Leveraging Comprehensive Echo Data to Power Artificial Intelligence Models for Handheld Cardiac Ultrasound by D.M. Anisuzzaman, PhD, Jeffrey G. Malins, PhD, John I. Jackson, PhD, Eunjung Lee, PhD, Jwan A. Naser, MBBS, Behrouz Rostami, PhD, Grace Greason, BA, Jared G. Bird, MD, Paul A. Friedman, MD, Jae K. Oh, MD, Patricia A. Pellikka, MD, Jeremy J. Thaden, MD, Francisco Lopez-Jimenez, MD, MSc, MBA, Zachi I. Attia, PhD, Sorin V. Pislaru, MD, PhD, Garvan C. Kane, MD, PhD

“…Objective: To develop a fully end-to-end deep learning framework capable of estimating left ventricular ejection fraction (LVEF), estimating patient age, and classifying patient sex from echocardiographic videos, including videos collected using handheld cardiac ultrasound (HCU). …”
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Attention to Monkeypox: An Interpretable Monkeypox Detection Technique Using Attention Mechanism by Avi Deb Raha, Mrityunjoy Gain, Rameswar Debnath, Apurba Adhikary, Yu Qiao, Md. Mehedi Hassan, Anupam Kumar Bairagi, Sheikh Mohammed Shariful Islam

“…To address this, the deployment of deep learning models on edge devices presents a viable solution for the rapid and accurate detection of monkeypox. …”
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Recent development and prospective of video coding by Meng GUO, Xiuying NIE, Gengsheng HUANG

“…The next generation video coding standard HEVC(high efficiency video coding) and AVS2 have been developed.The key technologies of video coding were surveyed,specifically including coding unit,intra-prediction,inter-prediction and de-blocking filter.Besides that,the new development directions were analyzed and discussed,such as scene video coding,cloud coding,deep learning video coding,and so on.…”
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Attention-guided convolutional network for bias-mitigated and interpretable oral lesion classification by Adeetya Patel, Camille Besombes, Theerthika Dillibabu, Mridul Sharma, Faleh Tamimi, Maxime Ducret, Peter Chauvin, Sreenath Madathil

“…Recent advances in deep learning have demonstrated potential in supporting clinical decisions. …”
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ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network by Xiao Feng, Xiao Feng, Zhongyuan Guo, Zhongyuan Guo, Sam Kwong

“…Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature. …”
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Efficient Anomaly Detection Algorithm for Heart Sound Signal by Zhihai Liu, Wen Liu, Zheng Gu, Feng Yang

“…Researchers have recently utilized deep learning methods to detect abnormal features in heart sound signals, thereby facilitating disease diagnosis. …”
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Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification by Getinet Yilma, Mesfin Dagne, Mohammed Kemal Ahmed, Ravindra Babu Bellam

“…Deep-learning plays a crucial role in large-scale health monitoring of agricultural plants. …”
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A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach by Ghulam Abbas, Wu Zhi, Aamir Ali

“…In the second stage, leveraging deep learning technology, the real‐time reactive power output of photovoltaics (PV) and wind power units is controlled at a 5‐min time scale throughout the day. …”
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Explainable Artificial Intelligence for Crowd Forecasting Using Global Ensemble Echo State Networks by Chamod Samarajeewa, Daswin De Silva, Milos Manic, Nishan Mills, Prabod Rathnayaka, Andrew Jennings

“…Crowd forecasting is typically achieved using deep learning models that learn the evolving nature of data streams. …”
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Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward Relay by Gangsan Jeong, Xianglan Jin

“…Subsequently, we introduce neural network-based deep learning methods to mitigate computational complexity. …”
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Robust Forest Sound Classification Using Pareto-Mordukhovich Optimized MFCC in Environmental Monitoring by Ahmad Qurthobi, Robertas Damasevicius, Vytautas Barzdaitis, Rytis Maskeliunas

“…This study focuses on the application of deep learning models for forest sound classification as an effort to establish an early threats detection system. …”
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An Efficient Network Based on Conjugate Gradient Optimization and Approximate Observation Model for SAR Image Reconstruction by Song Zhou, Jing Chen, Zao Wang, Yuhao Wang, Pin Wen

“…Deep learning has been successfully applied to solve the synthetic aperture radar (SAR) imaging problem, which shows superior imaging performance to compressive sensing (CS)-based methods under sparse sampling conditions. …”
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Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer by Zhichao Wang, Yan Hu, Jun Cai, Jinyuan Xie, Chao Li, Xiandong Wu, Jingjing Li, Haifeng Luo, Chuchu He

“…This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. …”
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Named Entity Recognition Model Based on the Fusion of Word Vectors and Category Vectors by Yang Zhou, Haoyang Zeng, Wei Zhang, Yuguang Zhang

“…Named entity recognition (NER) in deep learning mode heavily relies on the processing and analysis of text vectors. …”
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CR-DEQ-SAR: A Deep Equilibrium Sparse SAR Imaging Method for Compound Regularization by Guoru Zhou, Yixin Zuo, Zhe Zhang, Bingchen Zhang, Yirong Wu

“…The experimental results show that the proposed method outperforms existing deep learning-based SAR imaging methods regarding reconstruction performance and memory usage.…”
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Anomaly Detection and Business Process Orchestration for Low-Code Platform in Power System Based on Deep-Cross Model With Data Balancing by Jin Huang, Ming Sheng Xu, Chang Ming Zhu, Guo An Wang

“…Second, we propose xDeepCIN, a novel deep learning architecture that uniquely combines Compressed Interaction Networks with low-order crossing structures, demonstrating a 28% reduction in feature sparsity compared to existing deep cross models. …”
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Time Series Analysis of Production Decline in Carbonate Reservoirs with Machine Learning by Liqiang Wang, Mingji Shao, Gen Kou, Maoxian Wang, Ruichao Zhang, Zhengzheng Wei, Xiao Sun

“…Although machine learning methods based on multiple regression and deep learning have been applied to unconventional oil reservoirs in recent years, their application effects have been unsatisfactory. …”
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Weakly-Supervised Deep Shape-From-Template by Sara Luengo-Sanchez, David Fuentes-Jimenez, Cristina Losada-Gutierrez, Daniel Pizarro, Adrien Bartoli

“…We propose WS-DeepSfT, a novel deep learning-based approach to the Shape-from-Template (SfT) problem, which aims at reconstructing the 3D shape of a deformable object from a single RGB image and a template. …”
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Advanced Algorithmic Model for Real-Time Multi-Level Crop Disease Detection Using Neural Architecture Search by Slimani Hicham, El Mhamdi Jamal, Jilbab Abdelilah

“…We compare the performance of our proposed model with nine other deep learning models using transfer learning. Remarkably, transfer learning based on the NAS method achieves high classification accuracy, consistently exceeding 90.84% F1 scores. …”
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A novel wireless sensor network deployment for monitoring and predicting abnormal actions in medical environment and patient health state by R. Manikandan, S. Arunprakash, Rakan A. Alsowail, Tharani Pandiaraj

“…This study proposes a deep learning-based approach integrated with WSNs to address these challenges. …”
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