Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning

Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%

Algebraic service composition for user-centric IoT applications

The Internet of Things (IoT) requires a shift in our way of building applications, as it is aimed at providing many services to society in general. Non-developer people require increasingly complex IoT applications and support for their ever changing run-time requirements. Although service composition allows the combination of functionality into more complex behaviours, current approaches provide support for dealing with one IoT scenario at a time, as they allow the definition of only one workflow. In this paper, we present DX-MAN, an algebraic model for static service composition that allows the definition of composite services that encompass multiple workflows for run-time scenarios. We evaluate our proposal on an example in the domain of smart homes

A MapReduce-based modified Grey Wolf optimizer for QoS-aware big service composition

Big services are the collection of interrelated web services across virtual and physical domains, integrating service oriented computing and big data. The rapid growth of Big services that offer similar functionality with varying QoS attributes makes the process of selection and composition of these big services as highly challenging and complex. In this paper, we develop an efficient QoS-aware Big service composition approach by applying a MapReduce based Modified Grey Wolf Optimizer (MR-MGWO) that explores more search space, especially in a multidimensional environment. Our approach ensures an optimal balance of exploration and exploitation that enhances the convergence rate and minimizes the computational time. The empirical analysis illustrates that the performance of MR-MGWO is superior to other similar approaches for solving Big service composition

An efficient chaotic salp swarm optimization approach based on ensemble algorithm for class imbalance problems

Class imbalance problems have attracted the research community, but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an AdaBoost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization, and AdaBoost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of AdaBoost

Minecrafter: A secure and decentralized consensus protocol for blockchain-enabled vaccine supply chain

The vaccine supply chain (VSC) is crucial for the response to pandemics, ensuring that vaccines reach those in need. However, it faces security risks, network congestion, and inefficiencies. Existing blockchain-based solutions struggle with high energy consumption, centralization, and suboptimal validator selection. To address these challenges, we propose Minecrafter, a secure, decentralized consensus protocol. It selects validators dynamically using game theory, machine learning, and randomness. DBSCAN clustering is utilized for its ability to detect anomalies and adapt to varying network conditions without requiring a predefined number of clusters, reducing centralization risks. Unlike most proof-based protocols, Minecrafter operates with low energy and computational costs. The proposed work demonstrates that Minecrafter outperforms traditional consensus protocols in terms of security, transaction rate, and block latency through experiments. This new protocol is particularly useful for VSCs because it strikes an appropriate balance between speed, safety, and expandability. The results show that the proposed work can improve the reliability and strength of vaccine distribution networks while reducing the risks associated with traditional blockchain consensus methods

A Novel Recommendation System for Vaccines Using Hybrid Machine Learning Model

Advancements in the medical field have brought almost all diseases within the realm of vaccinology. Multiple manufacturers produce vaccines for various diseases. The efficacy rate of these vaccines varies based on different factors. Over the years researchers have developed various recommendation systems for drugs using different techniques with the common goal to help doctors in prescribing drugs after considering different factors. The recommendation system for vaccines is an unexplored area that requires extensive consideration of factors to recommend the vaccine that provides a high efficacy rate. In our paper, we propose a recommendation system for vaccines that considers host-based factors such as age, sex, medical history and also, vaccine-based factors such as post-vaccination recovery rate, death rate and symptoms faced by recipients. The algorithm is score-based where each vaccine is given a score and ranked accordingly for the patient. We created a hybrid machine learning model to extract useful information from the medical data to score the vaccines that are suitable for a particular recipient. The results produced by the system when run on simulated patient data show significant changes in recommended vaccines for the recipient based on different factors