Weather type reconstruction using machine learning approaches by L. Pfister, L. Pfister, L. Wilhelm, L. Wilhelm, Y. Brugnara, Y. Brugnara, Y. Brugnara, N. Imfeld, N. Imfeld, S. Brönnimann, S. Brönnimann

“…<p>Weather types are used to characterise large-scale synoptic weather patterns over a region. Long-standing records of weather types hold important information about day-to-day variability and changes in atmospheric circulation and the associated effects on the surface. …”
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Machine learning and microfluidic integration for oocyte quality prediction by Hassan Saffari, Davood Fathi, Peyman Palay, Hamid Gourabi, Rouhollah Fathi

“…Among them, Agglomerative Clustering yielded the best performance (Silhouette = 0.49, Davies–Bouldin = 0.73), showing meaningful grouping patterns among oocytes. These results demonstrate that integrating biomechanical profiling with machine learning can significantly improve the objectivity and accuracy of oocyte quality prediction. …”
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Interpretable Machine Learning Models for PISA Results in Mathematics by Ismael Gomez-Talal, Luis Bote-Curiel, Jose Luis Rojo-Alvarez

“…By preprocessing the PISA dataset, we categorized students into Low, Medium, and High proficiency levels and employed various binary classification models to discern predictive patterns. In addition, a stacking meta-model integrating the strengths of eight distinct predictive models was developed to enhance prediction accuracy. …”
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MACHINE LEARNING TECHNIQUES FOR RETINOPATHY DETECTION IN DIABETIC PATIENTS by Ajay Kushwaha, Ahankari Sachin Suresh, Chennoju Phanindra, Anil Kumar Sahu, Devanand Bhonsle, Yamini Chouhan

“…A range of techniques, from traditional clinical methods to advanced machine learning algorithms, are employed to detect retinopathies in diabetic patients. …”
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CNC machining data repository: Geometry, NC code &amp; high-frequency energy consumption data for aluminum and plastic machiningMendeley Data by Markus Brillinger, Muaaz Abdul Hadi, Stefan Trabesinger, Johannes Schmid, Florian Lackner

“…Potential use cases include optimizing machining parameters for energy reduction based on power consumption patterns, and enhancing digital twin models with real-world machining data. …”
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A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resili... by Yu Chen, Wenxing You, Lu Ou, Hui Tang

“…Supervised learning uses labeled data while unsupervised learning detects patterns in unlabeled data. Reinforcement learning optimizes rewards through trial-and-error interactions. …”
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Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study by Vladimir Koković, Kosta Pavlović, Andjela Mijanović, Slavko Kovačević, Ivan Mačužić, Vladimir Božović

“…This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. …”
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Review of Control Techniques for Dual Three-phase PMSM Drives with Low Carrier Ratios by Minrui Gu, Zheng Wang, Ming Cheng, Weifeng Su, Linyue Shang, Po Liu

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Pneumatic Sucker-Rod Machine for Dispersing Solid Mineral Fertilizers by V. A. Makarov, O. I. Zhuravleva, A. V. Shemyakin

“…It was determined that the quality of fertilizer dispersing with a pneumatic sucker-rod machine is mainly aff ected by the uniformity of their supply by reel-type units to the air stream and rods with holes, and the pattern of fertilizer distribution to ejectors depending on the distance between them. …”
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Machine learning-guided field site selection for river classification by Zhihao Wang, Gregory Brian Pasternack, Yufang Jin, Costanza Rampini, Serena Alexander, Nikhil Kumar, Rune Storesund, K. Martin Perales, Christopher Lim, Stephanie Moreno, Igor Lacan

“…This leads to an inadequate exploration of stream characteristics, resulting in incomplete or inaccurate classification. Machine learning has been recognized for discovering and extracting streams’ geomorphic patterns efficiently and accurately from data, but its application in field site sampling design is still in its infancy. …”
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Quantum Neural Network Based Machine Translator for Hindi to English by Ravi Narayan, V. P. Singh, S. Chakraverty

“…This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. …”
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Predicting Crime Time Intervals Using Machine Learning Models by Arief Deswandi, Widi Hastomo

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Interpretable machine learning models classify minerals via spectroscopy by R. Smith, Tyler L. Spano, Marshall McDonnell, Lance Drane, Ian Gibbs, Andrew Miskowiec, J. L. Niedziela, Ashley E. Shields

“…Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. …”
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Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions by Aikaterini Sakagianni, Christina Koufopoulou, Petros Koufopoulos, Sofia Kalantzi, Nikolaos Theodorakis, Maria Nikolaou, Evgenia Paxinou, Dimitris Kalles, Vassilios S. Verykios, Pavlos Myrianthefs, Georgios Feretzakis

“…It aims to determine the patterns from AMR gene data that are clinically relevant and, in public health, capable of informing strategies. …”
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Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction by Mutiara Khairunisa, Desak Made Sidantya Amanda Putri, I Gusti Ngurah Lanang Wijayakusuma

“…LSTM excelled in capturing complex temporal patterns in menstrual cycle data, while CNN effectively identified key patterns, and Decision Tree offered interpretability despite lower performance. …”
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The application and research of machine learning in malicious encrypted traffic detection by Tian Rui, Zhang Yaqin, Dong Wei, Li Zhicheng, Feng Zhi

“…The findings indicate that machine learning significantly improves the accuracy of malicious behavior detection, particularly in complex feature extraction and the identification of new attack patterns.…”
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Exploratory Data Analysis of the Monkeypox Virus Using Machine Learning by Kiran Dhanaji Kale, Pranav More, Prabhdeep Singh

“…This will be very helpful for researchers working on machine learning and seeking patterns for Monkeypox to easily predict Monkeypox cases in their country.…”
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Prediction of Customer Switching Using Support Vector Machine Method by Abu Tholib, Selfia Hafidatus Sholeha, Qurrotu Aini

“…The Neural Network model can be trained with better patterns to detect data and achieve high accuracy. …”
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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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Identifying the quantum properties of hadronic resonances using machine learning by Jakub Filipek, Shih-Chieh Hsu, John Kruper, Kirtimaan Mohan, Benjamin Nachman

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