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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Patil, Ashwini P. Hiremath, Manjunath Department of Computer Science Bengaluru India CMR Institute of Technology Department of Computer Application Bengaluru India

ISBN: (纸本)9798350335774

Chronic leukemia is a slow-progressing form of disease, If not diagnosed on time can progress and increase the risk of life-threatening complications. It is essential to develop a fully automated system to recognize and categorize type of leukemia for proper evaluation and treatment. This paper aims to provide a machine learning model to identify and classify chronic lymphocytic leukemia, chronic myeloid Leukemia and healthy cells. Digital microscopic blood smear images were automatically cropped into single nucleus and segmented using watershed algorithm. Grey level co-occurrence matrix (GLCM) and geometrical features were extracted from the segmented nucleus images and random forest algorithm is used to classify chronic leukemia and healthy cells. This prognosis aids pathologists and physicians in identifying leukemic patients early and selecting the most effective course of action. © 2023 IEEE.

关键词： Machine learning

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Detection of Mg II Absorption Lines Using Convolutional Neural Networks 6

Detection of Mg II Absorption Lines Using Convolutional Neur...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Liu, Han Cai, Zheng Pu, Qiao Qinghai University Department of Computer Technology and Application Xining China Tsinghua University Department of Astronomy Beijing China

ISBN: (纸本)9798350316520

Nowadays, with more sky survey projects, astronomical data are generated in large quantities, and the traditional astronomical data processing methods can no longer meet the current data processing needs. Deep learning techniques are becoming more and more mature in the field of astronomy. The Mg II λ λ 2796,2803 absorption line belongs to the metallic narrow absorption line and is one of the very important absorption lines. It is widely used to study the intergalactic medium and the circumgalactic medium. In this paper, convolutional neural network is applied to Mg II absorption line detection. It is capable of multitasking and is able to classify and at the same time go on to output the redshift and equivalent width of the Mg II absorption lines. A real dataset characterized by the double peak of the Mg II absorption line was created from the published SDSS DR16 Mg II catalog. Training resulted in a model with a final accuracy of 94.5% on the test dataset, with relatively small prediction errors for equivalent width of the Mg II absorption line. The deep learning method proposed in this paper is much faster than traditional methods and maintains high accuracy with little human interference. © 2024 IEEE.

关键词： Object detection

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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Multi Terminal Adaptive Layout Based on HTML 5 and CSS3 1

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作者： Zheng, Wei Zhou, Qiang Ou, Wenjisi Department of Computer Technology Jiangxi Vocational and Technical College of Information Application Nanchang330043 China

ISBN: (数字)9789819719792

ISBN: (纸本)9789819719785

Layout is a key link in web page design. In addition to PC, there are a large number of mobile terminals for accessing web pages. When the fixed layout is migrated to the mobile terminal, the user experience is likely to be insufficient. For these devices with different resolutions, CSS3 technology adopts different methods to solve the problem. This paper proposes a responsive layout solution based on HTML 5+ CSS3. First, use HTML5 semantic structural tags to transform DIV tags with semantic requirements in fixed layouts. Then through CSS3's media query and flexible layout, we design the responsive layout of the web page layout, and set the responsive code for pictures and text. The experimental research shows that this scheme realizes the free switching and smooth transition of web pages between different terminals and improves the user experience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data fusion

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A Survey of Complex Network Representation Learning Methods 23

A Survey of Complex Network Representation Learning Methods

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7th International Conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Wang, Keke Zhu, Yu Huang, Jiangqiang Wang, Xiaoying Department of Computer Technology and Application Qinghai University Xining810000 China

ISBN: (纸本)9781450399883

Network representation learning is a technique that embeds the nodes in a network into a low-dimensional vector space, providing strong support for network analysis tasks such as node classification, link prediction, and community detection. This paper provides a summary of the research in the field of network representation learning in recent years. Firstly, the definition of network representation learning is introduced. Then, traditional network representation learning methods are presented, including matrix factorization-based methods, random walk-based methods, and neural network-based methods. However, traditional network representation learning methods can only be applicable to the traditional networks abstracted as the graphs and not applicable to the hypernetworks abstracted as the hypergraphs. Therefore, this paper also introduces hypernetwork representation learning methods, including homogeneous hypernetwork representation learning and heterogeneous hypernetwork representation learning. Finally, this paper summarizes future research directions in network representation learning. © 2023 ACM.

关键词： Vector spaces

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Multivariate Deep Learning for Reconstruction of Spatial Missing Climate Data 3

Multivariate Deep Learning for Reconstruction of Spatial Mis...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Yao, Ziqiang Wu, Li Huang, Jianqia Wang, Xiaoying Department of Computer Technology and Application Qinghai University Xining810016 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Complete meteorological data is essential for meteorological research. However, due to sensor failure or occlusion, data loss always occurs. In order to deal with this problem, geoscience often uses Kriging and other traditional interpolation methods. Nevertheless, because the traditional method only considers the existing data, it cannot provide accurate reconstruction results when the spatial missing rate is large. Inspired by image inpainting algorithms, we propose a new deep learning method that combines other variables related to missing meteorological variables to provide more effective information for missing regions, and proves the good effect of the method through a series of experiments. To illustrate that our method can maintain good performance under different missing rates, we use the reanalysis data of the European Centre for Medium-Range Weather Forecasts to create a data set with three different missing rates of 30%, 50%, and 70% through the artificial mask. Under different missing rates, the average RMSE of the method based on deep learning decreased by 77% and 31% respectively compared with Kriging and DINEOF. In addition, by combining relevant variables, the accuracy of the deep learning model can be further improved by about 16%. © 2023 SPIE.

关键词： Kriging

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application of Improved SeMask-FPN Model in Plateau Pika Burrows Recognition 3

Application of Improved SeMask-FPN Model in Plateau Pika Bur...

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3rd International Conference on Electronic Information Engineering and computer Science, EIECS 2023

作者： Jiang, Jieteng Li, Chunmei Jin, Yunpeng Li, Kai Li, Haiyang Qinghai University Department of Computer Technology and Application Qinghai Xining China

ISBN: (纸本)9798350316674

The Three Rivers Source Region, situated on the Qinghai-Tibet Plateau, is an important natural reserve in China, with its grassland ecosystem playing a crucial role in maintaining regional ecological balance and water conservation. However, in recent years, the area has experienced severe grassland degradation due to factors such as overgrazing, grassland fires, climate change, and rodent infestations. Therefore, the urgent need for grassland restoration and management has emerged. To effectively address grassland degradation, accurate identification of its severity levels (e.g., first-degree degradation, second-degree degradation, etc.) is essential, followed by the implementation of appropriate restoration measures corresponding to each level. This study focuses on rodent infestations as a perspective to investigate grassland degradation. Through surveys, it has been found that the coverage ratio of rodent burrows, specifically those made by plateau pikas and field mice, can be used to determine the degree of grassland degradation. Accordingly, this research attempts to utilize semantic segmentation techniques to identify rodent burrows, particularly those of plateau pikas, and subsequently calculate the coverage ratio, enabling the determination of grassland degradation levels. Currently, due to the absence of a comprehensive publicly available rodent burrow dataset, we have conducted on-site data collection in the Three Rivers Source Region to establish a dataset based on rodent burrows. Additionally, we have improved the semantic segmentation model, SeMask-FPN. Addressing the insufficiency of global interactions in the SeMask-FPN model, we propose two different global fusion models: the first incorporates burrows convolution and self-attention mechanisms, introducing the Atrous Self-Attention (ASA) module to create the ASA-based SeMask-FPN model. The second model combines pooling and self-attention mechanisms, introducing the Pooling Self-Attention (PSA) module

关键词： Convolutional Neural Network Deep Learning Grassland Degradation Rodent Infestations Semantic Segmentation Transformer

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Intelligent hierarchical federated learning system based on semi-asynchronous and scheduled synchronous control strategies in satellite network

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Autonomous Intelligent Systems 2025年第1期5卷 1-14页

作者： Mei, Qiang Huang, Rui Li, Duo Li, Jingyi Shi, Nan Du, Mei Zhong, Yingkang Tian, Chunqi Operations Services Department China Satellite Network Application Co. Ltd Beijing100089 China College of Computer Science and Technology Tongji University Shanghai201804 China

Federated learning (FL) is a technology that allows multiple devices to collaboratively train a global model without sharing original data, which is a hot topic in distributed intelligent systems. Combined with satellite network, FL can overcome the geographical limitation and achieve broader applications. However, it also faces the issues such as straggler effect, unreliable network environments and non-independent and identically distributed (Non-IID) samples. To address these problems, we propose an intelligent hierarchical FL system based on semi-asynchronous and scheduled synchronous control strategies in cloud-edge-client structure for satellite network. Our intelligent system effectively handles multiple client requests by distributing the communication load of the central cloud to various edge clouds. Additionally, the cloud server selection algorithm and the edge-client semi-asynchronous control strategy minimize clients’ waiting time, improving the overall efficiency of the FL process. Furthermore, the center-edge scheduled synchronous control strategy ensures the timeliness of partial models. Based on the experiment results, our proposed intelligent hierarchical FL system demonstrates a distinct advantage in global accuracy over traditional FedAvg, achieving 2% higher global accuracy within the same time frame and reducing 52% training time to achieve the target accuracy. © The Author(s) 2025.

关键词： Federated learning

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Detecting phishing attacks using Convolutional Neural Network and LSTM 3

Detecting phishing attacks using Convolutional Neural Networ...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Jesmithaa, S. Eliyas, Sherin Hindustan Institute of Science and Technology Department of Computer Application Chennai India

ISBN: (纸本)9798350399264

Cybersecurity threats are increasing immensely due to the evolution of digitization. Due to this evolution, many security threats are happening around the world. One serious threat is phishing. It is a fraudulent activity that aims to steal a victim's private data using fraudulent emails and websites. Sensitive data such as login information, passwords, and bank account details are obtained and misused. In these types of attacks, the attackers appear to be legitimate. It is very hard to differentiate between what is real and phishing. A lot of research has been carried out to classify URLs as legitimate or related to phishing. However, a more efficient detection approach is needed to accurately predict and distinguish genuine sites from phishing sites. This issue can be solved by using deep learning algorithms such as convolutional neural networks (CNN) and long-term short-term memory (LSTM). This paper proposes a hybrid (CNN-LSTM) approach that provides maximum accuracy. © 2023 IEEE.

关键词： Cybersecurity

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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

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Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham Science Publishers.

关键词： Quantum computers

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