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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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A Review on the application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

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computers, Materials & Continua 2024年第1期78卷 197-225页

作者： Xiaozhong Yu Jinhua Zheng College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

In rice production,the prevention and management of pests and diseases have always received special *** methods require human experts,which is costly and *** to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is ***,deep learning technology has been employed to detect and identify rice diseases and *** paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing ***,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and *** study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

关键词： Deep learning rice diseases and pests image recognition object detection

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SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

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computers, Materials & Continua 2024年第6期79卷 4641-4657页

作者： Jiahui Liu Lang Li Di Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys ***,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within *** challenges significantly hinder key recovery *** paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these ***-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained ***,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each ***,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct ***,an intelligent search method is proposed to identify optimal parameters,further improving attack *** were conducted on both simulated environments and real power traces collected from the SAKURA-G *** the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

关键词： Side-channel analysis correlation power analysis genetic algorithm crossover mutation

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Side-Channel Leakage Analysis of Inner Product Masking

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computers, Materials & Continua 2024年第4期79卷 1245-1262页

作者： Yuyuan Li Lang Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

The Inner Product Masking(IPM)scheme has been shown to provide higher theoretical security guarantees than the BooleanMasking(BM).This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of *** previous work unfolds when certain(adversarial and implementation)conditions are met,and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected *** this paper,we investigate the security characteristics of IPM under different *** adversarial condition,the security properties of first-order IPMs obtained through parametric characterization are preserved in the face of univariate and bivariate *** implementation condition,we construct two new polynomial leakage functions to observe the nonlinear leakage of the IPM and connect the security order amplification to the nonlinear *** observe that the security of IPMis affected by the degree and the linear component in the leakage *** addition,the comparison experiments from the coefficients,signal-to-noise ratio(SNR)and the public parameter show that the security properties of the IPM are highly implementation-dependent.

关键词： Side-channel analysis inner product masking mutual information nonlinear leakage

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Enhancement of Farming Using IoT Based Unmanned Aerial Vehicles 2

Enhancement of Farming Using IoT Based Unmanned Aerial Vehic...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Tiwari, Vanshika Prakash, Shiva Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9798350371055

The agriculture sector faces the pressing challenge of increasing food production while optimizing resource use and minimizing environmental impact. One promising solution to address these challenges is the integration of Internet of Things (IoT) based Unmanned Aerial Vehicles (UAVs) into farming practices. This research paper explores the potential benefits, challenges and implications of using IoT-based UAVs in agriculture. It examines how this technology can enhance farming by providing real-time data, precision agriculture capabilities and improved resource management. There are detecting diseases in agriculture field to reduce background noise using the Median filters, Temporal filters. This helps to remove noise and vestiges caused by the movement of the drone or the target factory. © 2024 IEEE.

关键词： Image Processing Internet of Things (IoT) Median Filter Temporal Filter Unmanned Aerial Vehicles (UAVs)

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Pneumonia Identification and Classification Using CNN Model Based on Chest X-Ray Image in Healthcare 7th

Pneumonia Identification and Classification Using CNN Model ...

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7th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Kumar, Suraj Prakash, Shiva Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9789819950140

Pneumonia is a bacterial, viral, or fungal disease within one or even both lungs. It is a serious disease where the air sacs in both lungs fill up with pus as well as other substances. Many architectures and models were developed to evaluate such diseases, but still, the possibility to further enhancement. We built a CNN model to identify and categorise Pneumonia disease in the lungs using CXR images. The dataset was developed and pre-processed by the Kaggle CXR dataset. In the research, the convolutional neural network method has been used in conjunction with other data augmentation approaches to improve classification accuracy which will help to enhance the training and validation accuracies, as well as characterisation of the precision of the CNN model and achieve various results. Our model's training accuracy and validation accuracy are 0.9784 and 0.9511, respectively, and its training loss and validation loss are 0.0634 and 0.1476. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Methodologies, applications, and Challenges of Pneumonia Detection of Chest X-Ray images for COVID-19 using IoT-enabled Deep Learning

Methodologies, Applications, and Challenges of Pneumonia Det...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Verma, Garima Prakash, Shiva Madan Mohan Malaviya University of Technology Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9798350323887

There is a great advancement in the domain of Internet of medical Things (IoMT) including other domains of artificial intelligence, machine learning, deep learning which has an extensive possibility of exploring healthcare industry. The IoT devices like sensors, actuators and other devices gets connected to the internet and further they collect the data and store it to a specific location. For further processing of the data, the frameworks of machine learning, deep learning are utilized. These techniques help to get the clear insights of the patient's health data which enables to know the current health status of the patient. Recently, the Covid-19 outbreak has occurred which has influenced millions of people across the globe. This virus has taken life of many people and the infection rate of this virus is still increasing day by day. Researchers and medical staffs are exploring advanced techniques to utilize medical images of the infected person using IoMT and deep learning frameworks so that the root cause can be explored. Different techniques deep neural networks have been explored in this work to detect Covid-19 infected persons which utilizes a chest X-ray dataset. A lot of challenges are there that are being faced by the researchers to detect Covid-19 infected patients from Chest X-ray images. This exhaustive literature review presents different frameworks of deep learning architectures and a comparative study has also been done addressing the recent methodologies, datasets, issues, research gaps and so on. Further, some pre-trained models based on CNN architectures like Xception, VGG16, VGG19 and so on are also discussed. © 2023 IEEE.

关键词： Viruses

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Heart Disease Prediction and Diagnosis Using IoT, ML, and Cloud Computing 6th

Heart Disease Prediction and Diagnosis Using IoT, ML, and Cl...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Maurya, Jyoti Prakash, Shiva Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

ISBN: (纸本)9789819940707

Heart disease is currently regarded as the main cause of illness. Regardless of age group, heart disease is a serious condition nowadays because most individuals are not aware of their kind and level of heart disease. In this fast-paced world, it is essential to be aware of the different types of cardiac problems and the routine disease monitoring process. As per the statistics from the World Health Organization, 17.5 million deaths are because of cardiovascular disease. Manual feature engineering, on the other hand, is difficult and generally requires the ability to choose the suitable technique. To resolve these issues, IoT, machine learning models and cloud techniques, are playing a significant role in the automatic disease prediction in medical field. SVM, Naive Bayes, Decision Tree, K-Nearest Neighbor, and Artificial Neural Network are some of the machine learning techniques used in the prediction of heart diseases. In this paper, we have described various research works, related heart disease dataset, and comparison and discussion of different machine learning models for prediction of heart disease and also described the research challenges, future scope and discussed the conclusion. The main goal of the paper is to review the latest and most relevant papers to identify the benefits, drawbacks, and research gaps in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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Blockchain Enabled Autonomous Vehicle Based Vehicular IoT System

Blockchain Enabled Autonomous Vehicle Based Vehicular IoT Sy...

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

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

ISBN: (纸本)9781665462631

Blockchain technology is gaining its popularity in many areas with huge success which ultimately attracts all researchers to study this technology. And the result of that shows more and more research to be conducted. Which makes blockchain technology relevant in the field of transportation systems. It is expected that in near future their integration will achieve greater heights, as compared to other technology blockchain is still new and many more unknown things still need to be found out. The aim of this study is to find out how blockchain benefits in Autonomous vehicles. We have compared the blockchain integrated autonomous vehicle systems to get the picture of the present environment and the challenge of the future for this integration. AV's or we say intelligent transportation is the future. We are going to study the potential application of the blockchain which will help in improving this particular section of industry. Finally their limitations and challenges associated with this integration. © 2023 IEEE.

关键词： Blockchain

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Detecting and recognizing faces from video images using multi-deep CNN based rank-level fusion 2

Detecting and recognizing faces from video images using mult...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ghosh, Manas Sing, Jamuna Kanta RCC Institute of Information Technology Department of Computer Application Kolkata India Jadavpur University Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350371314

The advent of deep neural networks has provided a competitive edge in facial analysis efforts, especially the detection and recognition of faces from video images. This paper outlines a methodology wherein a face database is created from an outdoor video clip, and a rank-level fusion mechanism has been proposed for face recognition using a database that makes use of the probabilities produced by the softmax functions of multiple deep convolutional neural network (CNN) models. These probabilities are referred to as certainty indices for the potential classes to which a test sample may belong. For each of the CNN models, fuzzy ranks are obtained for a subset of classes with the top certainty indices by applying a Gaussian distribution function followed by a complementation operation. For each class, appearing in one or more subsets corresponding to deep network models, average of the certainty indices is complemented and fused with the sum of complemented fuzzy ranks. While calculating the cumulative sum of the fuzzy ranks, a penalty, is foisted to the class for not being present in any subset. This can avoid the chance of becoming an unexpected winner. The class with the lowest final ranking was identified as the class pertaining to the test sample. In addition to testing with the custom-made outdoor video-based face dataset, the projected method was evaluated with a standard surveillance indoor dataset, ChokePoint. Considering all test sets on ChokePoint dataset, the average recognition accuracy of the proposed method is 99.54%. The same for custom-made dataset is 93.96%. For both datasets, precision and macro-average scores of the proposed method in all cases are higher than the individual deep CNN models. © 2024 IEEE.

关键词： Face recognition

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