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A robust blockchain-based watermarking using edge detection and wavelet transform

Multimedia Tools and Applications 2025年第14期84卷 12739-12763页

作者： Mannepalli, Praveen Kumar Richhariya, Vineet Gupta, Susheel Kumar Shukla, Piyush Kumar Dutta, Pushan Kumar Chowdhury, Subrata Hu, Yu-Chen G H Raisoni Institute of Engineering and Technology Nagpur India LNCT University Bhopal India University Institute of Technology RGPV Bhopal India School of Engineering and Technology Amity University Kolkata Kolkata India AP Chittoor India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

Nowadays, image security is a crucial component of the scientific community. The transparent marking is often used to keep medical imaging information from being stolen without permission. In this work, a novel combination of blockchain-based encryption and invisible picture watermarking was presented. In order to achieve watermarking, edge detection of discrete wavelet transform coefficients is being used. Using the discrete wavelet transform to break up the medical image gives you multi-resolution coefficients. For the HH wavelet band, the edge detecting method is employed to make the edge coefficients. For watermark embedding, the difference between dilation and edge coefficient is used in order to enhance robustness. The image with the watermark is encrypted by employing a hashing process that is based on blockchain technology and was developed particularly for medical pictures. After the decryption procedure is finished at the conclusion of the decoding process, the picture is then regenerated. The results of both stages are presented in a sequential order. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Enhancing Microgrid Stability: Optimal BESS Sizing via Modified PSO for Frequency Control and Cost Efficiency 7

Enhancing Microgrid Stability: Optimal BESS Sizing via Modif...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Singh, S. Vikram Revathi, V. Babu, B Sankara Bhavana, Munugapati Singh, Navdeep Nagamani, G. Amity University Amity School of Engineeringh and Technology Uttar Pradesgh Greater Noida India New Horizon College of Engineering Department of Applied Sciences Bangalore India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

When constructing a Battery Energy Storage System (BESS) to be incorporated in a microgrid, the main objective is to minimise the risk of microgrid stability and system failure. An improper or careless sizing of the BESS may lead to system losses, excessive capacity, and high installation costs. Consequently, this study suggest a novel technique for managing the frequency of the independent microgrid using an improved particle swarm optimisation (PSO), which aims to find the optimum size of the BESS while minimising its total cost. Our research focuses on finding the most cost-effective way to deploy BESS in microgrids for a period of 15 years. We will also recommend the optimal measurements of BESS to minimise microgrid volatility and system failure in the case of utility grid outage. We will use the PSO approach for frequency regulation. The next step is to examine a standard microgrid and evaluate BESS in comparison to other existing storage systems in terms of their economic performance. By comparing the optimum size of BESS- modified PSO method to both the conventional size of BESS and the ideal size of BESS-based empirical approach, the findings show that the latter may improve the dynamic system effectiveness. In a typical microgrid, the polysulfide-chromium BESS is predicted to be cheaper throughout the 15 years of deployment compared to the chrome redox BESS, when evaluating the financial health of the two storage techniques. Findings indicate that microgrid planning flexibility, grid security, and power system stability may all be greatly enhanced using the suggested PSO method-based frequency management. Simultaneously, it is able to meet the needs for frequency control economically. © 2024 IEEE.

关键词： Cost effectiveness

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A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare System

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1683-1690页

作者： Zia, Maryam Obaidat, Mohammad S. Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Chaudhry, Shehzad Ashraf Comsats University Islamabad Department of Computer Science Sahiwal57000 Pakistan University of Jordan King Abdullah Ii School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University UP Noida201301 India National Yunlin University of Science and Technology Future Technology Research Center Yunlin64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul34398 Turkey

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool. © 2005-2012 IEEE.

关键词： Authentication

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Heart Disease Analysis and Prediction Using EDA and ML Classifiers 23

Heart Disease Analysis and Prediction Using EDA and ML Class...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Humra Khan Pawan Singh Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University India

ISBN: (纸本)9798400709418

Heart is one of the most important organs as blood is pumped throughout the body by the heart. Heart disease is widely regarded as the illness that kills individuals most quickly. To prevent catastrophic risks and diminish heart-related problems, prompt identification of heart disease is essential. The disease prediction system helps clinicians identify serious illnesses as early as possible. This research paper aims to provide a complete examination of the features contributing to heart disease with respect to by means of exploratory data analysis and predict the possibility of heart disease with the use of machine learning algorithms. Through EDA, it was discovered that, of all these features, the number of major vessels (0–3) stained by fluoroscopy, different types of chest pain, greatest heart rate reached, exercise-induced angina, exercise-induced ST depression compared to rest, and inclination of the peak exercise ST segment are the ones that have the greatest impact on the diagnosis of heart disease. Utilising the UCI heart disease dataset containing the features, three machine learning classifiers are implemented: decision tree (DT), random forest (RF), and K-nearest neighbour (KNN). When compared to other heart disease prediction models, Random Forest has the best rate of accuracy (85.25%).

关键词： Exploratory Data Analysis (EDA)

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Forecasting Continuous Air Pollution with Cloud-Based Big Data Platform using Spatial-Temporal Visualization 8

Forecasting Continuous Air Pollution with Cloud-Based Big Da...

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Chang, Yue Shan Abimananan, Satheesh Shukla, Saurabh Satheesh, Dhivyadharsini National Taipei University Department of Computer Science and Information Engineering New Taipei City Taiwan Amity University Maharashtra Department of Computer Science and Engineering Mumbai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350334012

In today's era of rapidly growing data, having a well-designed platform for analyzing and visualizing big data is becoming increasingly important. This study presents a cloud-based big data platform for analyzing and visualizing continuous spatial-temporal air pollution. The platform combines a Spark Hadoop-based machine learning environment with a TensorFlow-based deep learning framework for forecasting and a map for demonstration and visualization of the results. The platform uses the Inverse Distance Weighted (IDW) method to estimate the concentration of particulate matter in the atmosphere and a cloud based non-SQL database to store the pre-calculated air quality images. The platform also features a sliding window mechanism, which allows users to easily visualize the results over time. This work provides an instant overview of air quality in Taiwan and evaluates the performance of the platform, concluding that it is efficient and capable of providing continuous spatial-temporal results. This platform represents a significant step forward in the field of air quality monitoring and forecasting. © 2023 IEEE.

关键词： Data visualization

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Proposed AES-based Obfuscation and Encryption Model for Cloud Data Security

Proposed AES-based Obfuscation and Encryption Model for Clou...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Prabhdeep Singh Pawan Singh Abhay Kumar Agarwal Anil Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow India Department of Computer Science and Engineering Kamla Nehru Institute of Technology Sultanpur India Amity School of Engineering and Technology Amity University Lucknow India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Cloud Computing (CC) offers a diverse range of services along with huge data storage across a network. CC has collaborated with varied emerging technologies like IoT because of its numerous advantages. Despite CC's benefits, cloud-based servers are not always trustworthy in real-world scenarios. Users' privacy will be compromised if they send their data straight to cloud platforms since crucial information will be exposed to cloud servers. Thereby, this work develops a data security model in the cloud with two stages, proposed secured data storage and proxy re-encryption. Initially, the data is stored securely in the cloud based on improved AES-based encryption as well as obfuscation-based encryption. Based on the request from the data user, proxy re-encryption is done using Kronecker product-based encryption. The enhanced AES achieved the lowest value of 0.025 for the CCA attack with a key size of 128 bits. Thus, the final encoded key is obtained.

关键词： Cloud computing Data privacy Quantum computing Memory Elliptic curve cryptography Data models Encryption Servers Intelligent systems Homomorphic encryption

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Integrating Renewable Energy Sources into Smart Grids with an Aggregator-Based Energy Management System for Efficiency and Resilience 7

Integrating Renewable Energy Sources into Smart Grids with a...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kumar, B. Santhosh Devi, Anusuya Sharma, Smita Bhavana, Munugapati Babu, B. Sankara Singh, Navdeep Institute of Aeronautical Engineering Department of Cse Ai and Ml Telangana Hyderabad India New Horizon College of Engineering Department of Applied Science Karnataka Bangalore India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350350067

Integrating renewable energy sources (RESs) into smart grids (SGs) is challenging due to the intermittent and unpredictable nature of the sources, which are often the sun or wind. Additionally, it is important to think about how to get customers involved in the electricity sector so that we can lower energy prices globally. According to this article, an energy management system (EMS) may help the SG run more efficiently. The EMS aggregates distributed energy resources to make SG market participation easier. Integrating demand-side management (DSM) with active management strategies (AMS) has financial and environmental benefits as it increases the use of renewable energy sources while decreasing consumer energy costs. By incorporating their preferences and costs into market processes, energy supply/demand and distribution system operators (DSOs) may respond to real-time price signals. This has the potential to make the grid more adaptable and resilient. To verify the effectiveness of the proposed EMS, a 23-bus, 11-kV transmission network is used. © 2024 IEEE.

关键词： Clean energy

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An Analysis of Object Detection in Bad Weather Conditions using Deep Learning Models

An Analysis of Object Detection in Bad Weather Conditions us...

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Janvi Verma Harsh Verma Supriya Raheja Computer Science Engineering Amity School of Engineering and Technology Amity University Noida India

Object detection, a task, in the field of computer vision faces obstacles when dealing with weather conditions such as fog, rain, snow, and low light situations. This paper provides an overview of advancements in the realm of object detection under challenging weather conditions. It delves into groundbreaking research that utilizes learning techniques adjustments to frameworks and the significance of datasets. This paper proposed the YOLOv5 model to detect the object in bad weather conditions. The paper presents the comparison between the YOLOv5 model presented in literature and the proposed YOLOv5 model in terms of accuracy, precision, recall and F1-score. The evaluation results prove the better performance of our proposed model.

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Scalability Performance Analysis of Blockchain Using Hierarchical Model in Healthcare

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Blockchain in Healthcare Today 2024年第1期7卷 7-7页

作者： Sadath, Lipsa Mehrotra, Deepti Kumar, Anand Computer Science Engineering Department School of Engineering Amity University Dubai United Arab Emirates Computer Science Engineering Department Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India Electronics Engineering Department School of Engineering Amity University Dubai United Arab Emirates

Blockchain technology has become crucial in improving the privacy and security of enterprise applications in the cyber world. However, scalability has become a significant concern for researchers in large organizations, especially those with complex hierarchies and access privileges. As a result, the existing models and consensus algorithms suffer from various issues. Medical centers and healthcare providers are particularly affected by this problem due to the vast amount of data, making it a critical weakness of traditional database management systems. To address this issue, the authors propose a hierarchical model within the Hyperledger Fabric enterprise application, focusing on the healthcare sector as a use case. This model includes multiple organizations at different levels of the hierarchy, such as hospitals, hospital governance, and insurance companies. The initial implementation of this model includes two levels of hierarchy, demonstrating networks of hospitals joining an insurance company. The primary objective of the experiment is to test and improve the network’s performance using this model. The model’s performance is evaluated by manipulating and scaling environmental factors such as the number of organizations, transaction numbers, channels, block intervals, and block sizes. The benchmarking tool used for this assessment is Hyperledger Caliper, which measures indicators such as success and failure rates, throughput, and latency. Currently, the research focuses only on testing the model’s scalability using patient data. © 2024, Partners in Digital Health. All rights reserved.

关键词： Blockchain

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Cloud Resource Management: Monitoring 23

Cloud Resource Management: Monitoring

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Anshika Rawat Shubham Singh Pawan Singh Department of Computer Science and Engineering Amity School of EngineeringandTechnology Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India India Computer Engineering and Application Gla University India Department of Computer Science and Engineering Amity School of of Engineering and Technology Amity University Uttar Pradesh India

ISBN: (纸本)9798400709418

The management and use of computer resources within enterprises has changed as a result of the emergence of cloud computing as a paradigm shift. With its diverse and comprehensive portfolio of cloud services, including AWS CloudWatch for monitoring, Amazon Web Services (AWS) is at the vanguard of this revolution. In order to fully utilize the capabilities of these platforms and achieve cost- effectiveness, scalability, and optimal performance, proper cloud resource management is essential. This, study offers a thorough investigation of AWS's cloud resource management, with a focus on Particle Swarm Optimization (PSO) integration as a cutting-edge optimization technique. The dynamic and ever-changing workloads seen in cloud computing systems present a special set of issues for resource allocation and optimization. However, finding the ideal compromise between performance and cost-efficiency is still a challenging task. This study examines the changing environment of cloud resource management with an eye toward the future. In order to enable even more intelligent resource allocation decisions, we take into account the possible integration of machine learning and predictive analytics into our PSO framework. We also look at the broader implications of PSO-driven resource management.

关键词： Amazon Web Services (AWS)

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