Multi-Candidate Voting Model Based on Blockchain

Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.

An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process.This study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human health.The goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous wastes.The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and DenseNet121.In addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models.Moreover,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.