Enhancing transaction verification through pruned merkle tree in blockchain

A Merkle tree is a data structure employed within Blockchain technology to securely verify information or transactions within a vast data collection. This paper proposes a new and improved verification method, Pruned Merkle Tree (PMT), for hash nodes marching to the Merkle Root in a Minimal duration. PMT is a unique mechanism for verifying unpaired transactions in a block. The future influence of cryptocurrency will be immense, and PMT showcases its effectiveness in terms of transaction speed and node repetition. Our method allows any block to validate the full availability of transactions without repeating hash nodes and focuses on improving the transaction process through the Pruned Merkle Tree and achieving remarkable results. To assess the performance of the proposed system, we used Hyperledger Caliper, a benchmarking tool specifically designed for measuring the performance of Hyperledger-based blockchain solutions. The evaluation results show a significant improvement in throughput, with a value of 30450kbps recorded. The processing time has also increased noticeably, reaching 1660ms. Security measures have also been strengthened, yielding an impressive 99.60%. The energy consumption factor plays a crucial role, and the PMT exhibits the lowest value at 235 joules. Keywords: Blockchain, Merkle Tree, Pruned Merkle Tree, Security, Transaction Verificatio

Improved Computational Efficiency of Machine Learning Algorithm based on Evaluation Metrics to control the spread of Coronavirus in the UK

The Covid-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast the covid-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total covid cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new Covid cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new Covid 19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new Covid cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new Covid cases, which could help the health sector to take relevant control measures for the spread of the virus

Predictive Analysis of Online Television Videos Using Machine Learning Algorithms

In recent years, intelligent machine systems promote different disciplines and facilitate reasonable solutions in various domains. Machine learning offers higher-level services for organizations to build customized solutions. Machine learning algorithms are widely integrated with image, video analytics, and evolving technologies such as augmented and virtual reality. The advanced machine learning approach plays an essential key role in handling the huge volume of time-dependent data and modeling automatic detection systems. The data grows exponentially with varying sizes, formats, and complexity. Machine learning algorithms are developed to extract meaningful information from huge and complex datasets. Machine learning algorithms or models improve their efficiency by the training process. This chapter commences with machine learning fundamentals and focuses on the most prominent machine learning process of data collection, feature extraction, feature selection, and building model. The significance and functions of each method on the live streaming television video dataset are discussed. We addressed the dimensionality reduction and machine learning incremental learning process (online). Finally, we summarized the performance assessment of decision tree, J48 graft, LMT tree, REP tree, best first (BF), and random forest algorithms based on their classification performance to build a predictive model for automatic identification of advertisement videos

Fundamental Concepts and Applications of Blockchain Technology

Blockchain is the most influential technology when the Internet was invented. In the due course of time, the researcher’s focus shifted to explore the hallucinations of technology and it has a wide range of impacts on many applications including finance, medicine, logistics, supply chain, currencies, education, music, voting, and identity management. Blockchain, a digital database, is a combination of cryptography and peer-to-peer networking and can be identified as distributive ledger technology. The key concept of blockchain technology is to protect the distributed ledger. The major benefits of this technology are to reduce the complexity of time, space, and cost. Blockchain is the most prominent technology for efficient financial, consistent trading process and it also improves the authorization control, since the transactions are immutable, secured, and updated immediately within the peers in the network. Over a period blockchain is gaining momentum in the Internet systems to address global business challenges and to sustain environmental advances. Privacy and security factors play an important part in the execution of blockchain technology. Privacy Leakage, Scalability, and Selfish Mining are some of the major challenges in blockchain and have been the emphasis of both industry experts and academic researchers. In this chapter, we present a complete perspective about blockchain technology, security risks, and challenges that prevail due to the vulnerability of being a popular technology. We summarize the empowering technologies for being scalable, privacy-free, and honest mining blockchain systems. Finally, we discuss various applications, where blockchain technology is genuinely used