LUNA: Loss-Construct Unsupervised Network Adjustment for Low-Dose CT Image Reconstruction by Ritu Gothwal, Shailendra Tiwari, Shivendra Shivani

“…We propose an unsupervised CT reconstruction technique that leverages the power of Deep convolutional neural networks (Deep CNNs), demonstrating that a randomly initialized neural network can serve as a prior. …”
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Deep learning-based time series prediction for precision field crop protection by Tao He, Meijin Li, Dong Jin

“…RAADA dynamically adapts decisions based on real-time field responses, ensuring efficiency and sustainability.ResultsThe experimental findings obtained from large-scale agricultural datasets show that our framework far exceeds the existing most advanced methods in terms of the accuracy of yield prediction, resource optimization, and environmental impact mitigation.DiscussionThis research offers a transformative solution for precision agriculture, aligning with the pressing need for advanced tools in sustainable crop management.…”
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Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development by Yiheng Pang, Yun Wang, Zhiqiang Niu

“…The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory (CNN-LSTM) in part II.…”
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PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network by Munira Islam, Khadija Akter, Md. Azad Hossain, M. Ali Akber Dewan

“…This study adopts Italian raw audio data to establish an efficient detection framework specifically designed to classify the vocal data into two distinct categories: healthy individuals and patients diagnosed with Parkinson’s disease. …”
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Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…In this paper, we present a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) alongside a 3D-Echo Fusion approach and a Dual Attention Model for heart valve disease classification using echocardiogram videos. …”
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A Mobile Deep Learning Classification Model for Diabetic Retinopathy by Daniel Rimaru, Antonio Nehme, Musaed Alhussein, Khaled Mahbub, Khusheed Aurangzeb, Anas Khan

“…An Android application has then been developed, that makes calls to this model and then displays the results on screen with a simple-to-understand interface developed using the Kivy framework.…”
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Historical Blurry Video-Based Face Recognition by Lujun Zhai, Suxia Cui, Yonghui Wang, Song Wang, Jun Zhou, Greg Wilsbacher

“…Next, we build a deep neural network-integrated object-tracking algorithm to compensate for failed recognition over one or more video frames. The framework combines simple online and real-time tracking with deep data association (Deep SORT), and TB-MTCNN with the residual neural network (ResNet) model. …”
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Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations by Claudio Ronchetti, Sara Marchio, Francesco Buonocore, Simone Giusepponi, Sergio Ferlito, Massimo Celino

“…In the present work, we report cutting-edge research, where we explored a wide range of compositions of cathode materials for Na-ion batteries by first-principles calculations using workflow chains developed within the AiiDA framework. We trained crystal graph convolutional neural networks and geometric crystal graph neural networks, and we demonstrate the ability of the machine learning algorithms to predict the formation energy of the candidate materials as calculated by the density functional theory. …”
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MAK-Net: A Multi-Scale Attentive Kolmogorov–Arnold Network with BiGRU for Imbalanced ECG Arrhythmia Classification by Cong Zhao, Bingwei Lai, Yongzheng Xu, Yiping Wang, Haorong Dong

“…To address these limitations, we introduce MAK-Net, a hybrid deep learning framework that combines: (1) a four-branch multiscale convolutional module for comprehensive feature extraction across diverse waveform morphologies; (2) an efficient channel attention mechanism for adaptive weighting of clinically salient segments; (3) bidirectional gated recurrent units (BiGRU) to capture long-range temporal dependencies; and (4) Kolmogorov–Arnold Network (KAN) layers with learnable spline activations for enhanced nonlinear representation and interpretability. …”
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MFCANet: Multiscale Feature Context Aggregation Network for Oriented Object Detection in Remote-Sensing Images by Honghui Jiang, Tingting Luo, Hu Peng, Guozheng Zhang

“…To address this gap, we have extended the RTMDet framework by introducing three modules: the Focused Feature Context Aggregation Module, the Feature Context Information Enhancement Module, and the Multi-scale Feature Fusion Module. …”
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Machine Learning for Fire Safety in the Built Environment: A Bibliometric Insight into Research Trends and Key Methods by Mehmet Akif Yıldız

“…This study aims to identify research trends and provide a guiding framework for researchers by systematically reviewing the literature on integrating machine learning-based predictive methods into building fire safety design using bibliometric methods. …”
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Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks by Do-Kyeong Lee, Jae-Sung Shin, Jae-Sung Choi, Min-Hyung Choi, Min Hong

“…To redress this, we propose a thermal imaging-based framework for respiratory segmentation aimed at estimating non-invasive pulmonary function. …”
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Enhancing Security in CPS Industry 5.0 using Lightweight MobileNetV3 with Adaptive Optimization Technique by Mohammed A. Aleisa

“…Computational efficiency is maximized through the implementation of MobileNetV3, a thin convolutional neural network optimized for mobile and edge devices. …”
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Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning by Alfredo Petrella, Marco Miozzo, Paolo Dini

“…To do so, we propose a novel prediction framework based on edge computing and Deep Transfer Learning (DTL) techniques, using datasets obtained at the edge through a large measurement campaign. …”
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Recognition of Underwater Engineering Structures Using CNN Models and Data Expansion on Side-Scan Sonar Images by Xing Du, Yongfu Sun, Yupeng Song, Lifeng Dong, Changfei Tao, Dong Wang

“…This work establishes a robust framework for SSS image recognition, advancing applications in marine geophysical exploration and underwater object detection.…”
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A Data-Driven Approach for Generating Synthetic Load Profiles with GANs by Tsvetelina Kaneva, Irena Valova, Katerina Gabrovska-Evstatieva, Boris Evstatiev

“…This paper proposes a data-driven framework based on a lightweight 1D Convolutional Wasserstein GAN with Gradient Penalty (Conv1D-WGAN-GP) for generating high-fidelity synthetic 24 h load profiles. …”
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Advancing personalized diagnosis and treatment using deep learning architecture by Rahat Ullah, Nadeem Sarwar, Mohammed Naif Alatawi, Abeer Abdullah Alsadhan, Hathal Salamah Alwageed, Maqbool Khan, Aitizaz Ali

“…This study proposes ImmunoNet, a deep learning-based framework that integrates genetic, molecular, and clinical data to enhance the accuracy of autoimmune disease diagnosis and treatment. …”
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Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding by Yu Song, Yu Song, Liyuan Han, Tielin Zhang, Bo Xu, Bo Xu

“…Initially, the MFSNN employs temporal convolutional networks and channel attention mechanisms to extract spatiotemporal features from raw data. …”
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A Hierarchical and Self-Evolving Digital Twin (HSE-DT) Method for Multi-Faceted Battery Situation Awareness Realisation by Kai Zhao, Ying Liu, Yue Zhou, Wenlong Ming, Jianzhong Wu

“…To address this gap, we propose a Hierarchical and Self-Evolving Digital Twin (HSE-DT) method that enhances battery state estimation by coordinating multiple estimation techniques in a hierarchical framework and enabling adaptive updating through transfer learning. …”
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CareAssist GPT improves patient user experience with a patient centered approach to computer aided diagnosis by Ali Algarni

“…CareAssist-GPT combines high-resolution X-ray images, real-time physiological vital signs, and clinical notes within a unified predictive framework using deep learning. Feature extraction is performed using convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformer-based NLP modules. …”
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