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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Chauhan, Neelam Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Deptt. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9789380544441

Nowadays, the Internet of Things (IoT) is an emerging technology to interconnect the physical things for providing the best services in hands. IoT has different applications in many areas viz., Smart Health, Smart Transportation, and Smart Cities. Healthcare IoT is an information system to provide quick health services to patients. In medical service applications, IoT facilitates correspondence among specialists and remote patients who are equipped with wearable sensors. Patients' data is critical and any security violation to such data may lead to severe problems. Some existing healthcare frameworks may prompt infringement on the security of the patients. Therefore, security must be implemented carefully in such systems. Blockchain is one of the secure techniques and has been applied to most IoT systems. Scarcely, the significant purposes behind using the Blockchain in medical care frameworks are its conspicuous provisions, i.e., Decentralization, Immutability, Transparency, Security, and Privacy. In this paper, we proposed a blockchain-based healthcare IoT system for the security of Electronic Health Records (EHRs). In this methodology, the health data of the patients are encrypted and stored at the hospital servers and related hash values are stored in the blockchain. Our primary intention is to give an appropriate, secure, and approved admittance to this critical clinical data of the patients using the Ethereum blockchain. This Ethereum based architecture provides an adaptable and secure framework for a smart healthcare system and outperforms the existing methods. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Smart contract

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A Comparative Analysis of the Techniques for Influence Maximization in Social Networks 9

A Comparative Analysis of the Techniques for Influence Maxim...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Shakeel, Nida Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Deptt. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9789380544441

Now-a-days, social networks have become an important part of the individuals' day-to-day life. It plays a significant role in spreading data over the network. In viral advertising, organizations desire to propagate their items by utilizing the network of the organization and potentials of the influence propagation. Usually, they give the items at no cost to the chosen clients, promote them throughout the network, and maximize the acquisition. There should be a good spreading plan for the free-of-charge items with the objective of the organizations for choosing the ideal seed set to boost the influence spread. Identifying the seed nodes in the network which can maximize the influence is known as Influence Maximization (IM). Influence maximization is a principal issue to recognize a tiny arrangement of people which contain maximal impact within the community. Centrality measures help to identify the influential node in the network. This paper presents a comparative analysis of the effectiveness of the Influence Maximization techniques viz., Heuristic Algorithm (HA), Greedy Algorithm (GA), Cost Effective Lazy Forward (CELF) ++ Algorithm, and Discrete Shuffled Frog-Leaping Algorithm (DSFLA). We evaluated the performance on various social network datasets available in the public domain viz., Facebook, Skitter, Epinions, and Twitter. We observed that DSFLA is an efficient technique that outperforms in terms of the computational time as compared to the other influence maximization techniques viz., HA, GA, and CELF++. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Heuristic algorithms

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Continuous and generalized visual odometry based on FLANN_PSC-RANSAC_LSTM

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 15-28页

作者： Li, Dengao Feng, Ran Wu, Fanming Zhao, Jinhua Zhao, Jumin College of Computer Science and Technology College of Data Science Taiyuan University of Technology Shanxi Taiyuan China College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan University of Technology Shanxi Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China

In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations. © 2024 - IOS Press. All rights reserved.

关键词： Long short-term memory

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A Comparative Analysis of Container Orchestration Tools in Cloud Computing 9

A Comparative Analysis of Container Orchestration Tools in C...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Malviya, Anshita Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Deptt. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9789380544441

Cloud Computing is an emerging technology that is used not only by developers but also by end-users. It has vital importance in the information technology (IT) industries as its future would create a great transition from conventional IT services. These days, containerization in cloud computing has become an important research area. The selection of container orchestration tools is one of the difficult tasks for the organizations involved in the management of the vast number of containers. These tools have their strengths, weaknesses, and functionalities which need to be considered. This paper presents a comparative analysis of the container orchestration tools. This analysis would help the professionals to decide whether they need an orchestrator bound to a single technology or an orchestrator which provides the independent solution. In this paper, four popular orchestration tools viz., Kubernetes, Docker Swarm, Mesos, and Redhat OpenShift are analyzed on various parameters viz., security, deployment, stability, scalability, cluster installation, and learning curve. We observed that Kubernetes has the best scheduling features whereas Docker Swarm is easy to use. We also found that Mesos has good scalability whereas OpenShift is a highly secure orchestration tool. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Containers

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Enhancing Security and Privacy in Health Care Using Cyber-physical Systems Through Machine Learning 8th

Enhancing Security and Privacy in Health Care Using Cyber-ph...

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8th International Conference on information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Mahalle, Parikshit N. Mehta, Pradnya S. Kashid, Shraddha S. Sule, Bipin Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

The importance of securing cyber-physical systems (CPS) is paramount to safeguarding the interconnected networks and devices, especially in sectors such as blockchain, health care, and the "Internet of Things" (IoT). The importance of authentication for users and devices in CPS security is highlighted in this paper. As a result of the intricate interconnectivity of networks, the integration of cyber-physical systems (CPS) in health care has revolutionized the delivery of medical services in an era of rapidly advancing technology. There are, however, serious concerns surrounding the privacy and security of sensitive healthcare data as a result of this integration. In this paper, a revolutionary approach to address these concerns is presented by utilizing machine learning techniques to address these problems. To enhance security measures within healthcare CPSs and protect privacy, Random Forest, a powerful ensemble learning algorithm, is employed as an integral part of the security measures. The proposed system utilizes a combination of anomaly detection, adaptive learning, and multi-attribute feature selection in order to identify potential security breaches while simultaneously ensuring patient confidentiality. As an additional security precaution, encryption protocols are implemented to ensure that the transmission of data is secure, preventing unauthorized access to the data as well. "As a comprehensive evaluation of the system's performance, a number of evaluation metrics are used, including Accuracy, Precision, Recall, F1 Score, and AUC-ROC". Based on these results, it is evident that this approach is effective in providing robust security and privacy protections in healthcare CPS, thereby establishing a new paradigm for delivering safe and confidential health care in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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A Secure Design of the Smart Vehicular IoT System using Blockchain technology 9

A Secure Design of the Smart Vehicular IoT System using Bloc...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Shrivastava, Atul Lal Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Deptt. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9789380544441

These days, Internet of Things (IoT) is widely used in several real-life applications viz., smart agriculture, smart city, smart healthcare, and smart vehicles. Smart vehicles are interconnected and deliver a variety of sophisticated services to their owners, transit authorities, automobile manufacturers, and other service providers. Smart cars could be exposed to a number of security and privacy risks, including Global Positioning System (GPS) tracking and remote vehicle hijacking. In vehicular IoT, a variety of data are stored in cloud and any alteration in data can cause major road accidents. Blockchain is an evolving technology that can be used for security using decentralization and smart contracts. It is a viable solution for implementing security in vehicular IoTs. In this paper, we presented a blockchain-based methodology to preserve users' privacy while simultaneously boosting vehicle security. The proposed methodology keeps the vehicle data at the servers and related hash values are stored in the blockchain. Thus, the proposed model offers security using decentralization of data. This method outperforms the existing methods in terms of security as well as efficiency. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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An Image Semantic Communication System Based on GAN and Relative Position-Encoding

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IEEE Communications Letters 2025年第6期29卷 1280-1284页

作者： Zhang, Ziqi Tan, Weijie Li, Chunguo Guizhou University College of Big Data and Information Engineering Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptography Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

In image semantic communication, the complex wireless channel environment leads to the loss of image details and performance degradation during transmission. To address this issue, we propose an image semantic communication system based on Generative Adversarial Networks (GAN) and Relative Position-Encoding (RPE), named GAN-RpeSC. Specifically, in the system design, we introduce the GAN network at the output of the semantic communication framework to enhance the image restoration. We integrate the RPE module into the Visual Transformer (ViT) module, adding the RPE module to its Multihead Self Attention (MHSA), so that the model can better capture the spatial location information of the image. Experimental results show that the proposed GAN-RpeSC has significant advantages in improving image transmission quality, with a 0.3 dB improvement in peak signal-to-noise ratio (PSNR) compared to the ViT module alone. © 1997-2012 IEEE.

关键词： Encoding (symbols)

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Improving Global Precipitation in Numerical Weather Prediction Systems based on Deep Learning Techniques 25

Improving Global Precipitation in Numerical Weather Predicti...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhao, Haixiang Zhang, Guojing Du, Ming Wang, Xiaoying Qinghai University Department of Computer Technology and Applications Xining810016 China Geospatial Information Technology and Application Qinghai Provincial Key Laboratory Xining810016 China

ISBN: (纸本)9798350330014

Accurate weather forecasts are crucial for many aspects of people's lives. Traditional short-term precipitation forecasting methods mainly rely on numerical weather predictions based on meteorological dynamics modelling. However, numerical weather predictions based on high-resolution physical models require hours of computation on supercomputers. Deep learning networks possess strong feature extraction capabilities, directly mapping images, meteorological data, and other information to high-dimensional feature spaces. Neurons extract abstract features, enabling continuous training and learning for predictions. Therefore, deep learning provides a new solution for short-term precipitation forecasting. In the past, deep learning has been used for precipitation forecasting by employing radar echo extension methods;however, these methods are limited to short-term forecasts and do not utilize other meteorological data, resulting in inaccurate predictions. This study proposes a model containing an attention mechanism, a convolutional extended short-term neural network (Att-PredRNN), based on 5.625° (32×64 grid points) resolution ECMWF reanalysis data and three years of global meteorological and daily precipitation data. The network uses multiple meteorological fields as input factors to predict spatial precipitation distribution for 24-hour precipitation forecasting. The model significantly improves the capture of spatial and temporal correlations through the attention matrices focusing on different parts of the input sequence. Experimental results show that the proposed Att-PredRNN model outperforms other comparison models in terms of RMSE, CSI, POD, and FAR overall and dramatically enhances the accuracy of short-term precipitation forecasts. Our code and data are publicly available. © 2023 IEEE.

关键词： Supercomputers

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Auto-PIP: Real-time Identification of Critical Performance Inflection Points in Software Stress Testing 35

Auto-PIP: Real-time Identification of Critical Performance I...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Shenglin Xiong, Xiao Li, Mengyao Sun, Yongqian Zhao, Yongxin Chen, Xia Deng, Bowen Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area"(SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research. © 2024 IEEE.

关键词： Software testing

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Multivariate Time Series Anomaly Detection based on Pre-trained Models with Dual-Attention Mechanism 35

Multivariate Time Series Anomaly Detection based on Pre-trai...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Sun, Yongqian Guo, Yang Liang, Minghan Wen, Xidao Kuang, Junhua Zhang, Shenglin Li, Hongbo Xia, Kaixu Pei, Dan Nankai University China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China BizSeer Haihe Laboratory of Information Technology Application Innovation China Tencent Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

In major tech companies, monitoring server performance data with anomaly detection algorithms is crucial for assessing operational status. Existing models often require separate training or fine-tuning for each server due to generalization limitations, leading to increased storage, memory, and training costs. As the number of servers grows, this approach becomes impractical. To address this, we propose using pretrained language models for time series anomaly detection, leveraging their strong generalization capabilities. Specifically, we employ two pre-trained GPT-2 models as backbones and implement a two-stage fine-tuning strategy to retain learned knowledge while adapting to specific business data characteristics. Our experiments on multiple anomaly detection datasets demonstrate that our method achieves the best average F1-Score, outperforming the leading baseline by 7%. © 2024 IEEE.

关键词： Time series

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