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2024 International Conference on Advances in Computing Research on Science Engineering and technology, ACROSET 2024

作者： Loonkar, Shweta Ahmed, Zahid Kavitha, R. Atlas SkillTech University Department of Isme Maharashtra Mumbai India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350388800

In the context of Web3.0, the rapid pace of technological innovation has resulted in the emergence of novel computing paradigms. This is a natural result of the exponential growth of technology. Within the ecosystem of Web 3.0, the major emphasis of this research is on rethinking architecture and protocols in order to be ready for the revolution in edge computing. To improve the efficacy, security, and scalability of data processing and transmission, DECABI provides a dynamic edge computing architecture with integrated blockchain characteristics. The study accelerates data transfers without compromising their integrity by using dynamic resource allocation, encrypted data transmission, and a consensus protocol for edge computing (CPEC). The suggested method has much better performance than the alternatives. This exemplifies how the method has the ability to completely alter the way edge computing is carried out in the Web3.0 era. Countless simulations and exhaustive studies confirmed the existence of this option. This study lays the groundwork for a more stable and secure Web3.0 ecosystem, and it also makes a substantial contribution to the current conversation about the future of computer architectures and protocols. © 2024 IEEE.

关键词： Data integrity

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Sentiment Analysis using Dictionary-Based Lexicon Approach: Analysis on the Opinion of Indian Community for the Topic of Cryptocurrency

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Annals of Data Science 2024年第6期11卷 2019-2034页

作者： Loomba, Sankalp Dave, Madhavi Arolkar, Harshal Sharma, Sachin School of Cyber Security and Digital Forensics National Forensic Sciences University Gujarat Gandhinagar India Adani Institute of Digital Technology Management Gujarat Ahmedabad India Faculty of Computer Application and Information Technology GLS University Gujarat Ahmedabad India Indrashil University Gujarat Mehsana India

Due to the ever-increasing computing power and easy availability, social-networking platforms like Facebook, Twitter, etc. have become a popular medium to express one’s views instantly, be it about political situations, commercial products, or social occurrences. Twitter is a powerful source of information, whose data can be utilized to investigate the opinions of users through a process called Opinion Mining or Sentiment Analysis. Using the principles of Natural Language Processing and data science, this paper presents a comparative evaluation of multiple lexicon-based sentiment analysis algorithms to extract public opinion from tweets. The study explores the nuances of sentiment analysis using data science methodology, assessing how various lexicon-based algorithms may successfully identify and classify sentiments expressed in tweets from the Indian community about cryptocurrency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Sentiment analysis

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IoT Based Smart Systems using Artificial Intelligence and Machine Learning: Accessible and Intelligent Solutions 6

IoT Based Smart Systems using Artificial Intelligence and Ma...

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6th International Conference on information Systems and computer Networks, ISCON 2023

作者： Ahmed, Bilal Shuja, Mirza Mishra, Hari Mohan Qtaishat, Ahmed Kumar, Mukesh Lovely Professional University School of Computer Application Punjab Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

The Internet of Things (IoT) has grown in importance in both the technological and social spheres as consumers want smarter homes, more efficient businesses, and more efficient healthcare systems. The social setting is very important for this. Communication networks provide uninterrupted internet connectivity for both fixed and mobile devices like PCs and smartphones. Customers may now keep up their level of engagement while interacting with one or more apps at any time and from any location, thanks to developments in information and communication technologies. Many kinds of smart devices might potentially coordinate their responses to events, sending out alerts or keeping records of the system's condition in line with a predetermined policy. Constant progress toward intelligent solutions is improving many facets of modern life, including manufacturing, transportation, electronic healthcare, electronic education, and many more. Machine-to-machine (M2M) communication in the IoT is autonomous and dynamic. Diverse approaches are being developed to address the challenges posed by disparate data sources, fluctuating signal quality, and overwhelming quantities of data. From the perspectives of intelligent system technologies, security, vulnerabilities, and the potential role that intelligent solutions based on Machine Learning (ML) and Artificial Intelligence (AI) may play, this article examines the IoT. Concerns about privacy and safety have arisen as a major obstacle to the widespread adoption of the Internet of Things (IoT). We propose that the Standard Security Framework (SSF) be made mandatory for all distributed networks to ensure that hardware and software modules may communicate with one another regardless of their respective platforms. In addition, the effectiveness and security of new innovations in using fog and edge computing in conjunction with cloud applications have been studied.. © 2023 IEEE.

关键词： Internet of things

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Remote Sensing Target Recognition Model Based on Small Sample Image Generation 4

Remote Sensing Target Recognition Model Based on Small Sampl...

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4th International Symposium on computer Engineering and Intelligent Communications, ISCEIC 2023

作者： Chen, Lei Sun, Liting Space Engineering University School of Space Information Beijing China Beijing Institute of Computer Technology and Application General Department of Network and Information Security Beijing China

ISBN: (纸本)9798350306323

In recent years, remote sensing object recognition methods based on deep learning have received extensive attention. Deep learning methods need to use a large number of labeled data to carry out network training. However, the cost of labeling remote sensing image data is very high. Therefore, how to use a small number of labeled target images to train the remote sensing target classifier with high performance is an urgent problem to be solved. In view of the above problems, this paper has carried out the following work. Firstly, based on WGAN-GP, this paper optimizes the constraint conditions of neural network and proposes a depth generation model, namely CCWGAN-GP, for remote sensing image generation. Secondly, this paper proposes a remote sensing target classification model SCLNet, which makes full use of the original one-hot label and the generated soft classification label to train the neural network. Experimental results show that the proposed model can generate highly similar images to real images, and can significantly improve the performance of the classifier under training conditions with only a few labels. © 2023 IEEE.

关键词： Remote sensing

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Performance Issues and Research Challenges in Designing a Secure IoT with Blockchain 3

Performance Issues and Research Challenges in Designing a Se...

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3rd International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Hemani Singh, Dayashankar Dwivedi, Rajendra Kumar Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665482684

Now a day, Internet of Things (IoT) are being used in several areas such as vehicular systems, smart city, healthcare, and supply chain management. Sensor data is stored in the cloud in an IoT system. Therefore, there is always a security concern with this data. There are several techniques for maintaining security, but most of them work in a centralized manner. However, blockchain is an emerging technology that works in a distributed manner and is based on peer-to-peer computing. Security can be enhanced using blockchain in any system. Blockchain technology offers decentralised security and privacy. Sensors used in loT systems are resource constrained. Therefore, implementing blockchain in any loT system creates several research challenges. This paper provides a discussion on such performance issues and research challenges while integrating blockchain with loT. The paper also gives some insights to overcome such issues. © 2022 IEEE.

关键词： Smart contract

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LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices 35

LabelEase: A Semi-Automatic Tool for Efficient and Accurate ...

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35th IEEE International Symposium on Software Reliability Engineering, ISSRE 2024

作者： Zhang, Shenglin Che, Zeyu Pan, Zhongjie Nie, Xiaohui Sun, Yongqian Pan, Lemeng Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Chinese Academy of Sciences Computer Network Information Center China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei China Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350353884

Trace data is crucial for system observability and maintainability within microservices architectures, and many operation algorithms depend heavily on trace data, including anomaly detection, root cause analysis, etc. However, the actual performance of these algorithms might be unsatisfactory due to the absence of high-quality labeled datasets for effective training and evaluation. Since billions of traces could be generated daily for large-scale microservices, labeling overhead is the main hurdle to obtaining high-quality trace *** this paper, we propose LabelEase, a novel semi-Automatic trace labeling tool, which uses active learning techniques to achieve efficient and accurate trace labeling. For anomaly trace labeling, LabelEase clusters similar traces with a graph-based trace representation technique and selects a few representative traces for human labeling, avoiding labeling most of the traces. For root cause labeling, LabelEase aggregates the labeled anomalous traces and identifies the service's failures for operators to label. Our systematic experiments on two large-scale datasets show that LabelEase achieves over 0.98 F1-score in anomaly trace labeling and 0.89 precision of failure detection in root cause labeling, LabelEase can reduce operators' labeling overhead by more than 99.9%. To the best of our knowledge, we are the first to propose a semi-Automatic trace labeling tool capable of achieving efficient and accurate trace labeling. © 2024 IEEE.

关键词： Labeled data

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Applying Non-Local Means Filter on Seismic Exploration

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computer Systems Science & Engineering 2022年第2期40卷 619-628页

作者： Mustafa Youldash Saleh Al-Dossary Lama AlDaej Farah AlOtaibi Asma AlDubaikil Noora AlBinali Maha AlGhamdi College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal UniversityDammamP.O.1982Saudi Arabia Geophysical Application Division Exploration Application Services DepartmentSaudi AramcoDhahranSaudi Arabia

The seismic reflection method is one of the most important methods in geophysical *** are three stages in a seismic exploration survey:acquisition,processing,and *** paper focuses on a pre-processing tool,the Non-Local Means(NLM)filter algorithm,which is a powerful technique that can significantly suppress noise in seismic ***,the domain of the NLM algorithm is the whole dataset and 3D seismic data being very large,often exceeding one terabyte(TB),it is impossible to store all the data in Random Access Memory(RAM).Furthermore,the NLM filter would require a considerably long *** factors make a straightforward implementation of the NLM algorithm on real geophysical exploration data *** paper redesigned and implemented the NLM filter algorithm to fit the challenges of seismic *** optimized implementation of the NLM filter is capable of processing production-size seismic data on modern clusters and is 87 times faster than the straightforward implementation of NLM.

关键词： Seismic exploration parallel programming seismic processing optimizing methods

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Millimeter Wave Beamforming for Massive MIMO in 6G Communication Systems

Millimeter Wave Beamforming for Massive MIMO in 6G Communica...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shree, Ritu Verma, Pradeep Kumar Kavitha, R. Vivekananda Global University Department of Computer Science & Application Jaipur India College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Millimeter wave beamforming for large MIMO in 6G verbal exchange systems is a promising era that could dramatically improve the overall performance and spectral efficiency of 6G networks. This generation entails the use of more than one antenna and antenna array to direct and recognize the electromagnetic power in a specific path. One of the core variations of millimeter wave beamforming in comparison with traditional more than one-antenna approaches is using fairly directional beams. This provides massive blessings in the communique link, together with improved bandwidth, improved user enjoyment, and stepped forward protection. Similarly, the use of a couple of antenna factors in this technology allows for beamforming, which could substantially lessen interference and enhance spectrum performance. Ultimately, the deployment of this era additionally calls for effective signal processing techniques, along with coding and modulation that can similarly boost the overall gadget capability. In conclusion, millimeter wave beamforming for huge MIMO in 6G verbal exchange systems can load up a huge range of possibilities and blessings, which could substantially enhance the user experience and warfare congestion in 6G networks. © 2024 IEEE.

关键词： Antenna Beamforming Dramatically Millimeter Networks Traditional

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Automated Detection of Abnormalities in Ultrasound Images Using Deep Learning

Automated Detection of Abnormalities in Ultrasound Images Us...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumar, Amit Bhuvana, J. Kharbas, Vikas Kumar College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Deep getting to know has been utilized in scientific imaging studies to explore challenges with diagnosing ailments and detecting anomalies. This examination focused on the automated detection of bizarre tissues in ultrasound pix. Pics must adhere to a homogeneous, properly-described, predictable anatomy for hit-deep getting-to-know applications. It looks at the proposed artificial neural network (ANN) and lengthy quick-time period memory (LSTM) based version referred to as ALunoNet to detect anomalies in ultrasound pictures. The models were skilled on a 10,000 M-mode and B-mode ultrasound pictures dataset. After ten epochs, their checking out accuracy was eighty three.16%. They look at comparing the performance of ALunoNet with other popular CNN and LSTM models. The observed effects demonstrate that ALunoNet performs higher than the same old fashions and that its overall performance is consistent even on datasets with constrained information. It has a look at highlights the capability of deep-gaining knowledge to automatize the detection of strange tissues in ultrasound pics. © 2024 IEEE.

关键词： abnormal automatized homogeneous performance ultrasound

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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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