cigFacies: a massive-scale benchmark dataset of seismic facies and its application by H. Gao, X. Wu, X. Sun, M. Hou, M. Hou, H. Gao, G. Wang, H. Sheng

“…However, unlike the CV domain, the field of seismic exploration lacks a comprehensive benchmark dataset for seismic facies, severely limiting the development, application, and evaluation of deep-learning approaches in seismic facies classification. …”
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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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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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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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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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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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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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p-Norm Broad Learning for Negative Emotion Classification in Social Networks by Guanghao Chen, Sancheng Peng, Rong Zeng, Zhongwang Hu, Lihong Cao, Yongmei Zhou, Zhouhao Ouyang, Xiangyu Nie

“…Most existing methods are based on deep learning models, facing challenges such as complex structures and too many hyperparameters. …”
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InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks by Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi

“…Our results demonstrate that InceptionDTA outperforms various sequence-based, transformer-based, and graph-based deep learning approaches across warm-start, refined, and cold-start splitting settings. …”
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Research on Feature Extracted Method for Flutter Test Based on EMD and CNN by Hua Zheng, Zhenglong Wu, Shiqiang Duan, Jiangtao Zhou

“…The measured signals from a wind tunnel test were manually labeled “flutter” and “no-flutter” as the foundational dataset for the deep learning algorithm. After the normalized preprocessing, the intrinsic mode functions (IMFs) of the flutter test signals are obtained by the EMD method. …”
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Multiple Morphological Constraints-Based Complex Gland Segmentation in Colorectal Cancer Pathology Image Analysis by Kun Zhang, JunHong Fu, Liang Hua, Peijian Zhang, Yeqin Shao, Sheng Xu, Huiyu Zhou, Li Chen, Jing Wang

“…In this project, we use deep learning to achieve stain separation by predicting the stain coefficient. …”
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Deep Temporal Clustering of Pathological Gait Patterns in Post-Stroke Patients Using Joint Angle Trajectories: A Cross-Sectional Study by Gyeongmin Kim, Hyungtai Kim, Yun-Hee Kim, Seung-Jong Kim, Mun-Taek Choi

“…The results demonstrate the effectiveness of end-to-end deep learning-based clustering, yielding significant performance improvements without the need for manual feature extraction. …”
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SC-ResNeXt: A Regression Prediction Model for Nitrogen Content in Sugarcane Leaves by Zihao Lu, Cuimin Sun, Junyang Dou, Biao He, Muchen Zhou, Hui You

“…Compared with four classical deep learning algorithms, SC-ResNeXt exhibited superior regression prediction performance. …”
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A comparative study on different machine learning approaches with periodic items for the forecasting of GPS satellites clock bias by Longjiang Song, Jiahao Liu, Leilei Wang, Ziyi Wang, Yibo Yuan

“…In recent applications, deep learning models have significantly improved handling time-series data. …”
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Foodborne Event Detection Based on Social Media Mining: A Systematic Review by Silvano Salaris, Honoria Ocagli, Alessandra Casamento, Corrado Lanera, Dario Gregori

“…Study variables included social media platforms, ML techniques (shallow and deep learning), and model performance, with a risk of bias assessed using the PROBAST tool. …”
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