MBGPIN: Multi-Branch Generative Prior Integration Network for Super-Resolution Satellite Imagery by Furkat Safarov, Ugiloy Khojamuratova, Misirov Komoliddin, Furkat Bolikulov, Shakhnoza Muksimova, Young-Im Cho

“…Traditional interpolation methods often fail to recover fine details, while deep-learning-based approaches, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), have significantly advanced super-resolution performance. …”
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ML-Driven Energy Savings for Cellular Baseband Units via Traffic Prediction by Aneta Kolackova, Viet Anh Phan, Jan Jerabek, Sergey Andreev, Jiri Hosek

“…Traditional static energy management approaches frequently waste resources and lead to increased costs, highlighting the need for more dynamic methods that adapt to changing network conditions. …”
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CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement by Guohong Gao, Yuxin Ma, Jianping Wang, Zhiyu Li, Yan Wang, Haofan Bai

“…Traditional contact cattle identification methods are costly; are easy to lose and tamper with; and can lead to a series of security problems, such as untimely disease prevention and control, incorrect traceability of cattle products, and fraudulent insurance claims. …”
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Do more with less: Exploring semi-supervised learning for geological image classification by Hisham I. Mamode, Gary J. Hampson, Cédric M. John

“…Labelled datasets within geoscience can often be small, with data acquisition both costly and challenging, and their interpretation and downstream use in machine learning difficult due to data scarcity. …”
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A hybrid hierarchical health monitoring solution for autonomous detection, localization and quantification of damage in composite wind turbine blades for tinyML applications by Nikhil Holsamudrkar, Shirsendu Sikdar, Akshay Prakash Kalgutkar, Sauvik Banerjee, Rakesh Mishra

“…This HHMLM model achieved 96.4% overall accuracy with less computation time than 83.8% for separate conventional Convolutional Neural Network (CNN) models. The developed SHM solution provides a more effective and practical solution for in-service monitoring of wind turbine blades, particularly in wind farm settings, with the potential for future wireless sensors with tiny ML applications.…”
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Accurate bladder cancer diagnosis using ensemble deep leaning by Rana A. El-Atier, M. S. Saraya, Ahmed I. Saleh, Asmaa H. Rabie

“…The methods used today for diagnosing and monitoring bladder cancer are intrusive, costly, and time-consuming. In clinical practice, invasive biopsy followed by histological examination continues to be the gold standard for diagnosis. …”
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Less Is More: Brain Functional Connectivity Empowered Generalizable Intention Classification With Task-Relevant Channel Selection by Haowei Lou, Zesheng Ye, Lina Yao, Yu Zhang

“…Meanwhile, despite previous studies using either convolutional neural networks (CNNs) or graph neural networks (GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. …”
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Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations by Khaled Sarieddine, Mohammad Ali Sayed, Sadegh Torabi, Ribal Atallah, Chadi Assi

“…The results demonstrate the effectiveness of the implemented approach with the Convolutional Long-Short Term Memory model producing optimal classification accuracy (99.4%). …”
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Assessing Revisit Risk in Emergency Department Patients: Machine Learning Approach by Wang-Chuan Juang, Zheng-Xun Cai, Chia-Mei Chen, Zhi-Hong You

“…ResultsThe evaluation results indicate that incorporating convolutional neural network–based feature extraction from unstructured ED physician narrative notes, combined with structured vital signs and demographic data, significantly enhances predictive performance. …”
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SkinIncept: an ensemble transfer learning-based approach for multiclass skin disease classification using InceptionV3 and InceptionResNetV2 by Md. Hasan Imam Bijoy, Md. Mahbubur Rahman, Abdus Sattar, Aminul Haque, Mohammad Shamsul Arefin, Pranab Kumar Dhar, Tetsuya Shimamura

“…The lack of access to dermatological expertise and resource constraints in rural areas exacerbate delayed or inaccurate diagnoses, leading to worsening conditions and higher treatment costs. This study addresses this critical issue by developing a robust and accurate system for classifying Bangladesh’s ten most common skin diseases using convolutional neural networks (CNNs)-based transfer learning models. …”
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Improved Tomato Leaf Disease Recognition Based on the YOLOv5m with Various Soft Attention Module Combinations by Yong-Suk Lee, Maheshkumar Prakash Patil, Jeong Gyu Kim, Seong Seok Choi, Yong Bae Seo, Gun-Do Kim

“…To reduce production costs, environmental effects, and crop losses, tomato leaf disease recognition must be accurate and fast. …”
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Deep Learning-Based Sound Source Localization: A Review by Kunbo Xu, Zekai Zong, Dongjun Liu, Ran Wang, Liang Yu

“…In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. …”
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Prediction of Sea Surface Current Around the Korean Peninsula Using Artificial Neural Networks by Jeong‐Yeob Chae, Hyunkeun Jin, Inseong Chang, Young Ho Kim, Young‐Gyu Park, Young Taeg Kim, Boonsoon Kang, Min‐su Kim, Ho‐Jeong Ju, Jae‐Hun Park

“…To compensate for these high computational costs, novel approaches with efficient computational costs combined with numerical model outputs need to be developed. …”
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Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models by Saad Hameed, Basheer Qolomany, Samir Brahim Belhaouari, Mohamed Abdallah, Junaid Qadir, Ala Al-Fuqaha

“…Comprehensive experiments across three scenarios—(1) optimizing the Rastrigin function, (2) using Long Short-Term Memory (LSTM) networks for time series regression, and (3) using Convolutional Neural Networks (CNNs) for material classification—show that the method significantly improves convergence rates and lowers computational costs. …”
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Explainable Siamese Neural Networks for Detection of High Fall Risk Older Adults in the Community Based on Gait Analysis by Christos Kokkotis, Kyriakos Apostolidis, Dimitrios Menychtas, Ioannis Kansizoglou, Evangeli Karampina, Maria Karageorgopoulou, Athanasios Gkrekidis, Serafeim Moustakidis, Evangelos Karakasis, Erasmia Giannakou, Maria Michalopoulou, Georgios Ch Sirakoulis, Nikolaos Aggelousis

“…Methods: By leveraging convolutional neural networks (CNNs) and Siamese neural networks (SNNs), the proposed framework effectively addresses the challenges of limited datasets and delivers robust predictive capabilities. …”
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Hybrid CNN-Ensemble Framework for Intelligent Optical Fiber Fault Detection and Diagnosis by Salem Titouni, Idris Messaoudene, Yassine Himeur, Omar Alnaseri, Farouk Chetouah, Boualem Hammache, Massinissa Belazzoug, Shadi Atalla, Wathiq Mansoor

“…This paper introduces a novel Hybrid CNN-Ensemble framework, combining convolutional neural networks (CNNs) for deep feature extraction with ensemble learning techniques including XGBoost, Support Vector Machines (SVM), and Random Forest (RF). …”
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Dynamic Optimization of Recurrent Networks for Wind Speed Prediction on Edge Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Muhammad Asim, Samia Allaoua Chelloug, Mohammed A. ELAffendi

“…To address this gap, we propose a framework that co-optimizes the discrete hyperparameter spaces of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN) models under strict memory constraints. …”
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Leveraging Synthetic Data to Develop a Machine Learning Model for Voiding Flow Rate Prediction From Audio Signals by Marcos Lazaro Alvarez, Alfonso Bahillo, Laura Arjona, Diogo Marcelo Nogueira, Elsa Ferreira Gomes, Alipio M. Jorge

“…Sound-based uroflowmetry (SU) is a non-invasive technique emerging as an alternative to traditional uroflowmetry (UF) to calculate the voiding flow rate based on the sound generated by the urine impacting the water in a toilet, enabling remote monitoring and reducing the patient burden and clinical costs. This study trains four different machine learning (ML) models (random forest, gradient boosting, support vector machine and convolutional neural network) using both regression and classification approaches to predict and categorize the voiding flow rate from sound events. …”
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Optimizing Backbone Networks Through Hybrid–Modal Fusion: A New Strategy for Waste Classification by Houkui Zhou, Qifeng Ding, Chang Chen, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu, Tao He

“…Traditional manual methods are time-consuming, labor-intensive, costly, and error-prone, resulting in reduced accuracy. …”
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An Artificial Intelligence Solution for Automated Dental Inspection and Charting by M.A. Moufti, M. Abu Talib, S. Al Mokdad, A. Alhouria, M. Alsaffarini, N. Almahmeed, S. Eldessouky, W. Kharoufah, Q. Nasir

“…This research presents a novel approach to automate dental inspection and charting, offering the potential to reduce clinical time and costs, improve patient experience and outcomes, and enhance patient access to dental care.…”
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