A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet by Shida Zhao, Guangzhao Hao, Yichi Zhang, Shucai Wang

“…The characteristics of each part of the sheep carcass are connected to each other and have similar features, which make it difficult to identify and detect, but with the development of image semantic segmentation technology based on deep learning, it is possible to explore this technology for real-time recognition of the 3 parts of the sheep carcass. …”
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DR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management prediction by Salem Knifo, Ahmad Alzubi

“…Therefore, in order to resolve these limitations; a deep learning-based model is developed in this study for efficient financial management prediction. …”
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Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism by Aarthi Chelladurai, D.P. Manoj Kumar, S. S. Askar, Mohamed Abouhawwash, Mohamed Abouhawwash

“…Thus, numerous research studies have been introduced based on deep learning models for the efficient classification of tomato leaf disease. …”
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A hybrid CNN-LSTM model with adaptive instance normalization for one shot singing voice conversion by Assila Yousuf, David Solomon George

“…Traditional voice conversion techniques primarily emphasize singer similarity, often leading to robotic-sounding singing voices. Deep learning-based singing voice conversion techniques, however, focus on disentangling singer-dependent and singer-independent features. …”
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Multi-Sensor Information Fusion with Multi-Scale Adaptive Graph Convolutional Networks for Abnormal Vibration Diagnosis of Rolling Mill by Rongrong Peng, Changfen Gong, Shuai Zhao

“…However, most of the existing deep learning (DL) based methods exploit only single sensor information and Euclidean space data, which results in incomplete information contained in the features extracted by in-depth networks. …”
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IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…Transformers are becoming dominant deep learning backbones for both computer vision and natural language processing. …”
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Empirical Analysis for Improving Food Quality Using Artificial Intelligence Technology for Enhancing Healthcare Sector by S. K. UmaMaheswaran, Gaganpreet Kaur, A. Pankajam, A. Firos, Piyush Vashistha, Vikas Tripathi, Hussien Sobahi Mohammed

“…Different AI technologies such as “Machine Learning (ML),” “Neural Language Processing (NLP),” “Rule-Based Expert Systems (RESs),” “Deep Learning (DL),” and so on are used in healthcare and food industries for big “medical data” analysis. …”
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Comparison Between Convolutional Neural Network CNN and SVM in Skin Cancer Images Recognition by Zaid Ghazi Hadi, Ahmed R. Ajel, Ayad Q. Al-Dujaili

“…This work presents a dermatologist-level classification of skin cancer by using residual network (ResNet-50) as a deep learning convolutional neural network (DLCNN) that maps images to class labels. …”
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Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence by Kilian Michiels, Eva Heffinck, Patricio Astudillo, Ivan Wong, Peter Mortier, Alessandra Maria Bavo

“…Methods. Different deep learning models were trained, validated, and tested using a cohort of 583 patients for which manually annotated data were available. …”
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A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Sardar Sadaqat Ali

“…Machine learning (ML) and deep learning (DL) have emerged as vital tools in cybersecurity, enabling the analysis of extensive datasets to identify potential cyber threats. …”
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Reducing lead requirements for wearable ECG: Chest lead reconstruction with 1D-CNN and Bi-LSTM by Kazuki Hebiguchi, Hiroyoshi Togo, Akimasa Hirata

“…This study aims to develop a deep learning model capable of reconstructing complete 12-lead ECG waveforms using a minimal number of chest leads, thereby optimizing lead configurations for wearable ECG systems. …”
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CDR-Detector: a chronic disease risk prediction model combining pre-training with deep reinforcement learning by Shaofu Lin, Shiwei Zhou, Han Jiao, Mengzhen Wang, Haokang Yan, Peng Dou, Jianhui Chen

“…Current studies mainly focused on developing well-designed deep learning models to predict the disease risk based on large-scale and high-quality longitudinal EHR data. …”
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Skeleton-Based Data Augmentation for Sign Language Recognition Using Adversarial Learning by Yuriya Nakamura, Lei Jing

“…In recent years, visual-based sign language recognition (SLR) has become an active research area with the advancement of deep learning. However, it is difficult to collect sign language data, and many datasets suffer from data lack and imbalance, leading to overfitting and reduced accuracy in machine learning. …”
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Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network by Guimei Yin, Jie Yuan, Yanjun Chen, Guangxing Guo, Dongli Shi, Lin Wang, Zilong Zhao, Yanli Zhao, Manjie Zhang, Yuan Dong, Bin Wang, Shuping Tan

“…Abstract Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. …”
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Equitable artificial intelligence for glaucoma screening with fair identity normalization by Min Shi, Yan Luo, Yu Tian, Lucy Q. Shen, Nazlee Zebardast, Mohammad Eslami, Saber Kazeminasab, Michael V. Boland, David S. Friedman, Louis R. Pasquale, Mengyu Wang

“…Research indicates a disproportionate impact of glaucoma on racial and ethnic minorities. Existing deep learning models for glaucoma detection might not achieve equitable performance across diverse identity groups. …”
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Machine Learning-Based Normal White Blood Cell Multi-Classification Optimization by Taeyeon Gil, Sukjun Lee, Onseok Lee

“…Several studies have employed deep learning (DL) or machine learning (ML) methods; no significant difference in performance between the two methods has been demonstrated. …”
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PlantAIM: A new baseline model integrating global attention and local features for enhanced plant disease identification by Abel Yu Hao Chai, Sue Han Lee, Fei Siang Tay, Hervé Goëau, Pierre Bonnet, Alexis Joly

“…Conventionally, detection has relied on plant pathologists, but recent advances in deep learning, particularly the Vision Transformer (ViT) and Convolutional Neural Network (CNN), have made it feasible for automated plant disease identification. …”
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DC-NNMN: Across Components Fault Diagnosis Based on Deep Few-Shot Learning by Juan Xu, Pengfei Xu, Zhenchun Wei, Xu Ding, Lei Shi

“…In recent years, deep learning has become a popular topic in the intelligent fault diagnosis of industrial equipment. …”
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Dual-Granularity Feature Alignment for Change Detection in Remote Sensing Images by Feng Zhou, Xinyu Zhang, Hui Shuai, Renlong Hang, Shanshan Zhu, Tianyu Geng

“…Deep learning has emerged as the preferred method for remote sensing change detection owing to its ability to automatically extract discriminative features from bitemporal images. …”
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Analysis of the integrated role of the Yangtze River Delta based on the industrial economic resilience of cities during COVID-19 by Cai Jia, Zini Cao, Jinkang Hu, Xudong Wang, Long Zhao, Junjun Zhi, Wangbing Liu, Gaohua Zhang, Shilong Ding, Yan Li, Luzhou Lin

“…The study employed the UNet deep learning method to detect the land use types in Wu-ma-Chu. …”
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