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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Protein-ligand binding affinity prediction using multi-instance learning with docking structures by Hyojin Kim, Heesung Shim, Aditya Ranganath, Stewart He, Garrett Stevenson, Jonathan E. Allen

“…IntroductionRecent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. …”
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Deep Spatio-Temporal Illuminant Estimation Under Time-Varying AC Lights by Jun-Sang Yoo, Kang-Kyu Lee, Chan-Ho Lee, Ji-Min Seo, Jong-Ok Kim

“…In contrast to previous color constancy methods that exploited the spatial color information, we propose a novel deep learning-based color constancy method that exploits the temporal variations exhibited by AC-powered lights. …”
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Predicting noncoding RNA and disease associations using multigraph contrastive learning by Si-Lin Sun, Yue-Yi Jiang, Jun-Ping Yang, Yu-Han Xiu, Anas Bilal, Hai-Xia Long

“…Predicting associations between noncoding RNAs and diseases can significantly improve the accuracy of early diagnosis.With the continuous breakthroughs in artificial intelligence, researchers increasingly use deep learning methods to predict associations. Nevertheless, most existing methods face two major issues: low prediction accuracy and the limitation of only being able to predict a single type of noncoding RNA-disease association. …”
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AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer by Pierre-Antoine Bannier, Charlie Saillard, Philipp Mann, Maxime Touzot, Charles Maussion, Christian Matek, Niklas Klümper, Johannes Breyer, Ralph Wirtz, Danijel Sikic, Bernd Schmitz-Dräger, Bernd Wullich, Arndt Hartmann, Sebastian Försch, Markus Eckstein

“…To overcome these limitations, we develop a deep-learning model that detects FGFR3 mutations using routine hematoxylin-eosin slides. …”
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A 3D Dual Encoder Mirror Difference ResU-Net for Multimodal Brain Tumor Segmentation by Qiwei Xing, Zhihua Li, Yongxia Jing, Xiaolin Chen

“…Brain tumors are characterized by their relatively high incidence and mortality rates, highlighting the utmost importance of precise automatic segmentation for subsequent diagnosis and treatment. Although deep learning has significantly advanced the field of accurate and efficient automatic brain tumor segmentation, several challenges still persist. …”
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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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Seamless monitoring of marine heatwaves: capability of gap-filled, geostationary satellite sea surface temperature data by Seonju Lee, Myung-Sook Park

“…Moreover, these seamless SST data serve as inputs for numerical modeling and the deep learning algorithms and has immense potential for both forecasting marine heatwaves and implementing a 24-h continuous monitoring technique. …”
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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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Deciphering of emergency construction objects using satellite imagery and sub-satellite monitoring data in the Arctic by M. L. Kazaryan, A. A. Richter, M. A. Shakhramanyan, S. M. Grigoriev

“…These are hierarchical deep learning method based on Google Street View images, information modelling of historical buildings, photogrammetry using UAVs, 3D shooting.Materials and methods. …”
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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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Exploring happiness factors with explainable ensemble learning in a global pandemic. by Md Amir Hamja, Mahmudul Hasan, Md Abdur Rashid, Md Tanvir Hasan Shourov

“…This paper predicts happiness scores using Machine Learning (ML), Deep Learning (DL), and ensemble ML and DL algorithms and examines the impact of individual variables on the happiness index. …”
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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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Changing educational trends in the context of global digitalization of society by Yuliya D. Ermakova

“…The very concept of 'deep learning', which is key to progress in the use of digital resources and artificial intelligence, changes the basic, well-established goals of universities and can create new competition for them. …”
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A Low‐Latitude Three‐Dimensional Ionospheric Electron Density Model Based on Radio Occultation Data Using Artificial Neural Networks With Prior Knowledge by Ding Yang, Hanxian Fang

“…Our work demonstrates a new possibility of applying deep learning methods with prior knowledge to a broader field of geosciences, particularly for problems of prediction.…”
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