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124 results on '"Gupta, Namit"'

16. Reliability Enhancement of Wireless Sensor Network Using Error Rate Estimation (ERE) Method

Author

Verma, Usha Soni, Gupta, Namit, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Manoj Kumar, editor, Dhaka, Vijaypal Singh, editor, Perumal, Thinagaran, editor, Dey, Nilanjan, editor, and Tavares, João Manuel R. S., editor

Published
2021
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17. Optimal Nodes Communication Coverage Approach for Wireless Sensor Network Path Planning

Author

Verma, Usha Soni, Gupta, Namit, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Manoj Kumar, editor, Dhaka, Vijaypal Singh, editor, Perumal, Thinagaran, editor, Dey, Nilanjan, editor, and Tavares, João Manuel R. S., editor

Published
2021
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18. An Optimized Sequence for Sparse Channel Estimation in a 5G MIMO System.

Author

Soni, Chanchal and Gupta, Namit

Subjects
*ADDITIVE white Gaussian noise, *MEAN square algorithms, *METAHEURISTIC algorithms, *FREQUENCY division multiple access, *BIT error rate
Abstract

Recently, the massive Multiple-Input Multiple-Output (MIMO) system has been integrated with machine learning approaches to realise automatic channel state information detection. These methods need high computational complexity due to the lack of optimal training sequences and the failure to control the sparsity assembly based on massive MIMO. Moreover, it does not consider optimised training sequences based on a compressive sensing approach in the presence of contamination. Thus, this paper proposed an enhanced deep learning model with an optimised pilot training sequence for sparse channel estimation in a 5 G massive MIMO system. Initially, the system and sparse block channel impulse response model will be constructed by considering the time-domain synchronous Generalized Frequency Division Multiplexing (GFDM) system and additive white Gaussian noise (AWGN). In the proposed work, a seagull optimisation algorithm is developed to design the training pilot sequence depending on the coherence properties of the sensing matrix, which is used to recover the channel impulse response. Then, the sparse massive MIMO channel is evaluated by proposing a new deep channel estimator with an enhanced stacked auto encoder (D-ESAE). The proposed channel MSE is 10−6, the bit error rate is 0.04, and NMSE is 10−9 at 40 dB SNR through Matlab simulation. [ABSTRACT FROM AUTHOR]

Published
2025
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19. An Efficient Layout of Single-Layer Full Adder Using QCA

Author

Patidar, Nilesh, Gupta, Namit, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Pant, Millie, editor, Kumar Sharma, Tarun, editor, Arya, Rajeev, editor, Sahana, B.C., editor, and Zolfagharinia, Hossein, editor

Published
2020
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24. Efficient Design and Simulation of Novel Exclusive-OR Gate Based on Nanoelectronics Using Quantum-Dot Cellular Automata

Author

Patidar, Mukesh, Gupta, Namit, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Nath, Vijay, editor, and Mandal, Jyotsna Kumar, editor

Published
2019
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35. Enhanced SVM-Based Novel Detection of Intrusions for Wireless Sensor Networks (WSNS)

Author

Gupta, Namit, Jain, Sandeep Kumar, Sagar, Vikas, and Karale, Sonali Gowardhan

Subjects
intrusion detection (ID), enhanced support vector machine (ESVM), Wireless sensor network (WSN), DoS attacks, chaotic levy grasshopper optimization (CLGO)
Abstract

The use of wireless sensor networks (WSNs) is expanding rapidly due to the quick advancement of wireless sensor technology. WSNs have significant military significance as well as a wide range of potential commercial applications. However, it has significant security issues because of factors such as the lack of resources available for terminal equipment and the nature of the wireless communication environment. Wireless sensor networks (WSNs) are susceptible to a variety of assaults because of their dispersed architecture and limited resources, making intrusion detection for WSNs an essential part of network security. The purpose of intrusion detection systems (IDS) in WSNs is to detect and react to intrusion attempts and other harmful activity. It takes a long time to perform a conventional intrusion detection algorithm. As a result, we have developed a novel intrusion detection framework for the wireless sensor network to help prevent this issue. This paper's major contribution is the proposal of an enhanced support vector machine (ESVM)-based intrusion detection algorithm and the construction of an intrusion detection system (IDS) for WSN's DoS attacks. Additionally, the suggested method's performance is enhanced by chaotic levy grasshopper optimization (CLGO). From the perspectives of detection rate, packet delivery rate, transmission delay, and energy consumption analysis, the proposed IDS can significantly improve network performance by identifying and removing malicious nodes in the network. It has the features of a simple structure, a short computation time, and a high detection rate.

Published
2023

37. AN ANALYSIS OF ENERGY DEMAND IN IOT INTEGRATED SMART GRID BASED ON TIME AND SECTOR USING MACHINE LEARNING.

Author

MANAGRE, Jitendra and GUPTA, Namit

Subjects
DEMAND forecasting, MACHINE learning, ENERGY consumption, TIME management, ELECTRIC power consumption, SMART meters, COMMUNICATION infrastructure
Abstract

Smart Grids (SG) encompass the utilization of large-scale data, advanced communication infrastructure, and enhanced efficiency in the management of electricity demand, distribution, and productivity through the application of machine learning techniques. The utilization of machine learning facilitates the creation and implementation of proactive and automated decision-making methods for smart grids. In this paper, we provide an experimental study to understand the power demands of consumers (domestic and commercial) in SGs. The power demand source is considered a smart plug reading dataset. This dataset is large dataset and consists of more than 850 user plug readings. From the dataset, we have extracted two different user data. Additionally, their hourly, daily, weekly, and monthly power demand is analysed individually. Next, these power demand patterns are utilized as a time series problem and the data is transformed into 5 neighbour problems to predict the next hour, day, week, and month power demand. To learn from the transformed data, Artificial Neural Network (ANN) and Linear Regression (LR) ML algorithms are used. According to the conducted experiments, we found that ANN provides more accurate prediction than LR Additionally, we observe that the prediction of hourly demand is more accurate than the prediction of daily, weekly, and monthly demand. Additionally, the prediction of each kind of pattern needs an individually refined model for performing with better accuracy. [ABSTRACT FROM AUTHOR]

Published
2023
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50. Diabetic mastopathy

Author

Dhanju, Avtar S., primary, Batra, Pranjali, additional, Gupta, Namit, additional, Manekar, Praneet, additional, Khubber, Manisha, additional, Singla, Deepshikha, additional, Thiyagu, K., additional, and Ohri, Poonam, additional

Published
2021
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