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检索条件"机构=Faculty of Software Engineering and Computer Engineering"

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Reinforcement-Learning Based Method for Accelerating Functional Coverage Closure of Traffic Light Controller Dynamic Digital Design 32

Reinforcement-Learning Based Method for Accelerating Functio...

引用

32nd International Conference on computer Theory and Applications, ICCTA 2022

作者： Halim, Youstina M. Ismail, Khaled A. Abd El Ghany, Mohamed A. Ibrahim, Sameh A. Halim, Youssef M. Ain Shams University Faculty of Engineering Mixel Egypt. Electronics and Communications Engineering Dept. Cairo Egypt Germany Electronics Department German University in Cairo Egypt Integrated Electronic Systems Lab Tu Darmstadt Germany Ain Shams University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Ain Shams University Faculty of Engineering Computer Engineering and Software Systems Dept. Cairo Egypt

ISBN: (纸本)9798350320190

Functional verification of digital designs is an increasingly complex and time-consuming endeavor. One of the major challenges in functional verification is achieving functional coverage closure in a timely manner. Verification experts have moved from using directed stimulus, towards constrained random stimulus and finally, towards using machine learning to direct stimulus. In this work, reinforcement learning (RL) was used, incorporating three different kinds of RL agents to direct stimulus generation for a traffic light controller system. Two of the agents are based on Q-learning (DQN and SARSA), and one is based on Monte-Carlo method (CEM). The maximum reduction in simulation time needed to achieve a targeted percent of coverage was 97%. The average reduction of simulation time across all testcases and agents used was 70%. The solution is highly reusable, the architecture was integrated with different agents and testcases. © 2022 IEEE.

关键词： Reinforcement learning

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Automated Glaucoma Detection based on LBP Representation and GLRLM Feature Extraction Method

Automated Glaucoma Detection based on LBP Representation and...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Ahmed, Zakia Lawgali, Ahmed Abdalla, Mohamed University of Omar Al-Mukhtar Faculty of Sciences Department of Computer Science Albayda Libya University of Benghazi Faculty of Information Technology Department of Computer Science Benghazi Libya University of Benghazi Faculty of Information Technology Department of Software Engineering Benghazi Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

Glaucoma is a chronic and degenerative disease that causes irreversible damage to the nerve system of an eye, due to an increase in the Intra-ocular pressure in the retina. It reduces the vision area and leads to blindness. Early detection is critical to prevent permanent vision loss. The automated analysis of medical images contributes to the increase in the performance of classifying retinal images to pathological and nonpathological. This paper presents an automated glaucoma diagnosis approach by analyzing the texture of fundus images. The analysis was performed using Local Binary Pattern descriptor to represent grayscale and channels of retinal images and applied Gray Level Run-Length Matrix to describe patterns of texture. The classification was performed using two classifiers. The proposed approach provided the best classification results with 98%, 91%, and 97% of the three versions of RIM-ONE databases using Support Vector Machine classifier. Findings showed that local binary pattern representations and concatenated gray level run length matrix features achieved high results. Consequently, proving this approach is reliable and promising. © 2022 IEEE.

关键词： Local binary pattern

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A Review of Artificial Intelligence in Serious Game for Public Health 3

A Review of Artificial Intelligence in Serious Game for Publ...

引用

3rd International Conference on Computational Science and Information Management, ICoCSIM 2019

作者： Rafiq, Abdul Asmawaty Abdul Kadir, Tuty Normaziah Ihsan, Siti Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Pahang Kuantan Malaysia

Serious games used in public health for purposes including training, learning, prediction, coaching, diagnostic, rehabilitation and supporting. However, the serious game focuses mainly on user experience and graphical application, but less attention on applying artificial intelligence (AI). By enhancing artificial intelligence, it will have the capability to solve issues, especially in the field of public health. This article discussed a review of the use of artificial intelligence in a serious game for public health. The main idea of this paper is to gather all the related articles and create a trend analysis of the use of Artificial Intelligence (AI) in a serious game for public health. The related articles were applied to Artificial intelligence (AI) in the area of decision-making. The final section discussed the new trend and the future of Artificial intelligence using current AI techniques. © Published under licence by IOP Publishing Ltd.

关键词： Public health

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Traffic signal image detection technology based on YOLO

Traffic signal image detection technology based on YOLO

引用

2021 International Conference on computer engineering and Innovative Application of VR, ICCEIA VR 2021

作者： Gao, Hui Wang, Wenhao Yang, Chengjin Jiao, Weile Chen, Ziwei Zhang, Tong Faculty of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huai'an223003 China

The detection and recognition of traffic signal image is an important content in intelligent transportation system. It can be applied to driver assistance system to effectively recognize traffic signal signs on the road, so as to reduce the occurrence of traffic accidents. At the same time, it also provides strong technical support for the future unmanned driving system. The main content of this paper is based on the deep learning method, using YOLOv3 and YOLOv4 algorithm to detect and recognize the traffic signal image on the road. The experimental results show that the recognition result of YOLOv4 network is better than that of YOLOv3 network. © Published under licence by IOP Publishing Ltd.

关键词： Traffic signals

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Depth3DSketch: Freehand Sketching Out of Arm’s Reach in Virtual Reality

Depth3DSketch: Freehand Sketching Out of Arm’s Reach in Vir...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Bashar, Mohammad Raihanul Amini, Mohammadreza Stuerzlinger, Wolfgang Sarac, Mine Pfeuffer, Ken Machuca, Mayra Donaji Barrera Batmaz, Anil Ufuk Department of Computer Science & Software Engineering Concordia University MontrealQC Canada Simon Fraser University VancouverBC Canada Kadir Has University Istanbul Turkey Department of Computer Science Aarhus University Aarhus Denmark Faculty of Computer Science Dalhousie University HalifaxNS Canada

ISBN: (纸本)9798400713958

Due to the increasing availability and popularity of virtual reality (VR) systems, 3D sketching applications have also boomed. Most of these applications focus on peripersonal sketching, e.g., within arm’s reach. Yet, sketching in larger scenes requires users to walk around the virtual environment while sketching or to change the sketch scale repeatedly. This paper presents Depth3DSketch, a 3D sketching technique that allows users to sketch objects up to 2.5 m away with a freehand sketching technique. Users can select the sketching depth with three interaction methods: using the joystick on a single controller, the intersection from two controllers, or the intersection from the controller ray and the user’s gaze. We compared these interaction methods in a user study. Results show that users preferred the joystick to select visual depth, but there was no difference in user accuracy or sketching time between the three methods. © 2025 Copyright held by the owner/author(s).

关键词： Virtualization

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Efficient feature selection analysis for accuracy malware classification 7

Efficient feature selection analysis for accuracy malware cl...

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7th International Conference on Mathematics, Science, and Education 2020, ICMSE 2020

作者： Romli, R.N. Zolkipli, M.F. Osman, M.Z. Faculty of Computer Systems and Software Engineering University Malaysia Pahang Gambang26200 Malaysia

Android is designed for mobile devices and its open-source software. The growth and popularity of android platform are high compared to another platform. Due to its glory, the number of malware has been increasing exponentially. Android system used a permission mechanism to allow users and developers to manage their access to private information, system resources, and data storage required by Android applications (apps). It became an advantage to an attacker to violent the data. This paper proposes a novel framework for Android malware detection. Our framework used three major methods for effective feature representation on malware detection and used this method to classify malware and benign. The result demonstrates that the Random forest is with 23 features is more accurate detection than the other machine learning algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Open source software

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Traffic sign detection based on Haar and adaBoost classifier 4

Traffic sign detection based on Haar and adaBoost classifier

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2021 4th International Conference on Advanced Algorithms and Control engineering, ICAACE 2021

作者： Wang, Wenhao Gao, Hui Chen, Xiaobing Yu, Zhenyang Jiang, Mingxing Faculty of Computer and Software Engineering HuaiYin Institute of Technology Huai'an Jiangsu223003 China

Intelligent transportation system is a hot issue in the field of computer vision. Traffic sign detection is an important part of intelligent transportation system. A traffic sign detection algorithm based on Haar feature and AdaBoost algorithm is proposed in this paper. Firstly, Haar features are extracted from the positive and negative samples of the training set, and then these features are used to train the AdaBoost cascade classifier. Finally, the Haar features are extracted from the test image, and the possible traffic signs in the image are detected, the experimental results show that the method can achieve good detection effect, and provide a new idea for the detection of traffic signs. © Published under licence by IOP Publishing Ltd.

关键词： Traffic signs

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Light-weight Sketch Recognition with Knowledge Distillation 6

Light-weight Sketch Recognition with Knowledge Distillation

引用

6th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Huynh, Viet-Tham Nguyen, Tam.V Tran, Minh-Triet Software Engineering Laboratory Faculty of Information Technology University of Science VNU-HCM Viet Nam Vietnam National University Ho Chi Minh City Viet Nam University of Dayton Department of Computer Science United States

ISBN: (纸本)9798350327410

Recognizing hand-drawn sketches is a promising starting point for various applications, such as assisting artists in creating 3D environments for games or virtual environment scenes quickly and efficiently from concept arts. In addition, by understanding drawings, we can generate 3D models that can be used for further design and development. Thus, in this paper, we aim to develop a novel lightweight network that can accurately recognize sketch drawings. We propose a lightweight-yet-efficient neural network based on MobileNetV2 for sketch recognition and employ knowledge distillation to train the proposed model from EfficientNet-B4. To evaluate the accuracy of the proposed method, we collect a dataset of sketches comprising 1800 drawings in 12 categories, ranging from furniture to animals. The experimental results show that our network model achieves an accuracy of 96.7%, with 96.9% precision, 96.7% recall, and 96.7% F1-score. These results demonstrate that the proposed approach has great potential for practical sketch recognition applications, such as interior design or VR scene generation. © 2023 IEEE.

关键词： Distillation

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Vision-based road segmentation for intelligent vehicles using deep convolutional neural networks

Vision-based road segmentation for intelligent vehicles usin...

引用

2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021

作者： Oztel, Gozde Yolcu Sakarya University Faculty of Computer and Information Sciences Department of Software Engineering Sakarya Turkey

ISBN: (纸本)9781665436038

Intelligent Transportation Systems are crucial to enhance traffic security and reduce traffic violations. Recent developments in artificial intelligence have also motivated researchers to work on these systems. In order to reduce the risk of road accidents, accurately determining the driving area is important. On the other hand, automated road segmentation is challenging because of different weather, light, road conditions, etc. Also, the roads' design characteristics may be different and may include very small and large features. In this paper, a vision-based road segmentation system is presented. To apply this system, a CNN architecture based on consecutive triple filter size has been designed. The consecutive triple filter size approach avoids missing both small and large features. Proposed CNN architecture consists of an encoder and a decoder subnetwork. Preliminary experimental results achieved 95.84% IoU, 97.86% TPR, and 2.34% FPR indexes for road segmentation tasks on the CamVid database. This study presents a low-cost, reliable, and basic road segmentation system for intelligent vehicles. Also, it is a step toward a more complex system to alert the driver about dangerous situations. Owing to the proposed system, a successful road segmentation is possible using a simple camera. © 2021 IEEE.

关键词： Convolutional neural networks

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Performance Improvement in Blood Cell Segmentation with Deep Learning-based Image Fusion Approach

Performance Improvement in Blood Cell Segmentation with Deep...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Fatma Tuana Dogu Hulya Dogan Ramazan Ozgur Dogan F. Sena Sezen Department of Electrical and Electronics Engineering Faculty of Engineering Karadeniz Technical University Trabzon Turkey Department of Software Engineering Faculty of Engineering Karadeniz Technical University Trabzon Turkey Department of Artificial Intelligence Engineering Faculty of Computer and Information Sciences Trabzon University Trabzon Turkey Department of Pharmacology Faculty of Pharmacy Karadeniz Technical University Trabzon Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

To overcome the limitations of examining blood cells under a microscope, several investigations have been carried out using artificial intelligence techniques. These investigations aim to automate the processes of detecting, counting, segmenting, and classifying blood cells. Typically, these investigations employ two-dimensional (2D) images to apply artificial intelligence and image processing techniques. Nevertheless, these 2D images occasionally lack optimal focus as a result of the microscope’s depth of focus (DOF). Inadequate achievement of optimally focused imaging has a detrimental impact on the efficiency of automated processes. In this context, the objective of this study is founded around two primary roles. The first of these is the performance improvement of blood cell segmentation by developing a deep learning-based image fusion approach. The second is the preparation of new data sets containing images with different focuses for optimally focused imaging of blood cells. In the study, four different performance evaluation criteria, namely Accuracy (ACC), Sensitivity (SEN), Specificity (SPE), and Jaccard, are utilized to demonstrate the efficacy of the deep learning-based image fusion approach in blood cell segmentation. Both the experimental results of the performance evaluation criteria and the visual findings obtained in the study indicate that the performance in blood cell segmentation is improved with a deep learning-based approach.

关键词： Performance evaluation Image segmentation Visualization Accuracy Microscopy Two-dimensional displays Cells (biology) Artificial intelligence Blood Image fusion

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