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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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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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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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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Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection by Brendan Ubochi, Abayomi E. Olawumi, John Macaulay, Oyawoye I. Ayomide, Kayode F. Akingbade

“…The obtained results demonstrate the peculiarity of the dataset, its selectiveness of the most appropriate model, and the potential of deep neural networks (DNNs) as an effective screening tool for glaucoma, enabling prompt interventions, reducing healthcare costs, and helping optometrists make swift decisions.…”
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Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub by Thirunavukarasu Balasubramaniam, Wathsala Anupama Mohotti, Kenneth Sabir, Richi Nayak

“…Utilizing remote sensing and climate data, covering 196 farms (and 6885 paddocks) across Australia, we applied several machine learning techniques, including XGBoost, random forest, linear regression, deep neural networks, stacking, and bootstrapping. Our results show that incorporating averaging-based feature-engineered climate attributes significantly improves pasture yield predictions, with enhancements of up to 20.28%, 31.81%, and 31.11% across the three evaluation measures, RMSE, MAE, and R2, respectively. …”
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Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier by Zainab N. Ali, Iman Askerzade, Saddam Abdulwahab

“…The results indicate that the proposed FWRVM-based classifier estimates the quality of the breads with 96.67% accuracy, 96.687% precision, 96.6% recall, and 96.6% F-measure within 8.96726 seconds processing time which is better than the compared Support vector machine (SVM), RVM, and Deep Neural Networks (DNN) classifiers.…”
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A convolutional autoencoder framework for ECG signal analysis by Ugo Lomoio, Patrizia Vizza, Raffaele Giancotti, Salvatore Petrolo, Sergio Flesca, Fabiola Boccuto, Pietro Hiram Guzzi, Pierangelo Veltri, Giuseppe Tradigo

“…Analysis of time varying signals may be done by using autoencoders (AEs) deep neural networks. AE specialized for signal data, named Convolutional Autoencoder (CAE), showed the best performances in the analysis of ECG signals.This paper presents a CAE-based framework for ECG signal analysis and anomaly identification. …”
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Multi-label classification with deep learning techniques applied to the B-Scan images of GPR by El Karakhi, Soukayna, Reineix, Alain, Guiffaut, Christophe

“…With the emergence of deep neural networks and with a learning phase on a large number of Bscan, it becomes possible to extract almost instantaneously the characteristics of GPR radar data. …”
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Impact of stain variation and color normalization for prognostic predictions in pathology by Siyu Lin, Haowen Zhou, Mark Watson, Ramaswamy Govindan, Richard J. Cote, Changhuei Yang

“…Abstract In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. …”
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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

“…Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.…”
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Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis by Sunil L. Bangare, Deepali Virmani, Girija Rani Karetla, Pankaj Chaudhary, Harveen Kaur, Syed Nisar Hussain Bukhari, Shahajan Miah

“…The researchers have identified that critical algorithms like CART support the predictability of the disease by 93.3% whereas the conventional models possess vert less specificity. Furthermore, deep neural networks can be applied for analyzing and detecting heart failures effectively and supporting medical practitioners in making better and more critical clinical decisions making. …”
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Predicting nighttime black ice using atmospheric data for efficient winter road maintenance patrols by Jinhwan Jang

“…In this context, the present study investigates machine learning techniques, including Random Forest, CatBoost, and Deep Neural Networks, for forecasting nighttime icing on rural highways in Korea. …”
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Time Complexity of Training DNNs With Parallel Computing for Wireless Communications by Pengyu Cong, Chenyang Yang, Shengqian Han, Shuangfeng Han, Xiaoyun Wang

“…Deep neural networks (DNNs) have been widely used for learning various wireless communication policies. …”
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