Computer Engineering, Computer Networks and Communications, Multidisciplinary
29
Scopus Publications
Scopus Publications
High-performance multiband terahertz nanoantenna for advanced wireless nanocommunications Bhagwati Sharan, Raja Manjula Engineering Research Express, 2025 This article presents a novel multiband, biocompatible MIMO nanoantenna for terahertz applications, designed to address the growing demand for faster data transfer rates in future wireless nanocommunications systems. The design process began with a foundational single-element antenna (157.12 × 184.40 × 11 μm3) constructed from a gold patch and ground on a PTFE substrate. This initial element, resonating at 1.041, 1.602, 2.199, and 2.814 THz, was subsequently expanded into a two-port MIMO structure (157.12 × 276.60 × 11 μm3) to enhance channel capacity. The proposed MIMO nanoantenna operates nearly at the same frequencies as the single-element nanoantenna. Still, it delivers better performance, achieving a significantly higher channel capacity (up to 1.163 Tbps at 1.044 THz) and gain (up to 9.06 dBi at 2.214 THz). Furthermore, the MIMO system demonstrates excellent diversity performance, with an ECC close to zero and a consistently high DG of 9.999 dB—indicating effective signal fading mitigation and enhanced reliability. The proposed nanoantennas prove highly effective for multiband operations up to 3 THz, demonstrating significant potential for various advanced applications. These include low-power 6G communications, high-resolution terahertz imaging, in-vivo biomedical sensing, and other high-speed nanoscale communication systems.
Leveraging relief feature selection and multi-classifier stacking approach for improved Parkinson's disease diagnosis Taezeen Hamid, Megha Chhabra, Bhagwati Sharan Intelligent Decision Technologies, 2025 Parkinson's disease (PD) is a neurodegenerative disorder of the brain that primarily affects motor function. Clinical challenges associated with this condition include accurately diagnosing patients in the early stages of the disease and predicting how the condition will progress. This project aims to enhance PD detection by integrating feature selection and classification using supervised learning techniques. Two publicly available datasets—the speech and PD classification datasets—are utilized to evaluate model performance across diverse features. The proposed work employs class balancing through the Synthetic Minority Oversampling Technique (SMOTE) to address the issue of class imbalance in this highly unbalanced dataset. Subsequently, the Relief algorithm is used for feature selection to identify the most relevant predictors. An ensemble of models is applied using the RF-XGBoost-KNN classifiers due to their superior accuracy compared to other classifier combinations. The RF-XGBoost-KNN model stack achieved classification accuracies of 94.56% and 93.53% for the PD speech dataset and Parkinson's Disease Classification Dataset, respectively, demonstrating its potential as a robust tool for early and accurate PD diagnosis.
Intelligent waste classification approach based on improved multi-layered convolutional neural network Megha Chhabra, Bhagwati Sharan, May Elbarachi, Manoj Kumar Multimedia Tools and Applications, 2024 This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%.
Microstrip Planar Antennas for C-Band Wireless Applications Bhagwati Sharan, Anil Kumar Sagar, Nidhi Rajak International Journal of Experimental Research and Review, 2024 In recent years, wireless communications have evolved significantly, and many mobile devices have reduced in size. The antennas used in mobile terminals must be lowered in size to fulfil the downsizing standards. Planar antennas, like microstrip and printed antennas, have a low profile, compact size, and conformability to mounting hosts, making them particularly desirable candidates for achieving these needs. Additionally, planar antennas are also being used in communication devices for 2.4 GHz (2400 – 2484 MHz) and 5.2 GHz wireless local area network (WLAN) systems (5150). In wireless applications, an antenna is a crucial component. At the transmitter, it transforms electrical signals into RF signals, and at the receiver, it converts RF signals to electrical signals. The patch inside the antenna is made of a conducting material such as Cu (Copper) or Au (Gold), and it can be rectangular, round, triangular, or elliptical. Two unique designs of microstrip planar antennas with an operating frequency of 5.2 GHz having S11 parameters as -16.0 dB and -15.7 dB have been offered and their performance has been studied in this research article.
Human Activity Recognition Using Deep Learning Techniques for Healthcare Applications Raj Shekhar, Deepak Singh Tomar, Bhagwati Sharan, R. K. Pateriya 2024 15th International Conference on Computing Communication and Networking Technologies Icccnt 2024, 2024 The idea of smart healthcare is gradually gaining traction with the rapid advancement in information technologies. Smart healthcare is the intelligent transformation of the current medical system to make it more reliable, efficient, and individualized through the intelligent use of next-generation technologies like artificial intelligence, and the Internet of Things (IoT). This study offered the recent advancement of Deep learning (DL) techniques for healthcare systems, and the use of DL techniques to identify human physical activity with wearable sensors. In this paper, convolutional neural networks (CNN), CNN-LSTM, Multi Headed CNN-LSTM, and Multi Headed CNNBiLSTM are used. The obtained outcomes of these Deep Learning models are compared in terms of accuracy, precision, recall and F1 score. The Multi-Headed CNN-BiLSTM model exhibits better performance than the rest of the models when applied to the UCI HAR dataset.
Oil Spill Classification using Machine Learning Kiran Kumar Ravulakollu, Ritu Dewan, Kimmi Verma, Setu Garg, Sunil Kumar Mishra, et al. Proceedings of the 18th Indiacom 2024 11th International Conference on Computing for Sustainable Global Development Indiacom 2024, 2024
Software Defect Prediction using Machine Learning Sonia Setia, Kiran Kumar Ravulakollu, Kimmi Verma, Setu Garg, Sunil Kumar Mishra, et al. Proceedings of the 18th Indiacom 2024 11th International Conference on Computing for Sustainable Global Development Indiacom 2024, 2024
Comprehensive Analysis of Machine Learning Approaches for Breast Cancer Detection and Classification Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
Forge News Detection: Random Forest and Bi- LSTM-based Hybrid Approach Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
Human Activity Recognition with Smartphone using Classical Machine Learning Models Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
Cement Strength Prediction using Regression Techniques Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
Enhancing Productivity and User Experience with Advanced Notepad: A Comprehensive Study Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
Significance of State-of-Art Search Engine in Game Development Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
A Robust Approach for Analysis and Visualization of CO2and Greenhouse Gas Emission and Its Effect Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
Diabetes Prediction using Data Mining Techniques: A state-of-the-art Survey Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
Prediction of Chronic Diseases using Machine Learning Classifiers Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
A Nudity Detection Algorithm for Web-based Online Networking Platform Proceedings of the 17th Indiacom 2023 10th International Conference on Computing for Sustainable Global Development Indiacom 2023, 2023
Mobile Continuum: Necessity or Addiction- A Review Kiran Kumar Ravulakollu, Megha Chhabra, Bhagwati Sharan, Ruchi Agarwal, Ritu Dewan, et al. Proceedings of the 2022 9th International Conference on Computing for Sustainable Global Development Indiacom 2022, 2022
