An Authentication Technique for Accessing De-Duplicated Data from Private Cloud using One Time Password

Mohan, Prakash; Chelliah, Saravanakumar

doi:10.4018/ijisp.2017040101

Cited by 21 publications

(4 citation statements)

References 19 publications

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“…Various models are analyzed based on the parameters like AUC (Area Under the Curve), F1-score, Accuracy and MioU. AUC is a measurement of the 2D area under the ROC (receiver operating characteristic) curve [24,25]. F1-Score is a performance parameter which performs classification processes.…”

Section: Model Analysismentioning

confidence: 99%

Multilevel Augmentation for Identifying Thin Vessels in Diabetic Retinopathy Using UNET Model

Kumar¹,

Sasipraba²

2023

Intelligent Automation &Amp; Soft Computing

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Section: Model Analysismentioning

confidence: 99%

Multilevel Augmentation for Identifying Thin Vessels in Diabetic Retinopathy Using UNET Model

Kumar¹,

Sasipraba²

2023

Intelligent Automation &Amp; Soft Computing

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“…Fig. 7 investigates the average classification outcomes analysis of the MOFSS-ODL model on the test dataset [30][31][32][33][34][35]. The figure depicted that the MOFSS-ODL methodology has resulted in maximal average prec n , reca l , F measure , accu y and kappa of 0.9764, 0.9822, 0.9793, 0.96499 and 0.8263.…”

Section: Experimental Validationmentioning

confidence: 99%

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Babu¹,

MathuSoothana²,

Kumar³

2023

Intelligent Automation &Amp; Soft Computing

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Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students' grades and retention. Prediction of students' performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately investigate the outcome of students' performance model related to the feature selection techniques. With this motivation, this paper presents a new Metaheuristic Optimization-based Feature Subset Selection with an Optimal Deep Learning model (MOFSS-ODL) for predicting students' performance. In addition, the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers. Besides, the Chaotic Monarch Butterfly Optimization Algorithm (CBOA) is used for the selection of highly related features with low complexity and high performance. Then, a sailfish optimizer with stacked sparse autoencoder (SFO-SSAE) approach is utilized for the classification of educational data. The MOFSS-ODL model is tested against a benchmark student's performance data set from the UCI repository. A wide-ranging simulation analysis portrayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures. Compared to other methods, experimental results prove that the proposed (MOFSS-ODL) classification model does a great job of predicting students' academic progress, with an accuracy of 96.49%.

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“…In a usual Cloud situation, R = { CPU , memory , disk , network }, abstracted using the virtualization technique [ 18 ]. This research emphasizes on memory and CPU, the most committed resource in data center that affects the VM migration [ 22 , 23 ].…”

Section: Background Information and Problem Statementmentioning

confidence: 99%

Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers

Parthiban

Harshavardhan

Neelakandan

et al. 2022

Computational Intelligence and Neuroscience

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The amount of energy required by Cloud Data Centers (CDCs) has increased significantly in this digital age, and as a result, there is a pressing need to reduce CDC energy ingesting. Consolidation of virtual machines (VMs) and effective virtual machine placement (VMP) techniques are commonly employed in large data middles to reduce energy consumption. The VMP is an NP-hard subject with infeasible optimum explanations even for tiny data middles, and it is dealt with using the Metaheuristic Optimization Algorithm, which is an experiential approach to optimization. With this in mind, this study introduces a novel energy-aware VMP technique for CDCs that is founded on the Disordered Salp Swarm Optimization Algorithm (EAVMP-CSSA) and is enhanced for energy efficiency (EAVMP-CSSA). The EAVMP-CSSA technique attempts to reduce CDC energy ingesting by dropping the quantity of active servers supporting virtual machines. The recommended EAVMP-CSSA strategy also aims to balance the resource operation of active servers (i.e., CPU, RAM, and Bandwidth), hence reducing waste and increasing efficiency. Furthermore, by combining the ideas of chaotic maps with the standard Salp Swarm Optimization Algorithm (SSA), the CSSA is intended to improve overall performance and reduce computational costs (SSA). A comprehensive range of experimental analyses are performed to ensure that the EAVMP-CSSA technique performs better, and the findings are compared to current VMP techniques. The EAVMP-CSSA approach achieves an effective outcome with a maximum service rate of 98.12%, whereas the Random, FFD, ACO, and AP-ACO procedures achieve a minimum service rate of 74.40%, 78.80%, 90.70%, and 96.31%, respectively. The experimental results demonstrate that the EAVMP-CSSA approach outperforms other assessment metrics.

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An Authentication Technique for Accessing De-Duplicated Data from Private Cloud using One Time Password

Cited by 21 publications

References 19 publications

Multilevel Augmentation for Identifying Thin Vessels in Diabetic Retinopathy Using UNET Model

Multilevel Augmentation for Identifying Thin Vessels in Diabetic Retinopathy Using UNET Model

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers

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