, Mehdi Jamei, Mozhdeh Jamei, Zaher Mundher Yaseen

“…By incorporating the linear relationship and regularization techniques of ridge regression with the flexibility and adaptability of the SKRidge algorithm, the L-SKRidge model is able to capture complex patterns in the data while also preventing overfitting. …”
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Prediction of Airline Ticket Price Using Machine Learning Method by Hüseyin Korkmaz

“…Traditional methods often fall short of capturing the intricate and rapidly changing patterns of airfare pricing. With the advent of machine learning algorithms, there is a growing potential to enhance the accuracy and reliability of ticket price predictions. …”
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An AI recognition method for children's clinical operative pain by skin potential (SP) signal by Mingxuan Huang, Cangcang Fu, Linbo Chui, Jiadong He, Xiaozhi Wang, Jikui Luo, Bin Wu, Yonggang Chen, Shaohua Hu, Jihua Zhu, Yubo Li

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Application of machine learning techniques for churn prediction in the telecom business by Raji Krishna, D. Jayanthi, D.S. Shylu Sam, K. Kavitha, Naveen Kumar Maurya, T. Benil

“…The primary objective of this work is to examine various machine learning algorithms necessary for creating customer churn prediction (CP) models and identifying the reasons for churn. …”
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A Novel Support Vector Machine with Globality-Locality Preserving by Cheng-Long Ma, Yu-Bo Yuan

“…Support vector machine (SVM) is regarded as a powerful method for pattern classification. …”
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Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector by Martina De Giovanni, Mariangela Lazoi, Romeo Bandinelli, Virginia Fani

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