Enhancing dysarthria severity classification: efficient audio based deep learning models by Varsha J Varma, Aniruddha Jana, Arpan Kumar Samal, Aurobindo S, Rani Chinnappa Naidu, Somaya Al-Maadeed, Moutaz Saleh, Rajesh Kumar M, Prakash Ramachandran, Zachariah C Alex, Rajeev Khoodeeram

“…Abstract A complex motor speech disorder, dysarthria makes diagnosis and its severity classification extremely challenging, thereby affecting suitable therapy and intervention strategies. This paper presents a deep learning-based method based on TORGO dataset to overcome these challenges. …”
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Fault Diagnosis in Power Generators: A Comparative Analysis of Machine Learning Models by Quetzalli Amaya-Sanchez, Marco Julio del Moral Argumedo, Alberto Alfonso Aguilar-Lasserre, Oscar Alfonso Reyes Martinez, Gustavo Arroyo-Figueroa

“…The early detection of faults in power generators is essential to prevent cutoffs of the electrical supply in the power grid. This work presents a comparative analysis of machine learning (ML) models for the generator fault diagnosis. …”
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Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions by Mumin Adam, Abdullatif Albaseer, Uthman Baroudi, Mohamed Abdallah

“…Traditional machine learning (ML) models rely on centralized architectures, which, while powerful, often present significant privacy risks due to the centralization of sensitive data. …”
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Enhancing lung cancer detection through integrated deep learning and transformer models by Revathi Durgam, Bharathi Panduri, V. Balaji, Adil O. Khadidos, Alaa O. Khadidos, Shitharth Selvarajan

“…Abstract Lung cancer has been stated as one of the prevalent killers of cancer up to this present time and this clearly underlines the rationale for early diagnosis to enhance life expectancy of patients afflicted with the condition. …”
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Predictive Analytics in Maternal Health: A Machine Learning Approach for Classification of Preeclampsia by Pakiza Amin, Saima Gulzar Ahmad, Hikmat Ullah Khan, Ehsan Ullah Munir, Naeem Ramzan

“…With the help of real and public clinical data, the present work presents an effective and ecological approach that can help healthcare professionals make appropriate and timely decisions about the management of pre-eclampsia. …”
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A Deep Learning Approach for Automated Depression Assessment Using Roman Urdu by Ruba Mohmand, Usman Habib, Muhammad Usman, Jamel Baili, Yunyoung Nam

“…Depression is a highly complex and frequently unnoticed mental condition that presents substantial hazards to a person’s well-being. …”
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Developing new machine-learning intelligent models to predict the excavation-tunnel displacements by Abdollah Tabaroei, Muhand Jawad Jasim, Ali Mohammed Al-Araji, Amir Hossein Vakili

“…In order to get a better realization of the behavior of an existing tunnel due to a vicinity deep excavation, this research that consists of three main parts, propose new intelligent models for predicting the excavation-tunnel displacements using machine learning. For the purpose four equations present to predict displacements of the excavation-tunnel complex. …”
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Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna by Md Mahabub Alam, Nurhafizah Abu Talip Yusof, Ahmad Afif Mohd Faudzi, Md Raihanul Islam Tomal, Md Ershadul Haque, Md. Suaibur Rahman

“…Abstract Machine Learning (ML) has significantly transformed antenna design by enabling efficient optimization of geometrical parameters, modeling complex electromagnetic behavior, and accelerating performance prediction with reduced computational cost. …”
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Characterizing Storm-Induced Coastal Flooding Using SAR Imagery and Deep Learning by Deanna Edwing, Lingsheng Meng, Suna Lv, Xiao-Hai Yan

“…Previous studies have proven that synthetic aperture radar (SAR) is an effective tool for flooding observation due to its high-resolution and timely observations, and deep learning-based models can accurately extract water bodies from SAR imagery. …”
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An optimized LSTM-based deep learning model for anomaly network intrusion detection by Nitu Dash, Sujata Chakravarty, Amiya Kumar Rath, Nimay Chandra Giri, Kareem M. AboRas, N. Gowtham

“…Several researchers have recommended the implementation of machine learning approaches in IDSs to counteract the menace posed by network intruders. …”
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Predicting Sentiments in Spotify Comments: A Comparative Analysis of Machine Learning Models by Filipe Augusto Felix de Queiroz, Igor Barbosa Negreiros, Giovana de Souza, Débora Cordeiro de Sousa, Sílvio Fernando Alves Xavier Júnior

“…We compare different statistical modeling and Machine Learning techniques, identifying the ones with the greatest accuracy in predicting sentiments. …”
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A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring by Daniel Krauss, Lukas Engel, Tabea Ott, Johanna Braunig, Robert Richer, Markus Gambietz, Nils Albrecht, Eva M. Hille, Ingrid Ullmann, Matthias Braun, Peter Dabrock, Alexander Kolpin, Anne D. Koelewijn, Bjoern M. Eskofier, Martin Vossiek

“…However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. …”
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Development and Explainability of Models for Machine-Learning-Based Reconstruction of Signals in Particle Detectors by Kalina Dimitrova, Venelin Kozhuharov, Peicho Petkov

“…Machine learning methods are being introduced at all stages of data reconstruction and analysis in various high-energy physics experiments. …”
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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…Conclusion This work presents an interpretable, biologically informed deep learning approach for predicting SLE-associated epitopes. …”
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Predictive modeling of air quality in the Tehran megacity via deep learning techniques by Abdullah Kaviani Rad, Mohammad Javad Nematollahi, Abbas Pak, Mohammadreza Mahmoudi

“…Abstract Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO, O3, NO2, SO2, PM10, and PM2.5, from 2013 to 2023 in the Tehran megacity, Iran, via deep learning (DL) models and evaluate their effectiveness over conventional machine learning (ML) methods. …”
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An Efficient Framework for Personalizing EMG-Driven Musculoskeletal Models Based on Reinforcement Learning by Joseph Berman, I-Chieh Lee, Jie Yin, He Huang

“…Our present framework was compared to the baseline MM and a widely used MM parameter optimization method: simulated annealing (SA). …”
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SGSNet: a lightweight deep learning model for strawberry growth stage detection by Zhiyu Li, Jianping Wang, Guohong Gao, Yufeng Lei, Chenping Zhao, Yan Wang, Haofan Bai, Yuqing Liu, Xiaojuan Guo, Qian Li

“…Thus, the development of lightweight detection technologies suitable for portable devices has become essential.MethodsThis paper presents SGSNet, a lightweight deep learning model designed for the fast and accurate detection of various strawberry growth stages. …”
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Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems by Ahmed Al Hammadi, Lina Bariah, Sami Muhaidat, Mahmoud Al-Qutayri, Paschalis C. Sofotasios, Merouane Debbah

“…In the second stage, deep Q-learning is exploited in order to maximize the overall spectral efficiency (SE). …”
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Creative Practices in Mathematics Teaching: results of planning through Universal Design for Learning by Paula Regina Raksa, Heliza Colaço Góes, Anderson Roges Teixeira Góes

“…We analyze the excerpt of a creative pedagogical practice based on Universal Design for Learning (UDL) in Mathematics lessons for a 5th grade of Elementary School. …”
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Optimal mixture design for organic Rankine cycle using machine learning algorithm by Valerio Mariani, Saverio Ottaviano, Davide Scampamorte, Andrea De Pascale, Giulio Cazzoli, Lisa Branchini, Gian Marco Bianchi

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