RACH Traffic Prediction in Massive Machine Type Communications by Hossein Mehri, Hani Mehrpouyan, Hao Chen

“…Traffic pattern prediction has emerged as a promising approach for efficiently managing and mitigating the impacts of event-driven bursty traffic in massive machine-type communication (mMTC) networks. …”
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Application of Machine Learning Techniques for Bearing Fault Diagnosis by Sarra Eddai, Nabih Feki, Ahmed Ghorbel, Abdelkhalak El Hami, Mohamed Haddar

“…Machine learning enhances machine diagnostics through advanced data analysis, pattern recognition, and fault prediction. …”
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Analyzing the Sedimentological Parameters and Size Distribution Pattern in Hamoun Hirmand Sediments by Samira Zandifar, Adele Jamalian, Maryam Naemi

“…Identifying the characteristics and distribution pattern of sediments in the Hamon lagoon bed is the primary objective of this research. …”
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Addressable and perceptible dynamic reprogram of ferromagnetic soft machines by Yuxuan Sun, Boxi Sun, Xiang Cui, Weihua Li, Yue Zhang, Li He, Shutong Nong, Zhengqing Zhu, Jiyang Wu, Dongxiao Li, Xingxiang Li, Shiwu Zhang, Xiangyang Li, Mujun Li

“…This reprogramming process is equally effective for single or multiple machines, enabling versatile multi-pattern deformation of individual machines and cooperation of multiple ones. …”
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Gender classification performance optimization based on facial images using LBG-VQ and MB-LBP by Faruq Abdul Hakim, Tio Dharmawan, Muhamad Arief Hidayat

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ESTIMATING EFFICIENCY INCREASE PATTERNS OF GRAIN MATERIAL SEPARATION IN SEED REFINER by Andrey Vladimirovich Butovchenko

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Evaluating urban traffic dynamics: a study of mobility in Vellore city by S. Parkavi, A. Parthiban

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APPLYING SOME MATCHING ALGORITHMS FOR SEQUENCE IGNATURE TO ANALYZE AND DETECT ENTRIES INTO SYSTEM NETWORKS by Pham Nguyen Huy Phuong, Dang Le Nam

“…Additionally, tools for network monitoring such as open source munintools,are also used to analyze and evaluate the performance of network-attack. Next, the time of pattern identification in the Snort's machine, and the performance of Snort as well as the number of packets passing through Snort, the amount of alerts per second, connection speed in real time, the percentage of received data in pattern matching process, etc. are also measured based on intelligent algorithms built in Snort. …”
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Climate Change Analysis in Malaysia Using Machine Learning by Anishalache Subramanian, Naveen Palanichamy, Kok-Why Ng, Sandhya Aneja

“…Despite these challenges, many studies still predominantly rely on traditional statistical methods, which limit their capacity for making accurate climate predictions and developing effective policy solutions.This study effectively addresses the existing gap in research by analyzing extensive historical climate data using advanced machine learning (ML) techniques. The primary focus is on accurately forecasting trends in both precipitation patterns and surface air temperature fluctuations. …”
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Machine Learning Analysis of Informal Minibus Taxi Driving by Cele Nomfundo, Kibangou Alain, Musakwa Walter

“…This paper presents a machine learning analysis of driving behaviors in informal minibus taxis, focusing on both controlled and uncontrolled environments. …”
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Machine Learning-Based Ransomware Classification of Bitcoin Transactions by Suleiman Ali Alsaif

“…The proposed approach makes use of three supervised machine learning methods to learn the distinctive patterns in Bitcoin payment transactions, namely, logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost). …”
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Simulation of granular flows and machine learning in food processing by X. Cui, D. Adebayo, H. Zhang, M. Howarth, A. Anderson, T. Olopade, K. Salami, S. Farooq

“…Our DEM and continuum simulation results show good agreement in modeling the chute flow, and the machine learning approach demonstrates promising potential for predicting flow patterns. …”
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Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

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Research on House Price Prediction based on Machine Learning by Yang Xiangjun

“…This study aims to evaluate and compare the performance of various machine learning models on the task of house price prediction. …”
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Forecasting Solar Energy Production Using Machine Learning by C. Vennila, Anita Titus, T. Sri Sudha, U. Sreenivasulu, N. Pandu Ranga Reddy, K. Jamal, Dayadi Lakshmaiah, P. Jagadeesh, Assefa Belay

“…For reliable predictions of solar electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machine learning and statistical approaches is suggested for predicting future solar energy generation. …”
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Machine Learning Methods for Weather Forecasting: A Survey by Huijun Zhang, Yaxin Liu, Chongyu Zhang, Ningyun Li

“…In this survey, we critically examines machine learning (ML)-based weather forecasting methods, which demonstrate exceptional capability in handling complex, high-dimensional datasets and leveraging large volumes of historical and real-time data, enabling the identification of subtle patterns and relationships among weather variables. …”
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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 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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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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