A Deep Learning-Based Approach for Precise Emotion Recognition in Domestic Animals Using EfficientNetB5 Architecture by Rashadul Islam Sumon, Haider Ali, Salma Akter, Shah Muhammad Imtiyaj Uddin, Md Ariful Islam Mozumder, Hee-Cheol Kim

“…This study presents a unique emotion classification deep learning-based approach for pet animals. The actual and emotional status of dogs and cats have been classified using a modified EfficientNetB5 model. …”
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A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data by Mussadiq Abdul Rahim, Sultan Daud Khan, Salabat Khan, Muhammad Rashid, Rafi Ullah, Hanan Tariq, Stanislaw Czapp

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An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks by Shun Zeng, Hongyu Chen, Rui Jing, Wenzhuo Yang, Ligong He, Tianle Zou, Peng Liu, Bo Liang, Dan Shi, Wenhao Wu, Qiusheng Lin, Zhenyu Ma, Jinhui Zha, Yonghao Zhong, Xianbin Zhang, Guangrui Shao, Peng Gong

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Lentil plant disease and quality assessment: A detailed dataset of high-resolution images for deep learning researchMendeley Data by Eram Mahamud, Md Assaduzzaman, Shayla Sharmin

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Retracted: Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT by Journal of Electrical and Computer Engineering

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Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques by Yu Fujinami-Yokokawa, Nikolas Pontikos, Lizhu Yang, Kazushige Tsunoda, Kazutoshi Yoshitake, Takeshi Iwata, Hiroaki Miyata, Kaoru Fujinami, on behalf of Japan Eye Genetics Consortium

“…To illustrate a data-driven deep learning approach to predicting the gene responsible for the inherited retinal disorder (IRD) in macular dystrophy caused by ABCA4 and RP1L1 gene aberration in comparison with retinitis pigmentosa caused by EYS gene aberration and normal subjects. …”
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Multi-branch LSTM encoded latent features with CNN-LSTM for Youtube popularity prediction by Neeti Sangwan, Vishal Bhatnagar

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Unfixed Bias Iterator: A New Iterative Format by Zeqing Zhang, Xue Wang, Jiamin Shen, Man Zhang, Sen Yang, Fanchang Yang, Wei Zhao, Jia Wang

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Feasibility of YOLOX computer model-based assessment of knee function compared with manual assessment for people with severe knee osteoarthritis by Tao Yang, Jie Zhao, Ben Wang, Li Wang, Hengzhe Bao, Bing Li, Wen Luo, Huiwen Zhao, Jun Liu

Subjects: “…Deep learning…”
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MythicVision: a deep learning powered mobile application for understanding Indian mythological deities using weight centric decision approach by Tauseef Khan, Aditya Nitin Patil, Aviral Singh, Gitesh Prashant Bhavsar, Kanakagiri Sujay Ashrith, Sachi Nandan Mohanty

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Monitoring Soil Salinity in Arid Areas of Northern Xinjiang Using Multi-Source Satellite Data: A Trusted Deep Learning Framework by Mengli Zhang, Xianglong Fan, Pan Gao, Li Guo, Xuanrong Huang, Xiuwen Gao, Jinpeng Pang, Fei Tan

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Cross-border mangrove dynamics and management in the Beibu Gulf: Long-term remote sensing observation using object-oriented deep learning by Wenna Gao, Chunyan Lu, Nuocheng Yang, Yuqi Wu, Kexin Wu, Zhangjuan Chen

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High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI by Qinyang Shou, Chenyang Zhao, Xingfeng Shao, Megan M Herting, Danny JJ Wang

“…We further proposed a Transformer-based deep learning (DL) model with k-space weighted image average (KWIA) denoised images as reference for training the model. …”
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A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances by Jinlin Liao, Feng Zhang, Shiwen Zhang, Cheng Gong

“…This paper proposes a Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) method, which is a real-time TTR method based on deep learning. The proposed method takes the Gate Recurrent Unit (GRU) network as the decision network and uses the results produced by the Modified Genetic Algorithm (MGA) as the training set of the decision network. …”
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Analysis of the Reform Effectiveness of Vocational Skill Identification Based on the New Social Training Model in the New Era under Deep Learning Assessment by Yeming Wang, Yong Ran

“…In order to be able to make full use of domain knowledge to improve the performance of skill word extraction, this paper proposes a skill word extraction method based on a combination of deep learning and corpus features. Skill word extraction is transformed into a sequence annotation problem, and based on the basic model of sequence annotation, Bi-LSTM-CRF, corpus features are added to the input layer, and the output of the input layer is connected with the Bi-LSTM output as the input of the CRF layer. …”
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STURM-Flood: a curated dataset for deep learning-based flood extent mapping leveraging Sentinel-1 and Sentinel-2 imagery by Nicla Notarangelo, Charlotte Wirion, Frankwin van Winsen

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SGCL-LncLoc: An Interpretable Deep Learning Model for Improving lncRNA Subcellular Localization Prediction with Supervised Graph Contrastive Learning by Min Li, Baoying Zhao, Yiming Li, Pingjian Ding, Rui Yin, Shichao Kan, Min Zeng

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A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization by Ruiqin Duan, Xiaosheng Peng, Cong Li, Zimin Yang, Yan Jiang, Xiufeng Li, Shuangquan Liu

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Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation by Tristan Whitmarsh, Wei Cope, Julia Carmona-Bozo, Roido Manavaki, Stephen-John Sammut, Ramona Woitek, Elena Provenzano, Emma L. Brown, Sarah E. Bohndiek, Ferdia A. Gallagher, Carlos Caldas, Fiona J. Gilbert, Florian Markowetz

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Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models by Wang Yuxin

“…These findings underscore the significant potential of deep learning models, particularly GRU and Bidirectional LSTM, in improving the precision and reliability of network traffic predictions. …”
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