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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Rawat, Vandana Singh, Neelam Bijalwan, Rahul Verma, Poonam Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of Computer Applications Graphic Era Hill University Dehradun India

ISBN: (纸本)9798350398496

Internet of things (IoT) is a fast and conforming technology, having a standard emergence primarily connected to the wireless communication between actuators, gadgets, and electrical services all in general referred to as instructions. Developmental services refer to the activities and services promoting community development and are considered as an integral part of developmental services, which include seamless and coherent experience. It is a phenomenon rich in technical skills and broader experiences consisting of mechanical frameworks and platforms, which aim towards selecting the most efficient and cost-effective tools. The collection of valuable information and relaying on them accurately are the two most important features provided by the efficient IoT services. © 2023 IEEE.

关键词： Efficiency

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Bitcoin-Blockchain Technology: Security Perspective 3

Bitcoin-Blockchain Technology: Security Perspective

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Ansar, Syed Anas Arya, Swati Aggrawal, Shruti Yadav, Jaya Pathak, Prabhash Chandra Babu Banarasi Das University Department of Computer Application Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India University of Lucknow Department of Computer Science Lucknow India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Nowadays, Blockchain technology has also drawn mammoth attention as it is currently triggering multiple projects. This technology is the prevalent technology that allows Bitcoin to operate. Bitcoin has already cemented itself as a popular virtual currency, and it will soon become a pressing requirement for citizens. Furthermore, with digital currencies proliferating all over the world, security must not be overlooked, especially when there have been numerous attacks and security vulnerabilities on bitcoin's blockchain. However, the lucrative system has drawn opponents and attackers who are continually attempting to exploit flaws in the technology in order to get profit. This paper contemplates the bitcoin's transaction process, with a focus on obscuring the true identities of clients and their coins prior to transacting, and then elucidating on the following: SHA-256 function, the primary threats to Bitcoin's security, along with some countermeasures to be undertaken. © 2022 IEEE.

关键词： Blockchain

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Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

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IEEE Transactions on Computational Social Systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 IEEE.

关键词： Matrix algebra

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Extrapolative Preservation Management of Medical Equipment through IoT 1

Extrapolative Preservation Management of Medical Equipment t...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Tripathi, Rajeev Mishra, Vinay Kumar Maheshwari, Himani Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Gupta, Ashulekha School of Management Sciences Department of Computer Application and Sciences Lucknow India School of Computer Application Babu Banarasi das University Lucknow India School of Computing Graphic Era Hill University Uttrakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350330915

The healthcare industry is primarily driven by technological innovations as they are a significant source of inspiration to provide the best patient care. To provide patients with prompt, high-quality care, medical device manufacturing has grown at an unprecedented rate. Hospitals should use the best maintenance techniques to increase the life of their equipment while trying to reduce maintenance costs and efforts as medical device usage increases. This paper proposes a predictive maintenance method (PdM) to assist in identifying critical equipment failures with diverse and frequent failure modes. The proposed method is based on the understanding of the physics of the incident, real-time data collection of relevant parameters using Internet of Things (IoT) technology, and application of machine learning algorithms to predict and classify the condition of the device as good and bad. To demonstrate the feasibility and effectiveness of moving from traditional maintenance to PdM, an economic analysis should be provided. The objective of this study was to determine the challenge of medical device maintenance using an Internet of Things (loT) enabled automated health monitoring system for devices that generate large amounts of data. real-time data, at scale, in healthcare organizations. © 2023 IEEE.

关键词： Failure Management Internet of things IoT Medical Equipment Maintenance Predictive Maintenance Sensors

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Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

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computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

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Using Trust-Based Data Integrity Verification and Energy-Aware Hierarchical Data Aggregation for WSN 11

Using Trust-Based Data Integrity Verification and Energy-Awa...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Singh, Dushyant Gaganpreet Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9781665487320

Because of their numerous uses, wireless sensor networks are currently quite popular. These nodes are typically placed in severe environments and have limited power supplies, making it difficult to replace them. Because of these problems, reducing energy consumption is a top priority to increase network lifespan. To solve the challenging issue of data aggregate, we created a novel combining technique that carries out grouping & trusted computation procedures. This design divides the networks into three categories based on remaining energy: normal networks, advance networks, & super networks. The proposed model utilises a hierarchical structure, and we present a novel method for determining the best number of cluster. the clustered head of choice. After selecting the root node, we employ a confidence calculation strategy that offers sense confidence, connection trust, & nodes trust. The nodes trust is computed using indirect & direct trust. To protect the data integrity, this confidence mechanism is used hop by hopping. The empirical study shows that the proposed approach boosts efficiency while preserving WSN's safeguards. © 2022 IEEE.

关键词： Energy utilization

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Designing Blockchain Based Consensus Mechanism for Smart Healthcare IoT

Designing Blockchain Based Consensus Mechanism for Smart Hea...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Kumari, Trishla Kumar, Rakesh Dwivedi, Rajendra Kumar Madan Mohan Malviya University of Technology Dept. of Computer Science and Engineering Gorakhpur India Madan Mohan Malviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665462631

In today's world, healthcare system is improving day by day by using smart IoT based system. Blockchain based system is totally a decentralized system due to which attackers are unable to access the confidential records easily. Blockchain play an important role in controlling and management of sensitive information in healthcare system and insurance sector. Consensus mechanisms used in blockchain technology to validate whichever block is going to be added next in the blockchain network. In this paper we proposed a hybrid based system which solves trust issues arises in the blockchain. Hybrid consensus mechanism provides reputation according to the activities of blocks in a blockchain network. Four layer architecture is designed in Hybrid model. Five algorithms are used to create, validate, fork handling, reward and punishment scheme and Markle tree is constructed respectively. This model is implemented on Ethereum based platform which shows that HCM model is better than a PoXR model as it consume less memory (50 percent) and consume less CPU(below 2 percent). © 2023 IEEE.

关键词： Blockchain

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A High-capacity Information Hiding Scheme Based on Blakley Secret Sharing 4

A High-capacity Information Hiding Scheme Based on Blakley S...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Zhou, Jincheng Li, Yue Shen, Yujie Tang, Dan Cai, Hongliang Gong, Jing Bai, Ningchao School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Province Engineering Technology Research Center of Support Software of lnformatization Application Chengdu China School of Computer Science and Engineering Sichuan University of Science & Engineering Yibin China Sichuan Provincial Big Data Technical Service Center Chengdu China

ISBN: (纸本)9798331541729

A high-capacity reversible information hiding scheme based on Blakley secret sharing is proposed to address the issues of low embedding rate and weak disaster recovery performance of images when using reversible information hiding to protect data in scenarios with large data transmission. The scheme employs Euclidean division and a two-dimensional modulus function to establish a robust mapping relationship between the secret information and the pixels of the cover image. Subsequently, the values derived from the preprocessing of the secret information are incorporated into the secret sharing method proposed by Blakley as the elements of the secret vector. The proposed scheme exploits the disaster-tolerance property of the secret sharing method and enhances the redundancy of the secret-carrying encrypted image, thereby improving the embedding rate and security of the algorithm while maintaining the security of the secret sharing. © 2024 IEEE.

关键词： Euclidean division Information hiding Modulus function Secret sharing

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A range and radial velocity measurement method based on up-chirp and down-chirp LFM signal in pulsed radar

A range and radial velocity measurement method based on up-c...

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IET International Radar Conference 2023, IRC 2023

作者： Chen, Hao Lei, Yi Key Lab of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781839539954

Range accuracy and radial velocity accuracy are important indexes in pulsed radar. However, in pulsed radar using linear frequency modulation (LFM) pulse, the range-Doppler coupling of LFM signal will lead to large range error in high-speed target measurement. The target's radial velocity is computed using range differentiation in traditional method, which is also not accurate. This paper presents a measurement method based on up-chirp and down-chirp LFM signal to get the target's accurate range and radial velocity simultaneously. Experiments confirm that the proposed method outperforms traditional method in both range accuracy and radial velocity accuracy. © The Institution of engineering & Technology 2023.

关键词： Chirp modulation

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Exploring Drug Sentiment Analysis with Machine Learning Techniques 6

Exploring Drug Sentiment Analysis with Machine Learning Tech...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Rathod, Dharmendrasinh Patel, Kashyap Goswami, Akashgiri Jashvantgiri Degadwala, Sheshang Vyas, Dhairya Computer Science and Engineering Chandubhai S Patel Institute of Technology Charusat University India Department of Computer Engineering DEPSTAR Charotar University of Science and Technology India Silver Oak College of Computer Application Silver Oak Univercity Gota Gujarat Ahmedabad India Computer Engineering Department Sigma Institute of Engineering Gujarat Vadodara India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (纸本)9798350398496

This research investigates the use of machine learning techniques for sentiment analysis of drug re views, which is a crucial task for extracting valuable information from the vast amount of unstructured data available online. Drug reviews provide patients' opinions on various aspects of drugs, including their efficacy, side effects, and overall experience. The study explores the performance of different machine learning algorithms for sentiment analysis of drug reviews. Additionally, it examines the effectiveness of various feature engineering techniques, such as bag-of-words, n-grams, and word embeddings, in capturing the subtleties of drug reviews. The study evaluates the performance of these techniques on a publicly available dataset of drug reviews and compares the results based on accuracy, precision, recall, and F1-score. The experiments demonstrate that the appropriate use of machine learning algorithms and feature engineering techniques can accurately capture the sentiment of drug reviews, achieving high accuracy and F1-score. This study's findings can provide valuable insights for healthcare professionals and researchers to analyze patient opinions about drugs, identify potential side effects, and enhance drug development and regulatory processes. © 2023 IEEE.

关键词： Embeddings

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