Hyperspectral Image Classification With Re-Attention Agent Transformer and Multiscale Partial Convolution by Junding Sun, Hongyuan Zhang, Jianlong Wang, Haifeng Sima, Shuanggen Jin

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Autonomous Mobile Inspection Robots in Deep Underground Mining—The Current State of the Art and Future Perspectives by Martyna Konieczna-Fuławka, Anton Koval, George Nikolakopoulos, Matteo Fumagalli, Laura Santas Moreu, Victor Vigara-Puche, Jakob Müller, Michael Prenner

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A Micro-Segmentation Method Based on VLAN-VxLAN Mapping Technology by Di Li, Zhibang Yang, Siyang Yu, Mingxing Duan, Shenghong Yang

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CapsuleThermNet: A CNN-CapsuleNet Architecture for Early Detection of Suicide Risk in Depressed Patients Using Thermal Facial Imaging by Eddy Muntina Dharma, Harjanto Prabowo, Agung Trisetyarso, Tjhin Wiguna

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Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite Imagery: A Systematic Review by Antonia Ivanda, Ljiljana Šerić, Maja Braović

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An automatic laryngoscopic image segmentation system based on SAM prompt engineering: from glottis annotation to vocal fold segmentation by Yucong Zhang, Yucong Zhang, Yuchen Song, Juan Liu, Juan Liu, Ming Li, Ming Li

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A Lightweight Ultrasound Image Denoiser Using Parallel Attention Modules and Capsule Generative Adversarial Network by Anparasy Sivaanpu, Kumaradevan Punithakumar, Kokul Thanikasalam, Michelle Noga, Rui Zheng, Dean Ta, Edmond H.M. Lou, Lawrence H. Le

“…Existing traditional and deep learning-based US denoising approaches have many limitations, such as reliance on manual parameter configurations, poor performance for unknown noise levels, the requirement for a large number of training data, and high computational expense. To address these challenges, we propose a novel Generative Adversarial Network (GAN) based denoiser. …”
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SGO enhanced random forest and extreme gradient boosting framework for heart disease prediction by Anima Naik, Ghanshyam G. Tejani, Seyed Jalaleddin Mousavirad

“…However, the study has certain limitations. The evaluation was conducted solely on two benchmark datasets, which may not fully reflect the diversity and complexity of real-world clinical populations. …”
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A hybrid XAI-driven deep learning framework for robust GI tract disease diagnosis by Fadl Dahan, Jamal Hussain Shah, Rabia Saleem, Muhammad Hasnain, Maira Afzal, Taha M. Alfakih

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Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks by Zurisaddai Severiche-Maury, Carlos Eduardo Uc-Rios, Wilson Arrubla-Hoyos, Dora Cama-Pinto, Juan Antonio Holgado-Terriza, Miguel Damas-Hermoso, Alejandro Cama-Pinto

“…Following a systematic methodology that includes model building, training, and evaluation, the LSTM model achieved a low test set loss and mean squared error (MSE), with values of 0.0163 for individual consumption and usage time and 0.0237 for total consumption. …”
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Analytical Approach to UAV Cargo Delivery Processes Under Malicious Interference Conditions by Fazliddin Makhmudov, Andrey Privalov, Sergey Egorenkov, Andrey Pryadkin, Alpamis Kutlimuratov, Gamzatdin Bekbaev, Young Im Cho

“…This method allows for the evaluation of UAV effectiveness based on the probability of successful cargo delivery within a specified time limit. …”
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LIoTning: A Peer-Supervised Payment Channel Approach for Secure Micropayments in IoT Ecosystem by Erfan Bahrami, Amir M. Aghapour, Morteza Amini

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Enhancing stability and power quality in electric vehicle charging stations powered by hybrid energy sources through harmonic mitigation and load management by Sahar M. Abd Elazim, M. H. Elkholy, A. Elgarhy, Tomonobu Senjyu, Mahmoud M. Gamil, Dongran Song, Ehab S. Ali, Gul Ahmad Ludin, Ahmad Shah Irshad, Mohammed Elsayed Lotfy

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Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

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Improvement of SAM2 Algorithm Based on Kalman Filtering for Long-Term Video Object Segmentation by Jun Yin, Fei Wu, Hao Su, Peng Huang, Yuetong Qixuan

“…Dynamic thresholds, combined with multi-criteria evaluation metrics (e.g., motion coherence, appearance consistency), prioritize high-quality frames while adaptively balancing confidence scores and temporal smoothness. …”
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OPTICALS: A Novel Framework for Optimizing Predictive Trading Indicators in Cryptocurrency Using Advanced Learning Simulations by Hasib Shamshad, Fasee Ullah, Syed Adeel Ali Shah, Muhammad Faheem, Beena Shamshad

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A CrossMod-Transformer deep learning framework for multi-modal pain detection through EDA and ECG fusion by Jaleh Farmani, Ghazal Bargshady, Stefanos Gkikas, Manolis Tsiknakis, Raul Fernandez Rojas

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DCSLK: Combined large kernel shared convolutional model with dynamic channel Sampling by Zongren Li, Shuping Luo, Hongwei Li, Yanbin Li

“…This study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational complexity that stem from the utilization of large convolutional kernels. …”
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Combining Supervised and Reinforcement Learning to Build a Generic Defensive Cyber Agent by Muhammad Omer Farooq, Thomas Kunz

“…Sophisticated mechanisms for attacking computer networks are emerging, making it crucial to have equally advanced mechanisms in place to defend against these malicious attacks. …”
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Assessment of Scientific Creative-Potential by Near-Infrared Spectroscopy Using Brain-Network-Based Deep-Fuzzy Classifier by Sayantani Ghosh, Amit Konar, Atulya K. Nagar

“…The novelty of the classifier lies in: i) design of an enhanced graph convolution operation that encapsulates local and global structural information from the input graph, ii) use of the Smish activation function to improve performance, iii) inclusion of a one-dimensional spatial convolution layer for preserving relevant information within convolved embeddings, iv) design of a novel mapping function to mitigate uncertainty among the spatial convolved vectors in the type-2 fuzzy layer, and v) application of Takagi-Sugeno-Kang (TSK)-based fuzzy reasoning to reduce computational cost. Evaluation on three datasets, each comprising over 45 individuals from different scientific backgrounds, shows that EGCIFC improves classification accuracy by 2.25% over the nearest competitor and by 22.72% over the lowest-performing baseline. …”
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