Pixel-Based Machine Learning in Medical Imaging by Kenji Suzuki

“…Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computer-aided diagnosis, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require “learning from examples.” …”
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Learning control system of lifting machine motors by V. I. Zimovets, A. S. Chirva, O. I. Marishchenko

“…The main way of increasing the efficiency of the automated control system of lifting machine motors is giving it the properties of adaptability on the basis of ideas and methods of machine learning and pattern recognition. …”
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Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Induced by Microwave Radiation and Their Associated Strength Reduction Patterns: Employing Meta-Heuristic Opti... by Zhongyuan Gu, Xin Xiong, Chengye Yang, Miaocong Cao

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Variational Methods in Optical Quantum Machine Learning by Marco Simonetti, Damiano Perri, Osvaldo Gervasi

“…As a case study, we considered the binary classification of points belonging to a certain geometric pattern (the Two-Moons Classification problem) on a plane. …”
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SMOTEHashBoost: Ensemble Algorithm for Imbalanced Dataset Pattern Classification by Seema Yadav, Dhruvanshu Joshi, Soham Mulye, Sandeep S. Udmale, Girish P. Bhole

“…Class imbalance occurs frequently in machine learning, particularly in binary classification tasks where the majority class has a significantly larger number of samples than the minority class. …”
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GeoFAN: Point Pattern Recognition in Spatial Vector Data by Zhuoyi Yang, Zeyi Li, Haitao Zhang, Wei Zhang, Yanwei Wang, Yihang Huang

“…Firstly, these data are low signal-to-noise ratio data in which the point patterns are mixed with a large number of normal point clusters; thus, it is difficult to recognize point patterns from these unstructured data using traditional clustering or machine learning methods. …”
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Neuromorphic devices assisted by machine learning algorithms by Ziwei Huo, Qijun Sun, Jinran Yu, Yichen Wei, Yifei Wang, Jeong Ho Cho, Zhong Lin Wang

“…Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. …”
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Global patterns and drivers of soil dissolved organic carbon concentrations by T. Ren, T. Ren, A. Cai

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Performance optimization of D2 tool steel machining using novel multi-material cladded electrodes in electric discharge machining by Shoaib Mohammad, M. Wasiq Habib, Ammara Kanwal, Muhammad Asim, Muhammad Farooq, Imran Zahid, Noreen Sher Akbar, Hussein Togun, Fahid Riaz

“…Additionally, the differences as created on the machined surface by each of the material in either configuration yield interesting results which indicate feasibility of generating patterns/impressions in a single experiment run. …”
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Advanced Cancer Classification Using AI and Pattern Recognition Techniques by Haddou Bouazza Sara, Haddou Bouazza Jihad

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Analyzing the Application of Machine Learning in Anemia Prediction by Li Yuxi

“…This paper examines decision trees, random forests, support x'ector machines, and neural networks. emphasizing their efficacy in identifying patterns and risk factors associated with anemia. …”
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Application of machine learning to growth model in fisheries by Semra Benzer, Recep Benzer, Ali Gül

“…Their limitations in capturing nonlinear patterns necessitate alternative approaches. Machine learning (ML) techniques have recently gained attention as a powerful tool for enhancing predictive accuracy in biological studies. …”
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Geometric properties of quantum entanglement and machine learning by S. V. Zuev

“…Fast data analysis based on hidden patterns is one of the main issues for adaptive artificial intelligence systems development. …”
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Flax Harvesting Technologies for Flax Harvesting Machines by V. G. Chernikov, R. A. Rostovtsev, V. Yu. Romanenko

“…(Research purpose) To establish patterns and the degree of correlation between the qualitative operation indicators (pulling and deseeding quality, flax line stretching); design parameters; machine dynamic properties and harvesting conditions (height and density of flax stem, field surface, thickness and unevenness of flax straw, etc.). …”
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Classification of musculoskeletal pain using machine learning by Dalia Mohamed Fouad, Marwa Mahmoud Mahfouz, Mohammed Mostafa Mohamed, Mahmoud Yassin Elzanaty, Tarek Abd El-Hafeez

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Evaluating Machine Unlearning: Applications, Approaches, and Accuracy by Zulfiqar Ali, Asif Muhammad, Rubina Adnan, Tamim Alkhalifah, Sheraz Aslam

“…ABSTRACT Machine learning (ML) enables computers to learn from experience by identifying patterns and trends. …”
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Meta-Learning-Based LSTM-Autoencoder for Low-Data Anomaly Detection in Retrofitted CNC Machine Using Multi-Machine Datasets by Ji-Min Woo, Seong-Hyeon Ju, Jin-Hyeon Sung, Kyung-Min Seo

“…Retrofitted systems suffer from two critical obstacles: a severe scarcity of labeled data and substantial variability in operational patterns across machines and products. To overcome these issues, this study introduces a novel anomaly detection framework that integrates Model-Agnostic Meta-Learning (MAML) with a Long Short-Term Memory Autoencoder (LSTM-Autoencoder) under a multi-machine-based task formulation. …”
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Fine identification of vegetation types in open pit mining regions using combined UAV RGB imagery and LiDAR point cloud data by Suling He, Yuncheng Deng, Jinliang Wang, Mengjia Luo

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APPLICATION OF THE SUPPORT VECTOR MACHINE, LIGHT GRADIENT BOOSTING MACHINE, ADAPTIVE BOOSTING, AND HYBRID ADABOOST-SVM MODEL ON CUSTOMERS CHURN DATA by Felice Elena, Robyn Irawan, Benny Yong

“…Customer churn analysis is the process of identifying a pattern or trend in churning customers. In order to classify and predict churning customers, machine learning techniques are required to build the classifier model. …”
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Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning by Lucijano Berus, Jernej Hernavs, David Potocnik, Kristijan Sket, Mirko Ficko

“…Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. …”
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