Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors by Heba-Allah Ibrahim El-Azab, R. A. Swief, Noha H. El-Amary, H. K. Temraz

“…Where the whole database is split into four seasons based on demand patterns. This article’s integrated model is built on techniques for machine and deep learning methods: Adaptive Neural-based Fuzzy Inference System, Long Short-Term Memory, Gated Recurrent Units, and Artificial Neural Networks. …”
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Mapping Nationwide Subfield Division Dynamics in Saudi Arabia Using Temporal Patterns of Sentinel-2 NDVI and Machine Learning by Ting Li, Oliver Miguel Lopez Valencia, Matthew F. McCabe

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Combining the SHAP Method and Machine Learning Algorithm for Desert Type Extraction and Change Analysis on the Qinghai–Tibetan Plateau by Ruijie Lu, Shulin Liu, Hanchen Duan, Wenping Kang, Ying Zhi

“…In this work, five different machine learning algorithms are used to classify different desert types on the Qinghai–Tibetan Plateau (QTP), and their classification performance is evaluated on the basis of their classification results and classification accuracy. …”
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Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…The purpose of this study was to evaluate the performance accuracy of the proposed machine learning algorithms by spatial cross-validation method in combination with the cellular automata model to simulate urban growth.Material and methods: In this study, to analyze urban land-use changes, Landsat satellite images related to the years 1997, 2006, and 2015 were classified using the support vector machine algorithm. …”
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Enhancing Education with Machine Learning: Predicting Student Readability Scores by Claire Bell

“…The research leverages a dataset of 1,000 English texts to evaluate and compare the performance of RFC, the Sooty Tern Optimization Algorithm (STOA), and the Gold Rush Optimizer (GRO) in predicting readability ratings. …”
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Pattern and semantic analysis to improve unsupervised techniques for opinion target identification by Khairullah khan, Ashraf Ullah, Baharum Baharudin

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Learning control system of lifting machine motors by V. I. Zimovets, A. S. Chirva, O. I. Marishchenko

“…To increase the operational reliability and service life of a mine electric lifting machines the article offers an information and machine learning algorithm for extreme functional control systems with electric hyprnspherical classifier. …”
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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

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Predictors of depression among Chinese college students: a machine learning approach by Lin Luo, Junfeng Yuan, Chenghan Wu, Yanling Wang, Rui Zhu, Huilin Xu, Luqin Zhang, Zhongge Zhang

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USING REINFORCEMENT LEARNING ALGORITHMS FOR UAV FLIGHT OPTIMIZATION by O. Dutsiak, V. Yuzevych

“…Machine learning gives UAVs the ability to discern patterns and make predictions based on data, thus bypassing the need for pre-programmed instructions during autonomous flight. …”
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Using Machine Learning to Identify the Dynamic Evolution Patterns of Negative Emotions in Perinatal Women: A Longitudinal Study in Southwest China by Yuan Zhang, Wenlong Li, Jian Zou, Guohui Yang, Xiaoni Zhong, Biao Xie

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Snow Distribution Patterns Revisited: A Physics‐Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub‐Arctic by R. L. Crumley, C. L. Bachand, K. E. Bennett

“…Abstract Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year‐to‐year patterns due to local topographic, weather, and vegetation characteristics. …”
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Algorithms and Methods for Individual Source Camera Identification: A Survey by Jaroslaw Bernacki, Rafal Scherer

“…This paper presents a comprehensive review of the existing methods and algorithms used for this purpose. It discusses approaches based on matrix noise analysis, including methods utilizing sensor pattern noise, photo response non-uniformity, statistical methods, aberrations analysis, as well as modern techniques based on deep neural networks and machine learning. …”
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Identification of anoikis-related genes classification patterns and immune infiltration characterization in chronic rhinosinusitis with nasal polyps based on machine learning by Ziqi Chen, Lingmei Qu, Qing Hao, Shuang Teng, Shuo Liu, Qin Wu, Hongtian Yi, Xianji Shen, Liang Li, Zhaonan Xu, Yanan Sun

“…Immune cell infiltration was analysed using the CIBERSORT algorithm. CRSwNP samples were classified via consensus clustering. …”
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Utilizing machine and deep learning algorithms to identify learning-related features in electroencephalography data during second language acquisition by Talal A. Aldhaheri, Sonali B. Kulkarni, Pratibha R. Bhise, Mohammed Tawfik

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High-performance glass classification using advanced machine learning and deep learning algorithms with a comprehensive feature analysis by Mohammed Bouziane, Abdelghani Bouziane, Samia Larguech, Khatir Naima, Mohammad Salman Haque, Younes Menni

“…Advanced learning algorithms like Random Forest (RF), XGBoost, Support Vector Machines, and Bidirectional Long Short-Term Memory (BiLSTM) networks are applied for classification. …”
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Prediction and management of physical injuries caused by gym equipment and facilities using a Support Vector Machine (SVM) algorithm by Javad Shahlaei Bagheri

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Classification of Anxiety Levels of IGD Patients at RSU Royal Prima Medan Using Support Vector Machine (SVM) Algorithm by Kharisma Gunanta Ginting, Nugroho Prasetyo, Al Vino Gunawan, Magdalena Sihombing, Adli Abdillah Nababan

“…This study aims to develop a patient anxiety level classification model in the ED using the Support Vector Machine (SVM) algorithm with the application of the Synthetic Minority Oversampling Technique (SMOTE) to address the class imbalance issue. …”
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Multi objective optimization of FDM 3D printing parameters set via design of experiments and machine learning algorithms by Antonio Panico, Alberto Corvi, Luca Collini, Corrado Sciancalepore

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