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Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

SN computer science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

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Preserve the Medical Data using Secure Partially Homomorphic Encryption with Blockchain Technology in Smart Healthcare 2

Preserve the Medical Data using Secure Partially Homomorphic...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Kavitha, S. Pavithra, B.S. Shashikala, A.B. Jagadeesh, B.N. Mageswari, P. Uma Marichi, Dileep Nitte Meenakshi Institute Of Technology Department Of Mca Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Rns Institute Of Technology Department Of Information Science And Engineering Bengaluru India Presidency College Department Of Computer Applications Bangalore India

ISBN: (纸本)9798350381665

Healthcare information is vital for both individuals and service providers, necessitating secure sharing and maintenance of electronic healthcare records (EHR). Traditional EHR systems have relied on centralized architectures, such as the cloud, which may expose sensitive information to undisclosed intelligence. However, these systems have struggled to address the diverse needs of stakeholders while ensuring separation, security, and compliance with legal requirements. Blockchain technology offers a decentralized and distributed ledger system, providing secure, verifiable, and immutable data transfer capabilities. By employing cryptographic techniques like hashes, blockchain eliminates the need for a central authority, making data exploitation challenging and evident. This study proposes a blockchainbased architecture utilizing the Proof of Stake (POS) consensus mechanism to authenticate user identities and facilitate secure EHR exchange across multiple healthcare systems. Additionally, to enhance data security, Secure Partially Homomorphic Encryption (SPHE) is recommended for outsourced data, enabling users to perform operations on encrypted data. The proposed technique surpasses alternatives in terms of energy efficiency, authenticity, and protection of medical data. © 2024 IEEE.

关键词： Blockchain

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Gated Recurrent Units and Recurrent Neural Network Based Multimodal Approach for Automatic Video Summarization

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Traitement du Signal 2023年第3期40卷 1227-1234页

作者： Kaur, Lakhwinder Aljrees, Turki Kumar, Ankit Pandey, Saroj Kumar Singh, Kamred Udham Mishra, Pankaj Kumar Singh, Teekam Department of Electronics & Telecommunication Engineering Rungta College of Engineering and Technology Bhilai490024 India College of Computer Science and Engineering University of Hafr Al Batin Hafar Al Batin39524 Saudi Arabia Department of Computer Engineering & Applications GLA University Mathura281406 India Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan School of Computing Graphic Era Hill University Dehradun248002 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India

A typical video record aggregation system requires the concurrent performance of a large number of image processing tasks, including but not limited to image acquisition, pre-processing, segmentation, feature extraction, verification, and description. These tasks must be executed with utmost precision to ensure smooth system performance. Among these tasks, feature extraction and selection are the most critical. Feature extraction involves converting the large-scale image data into smaller mathematical vectors, and this process requires great skill. Various feature extraction models are available, including wavelet, cosine, Fourier, histogram-based, and edge-based models. The key objective of any feature extraction model is to represent the image data with minimal attributes and no loss of information. In this study, we propose a novel feature-variance model that detects differences in video features and generates feature-reduced video frames. These frames are then fed into a GRU-based RNN model, which classifies them as either keyframes or non-keyframes. Keyframes are then extracted to create a summarized video, while non-keyframes are reduced. Various key-frame extraction models are also discussed in this section, followed by a detailed analysis of the proposed summarization model and its results. Finally, we present some interesting observations about the proposed model and suggest ways to improve it. © 2023 Lavoisier. All rights reserved.

关键词： Feature extraction

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Lips and Tongue Cancer Classification Using Deep Learning Neural Network 6

Lips and Tongue Cancer Classification Using Deep Learning Ne...

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

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management and Research Gwalior India Mits Department of Computer Engineering Gwalior India Sos in Computer Science and Applications Jiwaji University Gwalior India

ISBN: (纸本)9798350346961

One of the major diseases in developing countries is Oral Cancer, caused by alcohol, tobacco product and smoking which creates uncontrolled and abnormal cells in parts of human body. Recent Convolution Neural Network (CNN) has helped in medical industry to used medical images for finding the different types of diseases. The objective of research to build new CNN model which use for analysis the oral cancer images and determine the cancerous and noncancerous image. In this paper, CNN technique and image processing are used to categorize cancer or non-cancer lips and tongue image dataset. Deep Learning approaches is used to develop and check the performance of proposed CNN model. For experiment small Kaggle dataset that contains cancerous and non-cancerous lips and tongue images and apply proposed CNN model (Oral_Cancer_Detection). The result of Oral_Cancer_Detection model is very effective and accurate with low complexity. The Oral_Cancer_Detection model found 94% accuracy in our experiment and achieved better accuracy results in less time.. © 2023 IEEE.

关键词： Neural network models

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Robustness Challenges in Deep Learning: Strategies for Enhancing Model Resilience 2

Robustness Challenges in Deep Learning: Strategies for Enhan...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Joshi, Prakash Shaikh, M.Z. Varshney, Neeraj Dwivedy, Bhoopendra Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Gla University Department of Computer Engineering and Applications UP Mathura India Galgotias University Department of School of Computer Science & Engineering UP Greater Noida India

ISBN: (纸本)9798350371055

Deep knowledge is the main way the improvement of gadgets, gaining knowledge of generation for more than a few applications from facial popularity to self-reliant vehicles. Deep learning fashions have carried out outstanding successes, even outperforming people in complex obligations. However, robustness demanding situations of deep getting-to-know systems remain an essential difficulty. Deep studying fashions are at risk of malicious inputs that could result in misclassifications or denial-of-provider attacks. Robustness towards entering noise, in addition to robust extraction of beneficial capabilities from noisy datasets, are just a number of the main challenges for deep mastering systems. Current research has discovered a plethora of attack vectors that can be deployed against deep getting-to-know fashions along with 'adversarial' assaults, which contain manipulating the enter records in a particular manner to purpose models to misclassify items. Moreover, dataset bias and absence of right validation can cause structures that are not sturdy towards facts from a domain extraordinary from the facts used for education. A good way to enhance the robustness of deep learning models, researchers are exploring solutions that include countermeasures, specialized architectures, and higher education and checking out approaches. While progress has been made, the robustness challenges of deep learning remain an important study vicinity that should be addressed so one can fulfill the promise of this generation. © 2024 IEEE.

关键词： Architectures Discovered Extraordinary Manipulating Researchers

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Search Engine for QnA using Distributed Inverted Index System 3

Search Engine for QnA using Distributed Inverted Index Syste...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dey, Snehasis Moharana, Bhimasen De, Utpal Chandra Samant, Tapaswini Behera, Trupti Mayee Banerjee, Shobhan College of Engineering Electronics & Telecommunication Engineering Bhubaneswar India Lovely Professional University Department of Computer Science & Engineering Punjab India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Birla Institute of Technology and Science Department of Engineering Technology Pilani India

ISBN: (纸本)9798350381931

The internet along with its global uses has also evolved as a platform where various people can pose their questions on certain sites and get them answered by others from the same domain. Certain sites like Quora, Reddit, Yahoo Answers, etc. are sites enabling people to enhance their range over the net to find solutions to various issues or answers to various questions. But this leads to redundancy in data & hence increased memory consumption where different users can pose the same question in different ways. The same thing stands for the answers posted by different users for the same question in various ways. In this paper, we have worked on a search engine system for Question & Answers where given a question, we can find similar questions with the same semantic meaning using Elastic Search which uses an inverted index system & looks at various aspects of the formulated problem statement. © 2024 IEEE.

关键词： Search engines

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Analytical Evaluation of Web Performance Testing Tools: Apache JMeter and SoapUI 12

Analytical Evaluation of Web Performance Testing Tools: Apac...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Tiwari, Vatsya Upadhyay, Sachin Goswami, Jayati Krishna Agrawal, Sanjiv Bsa College of Engineering & Technology Department of Computer Science & Applications Mathura India GLA Univ. Deparmtnent of Computer Engineering & Application Mathura India G L Bajaj Group of Institutions Computer Sciecnce & Engineering Mathura India

ISBN: (纸本)9781665462617

Computing on the Web is based on distributed system applications that use the client-server architecture. Since web-based applications have been beta-tested, the quality criteria cannot be fixed. Whenever a website is launched, the question is always raised as to how many HTTP requests of users can the website handle at once. As part of this paper, various tools for performance testing will be used, including Apache SoapUI, JMeter, etc., and selection tools for load testing will be discussed. © 2023 IEEE.

关键词： Load testing

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Risk Assessment and Safety-as-a-Service Provisioning in IoV Networks: A Roadmap 5

Risk Assessment and Safety-as-a-Service Provisioning in IoV ...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Pradhan, Patrali Chattopadhyay, Samiran Chowdhury, Subrata Rout, Suchismita Mishra, Manoj Ranjan Patra, Sudhansu Shekhar Haldia Institute of Technology Dept. of Computer Science and Engineering Haldia India Techno India University Kolkata India Sreenivasa Institute of Tech. and Management Studies Dept. of Computer Science and Engineering Andhra Pradesh Chittoor India Kiit Deemed to be University School of Computer Engineering Bhubaneswar India Kiit Deemed to be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350376685

This paper presents a framework for a Safety recommender system that offers Safety-as-a-Service (Safe-aaS) to on-road vehicles in IoT-based transportation systems. The proposed safety recommendation system integrates several phases, including preparing sensed data, self-supervised learning from the data, and predicting personalized safety plans. The customized safety plans, generated by analyzing on-road decision parameters such as dynamic road conditions, weather patterns, and driving assistance parameters, are suggested to registered users in real time through the Safe-aaS platform tailored to their specific *** a Safe-aaS enabled road transportation system, sensor nodes produce large volumes of data in a dynamic environment. Efficient analysis and storage of sensed data in edge devices poses a major challenge for service providers. Conversely, generating safety-related decisions with lower processing time ensures driver safety. The Safe-aaS-based recommender system provides safety-related decisions with low latency and processing delay. In the proposed recommendation system, a safety score is computed from the pre-processed sensed data across various parameters, followed by a user-specific similarity score and a corresponding risk score. We apply a fuzzy rule-based learning technique to predict optimized safety plans. To showcase the real-time scenario of our framework, we develop a user-friendly application interface at the primary stage. Extensive analysis shows that our system effectively detects on-road risks and adapts safety recommendations in dynamic weather and road conditions. © 2024 IEEE.

关键词： Sensor nodes

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Implementation of Clustering Approaches in Fuzzy Database through Weka Tools 14

Implementation of Clustering Approaches in Fuzzy Database th...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Rani, Santosh Singh, Tarkeshwar Singh, Sandeep Kumar Kumar, Santosh Galgotias College of Engineering & Technology Department of Computer Applications Greater Noida India School of Computing Science & Engineering Galgotias University Greater Noida India

The extraction of knowledge and useful information from large amounts of data stored in databases is known as data mining. Given the growing use of fuzzy object-oriented database systems in complex and uncertain database applications, studying data mining techniques for fuzzy object-oriented databases is essential. Users may be able to better comprehend, organize, and utilize the data in these databases as a result. Fuzzy object-oriented databases are used to implement data mining techniques in order to extract the useful latent regularities from the enormous, complicated, and uncertain database. In this study, we develop a knowledge-based system for a fuzzy object-oriented database using a variety of data mining methods. In addition, the application of this learned information may significantly improve the adaptability and strength of database browsing, database organization, and data and knowledge querying in fuzzy object-oriented databases. © Grenze Scientific Society, 2023.

关键词： Data mining

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Advancing Medical Imaging: A Focus on Efficient Net for Brain Tumor Classification 2

Advancing Medical Imaging: A Focus on Efficient Net for Brai...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Shashidhar, R. Manasa, R. Megha, K.M. Priyanga, P. Manjunath, A.S. Roopa, M. Jss Science and Technology University Department of Electronics and Communication Engineering Mysuru India Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Bengaluru India Jss Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9798350372892

In the field of medical diagnosis, the increasing occurrence of brain tumors calls for creative approaches to detect and intervene at early stages. This proposed work focus on the Efficient Net (EffNet) architecture to address the challenges associated with brain tumour recognition and sorting from Magnetic Resonance Imaging (MRI) scans. The main objectives are outlined to develop a robust model capable of discriminating between various tumour types and normal brain tissues. Four distinct classes - glioma tumour, pituitary tumour, meningioma tumour, and no tumour compose the dataset, providing diversity for training and evaluation. Using projected method got the accuracy of 98.08% accuracy. Our proposed method detects the all the four classes effectively. © 2024 IEEE.

关键词： Magnetic resonance imaging

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