Dr. Gaurav Saxena
@juet.ac.in
Assistant Professor (SG), Computer Science and Engineering
Jaypee University of Engineering and Technology Guna
Dr. Gaurav Saxena is currently working as Assistant Professor in Computer Science and Engineering Department, Jaypee University of Engineering & Technology (JUET) Guna, M.P, India. He joined Department of Computer Science and Engineering, JUET in 2017.
He has completed his Ph.D. degree in area of Digital Image Processing using Deep learning from M.P State Technological University (RGPV) -Bhopal, India. He received his Master of Technology in Electronics & Communication Technology from National Institute of Technology Kurukshetra (NIT, Kurukshetra), Haryana, India. His Master of Technology thesis is in Image Texture Segmentation and Enhancement Using Linear and Non-linear filters. Dr. Gaurav received his Bachelor of Engineering in Electronics Engineering from M.P State Technological University (RGPV) -Bhopal, India.
Dr. Gaurav has over two decades of extensive experience in Academics. His academic experience is more than 15 years at JUET. He was the faculty of Electronics and Communicat
EDUCATION
BE,M.Tech,Ph.D
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Signal Processing, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Khushi Jain, Harsh Bansal, Gaurav Saxena, and Rohit Kumar
IEEE
With the inception of big data, there is a lot of information that goes unfiltered, contributing to fake news. This false information can lead to catastrophic results in the long run. Hence, to curb this spread, we aim to provide constructive techniques that can help detect this kind of misleading news. In this paper, we have introduced a novelty model, the LSTM-Attention etwork, and shown the comparison of the novelty model with existing machine learning algorithms like aive Bayes, SVM, BERT, and DeBERTa. The analysis is based on the dataset, which was extracted from diverse sources and contains over 20,000 news samples. The proposed work results in an accuracy ranging from 85.7% to 99.9%, which is very good for classifying most of the information.
Nidhi Saxena, Gaurav Saxena, Neelu Khare, and Md Habibur Rahman
IET Image Processing Institution of Engineering and Technology (IET)
Nikos Armenatzoglou, Sanuj Basu, Naga Bhanoori, Mengchu Cai, Naresh Chainani, Kiran Chinta, Venkatraman Govindaraju, Todd J. Green, Monish Gupta, Sebastian Hillig,et al.
ACM
In 2013, AmazonWeb Services revolutionized the data warehousing industry by launching Amazon Redshift, the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools. This cloud service was a significant leap from the traditional on-premise data warehousing solutions, which were expensive, not elastic, and required significant expertise to tune and operate. Customers embraced Amazon Redshift and it became the fastest growing service in AWS. Today, tens of thousands of customers use Redshift in AWS's global infrastructure to process exabytes of data daily. In the last few years, the use cases for Amazon Redshift have evolved and in response, the service has delivered and continues to deliver a series of innovations that delight customers. Through architectural enhancements, Amazon Redshift has maintained its industry-leading performance. Redshift improved storage and compute scalability with innovations such as tiered storage, multicluster auto-scaling, cross-cluster data sharing and the AQUA query acceleration layer. Autonomics have made Amazon Redshift easier to use. Amazon Redshift Serverless is the culmination of autonomics effort, which allows customers to run and scale analytics without the need to set up and manage data warehouse infrastructure. Finally, Amazon Redshift extends beyond traditional data warehousing workloads, by integrating with the broad AWS ecosystem with features such as querying the data lake with Spectrum, semistructured data ingestion and querying with PartiQL, streaming ingestion from Kinesis and MSK, Redshift ML, federated queries to Aurora and RDS operational databases, and federated materialized views.
Gaurav Saxena, Sarita Singh Bhadauria, and Subodh Kumar Singhal
IEEE
Haze removal techniques are widely used in various computer vision applications like object detection, tracking, target recognition, and video surveillance. Therefore, in this paper, the classification of different fog removal techniques is presented. Further, recent dehazing algorithms related to each category are analyzed for the restoration of atmospherically degraded images. However, the performance of the different algorithms is evaluated based on the most commonly used image quality assessment parameters. Hence, different comparison parameters utilized for the evaluation of the performance of the various dehazing algorithms are also discussed. Finally, the qualitative and quantitative comparison of the various state-of-art defogging algorithms and research scope for further improvement is discussed.
Subodh K. SINGHAL, Sujit K. PATEL, Anurag MAHAJAN, and Gaurav SAXENA
The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS
Gaurav Saxena and Sarita Singh Bhadauria
Lecture Notes in Networks and Systems Springer Singapore
Gaurav SAXENA and Sarita SINGH BHADAURIA
Turkish Journal of Electrical Engineering and Computer Sciences The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM)
Gaurav Saxena and Sarita Singh Bhadauria
Multimedia Tools and Applications Springer Science and Business Media LLC
Atmospheric conditions induced by suspended particles such as fog, smog, rain, haze etc., severely affect the scene appearance and computer vision applications. In general, existing defogging algorithms use various constraints for fog removal. The efficiency of these algorithms depends on the accurate estimation of the depth models and the perfection of these models solely relies on pre-calculated coefficients through the training data. However, the depth model developed on the basis of these pre-calculated coefficients for dehazing may provide better accuracy for some kind of images but not equally well for every type of images. Therefore, training data-independent based depth model is required for a perfect haze removal algorithm. In this paper, an effective haze removal algorithm is reported for removing fog or haze from a single image. The proposed algorithm utilizes the atmospheric scattering model in fog removal. Apart from this, linearity in the depth model is achieved by the ratio of difference and sum of the intensity and saturation values of the input image. Besides, the proposed method also take care the well-known problems of edge preservation, white region handling and colour fidelity. Experimental results show that the proposed model is more efficient in comparison to the existing haze removal algorithms in terms of qualitative and quantitative analysis.
Subodh Kumar Singhal, B. K. Mohanty, Sujit Kumar Patel, and Gaurav Saxena
World Scientific Pub Co Pte Lt
Parallel prefix adder (PPA) is the core component of diminished-1 modulo ([Formula: see text]) adder structure. In this paper, group-carry selection logic based PPA design is proposed and it is free from redundant logic operations which otherwise present in the existing PPA design based on group sum selection logic. Further, the logic expression of pre-processing unit of PPA is also presented in a simplified form to save some logic resources. The proposed PPA design for bit-width 32-bit involves 26.1% less area, consumes 28.4% less power and marginally higher critical-path delay than the existing PPA design. An efficient diminished-1 modulo ([Formula: see text]) adder structure is presented using proposed PPA design and modified carry computation algorithm of existing design. The proposed diminished-1 modulo ([Formula: see text]) adder structure for bit-width 32-bit offers a saving of 25.5% in area-delay-product (ADP) and 24.1% in energy-delay-product (EDP) than the best of the existing modulo adder structure.
Gaurav Saxena, Manraj Singh Grover, and Shampa Chakervarty
IEEE
The process of clustering similar words is crucial for a broad range of applications such as text classification and word sense disambiguation. Several approaches for deriving word similarity have been proposed. Some, like latent semantic analysis, are derived from the distributional hypothesis. Others extract relationships between terms by drawing upon predefined linguistic patterns. In this work, we propose an innovative approach which combines the essence of both these approaches. In the first phase, our algorithm generates a graphical model of terms and their interrelations with the help of special lexico-syntactic patterns called Hearst Patterns. We then apply a graph clustering technique to find semantically related words.
Deepak Kumar Jain, Gaurav Saxena, and Vineet Kumar Singh
IEEE
Image Mosaicing algorithm based on random corner method is proposed. An image mosaic is a method of assembling multiple overlapping images of same scene into a larger one. The output of image mosaic will be the union of two input images. In this paper we have to use three step automatic image mosaic methods. The first step is taking two input images and finding out the corners in both the images, second step is removing out the false corner in both the images and then by using homography we find its matched corner pair and we get final output mosaic. The experimental results show the proposed algorithm produces an improvement in mosaic accuracy, efficiency and robustness.