STA-HAR: A Spatiotemporal Attention-Based Framework for Human Activity Recognition by Md. Khaliluzzaman, Md. Furquan, Mohammod Sazid Zaman Khan, Md. Jiabul Hoque

“…Furthermore, the utilization of an attention mechanism serves the purpose of dynamically selecting the significant segments within the sequence, thereby improving the model’s comprehension of context and enhancing the efficacy of deep neural networks (DNNs) in the domain of human activity recognition (HAR). …”
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AI-Based Screening Method for Early Identification of Invasive Ductal Carcinoma in Breast Cancer by Dominik Jánošík, Sila Yavuz

“…Next, by leveraging deep neural networks, we extracted effective features, and through a majority vote method, we performed data classification to establish a screening structure for the diagnosis of invasive ductal carcinoma of breast tumors. …”
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Contrastive Dual-Pool Feature Adaption for Domain Incremental Remote Sensing Scene Classification by Yingzhao Shao, Yunsong Li, Xiaodong Han

“…Remote sensing image classification has achieved remarkable success in environmental monitoring and urban planning using deep neural networks (DNNs). However, the performance of these models is significantly impacted by domain shifts due to seasonal changes, varying atmospheric conditions, and different geographical locations. …”
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GFA-Net: Geometry-Focused Attention Network for Six Degrees of Freedom Object Pose Estimation by Shuai Lin, Junhui Yu, Peng Su, Weitao Xue, Yang Qin, Lina Fu, Jing Wen, Hong Huang

“…However, the challenge of deep neural networks inadequately extracting features from object regions in RGB images remains. …”
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Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment by Soronzonbold Otgonbaatar, Dieter Kranzlmuller

“…Our quantum resource estimation showed that quantum machine learning (QML) models, with a sufficient number of T-gates, provide the quantum advantage if and only if they generalize on unseen data points better than their classical counterparts deployed on the HPC system and they break the symmetry in their weights at each learning iteration like in conventional deep neural networks. We also estimated the quantum resources required for some QML models as an initial innovation. …”
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Lung Diseases Diagnosis-Based Deep Learning Methods: A Review by Shahad A. Salih, Sadik Kamel Gharghan, Jinan F. Mahdi, Inas Jawad Kadhim

“…This review discusses the various DL methods that have been developed for lung disease diagnosis, including convolutional neural networks (CNNs), deep neural networks (DNNs), and generative adversarial networks (GANs). …”
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Reverse design of broadband sound absorption structure based on deep learning method by Yihong Zhou, Lifeng Ma, Xi Kang, Zhiyuan Zhu

“…Traditional methods require time-consuming individual numerical simulations followed by cumbersome calculations, whereas the deep learning design method significantly simplifies the design process, achieving efficient and rapid design objectives. By utilizing deep neural networks, a mapping relationship between structural parameters and the sound absorption coefficient curve is established. …”
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Specification-Based Testing of the Image-Recognition Performance of Automated Driving Systems by Kento Tanaka, Toshiaki Aoki, Takashi Tomita, Daisuke Kawakami, Nobuo Chida

“…Interestingly, deep neural networks (DNNs) have proven effective for object detection in these settings. …”
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Application of Machine Learning to Background Rejection in Very-high-energy Gamma-Ray Observation by Jie Li, Hongkui Lv, Yang Liu, Jiajun Huang, Yu Wang, Wenbin Lin

“…Machine learning (ML) models, including logistic regression, support vector machines, decision trees, random forests, XGBoost, CatBoost, and deep neural networks (DNN) were constructed and trained using data sets of four energy bands ranging from 10 ^12 to 10 ^16 eV, and finally fused using the stacking ensemble algorithm. …”
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Augmented prediction of vertebral collapse after osteoporotic vertebral compression fractures through parameter-efficient fine-tuning of biomedical foundation models by Sibeen Kim, Inkyeong Kim, Woon Tak Yuh, Sangmin Han, Choonghyo Kim, Young San Ko, Wonwoo Cho, Sung Bae Park

“…This study aimed to develop a predictive model leveraging deep neural networks to predict VC progression after OVCF using magnetic resonance imaging (MRI) and clinical data. …”
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A Multistage Detection Framework Based on TFA and Multiframe Correlation for HFSWR by Zongtai Li, Gangsheng Li, Ling Zhang, Lanjun Liu, Q. M. Jonathan Wu

“…In this article, TFA, multiframe correlation, and deep neural networks are integrated to develop a three-stage detection framework. …”
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Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images by Ting Shen MsD, Jiehui Jiang PhD, Jiaying Lu MD, Min Wang MsD, Chuantao Zuo MD, PhD, Zhihua Yu, Zhuangzhi Yan PhD

“…Our framework is composed of 4 steps: (1) image preprocessing: normalization and smoothing; (2) identification of regions of interest (ROIs); (3) feature learning using deep neural networks; and (4) classification by support vector machine with 3 kernels. …”
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A forestry investigation: Exploring factors behind improved tree species classification using bark images by Gokul Kottilapurath Surendran, Deekshitha, Martin Lukac, Martin Lukac, Jozef Vybostok, Martin Mokros

“…This study investigates why researchers often focus on segment-specific bark images for tree species classification via deep neural networks rather than large or entire tree images. …”
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Deep Reinforcement Learning Assisted UAV Path Planning Relying on Cumulative Reward Mode and Region Segmentation by Zhipeng Wang, Soon Xin Ng, Mohammed EI-Hajjar

“…The proposed region segmentation algorithm and cumulative reward model have been tested in different DRL techniques, where we show that the cumulative reward model can improve the training efficiency of deep neural networks by 30.8% and the region segmentation algorithm enables deep Q-network agent to avoid 99% of local optimal traps and assists deep deterministic policy gradient agent to avoid 92% of local optimal traps.…”
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Forecasting High‐Speed Solar Wind Streams From Solar Images by Daniel Collin, Yuri Shprits, Stefan J. Hofmeister, Stefano Bianco, Guillermo Gallego

“…The study shows that a small number of physical features explains most of the solar wind variation, and that focusing on these features with simple machine learning algorithms even outperforms current approaches based on deep neural networks and MHD simulations. In addition, we explain why the typically used loss function, the mean squared error, systematically underestimates the HSS peak velocities, aggravates operational space weather forecasts, and how a distribution transformation can resolve this issue.…”
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Robust Data-Driven Fault Detection: An Application to Aircraft Air Data Sensors by Yunmei Zhao, Hang Zhao, Jianliang Ai, Yiqun Dong

“…To address these issues, exemplifying the FD problem of aircraft air data sensors, we explore to develop a robust (accurate, scalable, explainable, and interpretable) FD scheme using a typical data-driven method, i.e., deep neural networks (DNN). To guarantee the scalability, aircraft inertial reference unit measurements are adopted as equivalent inputs to the DNN, and a database associated with 6 different aircraft/flight conditions is constructed. …”
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Quantum theory-inspired inter-sentence semantic interaction model for textual adversarial defense by Jiacheng Huang, Long Chen, Xiaoyin Yi, Ning Yu

“…Abstract Deep neural networks have a recognized susceptibility to diverse forms of adversarial attacks in the field of natural language processing and such a security issue poses substantial security risks and erodes trust in artificial intelligence applications among people who use them. …”
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Visual explanation method for reversible neural networks by Xinying MU, Bingbing SONG, Fanxiao LI, Yisen ZHENG, Wei ZHOU, Yunyun DONG

“…The issue of model explainability has gained significant attention in understanding the vulnerabilities and anonymous decision-making processes inherent in deep neural networks (DNN).While there has been considerable research on explainability for traditional DNN, there is a lack of exploration on the operation mechanism and explainability of reversible neural networks (RevNN).Additionally, the existing explanation methods for traditional DNN are not suitable for RevNN and suffer from issues such as excessive noise and gradient saturation.To address these limitations, a visual explanation method called visual explanation method for reversible neural network (VERN) was proposed for RevNN.VERN leverages the reversible property of RevNN and is based on the class-activation mapping mechanism.The correspondence between the feature map and the input image was explored by VERN, allowing for the mapping of classification weights of regional feature maps to the corresponding regions of the input image.The importance of each region for model decision-making was revealed through this process, which generates a basis for model decision-making.Experimental comparisons with other explanation methods on generalized datasets demonstrate that VERN achieves a more focused visual effect, surpassing suboptimal methods with up to 7.80% improvement in average drop (AD) metrics and up to 6.05% improvement in average increase (AI) metrics in recognition tasks.VERN also exhibits an 82.00% level of localization for the maximum point of the heat value.Furthermore, VERN can be applied to explain traditional DNN and exhibits good scalability, improving the performance of other methods in explaining RevNN.Furthermore, through adversarial attack analysis experiments, it is observed that adversarial attacks alter the decision basis of the model.This is reflected in the misalignment of the model’s attention regions, thereby aiding in the exploration of the operation mechanism of adversarial attacks.…”
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Pixel-Level Recognition of Pavement Distresses Based on U-Net by Deru Li, Zhongdong Duan, Xiaoyang Hu, Dongchang Zhang

“…Secondly, the U-net model, one of the most advanced deep neural networks for image segmentation, is combined with the ResNet neural network as the basic classification network to recognize distressed areas in the images. …”
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Attention-based interactive multi-level feature fusion for named entity recognition by Yiwu Xu, Yun Chen

“…Abstract Named Entity Recognition (NER) is an essential component of numerous Natural Language Processing (NLP) systems, with the aim of identifying and classifying entities that have specific meanings in raw text, such as person (PER), location (LOC), and organization (ORG). Recently, Deep Neural Networks (DNNs) have been extensively applied to NER tasks owing to the rapid development of deep learning technology. …”
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