Residual trio feature network for efficient super-resolution by Junfeng Chen, Mao Mao, Azhu Guan, Altangerel Ayush

“…Abstract Deep learning-based approaches have demonstrated impressive performance in single-image super-resolution (SISR). …”
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Disorder-induced enhancement of lithium-ion transport in solid-state electrolytes by Zhimin Chen, Tao Du, N. M. Anoop Krishnan, Yuanzheng Yue, Morten M. Smedskjaer

“…Here, we address this challenge by establishing and employing a deep learning potential to simulate Li3PS4 electrolyte systems with varying levels of disorder. …”
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Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard by V. Steidl, J. L. Bamber, J. L. Bamber, X. X. Zhu, X. X. Zhu

“…In this study, we use deep learning paired with physical knowledge to generate ice thickness estimates for all glaciers of Spitsbergen, Barentsøya, and Edgeøya in Svalbard. …”
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Foveated Denoising for Ray Tracing Rendering by Daiyun Guo, Yan Zhang, Xubo Yang

“…The central vision within 18.5° is rendered with deep learning (DL) based denoising, and the periphery is rendered with temporal anti-aliasing (TAA). …”
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An Improved Deep Belief Network IDS on IoT-Based Network for Traffic Systems by Rayeesa Malik, Yashwant Singh, Zakir Ahmad Sheikh, Pooja Anand, Pradeep Kumar Singh, Tewabe Chekole Workneh

“…Hence, there is a need to use an intelligent mechanism based on machine learning (ML) and deep learning (DL), to detect attacks. In this study, the authors have proposed an intrusion detection engine with a deep belief network (DBN) being the core. …”
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Compensating Sparse-view Inline Computed Tomography Artifacts with Neural Representation and Incremental Forward-Backward Network Architecture by Manuel Buchfink, Faizan Ahmad, Guangpu Yang, Ammar Alsaffar, Charles Clark, Ahmed Baraka, Xingyu Liu, Sven Simon

“…Therefore, this paper discusses two deep-learning-based approaches for removing such artifacts. …”
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Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review by S. Kumar Reddy Mallidi, Rajeswara Rao Ramisetty

“…It then examines various IDS architectures and delves into the integration of machine learning (ML) and deep learning (DL) technologies that improve detection capabilities and system responsiveness. …”
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Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram by Xu Zhang, Xi Hui, Pengwu Wan, Tengfei Hui, Xiongfei Li

“…To address these challenges, deep learning-based modulation mode recognition technique is investigated in this paper for low-speed asynchronous sampled signals under channel conditions with varying SNR and delay. …”
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Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning by Jianye Zhou, Xinyu Yang, Lin Zhang, Siyu Shao, Gangying Bian

“…To realize high-precision and high-efficiency machine fault diagnosis, a novel deep learning framework that combines transfer learning and transposed convolution is proposed. …”
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Smart Shift Decision Method Based on Stacked Autoencoders by Zengcai Wang, Yazhou Qi, Guoxin Zhang, Lei Zhao

“…Meanwhile, the network structure of SAE is determined through a comparative experiment on simple and deep-learning neural networks. Experimental results demonstrate that using the SAE intelligent shift control strategy to determine shift timing not only is feasible and accurate but also saves time and development costs.…”
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Using transformer-based models and social media posts for heat stroke detection by Sumiko Anno, Yoshitsugu Kimura, Satoru Sugita

“…This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks.…”
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Towards automated recipe genre classification using semi-supervised learning. by Nazmus Sakib, G M Shahariar, Md Mohsinul Kabir, Md Kamrul Hasan, Hasan Mahmud

“…Furthermore, we have demonstrated traditional machine learning, deep learning and pre-trained language models to classify the recipes into their corresponding genre and achieved an overall accuracy of 98.6%. …”
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Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning by Rui Liu

“…Aiming at the problem of unobvious feature extraction of multiclass mine microseismic signals, this paper is based on the unsupervised learning method in the deep learning method, combined with wavelet packet energy ratio and empirical modulus singular value decomposition, and proposes a method based on wavelet packet energy and empirical modulus singular value decomposition and proposes a method (M-W&E) based on wavelet packet energy and empirical modulus singular value decomposition. …”
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Enhancing Driving Safety and Environmental Consciousness through Automated Road Sign Recognition Using Convolutional Neural Networks by M. H. F. Md Fauadi, M. F. H. Mohd Zan, M. A. M Ali, L. Abdullah, S. N. Yaakop and A. Z. M. Noor

“…This study explores the application of Convolutional Neural Networks (CNNs) in automatically recognizing road signs. CNNs, as deep learning algorithms, possess the ability to process and classify visual data, making them well-suited for image-based tasks such as road sign recognition. …”
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A dataset of blood slide images for AI-based diagnosis of malariaDataverse by Rose Nakasi, Joyce Nakatumba Nabende, Jeremy Francis Tusubira, Aloyzius Lubowa Bamundaga, Alfred Andama

“…The datasets will support robust and accurate deep learning models for malaria diagnosis using thick and thin blood smear images with reasonable detection accuracies.…”
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Fine-Grained Tasks for Crowdsourced Entity Resolution by Tiezheng Nie, Hanyu Mao, Xin Liu, Sining Yu

“…In recent years, crowdsourcing approaches have provided new ideas for entity resolution, using human intelligence to bring entity resolution to a higher level that can meet the short-term needs of a variety of users, unlike deep learning models that require large amounts of labeled data to train the model and are therefore subject to more research and development. …”
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Custom YOLO Object Detection Model for COVID-19 Diagnosis by Noor Najah Ali, Aseel Hameed, Asanka G. Perera, Ali Al_Naji

“…Clinical staff can benefit from Computer Aided Diagnostics (CAD) systems that combine deep learning algorithms and image processing technologies as diagnostic tools for COVID-19. …”
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Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review by Mohamad Hazwan Mohd Ghazali, Wan Rahiman

“…A combination of time domain statistical features and deep learning approaches is expected to be widely applied in the future, where fault features can be automatically extracted from the raw vibration signals. …”
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Advanced Photovoltaic Emulator with ANN-Based Modeling Using a DC-DC Push-Pull Converter and LQR Control with Current Observer by Aboubakeur HADJAISSA, Mohammed BENMILOUD, khaled AMEUR, HALIMA BOUCHENAK, Maria DIMEH

“…This study focuses on developing a PVE model using deep learning techniques, specifically a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) with backpropagation as the learning algorithm. …”
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Rolling bearing fault diagnosis based on parameter optimized VMD and improved GoogLeNet by LI Haoran, LIU Deping

“…ObjectiveThe application of deep learning methods in the field of rolling bearing fault diagnosis is very effective, but traditional neural networks cannot extract features at multiple scales due to the use of a single scale convolution kernel, and do not consider the importance of different features in fault diagnosis. …”
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