Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine by Dagnew Yebeyen, Binyam Tesfaw Hailu, Worku Zewdie, Temesgen Abera, Gudeta W. Sileshi, Melaku Getachew, Sileshi Nemomissa

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Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments by Leyang Zhao, Weixi Wang, Qizhi He, Li Yan, Xiaoming Li

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Characteristics and Rapid Prediction of Seismic Subsidence of Saturated Seabed Foundation with Interbedded Soft Clay–Sand by Liuyuan Zhao, Miaojun Sun, Jianhong Ye, Fuqin Yang, Kunpeng He

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Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

“…While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. …”
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Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms by Ayesha Siddika, Momotaz Begum, Fahmid Al Farid, Jia Uddin, Hezerul Abdul Karim

“…The prediction of software defects is a crucial element in maintaining the stability and reliability of software systems. This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervised. …”
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Improving Attack Detection in IoV with Class Balancing and Feature Selection by Thierry Widyatama, Ifan Rizqa, Fauzi Adi Rafrastara

“…The ensemble algorithms evaluated in this research comprise Random Forest, Gradient Boosting, and XGBoost. …”
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Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis by Raut Komal, Balpande Vijaya

“…This paper proposed several machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression and Support Vector Machine and design an ensemble of these models to detect and classify Parkinson's disease. …”
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Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. by Sadia Nazim, Muhammad Mansoor Alam, Syed Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Suud

“…The trust of users in the models used for cybersecurity would be undermined by the ambiguous and indefinable nature of existing AI-based methods, specifically in light of the more complicated and diverse nature of cyberattacks in modern times. The present research addresses the comparative analysis of an ensemble deep neural network (DNNW) with different ensemble techniques like RUSBoost, Random Forest, Subspace, AdaBoost, and BagTree for the best prediction against imagery malware data. …”
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AN INTELLIGENT POSTOPERATIVE CHRONIC PAIN PREDICTION SYSTEM (I-POCPP) by Elif Kartal, Fatma Önay Koçoğlu, Zeki Özen, İlkim Ecem Emre, Gürcan Güngör, Pervin Sutaş Bozkurt

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Wavelet-Based ensembled intelligent technique for a better quality of fault detection and classification in AC microgrids by Nityananda Giri, Pravati Nayak, Ranjan Kumar Mallick, Sairam Mishra, Aymen Flah, Habib Kraiem, Lukas Prokop, Mohammad Kanan

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Fault Detection in Photovoltaic Systems Using a Machine Learning Approach by Jossias Zwirtes, Fausto Bastos Libano, Luis Alvaro de Lima Silva, and Edison Pignaton de Freitas

“…The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. …”
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Changes Detection of Mangrove Vegetation Area in Banyak Islands Marine Natural Park, Sumatra, Southeast Asia by Muhammad Arif Nasution, Helmy Akbar, Singgih Afifa Putra, Ammar AL-Farga, Esraa E. Ammar, Yudi Setiawan

“…Spectral index combinations, including NDVI, NDMI, MNDWI, and MVI, were analyzed using random forest classification, a tree-based machine learning algorithm. …”
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Combined L-Band Polarimetric SAR and GPR Data to Develop Models for Leak Detection in the Water Pipeline Networks by Yuyao Zhang, Hongliang Guan, Fuzhou Duan

“…The model features are selected with the Boruta wrapper algorithm based on the SAOCOM-1A images after pre-processing, and the SSRDC values at sampling locations within the research area are calculated with the reflected wave method based on the GPR data. …”
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Residential Building Renovation Considering Energy, Carbon Emissions, and Cost: An Approach Integrating Machine Learning and Evolutionary Generation by Rudai Shan, Wanyu Lai, Huan Tang, Xiangyu Leng, Wei Gu

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Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals by Siddhant Arora, Sridharan Naveen Venkatesh, Vaithiyanathan Sugumaran, Anoop Prabhakaranpillai Sreelatha, Vetri Selvi Mahamuni

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A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…The dataset was split into a 70%:15%:15% ratio for training, testing, and validation, respectively, and five machine learning algorithms were evaluated: AdaBoost, Support Vector Machine, Random Forest, Decision Tree, and Stochastic Gradient Descent. …”
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Performance Evaluation of Robotic Harvester with Integrated Real-Time Perception and Path Planning for Dwarf Hedge-Planted Apple Orchard by Tantan Jin, Xiongzhe Han, Pingan Wang, Yang Lyu, Eunha Chang, Haetnim Jeong, Lirong Xiang

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Hybrid Model for 6G Network Traffic Prediction and Wireless Resource Optimization by Mohammed Anis Oukebdane, A. F. M. Shahen Shah, Md Baharul Islam, John Ekoru, Milka Madahana

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Detection and Analysis of Malicious Software Using Machine Learning Models by Selman Hızal, Ahmet Öztürk

“…Our analysis encompasses binary and multi-class classification tasks under various experimental conditions, including percentage splits and 10-fold cross-validation. The evaluated algorithms include Random Tree (RT), Random Forest (RF), J-48 (C4.5), Naive Bayes (NB), and XGBoost. …”
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Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud by Chiranjit Singha, Kishore Chandra Swain, Satiprasad Sahoo, Ayad M. Fadhil Al-Quraishi, Joseph Omeiza Alao, Hussein Almohamad, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo

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