Pritesh Jain

Devi Ahilya Vishwavidyalaya

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Sandeep Kumar Jain

State Street (United States)

Dheeraj Rane

Indian Institute of Technology Indore

Shyam Patidar

Medi-Caps University

Lalit Bhanwrela

Sri Manakula Vinayagar Medical College and Hospital

Manoj Sindhwani

Lovely Professional University

Upendra Singh

Indian Institute of Technology Indore

Keyur Tank

Sandeep Jain

Hewlett-Packard (India)

Praveen Bhanodia

Indian Institute of Technology Indore

Sandeep Kumar Jain

University of Pittsburgh

Cooperative Institutions

Lovely Professional University

Patel Hospital

Banaras Hindu University

Graphic Era University

Chandigarh University

Indian Institute of Technology Indore

Vivekananda Global University

Galgotias University

Institute of Technology Management

Tata Technologies (India)

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Structural Optimization of Rollcage Using Finite Element Analysis and Design of Experiments Approach

SAE technical papers on CD-ROM/SAE technical paper series (2018)

Pritesh Jain

10.4271/2018-01-5014

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Citations (1)

Technique of intrusion detection based on Neural Network- A review

Anand Swarup Sen Pritesh Jain

In recent years, frequent introduce a many kinds of intrusion while the growing of technology. We need to improved intrusion detection techniques. Intrusion detection techniques can be classified on the basis of source of data and its behavior. A convenient way to detect the legitimate use is through the monitoring the unwanted activity. Current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. The strength of ANN is to identify and classify the network activities based on incomplete, nonlinear data source. Here we collate the development of the systems and the outcome of their implementation. It provides an introduction and activity of the key development within this field, in regard to making suggestions for future research.

10.1109/csibig.2014.7056936

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Citations (2)

List of contributors

Elsevier eBooks (2025)

Khalil Ahmed Mehar Arfi Chandani Bhasin G. Boopathi Raja Harpreet Kaur Channi

10.1016/b978-0-443-30080-6.00022-5

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PERFORMANCE SIMULATION AND BER COMPARISION OFDM-MIMO SYSTEMS IN AWGN AND RAYLEIGH FADING CHANNELS WITH BPSK MODULATION SCHEMES

ICTACT Journal on Communication Technology (2018)

Sandeep Jain Lalit Bhanwrela Pritesh Jain

Behavior of communication medium play vital role in the growth of wireless communication system. To understand various wireless channel environments such as additive white Gaussian noise (AWGN) and Rayleigh fading channels in orthogonal frequency division multiplexing (OFDM) along with multiple input and multiple output (MIMO) system, we analyze mathematical relation of bit error rate (BER) against Signal to Noise ratio (SNR) and verified the same using MATLAB simulation, while considering the BPSK modulation scheme. The performance measured in terms of BER is evaluated for various channels of OFDM-MIMO system along with variation in number of antennas at transmitter and receiver side. It has been demonstrated that the BER and probability of detection improves against SNR per bit, asthe diversity employs at transmitter or/and receiver end.

Modulation (music)

10.21917/ijct.2018.0251

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An Alternative Approach to Determine Johnson-Cook Material and Failure Model Parameters with Minimal Experimental Data

SAE technical papers on CD-ROM/SAE technical paper series (2022)

Pritesh Jain Charudatta Bandgar

10.4271/2022-01-0279

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A Novel Cloud Bursting Brokerage and Aggregation (CBBA) Algorithm for Multi Cloud Environment

Pritesh Jain Dheeraj Rane Shyam Patidar

Cloud computing is a paradigm of computing that aims at providing dynamically scalable computing resources over the Internet as a service. Users do not need to bother about the management of technology infrastructure. The practice of computing across two or more data centers separated by the Internet is growing in popularity due to an explosion in scalable computing demands. However, one of the major challenges that faces the cloud computing is how to secure and protect the data and processes the data of the user. The security of the cloud computing environment is a new research area requiring further development by both the academic and industrial research associations. In this paper we proposed a novel algorithm that is Cloud Bursting Brokerage and Aggregation (CBBA). In this approach we consider three clouds for bursting and aggregation operation. We also used secure sharing mechanism so that the cloud resources are shared among different cloud environment.

Popularity

10.1109/acct.2012.7

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Citations (22)

An Effective Content Based Image Retrieval System Based on Global Representation and Multi-Level Searching

2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) (2018)

Shilpa Garud Barkalle Pritesh Jain

To retrieve important images from a dissimilar collection by using visual queries as search arguments are the arduous and substantial open problems. In this paper the writers have mentioned the designs and implementations of a simple yet very effective Content-Based Image Retrieval (CBIR) system. The colors, textures and the shapes features are the vital parts of this system. With the three main consequent searching steps the searching becomes multilevel. Such propounded systems are very unique as they consider one feature at each step and use the results of the previous step as the input for the next coming step in multilevel pattern whereas in the earlier methods all the features are combined at once for the single-level search of an average CBIR system. The propounded method is very simple and comfortable to adopt. The retrieval grade of the propounded method is valuated using bi-benchmark datasets for an image classification. The above system of methods shows very good results in terms of amelioration in retrieval qualities, when compared with the literature. In proposed work we get accuracy like between 68.15 % to 94.86% in used different features.

Benchmark (surveying)

Content-Based Image Retrieval

Feature (linguistics)

Representation

Implementation

10.1109/icoei.2018.8553928

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Citations (2)

Challenges for the Effective Teachings

IOR INTERNATIONAL PRESS eBooks (2019)

Pritesh Jain Sandeep Kumar Jain

This book consists of a glimpse of many professors’ ideas towards improvement of teaching practices in India. The vital concept includes strategies in teaching, modernization in education, art of teaching, flipped class room, brain storming, active teaching and learning, challenges in teaching, creative teaching and design thinking. A collection of research letters presented over here has main intention to help the student community by adopting innovative teaching. Around 95 articles were contributed through research letters all over India. Notably, 68 articles were reviewed and presented in this book.

Brainstorming

10.34256/iorip19137

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Efficient Content Based Image Retrieval Using Color and Texture

Pritesh Jain

Image classification is perhaps the most important part of digital image analysis. Retrieval patternbased learning is the most effective that aim to establish the relationship between the current and previous query sessions by analyzing image retrieval patterns. We propose a new feedback based and content based image retrieval system. Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this paper we present content-based image retrieval system that uses color and texture as visual features to describe the content of an image region. Our contribution are we use Gabor filters to extract texture features from arbitrary shaped regions separated from an image after segmentation to increase the system effectiveness. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from color the whole image, and features extracted from Texture some image regions. That approach is more effective and efficient way for image retrieval.

Content-Based Image Retrieval

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Machine learning-driven antenna design

CRC Press eBooks (2024)

Sandeep Kumar Jain Pritesh Jain

The application of machine learning (ML) techniques in antenna design has become a subject of great interest in recent years. By harnessing the power of data analysis, pattern recognition, and optimization algorithms, ML techniques offer the potential to optimize antenna performance, enhance efficiency, and reduce design time. This chapter provides an introduction to the field of machine learning-driven antenna design, with a focus on performance metric optimization and the exploration of new design possibilities. Various ML techniques, including neural networks, genetic algorithms, and deep learning, are discussed, along with their applications in antenna design.

10.1201/9781003422440-5

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