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Institution

Thapar University

EducationPatiāla, Punjab, India
About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Cloud computing & Fuzzy logic. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.


Papers
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Journal ArticleDOI
TL;DR: The decolorization potential of two bacterial consortia developed from a textile wastewater treatment plant showed that among the two mixed bacterial culture SKB-II was the most efficient in decolorizing individual as well as mixture of dyes.

189 citations

Journal ArticleDOI
TL;DR: The potential and applications of blockchain in the IoE field, which enables automated data exchange, complex energy transactions, demand response management and Peer-to-Peer (P2P) energy trading etc, are discussed.

188 citations

Journal ArticleDOI
TL;DR: The performance of state-of-the-art techniques are analyzed to identify those that seem to work well across several crops or crop categories and a set of acceptable techniques are discovered.
Abstract: The symptoms of plant diseases are evident in different parts of a plant; however leaves are found to be the most commonly observed part for detecting an infection Researchers have thus attempted to automate the process of plant disease detection and classification using leaf images Several works utilized computer vision technologies effectively and contributed a lot in this domain This manuscript summarizes the pros and cons of all such studies to throw light on various important research aspects A discussion on commonly studied infections and research scenario in different phases of a disease detection system is presented The performance of state-of-the-art techniques are analyzed to identify those that seem to work well across several crops or crop categories Discovering a set of acceptable techniques, the manuscript highlights several points of consideration along with the future research directions The survey would help researchers to gain understanding of computer vision applications in plant disease detection

187 citations

Journal ArticleDOI
TL;DR: The results obtained demonstrate that the proposed cloud-based anomaly detection model is superior in comparison to the other state-of-the-art models (used for network anomaly detection), in terms of accuracy, detection rate, false positive rate, and F-score.
Abstract: With the emergence of the Internet-of-Things (IoT) and seamless Internet connectivity, the need to process streaming data on real-time basis has become essential. However, the existing data stream management systems are not efficient in analyzing the network log big data for real-time anomaly detection. Further, the existing anomaly detection approaches are not proficient because they cannot be applied to networks, are computationally complex, and suffer from high false positives. Thus, in this paper a hybrid data processing model for network anomaly detection is proposed that leverages grey wolf optimization (GWO) and convolutional neural network (CNN). To enhance the capabilities of the proposed model, GWO and CNN learning approaches were enhanced with: 1) improved exploration, exploitation, and initial population generation abilities and 2) revamped dropout functionality, respectively. These extended variants are referred to as Improved-GWO (ImGWO) and Improved-CNN (ImCNN). The proposed model works in two phases for efficient network anomaly detection. In the first phase, ImGWO is used for feature selection in order to obtain an optimal trade-off between two objectives, i.e., reduced error rate and feature-set minimization. In the second phase, ImCNN is used for network anomaly classification. The efficacy of the proposed model is validated on benchmark (DARPA’98 and KDD’99) and synthetic datasets. The results obtained demonstrate that the proposed cloud-based anomaly detection model is superior in comparison to the other state-of-the-art models (used for network anomaly detection), in terms of accuracy, detection rate, false positive rate, and F-score. In average, the proposed model exhibits an overall improvement of 8.25%, 4.08%, and 3.62% in terms of detection rate, false positives, and accuracy, respectively; relative to standard GWO with CNN.

185 citations

Journal ArticleDOI
TL;DR: A new cloud based user authentication scheme for secure authentication of medical data that provides the session-key security and protects active attacks and a detailed comparative analysis for the communication and computation costs along with security and functionality features which proves its efficiency in comparison to the other existing schemes of its category.
Abstract: Security and privacy are the major concerns in cloud computing as users have limited access on the stored data at the remote locations managed by different service providers. These become more challenging especially for the data generated from the wearable devices as it is highly sensitive and heterogeneous in nature. Most of the existing techniques reported in the literature are having high computation and communication costs and are vulnerable to various known attacks, which reduce their importance for applicability in real-world environment. Hence, in this paper, we propose a new cloud based user authentication scheme for secure authentication of medical data. After successful mutual authentication between a user and wearable sensor node, both establish a secret session key that is used for future secure communications. The extensively-used Real-Or-Random (ROR) model based formal security analysis and the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool based formal security verification show that the proposed scheme provides the session-key security and protects active attacks. The proposed scheme is also informally analyzed to show its resilience against other known attacks. Moreover, we have done a detailed comparative analysis for the communication and computation costs along with security and functionality features which proves its efficiency in comparison to the other existing schemes of its category.

185 citations


Authors

Showing all 3035 results

NameH-indexPapersCitations
Gaurav Sharma82124431482
Vinod Kumar7781526882
Neeraj Kumar7658718575
Ashish Sharma7590920460
Dinesh Kumar69133324342
Pawan Kumar6454715708
Harish Garg6131111491
Rafat Siddique5818311133
Surya Prakash Singh5573612989
Abhijit Mukherjee5537810196
Ajay Kumar5380912181
Soumen Basu452477888
Sudeep Tanwar432635402
Yosi Shacham-Diamand422876463
Rupinder Singh424587452
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202347
2022149
20211,237
20201,083
2019962
2018933