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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

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

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.

关键词： Industries Electric potential Transportation Blockchains Autonomous vehicles

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Privacy Preservation in Federated Learning: its Attacks and Defenses

Privacy Preservation in Federated Learning: its Attacks and ...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Jyoti Maurya Shiva Prakash Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

In the context of artificial intelligence, the traditional centralized server-based machine learning model training approaches have significant security and privacy issues. The federated learning (FL) approach was created to address these problems. FL uses a privacy-by-design architecture, in which several devices work together to develop any machine learning system that does not reveal users' personal information under the control of a single server. In federated learning, privacy preservation is achieved through several techniques that involve adding noise, secure aggregation, local training, and model compression. These techniques aid in making sure that raw data is not transmitted over the network and that only the required information is shared to train a global model. This research study has discussed about the federated learning-related privacy threats and defenses. The existing research works are divided into two types as attack methods and corresponding defense mechanism and further analyzed the relevant research papers from the last few years, and then further discussed the research challenges, and finally concluded the research study.

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Test Suites Generation using UML Modelling and Heuristic Techniques: A Systematic Study 22

Test Suites Generation using UML Modelling and Heuristic Tec...

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Proceedings of the 4th International Conference on information Management & Machine Intelligence

作者： Kirti Gupta Parul Goyal Computer Application and Information Technology Shri Guru Ram Rai University India

ISBN: (纸本)9781450399937

Software testing is not only crucial, but also a difficult stage in the system of software development life cycle. During the software testing phase, debuggers create test suites as input for the system being tested in order to evaluate the system's events. An eager role is played by creating test suites for identifying errors and enhancing the prominence of the product. This paper expands on a thorough analysis of generation of test suites processes used in many situations, including implementation of test suites utilizing a single UML diagram, a combinational UML diagram, and meta-heuristic methods. Together with this analysis, a selection of eminence literature from journals, symposia, workshops, and conferences is utilized. Issues with software testing have been identified by looking at the literature study that is based on methods used to develop test suites, coverage requirements, data foundation, testing level, and produced components. Deficits are acknowledged based on systematic study, and future efforts are advised. Through the use of inclusion-exclusion criteria, newly proposed coverage criteria, and test suite generating approaches, writers were able to identify the limitations of the work that was provided after examining the deliberative literature. The findings examined 74 significant studies in opposition to 6 research issues. By responding to the carefully considered study questions, the purpose has been satisfied. Following a review of the literature, it can be said that all kinds of generation techniques are frequently used in a range of testing scenarios, but that the period from 2010 to 2022 witnessed the greatest use of all techniques.

关键词： Meta-Heuristic Algorithms

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Explainable Approach for Species Identification using LIME 4

Explainable Approach for Species Identification using LIME

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4th IEEE Bombay Section Signature Conference, IBSSC 2022

作者： Nikam, Mihir Ranade, Ameya Patel, Rushil Dalvi, Prachi Karande, Aarti Sardar Patel Institute of Technology Department of Information Technology Mumbai India Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India Sardar Patel Institute of Technology Department of Master in Computer Application Mumbai India

ISBN: (纸本)9781665492911

Plant identification has a wide array of applications in the fields of agronomy and the discovery of natural and medicinal products. This research aims to explore various deep learning techniques like InceptionV3, Xpection, and ResNet to identify plants. Highly accurate machine learning models generally lack explainability and interpretability. Neural networks are usually opaque systems and thus a direct understanding of the interpretations becomes necessary. We aim to remove this ambiguity of how the model reaches its conclusion by introducing Explainable AI (XAI) techniques. Explainability aims to break such barriers by diminishing the lack of transparency in Artificial Intelligence and Machine Learning models, thus taking a step toward making AI reliable. In this paper, Convolutional Neural Network has been used to identify Vietnamese medicinal plant images based on the characteristics of the leaves, stems and other parts of the plant. Upon identification, our paper also elaborates on how each model predicts which part of the image helps the CNN model to make a prediction by integrating Explainable AI (XAI) using the Lime package. Through this research, we generated images using LIME package which highlight pixels that determine the result of our plant identification process. © 2022 IEEE.

关键词： Deep neural networks

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Privacy Preservation in Federated Learning, its Attacks and Defenses using SMC-GAN

Privacy Preservation in Federated Learning, its Attacks and ...

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International Conference on Secure Cyber Computing and Communication (ICSCCC)

作者： Jyoti Maurya Shiva Prakash Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

When using collaborative machine learning, maintaining privacy is a major concern because different parties want to train a model on their own data without sharing it with others. In collaborative machine learning, the approaches of Secure Multiparty Computation (SMC) and Generative Adversarial Networks (GAN) can be utilized to protect privacy. While GANs can create artificial data samples that are comparable to the real data, SMC allows participants to collaboratively compute a function without disclosing their inputs. In collaborative machine learning, this work provides a method for privacy preservation utilizing SMC and GANs. The proposed approach can produce high model accuracy while maintaining privacy, according to experimental data. The method is assessed using a number of benchmark datasets and contrasted with other privacy-preserving strategies. The findings demonstrate that, in terms of model correctness and privacy preservation, the suggested approach performs better than competing techniques.

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Evaluating the Factors of CGTMSE Scheme in Bank by Using Fuzzy AHP 6

Evaluating the Factors of CGTMSE Scheme in Bank by Using Fuz...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Nadeem, Mohd Ahmad, Mahfooz Ahmad, Masood Pathak, Prabash Chandra Gupta, Sonali Pandey, Harsh School of Computer Application Babu Banarsi das University Lucknow India Integral University Department of Electronics and Communication Lucknow India Babasaheb Bhimrao Ambedkar University Department of Information Technology Lucknow India

ISBN: (纸本)9798350304480

MSMEs (Micro, Small, and Medium Enterprises) are basic financial tools for India's economic growth and development. Many schemes have been created by the Indian government to support MSMEs with appropriate and timely access to structured funding. The Credit Guarantee Trust for Micro and Small Enterprises (CGTMSE), created in 2000, represents one of the significant initiatives. An MSME is a company that operates in a specialized market with an untested product or innovation. The designer-entrepreneur may be a respectable professional, but he or she is not a respectable leader. Issues arise as a result of a lack of administrative skills. Lack of money to invest in innovation becomes another barrier when mastery is severely demanded. He is unable to afford pricey advertising campaigns to get the word out. Administrative formality informs him of the need of upholding all of the regulations that everyone is required to go by, particularly concerning MSME. The authors employed the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) to examine these aspects impacting the CGTMSE designs. Based on our empirical assessment system, awareness was ranked first, with a weight of 0.325562. This method quantitatively assesses the components. This is an MCDM technique that reduced the vagueness of humans in the selection of the most prominent factors affecting the schemes. © 2023 IEEE.

关键词： CGTMSE credit guarantee credit guarantee scheme fuzzy AHP MSME

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Patients Medical Record Monitoring Using IoT Based Biometrics Blockchain Security System

Patients Medical Record Monitoring Using IoT Based Biometric...

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IoT, Communication and Automation technology (ICICAT), International Conference on

作者： Kamlesh Kumar Gautam Shiva Prakash R K Dwivedi Department of Information Technology & Computer Application Madam Mohan Malaviya University of Technology Gorakhpur UP

The Patient medical record monitoring using IoT based Biometrics and blockchain security system suggests an innovative strategy for patient monitoring of their health and disease that combines the Internet of Things (IoT) devices and blockchain technology through biometrics. The paper presents a proposed model of combine system architecture that uses IoT devices and biometrics system to collect patient health data and store it on hospital database and that data synchronized biometrics database which is used in system that is government data of Adhaar identification of patient data with a blockchain-based platform for secure and transparent sharing and analysis in the medical record of hospital and medical science. The proposed system architecture includes IoT devices such as smart wearable things such as smart watch, smart digital globs, digital face mask, smart goggles and sensors that collect patient health data and transmit it to a blockchain-based platform that is Ethereum node. The platform uses smart contracts to manage data access and sharing, ensuring that the patient data and information will be only shared to authorized person and parties. The platform also enables data analysis and machine learning algorithms to provide insights into patient health trends and enable early intervention in case of health issues.

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Multi-dimensional wind power prediction based on time-series characterization analysis and VMD-EMD quadratic decomposition

Multi-dimensional wind power prediction based on time-series...

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2023 Asia Conference on Power, Energy Engineering and computer technology, PEECT 2023

作者： Zhang, Zhe Gao, Jing Wang, Yongsheng Guan, ShiJie Luo, Jidong Inner Mongolia University of Technology School of Data Science and Application Hohhot China Inner Mongolia Agricultural University School of Computer and Information Hohhot China Inner Mongolia DongRun Energy Technology Co. Ltd Hohhot China

ISBN: (纸本)9798350313376

Wind power data receive wind volatility and have strong non-smoothness, making it difficult to achieve high accuracy in wind power prediction. To address this challenge, this paper proposes a wind power multi-step prediction model combining VMD (Variational Modal Decomposition), EMD (Empirical Modal Modal Decomposition), Hurst analysis and temporal entropy values. Firstly, the first decomposition of the historical wind power data is carried out by VMD decomposition;then Hurst analysis is performed on the components of the first decomposition, and the components with low regularity are decomposed twice using EMD decomposition;then the components of the second decomposition are further filtered using permutation entropy, and the components with high entropy values are compared with the high The components with high entropy, high Hurst and low entropy of the secondary decomposition are formed into high randomness irregular component, regular component and low randomness low regularity component;for the three types of components, BP neural network is used to predict them respectively, and they are reorganized into wind power prediction values. The experiments prove that the model proposed in this paper has higher prediction accuracy and faster running time than the current mainstream models, and can achieve more efficient wind power prediction. © 2023 IEEE.

关键词： Wind power

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Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

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computers, Materials & Continua 2024年第3期78卷 3323-3348页

作者： Umer Sadiq Khan Zhen Liu Fang Xu Muhib Ullah Khan Lerui Chen Touseef Ahmed Khan Muhammad Kashif Khattak Yuquan Zhang School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan432000China Key Lab of Mountain Hazards and Surface Processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China College of Aviation Zhongyuan University of TechnologyZhengzhou451191China Research Center of Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China School of Computer Science University of PoonchAzad Jammu Kashmir12350Pakistan School of Computer Science and Information Engineering Hubei UniversityWuhan430062China

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture *** the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation *** this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter *** proposed model highlights the residual region with considerable information and constructs color ***,we incorporate the content-based color saliency as spatial information in the Gaussian mixture *** segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum ***,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic *** experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge *** the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

关键词： Unsupervised segmentation color saliency spatial weighted GMM random color histogram

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

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

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

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

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

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.

关键词： Deep learning Databases Face recognition Computational modeling Surveillance Predictive models Convolutional neural networks

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