An ensemble deep learning framework for energy demand forecasting using genetic algorithm-based feature selection. by Mohd Sakib, Tamanna Siddiqui, Suhel Mustajab, Reemiah Muneer Alotaibi, Nouf Mohammad Alshareef, Mohammad Zunnun Khan

“…This study proposes an ensemble approach that integrates a genetic algorithm with multiple forecasting models to optimize feature selection. …”
Get full text

Rainfall Prediction in Khorasan Razavi Stations Using a Hybrid Neural Network and Genetic Algorithm Approach by Mahdi Naseri, Mahsa Mardani

“…This study proposes a novel hybrid approach, combining the Non-linear Auto Regressive with eXogenous inputs (NARX) neural network with a Genetic Algorithm (GA) for parameter optimization, aiming to improve daily rainfall prediction in Khorasan Razavi province, Iran. …”
Get full text

A comprehensive review of rice mapping from satellite data: Algorithms, product characteristics and consistency assessment by Husheng Fang, Shunlin Liang, Yongzhe Chen, Han Ma, Wenyuan Li, Tao He, Feng Tian, Fengjiao Zhang

“…We found that 1) rice fields with simple cropping patterns and intensive cultivation can be correctly recognized using various algorithms; 2) different products share low consistency in fragmented rice fields 3) the prevalence of clouds and complicated rice cropping patterns or diverse growing environments in subtropical and tropical regions poses challenges to accurate rice mapping. …”
Get full text

Hyperspectral Estimation of Chlorophyll Content in Ginseng Fruit Leaves Based on Wavelet Transform and VCPA-GA Algorithm by Guo Jinfeng, Zhang Zhicong, Umut Hasan, Zhou Zhongye, Xu Wenyu, Yusup Ahmat

“…We combined Variable Combination Pattern Analysis (VCPA) with Genetic Algorithm (GA), employing the combined VCPA-GA algorithm to extract sensitive bands from the full spectrum and each decomposed layer of the ginseng fruit leaf. …”
Get full text

Prediction of the Reaming Torque Using Artificial Neural Network and Random Forest Algorithm: Comparative Performance Analysis by M. C. Rakshith, Raghavendra C. Kamath, G. S. Vijay

“…In this regard, the ability of traditional statistical tools to identify intricate correlations and patterns in reaming operation data is limited. To overcome these issues, machine learning methods such as the Artificial Neural Network (ANN) provide reliable options. …”
Get full text

The use of artificial intelligence to analyze and optimize financial flows by М. А. Miakisheva

Get full text

Multi class aerial image classification in UAV networks employing Snake Optimization Algorithm with Deep Learning by Alanoud Al Mazroa, Nuha Alruwais, Muhammad Kashif Saeed, Kamal M. Othman, Randa Allafi, Ahmed S. Salama

“…Finally, the kernel extreme learning machine (KELM)-based classification algorithm is implemented to identify and classify the presence of various classes in aerial images. …”
Get full text

Toward Sustainable Campus Energy Management: A Comprehensive Review of Energy Management, Predictive Algorithms, and Recommendations by Noor Islam Jasim, Saraswathy Shamini Gunasekaran, Nouar AlDahoul, Ali Najah Ahmed, Ahmed El-Shafie, Mohsen Sherif, Moamin A Mahmoud

Get full text

GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds by D. Oakley, D. Oakley, C. Loiselet, T. Coowar, T. Coowar, V. Labbe, J.-P. Callot

“…In this paper, we present automated workflows for detecting geological folds from map data using both unsupervised and supervised machine learning. For the unsupervised case, we use regular expression matching to identify map patterns suggestive of folds along lines crossing the map. …”
Get full text

Predicting Weather Disruptions for the ICC Champions Trophy 2025 in Pakistan Using Machine Learning and Data Analytics by Syeda Faiza Nasim, Umm-e-Kulsoom, Syeda Alishba Fatima, Salka Naushad

“…Our approach comprised combining real-time analytics with historical weather data from Open-Meteo, as well as using Python tools and the Machine Learning algorithm to predict rain during a game. …”
Get full text

Enhanced Gold Ore Classification: A Comparative Analysis of Machine Learning Techniques with Textural and Chemical Data by Fabrizzio Rodrigues Costa, Cleyton de Carvalho Carneiro, Carina Ulsen

“…Several supervised and unsupervised machine learning methods and applications integrate a wide variety of algorithms that aim at the efficient recognition of patterns and similarities and the ability to make accurate and assertive decisions. …”
Get full text

Research on Rolling Bearing Fault Diagnosis Method Based on MPE and Multi-Strategy Improved Sparrow Search Algorithm Under Local Mean Decomposition by Haodong Chi, Huiyuan Chen

“…Furthermore, considering the instability of classification caused by the empirical setting of hidden layer nodes in the extreme learning machine (ELM), a multi-strategy improved sparrow search algorithm is proposed to optimize ELM parameters. …”
Get full text

Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS by Ruizhi Zhang, Dayong Zhang, Bo Shu, Yang Chen

“…A dataset comprising 2700 known geological hazard locations in Yibin City was analyzed to extract key environmental and topographic features influencing hazard susceptibility. Several machine learning models were evaluated, including random forest, XGBoost, and CatBoost, with model optimization performed using the Sparrow Search Algorithm (SSA) to enhance prediction accuracy. …”
Get full text

Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data by Pei-Chun Lin, Kuan-Yen Chen, Jenhung Wang

“…Unlike traditional accident-reporting systems, the ETC infrastructure provides a uniquely comprehensive and precise dataset that captures spatiotemporal traffic patterns and environmental conditions across the national highway network. …”
Get full text

A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes by J. Li, A. Geiss, Z. Feng, L. R. Leung, Y. Qian, W. Cui, W. Cui

Get full text

Machine Learning Classification of 3D Intracellular Trafficking Using Custom and Imaris-Derived Motion Features by Oleg Kovtun

“…The approach employs an extended gradient-boosted decision trees classifier trained on an array of synthetic trajectories designed to simulate diffusion behaviors typical of intracellular environments. <b>Results:</b> The machine learning classifier demonstrated a classification accuracy of over 90% on synthetic datasets, effectively capturing and distinguishing complex diffusion patterns. …”
Get full text

ML‐UrineQuant: A machine learning program for identifying and quantifying mouse urine on absorbent paper by Warren G. Hill, Bryce MacIver, Gary A. Churchill, Mariana G. DeOliveira, Mark L. Zeidel, Marcelo Cicconet

“…We have developed a machine learning algorithm based on Region‐based Convolutional Neural Networks (Mask‐RCNN) that was trained in object recognition to detect and quantitate urine spots across a broad range of sizes—ML‐UrineQuant. …”
Get full text

Machine Learning Insights into the Last 400 Years of Etna Lateral Eruptions from Historical Volcanological Data by Arianna Beatrice Malaguti, Claudia Corradino, Alessandro La Spina, Stefano Branca, Ciro Del Negro

“…Here, we applied a machine learning technique to automate the analysis of these datasets, handling intricate patterns that are not easily captured by explicit commands. …”
Get full text

Improving ICESat-2 photon classification and tree height estimation using Moran's I and machine learning by Mei-Kuei Lu, Sorin Popescu, Lonesome Malambo

“…However, its ATL08 data product, designed for canopy height and terrain classification, exhibits classification inaccuracies due to algorithm limitations and noise contamination. This study aimed to address these challenges by leveraging local spatial autocorrelation, Moran's I, as a feature input in machine learning methods to enhance photon classification accuracy. …”
Get full text

Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques by Joan D. Gonzalez-Franco, Alejandro Galaviz-Mosqueda, Salvador Villarreal-Reyes, Jose E. Lozano-Rizk, Raul Rivera-Rodriguez, Jose E. Gonzalez-Trejo, Alexei-Fedorovish Licea-Navarro, Jorge Lozoya-Arandia, Edgar A. Ibarra-Flores

“…By enabling more targeted interventions for high-risk patients, our unsupervised segmentation approach focuses on intrinsic data patterns rather than predefined diagnostic labels, serves as a powerful complement to traditional risk assessment tools.…”
Get full text