Capturing drug use patterns at a glance: An n-ary word sufficient statistic for repeated univariate categorical values. by Gabriel J Odom, Laura Brandt, Clinton Castro, Sean X Luo, Daniel J Feaster, Raymond R Balise, CTN-0094 Team

“…Further, machine readable use pattern summaries are a standardized method to calculate treatment outcomes and are therefore useful to all future SUD clinical trials. …”
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Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction by Shakeel Ahmad, Sheikh Muhammad Saqib, Asif Hassan Syed, Nashwan Alromema, Ali Kararay

“…However, in the context of the Arabic language, studies predominantly resort to machine learning or deep learning algorithms for sentiment and emotion analysis, often neglecting the utilization of current pre-trained language models. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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

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

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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. …”
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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. …”
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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach by Yang Chen, Xiaojie Su, Fei Yao, Yushan Liu, Hua Xing, Yubin Ju, Zhiran Kang, Wuquan Sun, Lijun Yao, Li Gong

“…Then both datasets were randomly assigned to a training and validating dataset and a test dataset in a ratio of 7:3. Six machine learning algorithms were selected and trained by our dataset. …”
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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. …”
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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

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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. …”
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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

“…We normalized and prepared the data, then we employed PCA and UMAP to reduce dimensionality and facilitate visualization. Using the k-means algorithm, we segmented the patients into distinct groups based on their clinical features. …”
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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. …”
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Predicting Accident Severity on Taiwan Highways Using Machine Learning and Electronic Toll Collection (ETC) Data by Pei-Chun Lin, Kuan-Yen Chen, Jenhung Wang

“…This study aims to develop a machine learning-based framework for predicting the severity of highway traffic accidents by leveraging high-resolution data from Taiwan’s Electronic Toll Collection (ETC) system. …”
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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. …”
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Identification of biomarkers related to Escherichia coli infection for the diagnosis of gastrointestinal tumors applying machine learning methods by Tingting Ge, Wei Wang, Dandan Zhang, Xubo Le, Lumei Shi

“…Conclusions: Overall, we identified and validated 8 robust genes related to E. coli applying bioinformatics and machine learning algorithms, providing theoretical foundations for the relationship between E. coli-related dysbiosis and gastrointestinal tumors.…”
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