Single Infrared Super-Resolution via a Shifted Full-Scale Non-Local Network by Honghong Lu, Zhenhua Li

“…By confining full-scale non-local to local windows while allowing for shifted-window connectivity, full-scale non-local residual block solves the problem that existed non-local structures are difficult to be reused and serves as backbone for super-resolution network. Qualitative and quantitative evaluation results show that our method has better performance on benchmark datasets of Set5, Set14, BSD100, and the self-built infrared dataset under the up-sample factor of <inline-formula> <tex-math notation="LaTeX">$\times 2$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\times 3$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula>.…”
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Estimating Self-Confidence in Video-Based Learning Using Eye-Tracking and Deep Neural Networks by Ankur Bhatt, Ko Watanabe, Jayasankar Santhosh, Andreas Dengel, Shoya Ishimaru

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Automated detection of weld defects in TOFD images for steel bridges using generative adversarial networks by Yanfeng Gong, Zihao Chen, Hong Zhang, Meng Xu, Wen Deng

“…The proposed method is evaluated on a self-constructed TOFD dataset of steel bridge welds. …”
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Distribution Network Fault Risk Assessment Method Considering Difference in Entropy Value of Rare Factors by HUANG Junxian, CHEN Chun, CAO Yijia, QUAN Shaoli, WANG Yi

“…External factors such as bad weather and external failure seriously affect the reliability of a distribution network. To comprehensively and accurately assess the risk of faults in a distribution network, this paper proposes a fault risk assessment method that considers the difference in entropy value of rare factors. …”
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Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction by Shweta Ashish Koparde, Sonali Kothari, Sharad Adsure, Kapil Netaji Vhatkar, Vinod V. Kimbahune

“…This research • Introduces a novel approach for predicting the host of influenza viruses by leveraging protein sequences. • Extraction of features, including sequence length, Amino Acid Composition (AAC), Dipeptide Composition (DPC), Tripeptide Composition (TPC), aromaticity, secondary structure fraction, and entropy from protein sequence. • Addresses the data imbalance and improves model generalization, the oversampling technique is applied for data augmentation.The prediction model employs a Deep Recurrent Neural Network (DRNN) optimized by Fractional Addax Optimization 34 Algorithm (FAOA), a hybrid of Addax Optimization Algorithm (AOA) and Fractional Concept (FC), designed to perform 35 influenza virus host prediction. …”
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Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification by Kien Trang, Fung Fung Ting, Bao Quoc Vuong, Chee-Ming Ting

“…This research presents a novel node-based Graph Convolutional Network (GCN) approach for the classification of breast cancer from ultrasound images. …”
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Optimizing Fractional-Order Convolutional Neural Networks for Groove Classification in Music Using Differential Evolution by Jiangang Chen, Pei Su, Daxin Li, Junbo Han, Gaoquan Zhou, Donghui Tang

“…This study presents a differential evolution (DE)-based optimization approach for fractional-order convolutional neural networks (FOCNNs) aimed at enhancing the accuracy of groove classification in music. …”
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Deep Learning for Intelligent and Automated Network Slicing in 5G Open RAN (ORAN) Deployment by Shu-Ping Yeh, Sonia Bhattacharya, Rashika Sharma, Hassnaa Moustafa

“…With this evolution towards AI-based features in the network, the Open RAN (ORAN) specification expanded the network functions virtualization to the RAN intelligence by introducing RAN Intelligent Controller (RIC) to enable AI applications for the network functions. …”
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An Advanced Spatio-Temporal Graph Neural Network Framework for the Concurrent Prediction of Transient and Voltage Stability by Chaoping Deng, Liyu Dai, Wujie Chao, Junwei Huang, Jinke Wang, Lanxin Lin, Wenyu Qin, Shengquan Lai, Xin Chen

“…This paper presents a novel concurrent prediction framework for transient and voltage stability using a spatio-temporal embedding graph neural network (STEGNN). The proposed framework utilizes a graph neural network to extract topological features of the power system from adjacency matrices and temporal data graphs. …”
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Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection by Safa Ben Atitallah, Maha Driss, Wadii Boulila, Anis Koubaa

“…Conventional machine learning methods and typical Graph Neural Networks (GNNs) often struggle to capture the complexity and uncertainty in IIoT network traffic, which hampers their effectiveness in detecting intrusions. …”
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Optimization of Table Tennis Swing Action Supported by the Temporal Convolutional Network Algorithm in Deep Learning by Shaoxuan Sun, Hongyu Zheng, Zhixin Lin

“…To enhance the navigation accuracy and interpretability of Unmanned Aerial Vehicles (UAVs) in sports analysis, this study proposes an improved model based on the Temporal Convolutional Network (TCN) algorithm, integrated with Explainable Artificial Intelligence. …”
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Brain Tumour Segmentation and Grading Using Local and Global Context-Aggregated Attention Network Architecture by Ahmed Abdulhakim Al-Absi, Rui Fu, Nadhem Ebrahim, Mohammed Abdulhakim Al-Absi, Dae-Ki Kang

“…Specifically, a global context attention network is developed for capturing multiple-scale features, and a local context attention network is designed for specific tasks. …”
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SpaGAN: A spatially-aware generative adversarial network for building generalization in image maps by Zhiyong Zhou, Cheng Fu, Robert Weibel

“…The proposed network was comprehensively evaluated with a synthetic and a real-world dataset. …”
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MNeuralTab: Integrating meta-modeling and neural networks for customer churn prediction in e-commerce by Arif Mohammad Asfe, Md. Rashadur Rahman, Md. Sabir Hossain

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LMSOE-Net: lightweight multi-scale small object enhancement network for UAV aerial images by Zhixing Ma, Peidong Luo, Xiaole Shen

“…To improve both detection performance and model efficiency, we introduce the Efficient Multi Scale Pyramid (EMSP) neck network. This versatile feature fusion network enhances multi-scale feature extraction and integration by using convolutional modules with varying kernel sizes. …”
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RLEAFS: Reinforcement Learning-Based Energy Aware Forwarding Strategy for NDN-Based IoT Networks by Naeem Ali Askar, Adib Habbal

“…The proposed RLEAFS Strategy consists of two schemes: one designed to handle the dynamic and complex nature of real-world IoT environments, and another focused on improving the interest forwarding strategy to reduce network overhead. We implemented RLEAFS in ndnSIM to evaluate its performance against state-of-the-art NDN-based IoT forwarding strategies. …”
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An eighteen-organ microphysiological system coupling a vascular network and excretion system for drug discovery by Jing Wang, Huixue Zhang, Yueyang Qu, Yang Yang, Shuhui Xu, Zhenni Ji, Yuxiu Wang, Xiuli Zhang, Yong Luo

“…Abstract Physiological supporting systems, such as the vascular network and excretion system, are crucial for the effective functioning of organs. …”
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Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight by Xueying Cao, Hongmin Gao, Ting Qin, Min Zhu, Ping Zhang, Ping Zhang, Peipei Xu, Peipei Xu

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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…Furthermore, two ML methods, Support Vector Machine (SVM) and Random Forest (RF) models, are trained on this reduced feature set, and their predictions are integrated using a majority voting approach for evaluating the final classification results, thereby enhancing overall accuracy and reliability. …”
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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection by Muhammad Shafiq, J. Kavitha, Dhruva R. Rinku, N. K. Senthil Kumar, Kamal Poon, Amar Y. Jaffar, V. Saravanan

“…Based on features, the sorted-out data gets evaluated through a GRU-LSTM (Gated Recurrent Unit - Long Short-Term Memory) network to identify the state of the infant as usual and suggestive of hypoglycemia—blood glucose below 70 mg/dL, pale complexion, profuse perspiration. …”
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