Deep learning approach for automated hMPV classification by Sivarama Prasad Tera, Ravikumar Chinthaginjala, Irum Shahzadi, Priya Natha, Safia Obaidur Rab

“…This study proposes a novel deep learning framework, referred to as hMPV-Net, which leverages Convolutional Neural Networks (CNNs) to facilitate the precise detection and classification of hMPV infections. …”
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Fine Tuning Hyperparameters of Deep Learning Models Using Metaheuristic Accelerated Particle Swarm Optimization Algorithm by Abdel-Hamid M. Emara, Ghada Atteia, Jawad Hasan Alkhateeb

“…APSO-CNN uses the global search capabilities of Particle Swarm Optimization (PSO) to automatically optimize hyperparameter configurations for architecture-determined Convolutional Neural Networks (CNNs). The main contribution of this work is the development of the APSO-CNN framework, which introduces an improved PSO variant specifically tailored for Convolutional Neural Networks (CNNs) hyperparameter tuning, thereby reducing manual intervention and significantly enhancing model performance. …”
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Comparison of deep learning models for predictive maintenance in industrial manufacturing systems using sensor data by Wenjun Li, Ting Li

“…Abstract This paper presents a comprehensive comparison of deep learning models for predictive maintenance (PdM) in industrial manufacturing systems using sensor data. We propose a framework that encompasses data acquisition, preprocessing, and model construction using various deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and their hybrid variants. …”
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Action Recognition with 3D Residual Attention and Cross Entropy by Yuhao Ouyang, Xiangqian Li

“…Core innovation integrates the attention mechanism into the 3D ResNet framework to emphasize key features and suppress irrelevant ones. …”
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A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning by Amin Pourmahboubi, Nazanin Arsalani Saeed, Hamed Tabrizchi

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Multi-modal expert system for automated durian ripeness classification using deep learning by Santi Sukkasem, Watchareewan Jitsakul, Phayung Meesad

“…These results highlight the potential of the proposed multi-modal approach as a practical and interpretable framework for automated durian ripeness assessment.…”
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R-Sparse R-CNN: SAR Ship Detection Based on Background-Aware Sparse Learnable Proposals by Kamirul Kamirul, Odysseas A. Pappas, Alin M. Achim

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MUFFNet: lightweight dynamic underwater image enhancement network based on multi-scale frequency by Dechuan Kong, Dechuan Kong, Yandi Zhang, Xiaohu Zhao, Yanqiang Wang, Lei Cai

“…The network introduces a frequency-domain-based convolutional attention mechanism to extract spatial information effectively. …”
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Enhancing Landmark Point Detection in <i>Eriocheir Sinensis</i> Carapace with Differentiable End-to-End Networks by Chong Wu, Shuxian Wang, Shengmao Zhang, Hanfeng Zheng, Wei Wang, Shenglong Yang

“…A 37-point localization framework was developed for the carapace, with the dataset augmented through random distortions, rotations, and occlusions to enhance generalization capability. …”
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Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks by Theodoros Leontiou, Anna Frixou, Marios Charalambides, Efstathios Stiliaris, Costas N. Papanicolas, Sofia Nikolaidou, Antonis Papadakis

“…<b>Methods</b>: In this study, we employed 3D convolutional neural networks (CNNs) to predict internal temperature fields. …”
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A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images by Pooja Govindaraj, Sasikaladevi Natarajan, Pradeepa Sampath, Akilesh Thimma Suresh, Rengarajan Amirtharajan

“…We propose a novel deep learning framework, the Compound Scaling Hypergraph Neural Network model (CSHG-CervixNet), for robust classification of cervical cancer subtypes. …”
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Personalized Solutions for Foot Health: Machine Learning-Based Foot Condition Detection, Classification, and Recommendation of Customized Footwear by Sonaa Rajagopal, Muralikrishnan Mani, Shyam Venkatraman, R. Suganya

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Mapping Coastal Soil Salinity and Vegetation Dynamics Using Sentinel-1 and Sentinel-2 Data Fusion With Machine Learning Techniques by Wen Liu, Tiezhu Shi, Zhinian Zhao, Chao Yang

“…These findings demonstrate the potential of integrating multisensor remote sensing with advanced machine learning techniques for coastal monitoring, providing a robust framework for sustainable land-use planning and ecological management.…”
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Improved YOLOv10 for Visually Impaired: Balancing Model Accuracy and Efficiency in the Case of Public Transportation by Rio Arifando, Shinji Eto, Tibyani Tibyani, Chikamune Wada

“…This study introduces the Improved-YOLOv10, a novel model designed to tackle challenges in bus identification and pov classification by integrating Coordinate Attention (CA) and Adaptive Kernel Convolution (AKConv) into the YOLOv10 framework. The Improved YOLOv10 advances the YOLOv10 architecture through the incorporation of CA, which enhances long-range dependency modeling and spatial awareness, and AKConv, which dynamically adjusts convolutional kernels for superior feature extraction. …”
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Towards precision agriculture: metaheuristic model compression for enhanced pest recognition by Sana Parez, Norah Saleh Alghamdi, Tahir Mahmood, Waseem Ullah, Muhammad Attique Khan, Taha Houda, Naqqash Dilshad

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Multi-Modal AI for Multi-Label Retinal Disease Prediction Using OCT and Fundus Images: A Hybrid Approach by Amina Zedadra, Mahmoud Yassine Salah-Salah, Ouarda Zedadra, Antonio Guerrieri

“…While previous computer-aided diagnostic systems have focused primarily on medical imaging, this paper proposes VisionTrack, a multi-modal AI system for predicting multiple retinal diseases, including Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), drusen, Central Serous Retinopathy (CSR), and Macular Hole (MH), as well as normal cases. The proposed framework integrates a Convolutional Neural Network (CNN) for image-based feature extraction, a Graph Neural Network (GNN) to model complex relationships among clinical risk factors, and a Large Language Model (LLM) to process patient medical reports. …”
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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

“…This study presents a non-invasive glucose monitoring framework that integrates a high-intensity Superbright optical sensor with an IoT-enabled data acquisition system using ESP32 microcontrollers and Raspberry Pi. …”
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An Informer-based multi-scale model that fuses memory factors and wavelet denoising for tidal prediction by Peng Lu, Yuchen He, Wenhui Li, Yuze Chen, Ru Kong, Teng Wang

“…In this paper, we used the Informer model as the foundational framework for further research and development. In comparative experiments, our proposed model outperformed LSTM, Informer, and MICN by 61.4%, 51.7%, and 23.8%, respectively. …”
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A prediction model for the mechanical properties of SUS316 stainless steel ultrathin strip driven by multimodal data mixing by Zhenhua Wang, Pengzhan Wang, Yunfei Liu, Yuanming Liu, Tao Wang

“…In this work, a deep learning framework for multimodal data fusion is constructed that couples a multi-layer perceptron (MLP) and a residual neural network (ResNet) to predict mechanical properties of SUS316 stainless steel ultrathin strips. …”
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Radiative Transfer Model-Integrated Approach for Hyperspectral Simulation of Mixed Soil-Vegetation Scenarios and Soil Organic Carbon Estimation by Asmaa Abdelbaki, Robert Milewski, Mohammadmehdi Saberioon, Katja Berger, José A. M. Demattê, Sabine Chabrillat

“…A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. …”
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