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An anonymous and fair auction system based on blockchain

Journal of Supercomputing 2023年第13期79卷 13909-13951页

作者： Ye, Zongli Chen, Chin-Ling Weng, Wei Sun, Hongyu Tsaur, Woei-Jiunn Deng, Yong-Yuan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung City413310 Taiwan Department of Computer Science Jilin Normal University Siping136000 China State Key Laboratory of Numerical Simulation Siping136000 China Computer Center National Taipei University New Taipei City237303 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City237303 Taiwan

Consumer approval of the online auction paradigm is demonstrated by rapidly expanding online auction transaction volumes. Bidders can access a wider variety of products, and sellers can reach a larger audience through online auction platforms. However, privacy concerns have reduced users’ trust in auction platforms, and complicated disputes have led consumers to lose faith in online auctions, negatively impacting market growth. The emergence of blockchain technology has brought a new approach to solving these problems. Blockchain is a new generation of information technology that is decentralized, transparent, traceable, anti-tampering, and unforgeable, following big data, cloud computing, and artificial intelligence. In this paper, we present a blockchain-based online auction solution as well as an auction protocol that uses ring signatures to guarantee anonymity. Three features summarize this essay: (1) Using blockchain technology to ensure that the auction transaction process is transparent, traceable, tamper-proof, and unforgeable. (2) The ring signature-based anonymous auction protocol allows for concealing bidders’ public keys in a group of public keys. Because malicious individuals are prevented from evaluating data by tracking numerous transactions of a single public key, this significantly protects bidders’ privacy. (3) The proposed scheme effectively resolves disputes that may arise during the auction. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Blockchain

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The OCPT Mechanism: An Innovative Methodology to Erect the Language Understanding to Build the Enhanced STT Model 4

The OCPT Mechanism: An Innovative Methodology to Erect the L...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Saranya, V. Devi, T. Deepa, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

Generative Pretrained Transformers (GPT) predict the sequence of words when building the language model in speech recognition. The traditional model inherits the high range biases during training the data, this issue leads to inappropriate outputs, the proposed research, the Optimized Constructive Pretrained Transformers(OCPT) mechanism emphasizes tuning GPT models specific to domain language modeling, accentuating the value of an organized approach that involves data preprocessing, model initialization, fine-tuning, and assessment. This model helps to build a more efficient and effective Speech-to-text model. The model obtained 97.12% accuracy on the validation set, compared with the AuGPT model which produces 91% accuracy, indicating the proposed model's ability to understand and generate contextually relevant text. During the training phase, the model was hyperparameter-tuned to improve performance metrics including learning rate, batch size, and training epochs. The Adam optimizer was important in accurately adjusting the model parameters to ensure convergence and stability. To avoid overfitting and ensure the model's generalizability, accuracy, and loss have been evaluated throughout the training and validation phases. The findings assessed the model's learning trajectories and performance indicators, demonstrating its efficacy. © 2024 IEEE.

关键词： Distribution transformers

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ACOVMD: Automatic COVID-19 misinformation detection in Twitter using self-trained semi-supervised hybrid deep learning model

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International Social science Journal 2024年第252期74卷 713-730页

作者： Birunda, S. Selva Devi, R. Kanniga Muthukannan, M. Babu, M. Mahesh Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Kelambakkam India Department of Civil Engineering KCG College of Technology Tamil Nadu Chennai India

During the COVID-19 pandemic, online social networks are extensively utilized, more than ever before by 8.4%, resulting in the propagation of false information related to COVID-19. Despite the existence of many fake news detection models;annotation inconsistency, memory consumption, accurate and self-trained efficient algorithms for detecting the emerging COVID-19 misinformation tweets are still challenging. Hence, the main aim of this work is to come up with a self-trained semi-supervised model that accurately and automatically detects the reliability of emerging COVID-19 tweets without delay. In this work, COVID-19 tweet dataset is created in English Language from the period January 2020 to January 2022 as a ground truth database. Then self-trained semi-supervised hybrid deep learning model is proposed to train both supervised and unsupervised components simultaneously using the created dataset. The proposed model is self-trained repeatedly and the model gets updated to predict the reliability of upcoming COVID-19 tweets that differ from training tweets. We performed experiments multiple times by limiting the percentage amount of labelled tweets shown to the model, namely 80%, 50%, 40%, 30%, 20% and 10% labelled tweets, respectively. Experimental results show that the proposed model achieves 80.92% accuracy and 98.15% accuracy in the 10% and 80% label-seen experiments, respectively. This shows a clear rising trend in the performance curve. Therefore, this technique will be useful for effectively classifying voluminous amounts of emerging tweets generated as part of the COVID-19 infodemic. The proposed model may efficiently use a huge amount of unlabelled tweets and enhance the model's generalization performance. © 2023 John Wiley & Sons Ltd.

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Hydroponic using Wick System for Herbal Cultivation using Random Forest Algorithm Comparing with Sift Algorithm 5

Hydroponic using Wick System for Herbal Cultivation using Ra...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jagan, D. Surendran, R. Logapriya, E. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

This study implements machine learning algorithms, including SIFT and Random Forest techniques, to monitor pH levels and fertilizers for optimal growth of hydroponic plants. The Random Forest Algorithm and the SIFT Algorithm function in conjunction. A collection of resources from Kaggle is used in this research. The Wick System enables the cultivation of cabbage in water, and the collection comprises instances of crops cultivated in various hydroponic settings and methods. In this study, two groups employed different methods to verify the authenticity of the images. Both Group 1 and Group 2 applied SIFT to a total of forty samples, with twenty samples in each group. The research utilized clinical data from *** and an 80% G-power value. A significance level of 0.05 was chosen, along with a type II error rate of 0.2. The Random Forest algorithm achieved an accuracy of 90.15%, while SIFT exhibited errors 62.2% of the time. A two-tailed significance test yielded a p-value of 0.001 (p © 2024 IEEE.

关键词： Hydroponics

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Privacy-Preserving Edge-Aided Eigenvalue Decomposition in Internet of Things

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IEEE Internet of Things Journal 2025年第12期12卷 19901-19914页

作者： Zhao, Xiaotong Zhang, Hanlin Lin, Jie Liang, Fan Kong, Fanyu Xu, Hansong Hua, Kun Qingdao University College of Computer Science and Technology Qingdao266071 China Xian Jiaotong University School of Electronic and Information Engineering Xian710049 China Sam Houston State University Department of Computer Science HuntsvilleTX77340 United States Shandong University School of Software Jinan250101 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China California Polytechnic State University Department of Electrical Engineering San Luis ObispoCA93407 United States

Eigenvalue decomposition (EVD) is a fundamental yet time-consuming operation with extensive applications in IoT. When the matrix dimension reaches millions, resource-limited IoT devices struggle to perform such computationally expensive operations. Edge computing, with its plentiful computing resources, offers an effective solution to this problem. However, privacy concerns arise because outsourced tasks may contain sensitive user data. In this paper, we propose the first privacy-preserving, edge-assisted EVD outsourcing scheme that securely enables users to outsource EVD tasks to edge servers. We design a privacy-preserving matrix transformation method to encode the original data, ensuring that edge servers cannot access users' private information. Additionally, we design a verification scheme that enables the user to verify the correctness of the results returned by the edge servers. Our protocol supports parallel computation by multiple edge servers, thus enhancing the efficiency of EVD. The feasibility of our proposed scheme is demonstrated through both theoretical and experimental perspectives. © 2025 IEEE.

关键词： Differential privacy

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Space Debris Clustering and Avoidance Using Chrono-Spatial Computational Frameworks

Space Debris Clustering and Avoidance Using Chrono-Spatial C...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Reddy V, Sathvik Kothapalli, Aditya Sahithya Varshini, K Sai Nandhan R, Raghu Vineetha, K.V. Ullas, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508432

This paper investigates the utilization of Spatio-Temporal data structures, their applications and how they can be implemented. This study also explores the temporal data management of spatial data that is dynamic in nature. Moreover, this integration of appropriate temporal aspects to the spatial data structures like Quad Tree is explored, thus the concept of Spatio-temporal data structures is developed. One of the primary takeaways from this paper is that it incorporates convex hull algorithm into the cluster management systems. This integration is exemplified in the context of spacecraft path optimization during orbital missions and the clustering of space debris in the atmosphere of the earth. The interaction will allow users to model rocket trajectories, so it would be possible to identify the optimum outputs. © 2024 IEEE.

关键词： Space debris

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Blockchain-Based Carbon Offset Tracking System for Sustainable Product Management 3

Blockchain-Based Carbon Offset Tracking System for Sustainab...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Padmavathi, A. Maurya, Laxmi Santhosh, Gauri Reddy, G Charan Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350354379

The agriculture sector of our country has been an important part of economic activity for a long time. Rather than the increasing demand shown by population growth, profitability remains difficult. This paper introduces an approach aimed at fixing agricultural marketing networks through the integration of blockchain technology. The proposed method uses blockchain to virtualize products and carbon offset certificates, providing a way for agricultural producers to mitigate carbon emissions and point out environmental concerns. With the help of the blockchain in this context, carbon offset credits can be securely tracked, traded, and utilized to promote sustainability practices. This innovative and new integration promises to revolutionize current agricultural product marketing methods, along with promoting environmental sustainability and empowering consumers with comprehensive information about the environmental footprint of their purchases. © 2024 IEEE.

关键词： Sustainable agriculture

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Leveraging CNN and AlexNet Algorithms for Improved Coffee Leaf Disease Identification and Detection 1

Leveraging CNN and AlexNet Algorithms for Improved Coffee Le...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Sree, Pranitha Surendran, R. Selvanarayanan, Raveena Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376517

The proposed study predicts Caffeine inflorescence diseases employing strong ML, and forecasting technologies. This research increases prediction abilities, evaluates cutting-edge technologies, and studies unique traits to forecast plant diseases faster and more correctly. Effective mitigation solutions are needed to prevent electrical disturbances from harming equipment, infrastructure, and system dependability in a rapidly evolving technological environment. Materials required are reputable data-gathering and analytics service Kaggle provided this study's core dataset. Group 1 uses convolutional neural networks. The study employed 48 sample sizes with 0.8 statistical power, 0.05 alpha, and 0.2 beta. Group 2 utilizes AlexNet. These figures are calculated using Clincalc. Google Colab runs accurate prediction software. Result: Group 1 used convolutional neural networks to achieve 78.65% accuracy, whereas Group 2 used AlexNet to achieve 68.24%. SPSS is used for accuracy and statistical data analysis. A two-tailed test determines if the two groups' accuracy differs statistically. Conclusion: Convolutional neural networks are more accurate than AlexNet algorithms. © 2024 IEEE.

关键词： Convolutional neural networks

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Service Composition in IoT: A Systematic Approach 9

Service Composition in IoT: A Systematic Approach

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Anoop, A. Sathya, D. Nidhishree, M.S. School of Computer Science Engineering Rv University Banaglore India Dayananda sagar college of engineering Bangalore India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

The Internet of Things (IoT) is an interconnection of several devices that work together to ease tasks. Cloud services are an integral part of IoT, which augments the functionality of the devices;each device can connect to the required cloud service assigned to the particular device. If the requested task is complex, i.e., cannot be fulfilled by one device or a single cloud service, a collection of services needs to be orchestrated and choreographed to achieve a common task with apt coordination of the services. This paper reviews the existing literature of the service composition approaches and different architectures in IoT and exhibits the comparative analysis of the various techniques and architectures in the contemporary works in the field. © 2024 IEEE.

关键词： Web services

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Financial Time Series Prediction on Apple Stocks Using Machine and Deep Learning Models 4

Financial Time Series Prediction on Apple Stocks Using Machi...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Saini, Agampreet Singh, Nisha Singh, Rahul Kumar Sachan, Manoj Kumar School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Sant Longowal Institute of Engineering and Technology Department of Computer Engineering Punjab Longowal India

ISBN: (纸本)9798350395914

The scope of financial time series prediction has evolved significantly, driven by the surging availability of data and computational advancements. This transition has seen the traditional statistical methods give way to present-day machine learning and deep learning techniques. Despite these strides, challenges continue to exist, with market unpredictability, dimensionality issues, and overfitting in high-frequency data standing as primary obstacles. Newer machine learning models are better than old ones at predicting stocks. When electronic trading and yield optimisation come together, they make these models even better. Deep learning has completely changed machine learning, especially in forecasting financial trends over time. Complex deep learning models lack interpretability, hindering real-world adoption. Considering this we have used two deep learning models which are Long Short-Term Memory and Convolutional Neural Network and trained these on apple stock market data over the years along with one machine learning model i.e. Linear Regression. Then we have compared the accuracy of all the models based on their R-squared Score value. © 2024 IEEE.

关键词： Adversarial machine learning

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