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8th International Conference on Multimedia and Image Processing, ICMIP 2023

作者： Zhang, Zixuan Shi, Fan Geng, Xinbo Tao, Yi He, Jing Key Laboratory of Computer Vision and System Ministry of Education School of Computer Science and Engineering Tianjin University of Technology China

ISBN: (纸本)9781450399586

In recent years, significant progress has been made in salient object detection. Nevertheless, there remains a need for further improvements in the effective combination of local and global perspectives. Combining global perception with the local focus can enhance the ability to capture the integrity of protruding objects while achieving accurate segmentation. In this paper, we introduce a newly proposed salient object detection network named GCA-Net, which is built upon three essential components. Firstly, we utilize atrous convolution to effectively aggregate contextual information and refine high-level semantic information via spatial pooling. Secondly, we employ 1-dim crossover operations to achieve global perception while minimizing computational effort. Lastly, We have innovatively integrated a contour-aware loss into our salient object detection task to constrain our model's predictions, leading to more accurate segmentation. After evaluating our model on the established salient object detection benchmark dataset, we conducted extensive experiments to demonstrate the exceptional performance of our method. As a result, our method achieved 1st in both Sm and MAE. © 2023 ACM.

关键词： Semantics

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A Novel Convolutional-Transformer Neural Network Architecture for Diagnosis of Pneumothorax 28

A Novel Convolutional-Transformer Neural Network Architectur...

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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Sanati, Amir Dashtestani, Mansoureh A. Rostami, Habib Azad, Saeed Talatian Persian Gulf University Faculty of Intelligent System Engineering and Data Science Computer Engineering Department Bushehr Iran

ISBN: (纸本)9798350338195

Pneumothorax is a life-threatening and urgent chest disease than can be detected using Chest X-Ray (CXR) image. CXR images are low resolution and diagnosis of pneumothorax based on them is error prone. Deep learning-based computer aided diagnosis systems can improve diagnosis performance of pneumothorax. Convolutional Neural Networks (CNNs) are default networks in deep learning-based medical image process. However, CNNs fail to capture long range features. On the other side, Transformer are proposed to exploit long range feature, but they cannot capture local features. In this paper, we propose a general method with a convolution and a transformer module which can classify CXR images to diagnose pneumothorax by extracting local features, global features and global features attended by local ones using a novel architecture. Results show that the proposed method outperforms base architectures and the other previous works. © 2023 IEEE.

关键词： Convolutional neural networks

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Study on Intelligent Decision Architecture of Online Data Acquisition and Analysis system 3

Study on Intelligent Decision Architecture of Online Data Ac...

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3rd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Yu, Chenglong Liu, Daoyuan Yan, Hongpo Li, Qiang Gong, Maozhen Beijing Institute of Computer Technology and Application Beijing China Jincheng Nanjing Engineering Institute of Aircraft System Nanjing China

ISBN: (纸本)9798331529222

To better improve the production capacity and control efficiency of discrete manufacturing workshops, a decision system architecture based on online acquisition and data analysis is proposed, and its architecture and operation process are described. Furthermore, the key supporting function such as manufacturing data detection and acquisition based on internet of things, data analysis for production control, production optimization decision based on data analysis were further studied. Ultimately, a prototype system was built, as well as its functionality was validated by the past actual data. It has good reference value to provides technical support and software foundation for subsequent research and performance verification. ©2024 IEEE.

关键词： Smart manufacturing

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Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

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China Communications 2024年第12期21卷 218-229页

作者： Xu Wenjing Wang Wei Li Zuguang Wu Qihui Wang Xianbin College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing 211106China Department of Electrical and Computer Engineering Western UniversityCanada

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless ***,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be *** this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different *** minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each *** apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal ***,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the *** results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

关键词： blockchain collaborative edge computing resource optimization task allocation

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Comparative Analysis of mmWave Radar-based Object Detection in Autonomous Vehicles

Comparative Analysis of mmWave Radar-based Object Detection ...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Sharifisoraki, Ziaaddin Amini, Marzieh Rajan, Sreeraman Carleton University School of Information Technology Ottawa Canada Carleton University System and Computer Engineering Department Ottawa Canada

ISBN: (纸本)9798350324136

Millimeter-wave radar technology is gaining popularity as a perception sensor in autonomous vehicles. This is due to its ability to detect nearby objects in adverse weather conditions, such as rain, snow, or fog, as well as its cost-effectiveness. In this paper, we explore the impact of different backbones and object detector heads on the performance of radar-based object detection algorithms. More specifically, we employ the RADDet dataset and its object detection algorithm which provides the entire Range-Azimuth-Doppler spectrum and incorporates an automatic annotation approach. We examine different backbones and object detector heads to identify optimal model combinations for autonomous driving applications. Our results show that using a YOLOv4 head integrated with a modified ResNet backbone leads to the highest mean average precision, reaching 66.3% with an intersection over union (IoU) of 0.1, and 43.6% with an IoU of 0.3. This observation will help to advance radar-based object detection, thereby enhancing safety and reliability in diverse environmental conditions. © 2024 IEEE.

关键词： mmWave Radar Object Detection ResNet VGG YOLO

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Combinatorial Kinetic Dualistic Auction Model for Metaverse Service Allocation 1

Combinatorial Kinetic Dualistic Auction Model for Metaverse ...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Singh, Raj Sarkar, Pritam Bandyopadhyay, Anjan Swain, Sujata Sardar, Tanvir Habib Pandey, Bishwajeet School of Computer Engineering KIIT 751024 India Department of Computer Science GITAM University Bengaluru India Department of Intelligent System and Cybersecurity Astana IT University Kazakhstan

ISBN: (纸本)9798350364378

The focus of this research work revolves around the utilization of a specialized"Combinatorial Kinetic Dualistic Auction Model" designed specifically for"metaverse Services." The main goal is to improve the allocation of resources in this ever-changing environment and enhance the overall experience for end-users. Through the exploration of metaverse services, we aim to further develop the comprehension and practical application of the Combinatorial Kinetic Dualistic Auction Model, ultimately contributing to a more immersive and responsive virtual experience. ©2024 IEEE |

关键词： Resource allocation

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Curve Monitoring and Alert system for Smart Transportation 1

Curve Monitoring and Alert System for Smart Transportation

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Natarajan, Yuvaraj Sri Preethaa K, R. Viswanathan, Shrinithi Jayarathinam, Sandhiya Balasubramaniam, Vikas Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kyungpook National University Department of Robot and Smart System Engineering Daegu Korea Republic of

ISBN: (纸本)9798350372748

The proposed IoT-based Smart Curve Monitoring and Alert system is designed to improve road safety through the fusion of embedded systems, machine learning and edge computing technologies. The proposed system is primarily powered by ESP32 microcontroller, continuously gathers data from an array of sensors, including accelerometers, gyroscopes and a potentiometer to set the speed limit. This collected data is then subjected to analysis by a Convolutional Neural Network (CNN) model, which has been trained to recognize driving patterns, especially when navigating curves. Here, CNN classifies the vehicle's behavior as safe and risky, and if a risky behavior is identified, it triggers an alert and dispatches relevant data to a NodeMCU device. The NodeMCU plays a crucial role in the system by monitoring the communication tasks. It receives the alert from the ESP32, validates the vehicle's speed against the preset limit, and, if necessary, activates a GSM module to send an immediate notification to designated guardian. To ensure real-time monitoring and awareness, all pertinent information will be displayed on the LCD screen, including the vehicle's behavior classification, current speed, speed limit and alert status. By addressing reckless driving behaviors, particularly on curves, the system contributes significantly to safer roads and reinforces the importance of responsible driving practices. © 2024 IEEE.

关键词： Convolution

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Malware Detection system Based on Static-Dynamic Preprocessing Techniques Combined in an Ensemble Model 15th

Malware Detection System Based on Static-Dynamic Preprocessi...

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15th International Conference on computer Science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Kim, Hae-Soo Kim, Mi-Hui School of Computer Engineering and Applied Mathematics Computer System Institute Hankyong National University 327 Jungang-ro Gyeonggi-do Anseong-si Korea Republic of

ISBN: (纸本)9789819724468

Malware distributors use persistence mechanisms in their malware to keep a PC infected for a long time. Techniques such as packing and obfuscation are often used so that the presence of malware cannot be determined by the simple executable code of the program. Static analysis of executable code, structures, and strings cannot determine obfuscated and packed malware. Therefore, dynamic analysis is required to determine the malicious behavior imposed on the system. In this paper, we used the CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) model, which preprocessed static data and dynamic data and combined them into an ensemble model to determine malicious and benign and achieved an accuracy of 97.68%, F1-Score of 97.67%, and false positive rate of 1.57%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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