Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approach by Lorenza Bonaldi, Carmelo Pirri, Federico Giordani, Chiara Giulia Fontanella, Carla Stecco, Francesca Uccheddu

“…Therefore, the lack of a clear understanding of the fascial system combined with the penalty related to the setting of the ultrasound acquisition has generated a gap that makes its effective evaluation difficult during clinical routine. The aim of the present work is to fill this gap by investigating the effectiveness of using a deep learning approach to segment the thoracolumbar fascia from ultrasound imaging. …”
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A User-Friendly Machine Learning Pipeline for Automated Leaf Segmentation in by Michelle Lynn Yung, Kamila Murawska-Wlodarczyk, Alicja Babst-Kostecka, Raina Margaret Maier, Nirav Merchant, Aikseng Ooi

“…Automated leaf segmentation pipelines must balance accuracy, scalability, and usability to be readily adopted in plant research. We present an end-to-end deep learning pipeline designed for practical use in plant phenotyping, which we developed and evaluated during a real-world plant growth experiment using Atriplex lentiformis . …”
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Advanced machine learning techniques for social support detection on social media by Olga Kolesnikova, Moein Shahiki Tash, Zahra Ahani, Ameeta Agrawal, Raúl Monroy, Grigori Sidorov

“…We use state-of-the-art transformer models and zero-shot learning techniques—including GPT-3, GPT-4, and GPT-4o. …”
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A review of deep learning models to detect malware in Android applications by Elliot Mbunge, Benhildah Muchemwa, John Batani, Nobuhle Mbuyisa

“…Despite the rise in Android applications’ usage and cyberattacks, the use of deep learning (DL) models to detect emerging malware in Android applications is still nascent. …”
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Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates by N. Ragu Ramalingam, K. Johnson, M. Pagani, M. Pagani, M. L. V. Martina

“…These simulation results are fit using a machine learning (ML)-based variational encoder–decoder model. …”
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Knee Osteoarthritis Detection and Classification Using Autoencoders and Extreme Learning Machines by Jarrar Amjad, Muhammad Zaheer Sajid, Ammar Amjad, Muhammad Fareed Hamid, Ayman Youssef, Muhammad Irfan Sharif

“…In this research, a unique deep learning model is presented that employs autoencoders as the primary mechanism for feature extraction, providing a robust solution for KOA classification. …”
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FEATURE-BASED IMPLEMENTATION OF MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR DISEASE PREDICTION by H. Singh, R. Tripathy, P. Kumar Sarangi, U. Giri, S. Kumar Mohapatra, N. Rameshbhai Amin

“…Heart-associated ailments are very frequent at present so it is essential to predict such illnesses. …”
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A deep learning based hybrid recommendation model for internet users by Amany Sami, Waleed El Adrousy, Shahenda Sarhan, Samir Elmougy

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Fuzzy evaluation and explainable machine learning for diagnosis of rheumatic and autoimmune diseases by Mohammed Fadhil Mahdi, Arezoo Jahani, Dhafar Hamed Abd

“…This work addresses three major challenges: (i) overlapping symptoms and complex clinical presentations, (ii) the lack of interpretability in traditional machine learning models, and (iii) the difficulty of selecting the best diagnosis model. …”
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Real‐Time Flood Inundation Modeling With Flow Resistance Parameter Learning by Alexander Young, John D. Albertson, Giovanni Moretti, Stefano Orlandini

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ODD-Net: a hybrid deep learning architecture for image dehazing by C. S. Asha, Abu Bakr Siddiq, Razeem Akthar, M. Ragesh Rajan, Shilpa Suresh

“…To overcome these challenges, we present ODD-Net, a hybrid deep learning architecture. …”
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Deepfake Image Forensics for Privacy Protection and Authenticity Using Deep Learning by Saud Sohail, Syed Muhammad Sajjad, Adeel Zafar, Zafar Iqbal, Zia Muhammad, Muhammad Kazim

“…This research focuses on the detection of deepfake images and videos for forensic analysis using deep learning techniques. It highlights the importance of preserving privacy and authenticity in digital media. …”
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Application of Machine Learning in Fault Detection And Classification in Power Transmission Lines by Michel Evariste Tshodi, Nathanael Kasoro, Freddy Keredjim, ALbert Ntumba Nkongolo, Jean-Jacques Katshitshi Matondo, Paul Mbuyi Balowe, Laurent Kitoko

“…Four distinct supervised machine learning classifiers are employed for comparison purposes, with the results presented in a confusion matrix. …”
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Predicting Phenoconversion in Isolated RBD: Machine Learning and Explainable AI Approach by Yong-Woo Shin, Jung-Ick Byun, Jun-Sang Sunwoo, Chae-Seo Rhee, Jung-Hwan Shin, Han-Joon Kim, Ki-Young Jung

“…We analyzed comprehensive clinical data from 178 individuals with iRBD over a median follow-up of 3.6 years and applied machine learning models to predict when phenoconversion would occur and whether progression would present with motor- or cognition-first symptoms. …”
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Integrating Sentiment Analysis With Machine Learning for Cyberbullying Detection on Social Media by Maram Fahaad Almufareh, Noor Zaman Jhanjhi, Mamoona Humayun, Ghadah Naif Alwakid, Danish Javed, Saleh Naif Almuayqil

“…This paper presents a unique framework that integrates sentiment analysis with machine learning algorithms to enhance the detection of cyberbullying on social media. …”
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Lessons learned from a muscle study in nail-patella syndrome by Luisa Paul, Anne Schänzer, Christel Depienne, Andreas Hentschel, Nicolai Kohlschmidt, Ulrike Schara-Schmidt, Christopher Jannik Nelke, Andreas Roos, Heike Kölbel

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A Multi-Task Based Clustering Personalized Federated Learning Method by Ao Xiong, Han Zhou, Yu Song, Dong Wang, Xu Wei, Da Li, Bo Gao

“…In response to the data heterogeneity issue, this paper presents a multi-task clustering-based personalized federated learning algorithm, which is applied to the prediction of carbon emissions in different regions and enterprises. …”
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Practical Recommendations for Artificial Intelligence and Machine Learning in Antimicrobial Stewardship for Africa by Tafadzwa Dzinamarira, Elliot Mbunge, Claire Steiner, Enos Moyo, Adewale Akinjeji, Kaunda Yamba, Loveday Mwila, Claude Mambo Muvunyi

“…The deployment of AI‐driven solutions presents unprecedented opportunities for optimizing treatment regimens, predicting resistance patterns, and improving clinical workflows. …”
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Resilience evaluation of memristor based PUF against machine learning attacks by Hebatallah M. Ibrahim, Heorhii Skovorodnikov, Hoda Alkhzaimi

“…Our objective is to test the resiliency of the security margins of the presented PUF using machine learning analysis tools, on-top of holistic NIST cryptographic randomness testing initially provided, to provide a high level of certainty in predicting the randomness output of the verified Memrister-based PUF. …”
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Automated Detection of Poor-Quality Scintigraphic Images Using Machine Learning by Anil K. Pandey, Akshima Sharma, Param D. Sharma, Chandra S. Bal, Rakesh Kumar

“…Objective In the present study, we have used machine learning algorithm to accomplish the task of automated detection of poor-quality scintigraphic images. …”
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