XTNSR: Xception-based transformer network for single image super resolution by Jagrati Talreja, Supavadee Aramvith, Takao Onoye

“…This paper presents a Deep Learning model for single-image super-resolution. …”
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Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches by Seifeddine Messaoud, Soulef Bouaafia, Amna Maraoui, Lazhar Khriji, Ahmed Chiheb Ammari, Mohsen Machhout

“…However, technological tests based on deep learning techniques and medical images could be useful in fighting this pandemic. …”
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A machine learning based classifier for topological quantum materials by Ashiqur Rasul, Md Shafayat Hossain, Ankan Ghosh Dastider, Himaddri Roy, M. Zahid Hasan, Quazi D. M. Khosru

“…The persistent homology pipeline in the proposed neural network integrates a topological analysis of crystal structures into the deep learning model, enhancing both robustness and performance. …”
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Functional feature extraction and validation from twelve-lead electrocardiograms to identify atrial fibrillation by Wei Yang, Rajat Deo, Wensheng Guo

“…Abstract Background Deep learning methods on standard, 12-lead electrocardiograms (ECG) have resulted in the ability to identify individuals at high-risk for the development of atrial fibrillation. …”
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Digital Core Modeling Based on Pretrained Generative Adversarial Neural Networks by Qing Zhang, Benqiang Wang, Xusheng Liang, Yizhen Li, Feng He, Yuexiang Hao

“…With the development of deep learning technology, a variety of deep learning algorithms have been applied to digital core modeling. …”
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Automated Dead Chicken Detection in Poultry Farms Using Knowledge Distillation and Vision Transformers by Ridip Khanal, Wenqin Wu, Joonwhoan Lee

“…Future work will explore integrating deep learning methods that incorporate temporal attention mechanisms and automated removal processes.…”
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Cardiac sound classification using a hybrid approach: MFCC-based feature fusion and CNN deep features by Mahbubeh Bahreini, Ramin Barati, Abbas Kamali

“…This paper presents a novel approach to PCG signal classification, leveraging a fusion of deep learning features and handcrafted features based on mutual information measurements. …”
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Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution by Keivan Faghih Niresi, Chong-Yung Chi

“…Recently, it has been shown that deep learning is a promising approach to image denoising. …”
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FABLDroid: Malware detection based on hybrid analysis with factor analysis and broad learning methods for android applications by Kazım Kılıç, İsmail Atacak, İbrahim Alper Doğru

“…Applications installed from third-party environments threaten users’ privacy and security. Deep learning-based methods are popular for detecting Android malware. …”
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Implementation Of Convolutional Neural Network (Cnn) Based On Mobile Application For Rice Quality Determination by Muhammad Zainal Altim, Abdullah Basalamah, kasman kasman

“…The purpose of this study is to design and build a CNN deep learning program modeling for a mobile application for rice quality classification and analyze the performance of a mobile application-based classification program as a means of halal information in real time. …”
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Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph by Tingpeng Li, Lei Wang, Danhua Peng, Jun Liao, Li Liu, Zhendong Liu

“…To elaborate, our approach emulates deep learning validation strategies by dividing the data into training and testing sets. …”
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Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

“…The proposed method demonstrates superior performance over traditional machine learning and deep learning baselines on real-world cooling system data. …”
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Squeeze-and-Excitation Vision Transformer for Lung Nodule Classification by Xiaozhong Xue, Yanhe Ma, Weiwei Du, Yahui Peng

“…This study combines 2 attention mechanisms and proposes a novel deep learning model called squeeze-and-excitation vision transformer (SE-ViT). …”
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A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI by Shenghan Lou, Jianxin Ji, Huiying Li, Xuan Zhang, Yang Jiang, Menglei Hua, Kexin Chen, Kaiyuan Ge, Qi Zhang, Liuying Wang, Peng Han, Lei Cao

“…Additionally, we explained two deep learning models used as validation examples using this dataset.…”
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Building a Framework for Visual Question Answering Systems by Maya Abu Hamoud, Wasim Safi

“…VQA (Visual Question Answering) systems are among the latest advancements in the fields of artificial intelligence and deep learning. They integrate image processing with natural language understanding to enable intelligent systems to answer questions related to image content. …”
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Application of Multiattention Mechanism in Power System Branch Parameter Identification by Zhiwei Wang, Liguo Weng, Min Lu, Jun Liu, Lingling Pan

“…The experiment shows that our proposed GTN is superior to other machine learning methods and deep learning methods and can still realize accurate branch parameter identification under various noise conditions.…”
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A New Deep Vision-Based Identifier As An Intelligent Herbicide Spraying Agent For Potato Farm Application by Halo Omer Anvar, Fardin Akhlaghian Tab, Mohsen Ramezani, Kaveh Mollazade

“…The classification results of 9 different weed species using the developed deep learning models were satisfactory; so that the overall accuracy of EN-Inception-V3, EN-VGG-16, and HCNN models was 99.82%, 99.89%, and 92.83% in the training phase, and 96.69%, 90.32%, and 82.67% in the test phase, respectively. …”
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Memristor-based feature learning for pattern classification by Tuo Shi, Lili Gao, Yang Tian, Shuangzhu Tang, Jinchang Liu, Yiqi Li, Ruixi Zhou, Shiyu Cui, Hui Zhang, Yu Li, Zuheng Wu, Xumeng Zhang, Taihao Li, Xiaobing Yan, Qi Liu

“…Abstract Inspired by biological processes, feature learning techniques, such as deep learning, have achieved great success in various fields. …”
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Learning Automata Based Incremental Learning Method for Deep Neural Networks by Haonan Guo, Shilin Wang, Jianxun Fan, Shenghong Li

“…Deep learning methods have got fantastic performance on lots of large-scale datasets for machine learning tasks, such as visual recognition and neural language processing. …”
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Cloud and Cloud Shadow Detection for Multi-Modal Imagery With Gap-Filling Applications by Keunhoo Cho, Seongwook Park, Boram Seong, Seongwhan Lee, Jae-Pil Park

“…Recent techniques for detecting CCS often employ deep learning methods, which are effective but typically require extensive training data specific to each type of satellite imagery. …”
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