Machine learning for experimental design of ultrafast electron diffraction by Mohammad Shaaban, Sami El-Borgi, Aravind Krishnamoorthy

“…We demonstrate that machine learning methods based on Convolutional Neural Networks trained on synthetic and experimental diffraction patterns can perform real-time analysis of diffraction data to resolve dynamical processes in a representative material, $${\textrm{MoTe}_{2}}$$ , and identify signs of material damage. …”
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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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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-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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Machine Learning Analysis of Informal Minibus Taxi Driving by Cele Nomfundo, Kibangou Alain, Musakwa Walter

“…Informal minibus taxis play a crucial role in urban transportation, particularly in developing countries, yet their driving patterns and safety implications remain under-explored. …”
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Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

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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

“…Based on the simulation data, we apply machine learning techniques such as Random Forest, Linear Regression, and Ridge Regression to evaluate the effectiveness of these models in predicting granular flow patterns. …”
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L’âme des machines dans The Ghost in the Shell by Bounthavy Suvilay

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Machine Learning Algorithms Performance Evaluation for Intrusion Detection by Shyla ., Kapil Kumar, Vishal Bhatnagar

“…The detection of Intrusion is the major research problem faced in the area of information security, the objective is to scrutinize threats or intrusions to secure information in the network Intrusion detection system (IDS) is one of the key to conquer against unfamiliar intrusions where intruders continuously modify their pattern and methodologies. In this paper authors introduces Intrusion detection system (IDS) framework that is deployed over KDD Cup99 dataset by using machine learning algorithms as Support Vector Machine (SVM), Naïve Bayes and Random Forest for the purpose of improving the precision, accuracy and recall value to compute the best suited algorithm.…”
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Improvement of signal detection based on using machine learning by Bassam Abd

“…The proposed system depends on the support vector machine (SVM). The SVM is one of the most popular learning algorithms in different fields, such as signal processing, image processing, communication, and pattern recognition. …”
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Research on House Price Prediction based on Machine Learning by Yang Xiangjun

“…For the house price prediction task, Random Forests generally perform better than Linear Regression and Single Decision Tree because they can better capture complex patterns in the data and reduce the risk of overfitting. …”
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Semantic Classification of Car Styling Using Machine Learning by Hung-Hsiang Wang, Yen-Ting Lu

“…The classification model trained with the Binary Pattern Pyramid Filter and the Random Forest classifier achieved an accuracy of 84.6%. …”
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Student Performance Prediction Using Machine Learning Algorithms by Esmael Ahmed

“…Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. …”
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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 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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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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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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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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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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