Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction by Raja Ayu Mahessya, Dian Eka Putra, Rostam Ahmad Efendi, Rayendra, Rozi Meri, Riyan Ikhbal Salam, Dedi Mardianto, Ikhsan, Ismael, Arif Rizki Marsa

“…This study developed and evaluated machine learning models for kyphosis prediction using a dataset of 81 pediatric patients by comparing the logistic regression and decision tree approaches. …”
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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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Exploring machine learning trends in poverty mapping: A review and meta-analysis by Badri Raj Lamichhane, Mahmud Isnan, Teerayut Horanont

“…Machine Learning (ML) has rapidly advanced as a transformative tool across numerous fields, offering new avenues for addressing poverty-related challenges. …”
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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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Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning by Dominic Benintendi, Kevin M. Tennant, Edward M. Sabolsky, Jay Wilhelm

“…Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. …”
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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

“…For localization systems leveraging LoRa signals, Machine Learning (ML) approaches are being increasingly explored, as ML-based solutions offer a powerful way to enhance the accuracy of positioning. …”
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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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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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Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis by Liang Xu, Jing Li, Wei Gong

“…Extracellular vesicles (EVs), as a category of nanoparticles, carry a wealth of biological information and play a crucial role in tumor initiation and progression, thereby offering novel approaches for early tumor diagnosis. In recent years, machine learning (ML) technology in the medical field has gained momentum, which utilize various algorithms to analyze input data, identify potential patterns and trends, develop predictive models, and generate high-precision predictions of unknown data, demonstrating its clinical potential in disease diagnosis. …”
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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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Integration of Deep Learning and Machine Learning Techniques for Advancing the Detection of Plant Diseases by Abdul Karim, Touqeer Ahmed Jumani, Muhammad Amir Raza, Shadi Khan Baloch, Nouman Qadeer Soomro, Muhammad Masud, Muhammad Salman Saeed, Mouaaz Nahas, Aamir Ali Patoli

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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

“…Based on the results of machine learning, we identified APOLD1 and EPYC as critical diagnostic genes in OA, which were further confirmed using ROC assays. …”
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Advanced Fault Diagnosis in Milling Machines Using Acoustic Emission and Transfer Learning by Muhammad Umar, Zahoor Ahmad, Saif Ullah, Faisal Saleem, Muhammad Farooq Siddique, Jong-Myon Kim

“…The accurate diagnosis of faults in milling machines is important to ensure manufacturing efficiency and minimize downtime. …”
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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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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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