Modeling Political Discourse in Indonesia’s 2024 Election Using Unsupervised Machine Learning by Malikhatul Ibriza, Maya Rini Handayani, Wenty Dwi Yuniarti, Khothibul Umam

“…The 2024 General Election in Indonesia has generated a large volume of diverse and unstructured digital political discourse, necessitating a machine learning-based analytical approach for efficient, objective, and scalable data processing. …”
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Optimizing UAV sprayer performance using field data and machine learning approaches by Doğan Güneş, Hideo Hasegawa

“…Additionally, particle-based simulations were conducted in Python to visualize droplet deposition patterns under six UAV flight scenarios. Results showed that flight speed and droplet size were the most influential factors affecting spray coverage. …”
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Comparison of ECG Between Gameplay and Seated Rest: Machine Learning-Based Classification by Emi Yuda, Hiroyuki Edamatsu, Yutaka Yoshida, Takahiro Ueno

“…These findings suggest that machine learning-based classification can effectively distinguish ECG patterns between gameplay and rest.…”
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Dataset Construction and Effectiveness Evaluation of Spoken-Emotion Recognition for Human Machine Interaction by Mitsuki Okayama, Tatsuhito Hasegawa

“…However, three key challenges persist in existing emotion recognition datasets: 1) most assume human-to-human interaction, neglecting shifts in speech patterns when users address a machine, 2) many include acted emotional expressions that differ from genuine internal states, and 3) even non-acted datasets often rely on third-party labels, creating potential mismatches with speakers’ actual emotions. …”
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Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia by Aditya Eaturu, Krishna Prasad Vadrevu

“…Furthermore, simpler models, such as Simple Persistence and MLP, showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating various ML and DL models before integrating them into any decision support systems (DSS) for fire management studies. …”
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Deep Reinforcement Learning-Based Multi-Access in Massive Machine-Type Communication by Nasim Ravi, Nuno Lourenco, Marilia Curado, and Edmundo Monteiro

“…The diverse applications of Machine-Type Communication (MTC) lead to exponential growth in Machine to Machine traffic. …”
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Machine Learning for Predicting Bank Stability: The Role of Income Diversification in European Banking by Karim Farag, Loubna Ali, Noah Cheruiyot Mutai, Rabia Luqman, Ahmed Mahmoud, Nol Krasniqi

“…These methods are applied to enhance the reliability and predictive power of the analysis by addressing the problem of endogeneity (via generalized method of moments) and capturing non-linearities, interactions, and high-dimensional patterns (via machine learning). The econometric findings reveal that income diversification can reduce non-performing loans, improve bank solvency, and enhance the Z-score, indicating the significant role of income diversification in improving bank stability. …”
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Advancing plant leaf disease detection integrating machine learning and deep learning by R. Sujatha, Sushil Krishnan, Jyotir Moy Chatterjee, Amir H. Gandomi

“…Our proposed method uses deep learning (DL) to extract features from photos of plant leaves and machine learning (ML) for further processing. To capture complex illness patterns, convolutional neural networks (CNNs) such as VGG19 and Inception v3 are utilized. …”
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Enhancing LoRa-Based Outdoor Localization Accuracy Using Machine Learning by Nur Kelesoglu, Marzena Halama, Anna Strzoda

“…This architecture integrates the strengths of Deep Learning and tree-based models, aiming to capture both temporal signal patterns and structured input correlations for improved localization accuracy. …”
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Prediction and Optimization of Civil Aviation Flight Delays Based on Machine Learning Algorithms by Qingwei Zhong, Yingxue Yu, Yiru Huang, Tianhang Zhang

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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…The field of dermatology has seen a change in recent years due to the convergence of artificial intelligence and medicine, which has produced creative methods for computer-aided diagnostics. Machine learning has become a potent tool in the search for more precise and effective diagnostic techniques because of its capacity to analyze enormous volumes of data and identify intricate patterns. …”
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Chlorophyll-a in the Chesapeake Bay Estimated by Extra-Trees Machine Learning Modeling by Nikolay P. Nezlin, SeungHyun Son, Salem I. Salem, Michael E. Ondrusek

“…One potential solution is machine learning, indirectly including non-<i>Chl-a</i> signals into the models. …”
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Machine Learning Traffic Flow Prediction Models for Smart and Sustainable Traffic Management by Rusul Abduljabbar, Hussein Dia, Sohani Liyanage

“…This study contributes to this objective by developing and evaluating advanced machine learning models that leverage multisource data to predict traffic patterns more effectively, allowing for the deployment of proactive measures to prevent or reduce traffic congestion and idling times, leading to enhanced eco-friendly mobility. …”
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Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners by Gulnur Arkin, Tangnur Abdukelim, Hankiz Yilahun, Askar Hamdulla

“…A speech corpus containing samples from advanced, intermediate, and elementary learners (N = 50) and standard speakers (N = 10) was constructed, with a total of 5880 samples. Support Vector Machine (SVM) and ID3 decision tree algorithms were employed to classify vowel formant parameters (F1-F2) patterns. …”
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Ensemble Learning-Based Metamodel for Enhanced Surface Roughness Prediction in Polymeric Machining by Elango Natarajan, Manickam Ramasamy, Sangeetha Elango, Karthikeyan Mohanraj, Chun Kit Ang, Ali Khalfallah

“…This paper proposes and demonstrates a domain-adapted ensemble machine learning approach for enhanced prediction of surface roughness (Ra) during the machining of polymeric materials. …”
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Identification of Biomarkers Associated with Diagnosis of Osteoarthritis Patients Based on Bioinformatics and Machine Learning by Yihao Liang, Fangzheng Lin, Yunfei Huang

“…SVM-RFE analysis and LASSO regression model were carried out to screen possible markers. The compositional patterns of the 22 types of immune cell fraction in OA were determined by the use of CIBERSORT. …”
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Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach. by Rodrigue Govan, Romane Scherrer, Baptiste Fougeron, Christine Laporte-Magoni, Roman Thibeaux, Pierre Genthon, Philippe Fournier-Viger, Cyrille Goarant, Nazha Selmaoui-Folcher

“…Our approach utilized a comprehensive strategy combining machine learning models trained on binarized incidences, along with descriptive techniques for identifying key factors. …”
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Interpretable machine learning insights into the association between PFAS exposure and diabetes mellitus by Cui Wang, Xinping Xu, Shuai Luo, Man Luo, Sha Li, Jianhong Si

“…Background: Diabetes Mellitus (DM) is a global health concern with rising prevalence, and its link to PFAS exposure remains unclear. No machine learning (ML) models have yet been developed to predict DM based on PFAS exposure. …”
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Multivariate machine learning algorithms for energy demand forecasting and load behavior analysis by Farhan Hussain, M. Hasanuzzaman, Nasrudin Abd Rahim

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Analysis of hydraulic breakdown and seepage of tail sealing system in shield tunnel machines by Dalong Jin, Zhuoyu Li, Dajun Yuan, Yangyang Gan, Jian Chen, Haipeng Guo

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