Exploring explainable machine learning algorithms to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 by Mequannent Sharew Melaku, Nebebe Demis Baykemagn, Lamrot Yohannes, Adem Tsegaw Zegeye

“…This study aimed to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 using machine learning algorithms. Data from Demographic and Health Surveys covering 147,466 men were analyzed. …”
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Predicting Live Weight for Female Rabbits of Meat Crosses From Body Measurements Using LightGBM, XGBoost and Support Vector Machine Algorithms by Hasan Önder, Cem Tirink, Taras Yakubets, Andriy Getya, Mykhalio Matvieiev, Ruslan Kononenko, Uğur Şen, Çağri Özgür Özkan, Tolga Tolun, Fahrettin Kaya

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AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector by Şenda Yıldırım, Ahmet Deniz Yücekaya, Mustafa Hekimoğlu, Meltem Ucal, Mehmet Nafiz Aydin, İrem Kalafat

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Environmental Sensitivity in AI Tree Bark Detection: Identifying Key Factors for Improving Classification Accuracy by Charles Warner, Fanyou Wu, Rado Gazo, Bedrich Benes, Songlin Fei

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TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds by Jonathan Henrich, Jan van Delden, Dominik Seidel, Thomas Kneib, Alexander S. Ecker

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Automated irrigation of apple trees based on dendrometer sensors by Thainná Waldburger, Thomas Anken, Marianne Cockburn, Achim Walter, Matthias Hatt, Camilo Chiang, Hassan-Roland Nasser

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The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population–Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis... by Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Refilwe Nancy Phaswana-Mafuya

“…Further investigation is needed to bridge this knowledge gap and inform evidence-based interventions to improve HIV testing. …”
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Application of machine learning algorithms to model predictors of informed contraceptive choice among reproductive age women in six high fertility rate sub Sahara Africa countries by Mequannent Sharew Melaku, Lamrot Yohannes, Berhanu Sharew, Mintesnot Hawaz Derseh, Eliyas Addisu Taye

“…Data cleaning, weighting, and descriptive statistical analyses were conducted using STATA version 17 and Excel 2019, while machine learning analysis was performed using Python 3.12. Furthermore, Random Forest, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Naïve Bayes, Decision Tree, Logistic Regression, and Adaptive Boosting (AdaBoost) were employed to predict informed contraceptive choice and to identify its predictors. …”
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Hard‐state Protocol Independent Multicast—Source‐Specific Multicast (HPIM‐SSM) by Ana Catarina Grilo, Pedro Oliveira, Rui Valadas

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Impact of Parameters and Tree Stand Features on Accuracy of Watershed-Based Individual Tree Crown Detection Method Using ALS Data in Coniferous Forests from North-Eastern Poland by Marcin Kozniewski, Łukasz Kolendo, Szymon Chmur, Marek Ksepko

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Blockchain-Based Decentralized Identity Management System with AI and Merkle Trees by Hoang Viet Anh Le, Quoc Duy Nam Nguyen, Nakano Tadashi, Thi Hong Tran

“…By employing Merkle Trees, the BDIMS ensures secure authentication with service providers without the need to disclose any personal information. …”
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Study protocol for a pragmatic randomised controlled trial in Belgian primary care and hospital settings on the effectiveness of an eHealth self-management support programme consis... by Josefien van Olmen, An De Groef, Nele Devoogdt, Lore Dams, Bart Morlion, Mira Meeus, G Lorimer Moseley, Louise K Wiles, Peter Hibbert, Patrick Neven, Ines Nevelsteen, Steffen Fieuws, Geert Crombez, Michel Mertens, Ceren Gursen, Wiebren Tjalma, Lander Willem, Lauren C Heathcote, Mark Catley, Anna Vogelzang, Marthe Van Overbeke, Emma Tack, Sophie Van Dijck, Annick L De Paepe, Rani Vanhoudt, Davina Wildemeersch, Femke De Backere

“…This delivery mode is believed to reduce barriers to pain self-management by providing timely, safe and cost-effective assistance addressing the biopsychosocial needs of patients. Utilising a chatbot format, the eHealth programme delivers pain science education and promotes physical activity (PA), personalised through decision-tree-based algorithms to support pain self-management. …”
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Applications of Multi-Robotic Arms to Assist Agricultural Production: A Review by Xiaojian Gai, Chang Xu, Yajia Liu, Qingchun Feng, Shubo Wang

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Machine Learning Predictive Models for Survival in Patients with Brain Stroke by Solmaz Norouzi, Samira Ahmadi, Shayeste Alinia, Farshid Farzipoor, Azadeh Shahsavari, Ebrahim Hajizadeh, Mohammad Asghari Jafarabadi

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Intelligent Course Plan Recommendation for Higher Education: A Framework of Decision Tree by Xiaoliang Chen, Jianzhong Zheng, Yajun Du, Mingwei Tang

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Does machine learning outperform logistic regression in predicting individual tree mortality? by Aitor Vázquez-Veloso, Astor Toraño Caicoya, Felipe Bravo, Peter Biber, Enno Uhl, Hans Pretzsch

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Extraction of individual tree attributes using ultra-high-density point clouds acquired by low-cost UAV-LiDAR in Eucalyptus plantations by Mei Zhou, Chungan Li, Zhen Li

“…Methods The framework consists of three independent yet interrelated approaches. Firstly, the tree trunks were detected using an approach based on the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm. …”
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The accuracy of image-based individual tree crown detection and delineation across vegetation types by N. Pucino, T. McVicar, S. Levick, A. van Dijk

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Traffic Classification in Software-Defined Networking Using Genetic Programming Tools by Spiridoula V. Margariti, Ioannis G. Tsoulos, Evangelia Kiousi, Eleftherios Stergiou

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Improved Cylinder-Based Tree Trunk Detection in LiDAR Point Clouds for Forestry Applications by Shaobo Ma, Yongkang Chen, Zhefan Li, Junlin Chen, Xiaolan Zhong

“…The study showed the following results: (1) The average difference between the inlier rates of tree trunks and non-tree points for the three sample plots using RANSAC-CyF were 0.59, 0.63, and 0.52, respectively, which were significantly higher than those using the Least Squares Circle Fitting (LSCF) algorithm and the Random Sample Consensus Circle Fitting (RANSAC-CF) algorithm (<i>p</i> < 0.05). (2) RANSAC-CyF required only 2 and 8 clusters to achieve a 100% detection success rate in Plot 1 and Plot 2, while the other algorithms needed 26 and 40 clusters. (3) The effective distance threshold range of RANSAC-CyF was more than twice that of the comparison algorithms, maintaining stable inlier rates above 0.9 across all tilt angles. (4) The RANSAC-CyF algorithm still achieved good detection performance in the challenging Plot 3. …”
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