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3rd IEEE International Conference on Human-Machine systems, ICHMS 2022

作者： Weng, Yu-Liang Chien, Shih-Yi Lin, Szu-Yin National Chengchi University Department of Management Information System Taipei Taiwan National Ilan University Department of Computer Science and Information Engineering Yilan Taiwan

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

This study aims to train a time-sequential deep learning model to classify reviews as genuine or fake. The real-life dataset from Yelp contains review text and user behavior, which are taken together as features within the LSTM model. The results suggest that a time-sequential model such as LSTM is promising to predict fake reviews with an 89% accuracy score. Besides, the textual features represented in word embedding vectors perform better than user behavior features. The results provided by deep learning models are expected to extend further to explainable AI (XAI) in future work. © 2022 IEEE.

关键词： Long short-term memory

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A Traffic Sign Recognition Method with Bi-Level Routing Attention 8

A Traffic Sign Recognition Method with Bi-Level Routing Atte...

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8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Liu, Ziqiong Li, Wenju Cui, Liu Gao, Xiaosong Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China ShangHai CRRC Ro-Intelligent System. Co. Ltd. Shanghai China

ISBN: (纸本)9798350369564

Traffic sign recognition has long been an essential part of automatic driving. Accurate traffic sign recognition algorithms are conducive to the development of automatic driving. However, traffic signs in natural scenes are often small targets, which contain less feature information and are difficult to detect and recognize. Aiming at the problem of low recognition accuracy of small targets in traffic sign recognition, a traffic sign recognition algorithm based on a Bi-Level Routing Attention is proposed. It utilizes a single-stage detector YOLOv5 as the basic network, with a double-layer routing attention mechanism introduced, realizing more flexible computing allocation and content awareness through double-layer routing, which could capture long-range data without incurring an increasing computational overhead and memory usage. Furthermore, our approach achieves noteworthy recognition accuracy by extracting fine-grained information of small targets. In particular, the experiment shows the appealing results on the TT100K dataset, which detection accuracy map of reaches 92.9%, and the frame processing rate is 119.8FPS. © 2023 IEEE.

关键词： Object detection

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Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road Networks

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China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer Science and Technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

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Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

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computers, Materials & Continua 2022年第2期70卷 2639-2653页

作者： V.Praveena A.Vijayaraj P.Chinnasamy Ihsan Ali Roobaea Alroobaea Saleh Yahya Alyahyan Muhammad Ahsan Raza Department of Computer Science and Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India Department of Information Technology Vignan’s Foundation for ScienceTechnology&ResearchGuntur522213India Department of Information Technology Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Community College in DwadmiShaqra University11961Saudi Arabia Department of Information Technology Bahauddin Zakariya UniversityMultan60000Pakistan

In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously *** such scenario,Internet of Things(IoT)network which is comprised of a set of Unmanned Aerial Vehicles(UAV),has received more attention from civilian tomilitary *** network security poses a serious challenge to UAV networks whereas the intrusion detection system(IDS)is found to be an effective process to secure the UAV *** IDSs are not adequate to handle the latest computer networks that possess maximumbandwidth and data *** order to improve the detection performance and reduce the false alarms generated by IDS,several researchers have employed Machine Learning(ML)and Deep Learning(DL)algorithms to address the intrusion detection *** this view,the current research article presents a deep reinforcement learning technique,optimized by BlackWidow Optimization(DRL-BWO)algorithm,for UAV *** addition,DRL involves an improved reinforcement learning-based Deep Belief Network(DBN)for intrusion *** parameter optimization of DRL technique,BWO algorithm is *** helps in improving the intrusion detection performance of UAV *** extensive set of experimental analysis was performed to highlight the supremacy of the proposed *** the simulation values,it is evident that the proposed method is appropriate as it attained high precision,recall,F-measure,and accuracy values such as 0.985,0.993,0.988,and 0.989 respectively.

关键词： Intrusion detection UAV networks reinforcement learning deep learning parameter optimization

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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

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computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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Comparative Analysis Of Deep Learning Architectures For The Classification Of Eye-Related Diseases 12

Comparative Analysis Of Deep Learning Architectures For The ...

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12th International Conference on Orange Technology, ICOT 2024

作者： Sultan, Muhammad Ali Kaburuan, Emil R. Rasool, Fezan Suwito, Misni Harjo Informatics Engineering Department Mercu Buana University Jakarta Barat Indonesia Computer Science Department Lahore University of Management Sciences Lahore Pakistan Information System Department Mercu Buana University Jakarta Barat Indonesia

ISBN: (纸本)9798331523602

Eye-related disorders are among the driving causes of vision impedances worldwide. Accurate classification of retinal conditions is crucial for adequate treatment and management. This research explores the potential of three advanced deep-learning models, namely—-VGG16, DenseNet, and Swin Transformers, to classify four eye-related disorders utilizing the Optical Coherence Tomography Image Retinal Database (OCTID) taken from ICPSR. The OCTID dataset contains retinal OCT images representing four distinct pathological conditions: age-related macular degeneration, diabetic retinopathy, central serous retinopathy, macular holes, and normal retinas. Evaluation is done based on metrics such as accuracy, precision, recall, and F1 score. The comparison of these models, paired with interpretability techniques such as GRADCAM and Integrated Gradients, reveals that VGG16 achieves the highest F1-score, demonstrating robust classification performance. At the same time, Swin Transformers develops competitive accuracy with practical interpretability through Integrated Gradients. DenseNet showed slightly lower metrics but performed adequately on the GRADCAM outputs. This study underscores the critical role of advanced deep learning frames, which work to enhance diagnostic accuracy for retinal disorders and provide a foundation for future developments in ophthalmic disease classification. © 2024 IEEE.

关键词： Optical coherence tomography

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Development of China's spaceborne SAR satellite,processing strategy,and application:take Gaofen-3 series as an example

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地球空间信息科学学报（英文版） 2024年第1期27卷 221-236页

作者： Guo Zhang Shunyao Wang Zhenwei Chen Yuzhi Zheng Ruishan Zhao Taoyang Wang Yu Zhu Xinzhe Yuan Wei Wu Weitao Chen State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Law Wuhan UniversityWuhanChina School of Geomatics Liaoning Technical UniversityFuxinChina School of Remote Sensing and Information Engineering Wuhan UniversityWuhanChina Institute of Spacecraft System Engineering China Academy of Space TechnologyBeijingChina National Disaster Reduction Center of China Ministry of Emergency ManagementBeijingChina School of Computer Science China University of GeosciencesWuhanChina Key Laboratory of Space Ocean Remote Sensing and Application Ministry of Natural Resources of the People's Republic of ChinaBeijingChina

After mastering the key technologies of manufacturing spaceborne Synthetic Aperture Radar(SAR),China's SAR satellites have been successfully launched into *** the only civil microwave satellite listed in the"National High-resolution Earth Observation system Major Project,"the Gaofen-3(GF-3)01 satellite is the first C-band multi-polarization SAR satellite with a resolution of 1 ***-3 series satellites stand out among civil SAR satellites worldwide because of their high resolution,wide swath,high geometric radiation quality,multiple imaging modes,and long operation *** GF-3 series satellites as an example,this study introduces the development of China's civil SAR satellites,as well as their processing strategies and *** success of the GF-3 series satellites shows that China's SAR remote sensing technology has stepped into a new era of high-quality and high-precision Earth observations.

关键词： High-resolution Synthetic Aperture Radar(SAR) GaoFen-3(GF-3)satellites earth observation

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Robust Network For Segmenting Roof Planes From Sparse Point Clouds 36

Robust Network For Segmenting Roof Planes From Sparse Point ...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Fu, Hang Zeng, Cheng Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan China School of Computer and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China

ISBN: (纸本)1891706594

The plane is the fundamental characteristic for describing the shape of polyhedral buildings. Roof plane segmentation of airborne LiDAR point clouds is an important step in 3D building model reconstruction. Existing methods are either non-automatic and efficient traditional methods, or hindered by the lack of datasets for roof plane segmentation and therefore fail to achieve good training for deep learning methods. To address the above issues, we proposes an end-to-end network called PUTr for roof plane segmentation of sparse point clouds. This network integrates a Point-cloud Upsampling module and Transformer to ensure effective generalization even when trained on low-density datasets, while also capturing dependency relationships within point cloud neighborhoods. Our model overcomes the low automation and manual intervention of existing methods, and the limited generalization of deep learning due to scarce high-quality datasets. Evaluations also demonstrate its effectiveness in roof plane segmentation of laser point cloud data. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： 3D reconstruction

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Improved Chest X-Ray Image Quality Using Median and Gaussian Filter Methods 4

Improved Chest X-Ray Image Quality Using Median and Gaussian...

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4th International Conference on Informatics, Multimedia, Cyber and information system, ICIMCIS 2022

作者： Kesuma, Lucky Indra Ermatita, Ermatita Erwin, Erwin Sari, Purwita Purabaya, Rudhy Ho Universitas Sriwijaya Doctoral Program in Engineering Faculty of Engineering Palembang Indonesia Universitas Sjakhyakirti India Universitas Sriwijaya Information System Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Departement of Computer System Palembang Indonesia Universitas Sriwijaya Informatic Management Faculty of Computer Science Palembang Indonesia Universitas Pembangunan Nasional Veteran Jakarta Information System Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665473279

The lungs are one of the organs of the body that are responsible for the human respiratory process and are very susceptible to dangerous diseases. For this reason, early detection and diagnosis of lung organs is needed, one of which is through examination of Chest X-Ray (CXR) images. Examination of the results of CXR images is still done manually by doctors and radiologists, this requires time and high accuracy. To facilitate the examination, it is necessary to image quality enhancement in order to get better image quality results so as to produce an accurate diagnosis. The initial stage in this research is to apply the contour improvement technique to the image using Morphology Opening, and followed by noise reduction using Median Filter and Gaussian filter. The results of the two methods of noise reduction are compared with the results of image quality in order to find out the best method that can be applied. The implementation of image quality enhancement results was measured quantitatively using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metrics (SSIM). In the Morphology Opening and Median Filter methods, the values obtained are 39.187, 22.252, and 0.952, respectively. Meanwhile, the Morphology Opening and Gaussian Filter methods obtained values of 38.717, 23.917 and 0.956. Based on these results, it can be concluded that both methods are able to improve image quality well. © 2022 IEEE.

关键词： Image quality

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Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

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IEEE Access 2024年 12卷 82443-82468页

作者： Sana, Laraib Nazir, Muhammad Mohsin Yang, Jing Hussain, Lal Chen, Yen-Lin Ku, Chin Soon Alatiyyah, Mohammed Alateyah, Sulaiman Abdullah Por, Lip Yee Lahore College for Women University Department of Computer Science Punjab Lahore54000 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia The University of Azad Jammu and Kashmir Department of Computer Science and IT Neelum Campus Azad Kashmir13230 Pakistan King Abdullah Campus The University of Azad Jammu and Kashmir Department of Computer Science and IT Azad Kashmir Muzaffarabad13100 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Prince Sattam Bin Abdulaziz University College of Sciences and Humanities-Aflaj Department of Computer Science Al-Kharj16278 Saudi Arabia Qassim University College of Computer Department of Computer Engineering Buraydah52571 Saudi Arabia

The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. This study demonstrates the power of our new methods for detecting and stopping attacks on Internet of Things (IoT) networks. This improved detection offers

关键词： Intrusion detection

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