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E-voting system using cloud-based hybrid blockchain technology

Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

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Retrieval of Interactive Requirements of Data Intensive Applications using Random Forest Classifier

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Informatica (Slovenia) 2023年第9期47卷 35-50页

作者： Raymond, Renita Anouncia, S. Margret School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Classifying requirements in data-intensive systems based on their interactions can assist the requirements engineering process in becoming more systematic and transparent, resulting in higher requirement compliance and software project completion. However, understanding the requirements centred on interactions with the system is particularly tough due to the increased complexity of big data. In most cases, awareness of interaction-based requirements is critical in moving forward with prediction and decision-making. As a result, the classification of interactive requirements plays a critical role in removing the difficulties from unclear requirements. Various approaches to effective requirement classification are being devised. However, due to inadequate requirement management reflecting the fast-changing organizational change, classification accuracy does not achieve its maximum potential. The best approach for reducing misclassification rate and retrieving interactive requirements for data-intensive systems would be to use Word Embedding and Random Forest Classifier retrieval mechanism, as none of the studies to date have emphasized it. It also assessed the impact by comparing the results to metrics derived from the Random Forest classifier's training on word count characteristics. The data set used to experiment with the classification, particularly for interaction-based needs, is unique to our work and has not been covered by any other studies to date. The researchers will benefit from this study as they will better understand the requirement classification process. With an F1 score of 0.91, precision of 0.89, and recall of 0.93, statistical analysis showed that Word Embedding followed by Random Forest Classifier produced a relatively high classification result to differentiate interactive requirements for data-intensive systems. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Classification (of information)

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Classifying Hematoxylin and Eosin Images Using a Super-Resolution Segmentor and a Deep Ensemble Classifier

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Intelligent Automation & Soft Computing 2023年第8期37卷 1983-2000页

作者： P.Sabitha G.Meeragandhi Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiTamilnaduIndia

Developing an automatic and credible diagnostic system to analyze the type,stage,and level of the liver cancer from Hematoxylin and Eosin(H&E)images is a very challenging and time-consuming endeavor,even for experienced pathologists,due to the non-uniform illumination and *** several Machine Learning(ML)and Deep Learning(DL)approaches are employed to increase the performance of automatic liver cancer diagnostic systems,the classi-ﬁcation accuracy of these systems still needs signiﬁcant improvement to satisfy the real-time requirement of the diagnostic *** this work,we present a new Ensemble Classiﬁer(hereafter called ECNet)to classify the H&E stained liver histopathology images *** proposed model employs a Dropout Extreme Learning Machine(DrpXLM)and the Enhanced Convolutional Block Attention Modules(ECBAM)based residual *** applies Voting Mechanism(VM)to integrate the decisions of individual classiﬁers using the average of probabilities ***,the nuclei regions in the H&E stain are seg-mented through Super-resolution Convolutional Networks(SrCN),and then these regions are fed into the ensemble DL network for classiﬁ*** effectiveness of the proposed model is carefully studied on real-world *** results of our meticulous experiments on the Kasturba Medical College(KMC)liver dataset reveal that the proposed ECNet signiﬁcantly outperforms other existing classiﬁca-tion networks with better accuracy,sensitivity,speciﬁcity,precision,and Jaccard Similarity Score(JSS)of 96.5%,99.4%,89.7%,95.7%,and 95.2%,*** obtain similar results from ECNet when applied to The Cancer Genome Atlas Liver Hepatocellular Carcinoma(TCGA-LIHC)dataset regarding accuracy(96.3%),sensitivity(97.5%),speciﬁcity(93.2%),precision(97.5%),and JSS(95.1%).More importantly,the proposed ECNet system consumes only 12.22 s for training and 1.24 s for ***,we carry out the Wilcoxon statistical test to determine whether the ECN

关键词： Convolutional block attention modules dropout ELM ensemble classiﬁer liver cancer segmentation voting mechanism

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Renaissance of Fuzzy and Fast Matching Entity with DSHS Algorithm

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SN computer science 2024年第6期5卷 734页

作者： Kari, Venkatram Amalanathan, Geetha Mary School of Computer Science and Engineering Vellore Institute of Technology University

Entity matching is a crucial aspect of data management systems, requiring the identification of real-world entities from diverse expressions. Despite the human ability to recognize equivalences among entities, machines struggle due to variations in expression. This challenge underscores the necessity for a robust framework capable of matching entities based on their attributes. While previous studies have explored transformation techniques, NLP, and deep learning, they often fall short in accuracy and scalability or overlook internal constraints. Such shortcomings result in costly rework and compromised business decisions. To address these issues, proposed “Deep Semantic Homophonic Synonymy” (DSHS) algorithm, a hybrid approach integrating rule-based, NLP, and deep learning techniques. By employing semantic matching, rule-based preprocessing, NLP enrichment, phonetic analysis, abbreviation variations, and spelling corrections, the DSHS algorithm effectively overcomes entity matching challenges. This solution, comprising six modules including Preprocessing, Transformation, Translation, Word Embedding, Attribute Similarity, and Classification, outperforms existing EM models in terms of accuracy and scalability. This solution is a comprehensive entity matching, offering significant advancements in the field and providing a reliable framework for resolving complex matching requirements. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data pipeline Data sync up MDDR Message queue Schema registry

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WSO-KELM: War Strategy Optimization-Based Kernel Extreme Learning Machine for Automatic Software Fault Prediction Model

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 145-163页

作者： Elci, J. Brundha Nandagopalan, S. Department of Computer Science and Engineering Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India Department of Information Science and Engineering Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India

The software development projects’ testing part is usually expensive and complex, but it is essential to gauge the effectiveness of the developed software. Software Fault Prediction (SFP) primarily serves to detect faults within software components. The scale of software projects is getting larger, making it impossible and time-consuming to analyze the flaws manually. Artificial Intelligence (AI) techniques are mainly incorporated to solve this problem. The massive data retrieved from the software repositories helps in identifying the features that are highly correlated with fault prediction and the features that are not. This paper introduces a Kernel Extreme Learning Machine (KELM) for SFP. To optimize the hyperparameters of the classifier and also enhance the classification accuracy, the War Strategy Optimization (WSO) algorithm is employed here. The experimental evaluations are conducted using different baseline actual-world datasets of software programs such as Java-coded open-source and PROMISE datasets. The experimental outcomes are conducted using different performance metrics that include sensitivity, Accuracy, and F1-score with values of 0.96%, 0.97%, and 0.96%, respectively. The results show that KELM is beneficial, and the optimization provided by the WSO algorithm also helps us obtain higher classification accuracy for the SFP problem and improve the fault detection process. © The Institution of Engineers (India) 2024.

关键词： Software reliability

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Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

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computer Systems science & engineering 2023年第4期45卷 483-495页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Owing to massive technological developments in Internet of Things(IoT)and cloud environment,cloud computing(CC)offers a highly flexible heterogeneous resource pool over the network,and clients could exploit various resources on *** IoT-enabled models are restricted to resources and require crisp response,minimum latency,and maximum bandwidth,which are outside the *** was handled as a resource-rich solution to aforementioned *** high delay reduces the performance of the IoT enabled cloud platform,efficient utilization of task scheduling(TS)reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user ***,this article concentration on the design of an oppositional red fox optimization based task scheduling scheme(ORFOTSS)for IoT enabled cloud *** presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud *** achieves the makespan by performing optimum TS procedures with various aspects of incoming *** designing of ORFO-TSS method includes the idea of oppositional based learning(OBL)as to traditional RFO approach in enhancing their efficiency.A wide-ranging experimental analysis was applied on the CloudSim *** experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

关键词： Metaheuristics task scheduling cloud computing internet of things makespan red fox optimizer

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Appearance and motion-based unusual crowd events detection using multiple moving objects

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Multimedia Tools and Applications 2024年第42期83卷 90177-90189页

作者： Babu, K. Atlas, L. Godlin Sreeji, S. Arjun, K.P. Rasmi, A. Senthilvel, P. Gururama Computational Intelligence SRM Institute of Science & Technology Chengalpet Kattankulathur603203 India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Gitam University Karnataka Bengaluru India Information Science and Engineering RRIT Bangalore India Saveetha University Chennai602 105 India

Unusual crowd analysis is an important problem in surveillance video due to their features cannot be extracted efficiently on the crowd scenes. To overcome this challenge, this paper introduced the appearance and motion based unusual events detection method by using multiple moving objects. This method includes appearance and motion based computation, and moving objects trajectory analysis. By using these all techniques, the unusual crowds and their activities can be obtained by analyzing moving objects size and the distance among multiple moving objects. Two benchmark datasets such as PETS 2009 and UMN are used for the experimental results. Also, several existing algorithms are compared with the proposed method to evaluate the effectiveness of the proposed work. The comparative analysis results proved that the proposed method achieved best accuracy for unusual crowd events. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： And trajectory analysis Appearance based analysis Distance based analysis Unusual crowd events detection

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Real-Time Elderly Fall Detection as Ambient Intelligence in Old Age Homes 3

Real-Time Elderly Fall Detection as Ambient Intelligence in ...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Shabadi, Laxmi Chaitra, M. Monik, L. Pavan, S.G. Deekshith, R.K. Narayan, Laxmi Sjb Institute of Technology Department of Computer Science and Engineering Bangalore India Bnm Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

The rapid expansion of autonomous technologies, the rise of computer vision, and edge computing present exciting opportunities in healthcare monitoring systems. Fall prevention is especially important for the elderly because falls from this age group often result in fatalities and serious injuries. Fall detection devices that can quickly recognize falls and alert emergency services have become more and more popular as a result. The primary goal of the project is to increase elderly home safety by implementing an ambient intelligence-based automated emergency recognition system. We present a unique method for fall posture detection utilizing an intelligence surveillance camera and a class of efficient models called MobileNets for mobile and embedded vision applications. A real-time embedded solution persuaded from 'edge AI' is implemented using the state-of-the-art Object detection: single shot detector (SSD) Mobile Net V2 and Internet of things embedded GPU platform NVIDIA's Jetson Nano. © 2025 IEEE.

关键词： Ambient intelligence

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Role of Artificial Intelligence and Deep Learning in Skin Disease Prediction: A Systematic Review and Meta-analysis

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Annals of Data science 2024年第6期11卷 2109-2139页

作者： Nancy, V. Auxilia Osvin Prabhavathy, P. Arya, Meenakshi S. Computer Science and Engineering College of Engineering and Technology SRM Institute of Science and Technology Vadapalani Tamil Nadu Chennai India Institute of Transportation IOWA State University IowaIA United States

Skin is a most essential and extraordinary part of the human structure. Exposure to chemicals such as nitrates, sunlight, arsenic, and UV rays due to pollution and depletion of the ozone layer is causing various skin diseases to spread rapidly. Digital healthcare offers many opportunities to reduce time, and human error, and improve clinical outcomes. However, the automatic recognition of skin disease is a major challenge due to high visual similarity between different skin diseases, low contrast, and large inter variation. Early detection of skin cancer can prevent death. Thus, Artificial intelligence (AI) and Machine Learning (ML) helps the physicians to improve clinical judgment or change manual perception. For skin cancer diagnostics, the ML/AI algorithm can outperform or match professional dermatologists in multiple studies. Different pre-trained architectures such as ResNet152, AlexNet, VGGNet, etc. are used for fusing different skin disease features such as texture, color, etc. and they are also utilized for conducting segmentation tasks. The variations in reflection, lesion size, shape, illumination, etc. often make automatic skin disease classification a complex task. ISIC 2019 and HAM 10000 are the widely used public datasets for skin disease prediction. More technical paper on skin cancer diagnosis is compared in this study. This report examines the majority of technical papers published between 2018 and October 2022 in order to appreciate current trends in the disciplines of skin cancer prediction. A study that combined clinical patient data with deep learning models (DL) increased the accuracy of predicting skin cancer. This article presents a visually attractive and well-organized summary of the current study findings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Deep learning

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An Effective Analysis and Exploration of Cutting-Edge Machine Learning for Protein Structure and Sequence Prediction

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SN computer science 2024年第6期5卷 1-12页

作者： Alam, Afaque Kumar, Mukesh Department of Computer Science and Engineering National Institute of Technology Patna

When processing large amounts of data, proteins play a key role in biological processes. Protein structure prediction relies on a procedure called relevant feature selection. To accomplish classification, a feature selection technique must first identify the most important features inside the massive dataset. The proposed methods improve protein structure prediction by providing data from the protein identification and sequencing process. Here we present a novel method for predicting protein structure using PSI sequencing. Gibbs Entropy Simulated Forging is used to pick important properties from a protein dataset. Gibbs Entropy is used to quickly locate the important amino acid properties from the massive dataset. The big data protein is sequenced using the Edman Firefly Optimization procedure after the appropriate features have been identified. A firefly's light intensity is calculated with the use of an objective function. Selected amino acid characteristics are ordered by light intensity rating. As a result, it is put to use in the fast and precise formation of protein sequences. This results in improved accuracy when sequencing proteins and a shorter sequencing time when determining each peptide's amino acid sequence. Accuracy and recall in the GESF algorithm procedure have increased while protein sequencing has required less time. The protein sequence is then constructed by applying Edman Firefly Optimization to a subset of characteristics. To improve protein correctness with little efforts investment, peptide residues are sequenced using EFO. Several large protein datasets are used to conduct an experimental evaluation in terms of precision, recall, false positive rate (FPR), sequencing accuracy (SQR), and sequencing time. The findings demonstrate that GESF-EFO improves upon existing methods of protein sequencing in the context of massive datasets. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accuracy EFO GESF SA ST

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