Nikhil Kumar Singh

Bio: Nikhil Kumar Singh is an academic researcher from Maulana Azad National Institute of Technology. The author has contributed to research in topics: Computer science & Patch antenna. The author has an hindex of 7, co-authored 24 publications receiving 227 citations. Previous affiliations of Nikhil Kumar Singh include Motilal Nehru National Institute of Technology Allahabad & Indian Institute of Information Technology, Allahabad.

Design and performance of wearable ultrawide band textile antenna for medical applications

Abstract: The concept of wearable products such as textile antenna are being developed which are capable of monitoring, alerting, and demanding attention whenever hospital emergency is needed, hence minimizing labor and resource. In the proposed work using textile material as a substrate, the ultra wideband antenna is designed especially for medical applications. Simulated and measured results here shows that the proposed antenna design meets the requirements of wide working bandwidth and provides 13.08 GHz bandwidth with very small size, washable (if using conductive thread for conductive parts) and flexible materials. Results in terms of bandwidth, radiation pattern, return loss as well as gain and efficiency are presented to validate the usefulness of the current proposed design. The work done here has many implications for future research and it could help patients with such flexible and comfortable medical monitoring techniques. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:1553–1557, 2015

Sentiment analysis: a review and comparative analysis over social media

Abstract: Sentiment analysis is the computational examination of end user’s opinion, attitudes and emotions towards a particular topic or product. Sentiment analysis classifies the message according to their polarity whether it is positive, negative, or neutral. Recently researchers focused on lexical and machine-learning based method for sentiment analysis of social media post. Social media is a micro blogger site in which end users can post their comment in slag language that contains symbols, idioms, misspelled words and sarcastic sentences. Social media data also have curse of dimension problem i.e. high dimension nature of data that required specific pre-processing and feature extraction, which leads to improve classification accuracy. This paper present comprehensive overview of sentiment analysis technique based on recent research and subsequently explores machine learning (SVM, Navies Bayes, Linear Regression and Random Forest) and feature extraction techniques (POS, BOW and HASS tagging) in context of Sentiment analysis over social media data set. Further twitter data-sets are scrutinized and pre-processed with proposed framework,which yield intersecting facts about the capabilities and deficiency of sentiment analysis methods. POS is most suitable feature extraction technique with SVM and Navie Bayes classifier. Whereas Random Forest and linear regression provide the better result with Hass tagging.

An Approach to Understand the End User Behavior through Log Analysis

Abstract: the end user in the web environment is a mind- numbing task. Huge amount of operational data is generated when end user interacts in web environment. This generated operational data is stored in various logs and may be useful source of capturing the end user activates. Pointing out the suspicious user in a web environment is a challenging task. To conduct efficient investigation in cyber space the available logs should be correlated. In this paper a prototype system is developed and implemented which is based on relational algebra to build the chain of evidence. The prototype system is used to preprocess the real generated data from logs and classify the suspicious user based on decision tree. At last various challenges in the logs managements are presented. Keywordsforensic; log file; correlation; decision tree,chain of evidence ,cyber crime;.

Design & Performance of Wearable Ultra Wide Band Textile Antenna for Medical Applications

Abstract: Abstract The concept of wearable products such as textile antenna are being developed which are capable of monitoring, alerting and demanding attention whenever hospital emergency is needed, hence minimizing labour and resource. In the proposed work by using textile material as a substrate the ultra wideband antenna is designed especially for medical applications.Simulated and measured results here shows that the proposed antenna design meets the requirements of wide working bandwidth and provides 13.08 GHz bandwidth with very small size, washable (if using conductive thread for conductive parts) and flexible materials. Results in terms of bandwidth, radiation pattern, return loss as well as gain and efficiency are presented to validate the usefulness of the current proposed design. The work done here has many implications for future research and it could help patients with such flexible and comfortable medical monitoring techniques.

Smart Textile-Integrated Microelectronic Systems for Wearable Applications

Abstract: The programmable nature of smart textiles makes them an indispensable part of an emerging new technology field. Smart textile-integrated microelectronic systems (STIMES), which combine microelectronics and technology such as artificial intelligence and augmented or virtual reality, have been intensively explored. A vast range of research activities have been reported. Many promising applications in healthcare, the internet of things (IoT), smart city management, robotics, etc., have been demonstrated around the world. A timely overview and comprehensive review of progress of this field in the last five years are provided. Several main aspects are covered: functional materials, major fabrication processes of smart textile components, functional devices, system architectures and heterogeneous integration, wearable applications in human and nonhuman-related areas, and the safety and security of STIMES. The major types of textile-integrated nonconventional functional devices are discussed in detail: sensors, actuators, displays, antennas, energy harvesters and their hybrids, batteries and supercapacitors, circuit boards, and memory devices.

The emergence of social media data and sentiment analysis in election prediction

Abstract: This work presents and assesses the power of various volumetric, sentiment, and social network approaches to predict crucial decisions from online social media platforms. The views of individuals play a vital role in the discovery of some critical decisions. Social media has become a well-known platform for voicing the feelings of the general population around the globe for almost decades. Sentiment analysis or opinion mining is a method that is used to mine the general population’s views or feelings. In this respect, the forecasting of election results is an application of sentiment analysis aimed at predicting the outcomes of an ongoing election by gauging the mood of the public through social media. This survey paper outlines the evaluation of sentiment analysis techniques and tries to edify the contribution of the researchers to predict election results through social media content. This paper also gives a review of studies that tried to infer the political stance of online users using social media platforms such as Facebook and Twitter. Besides, this paper highlights the research challenges associated with predicting election results and open issues related to sentiment analysis. Further, this paper also suggests some future directions in respective election prediction using social media content.

A transformer-based approach to irony and sarcasm detection

Abstract: Figurative language (FL) seems ubiquitous in all social media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of natural language processing, mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper, we employ advanced deep learning methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work (Potamias et al., in: International conference on engineering applications of neural networks, Springer, Berlin, pp 164–175, 2019), we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which is further enhanced with the employment and devise of a recurrent convolutional neural network. With this setup, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state-of-the-art methodologies and systems. Results demonstrate that the proposed methodology achieves state-of-the-art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.

Compact wearable MIMO antenna with improved port isolation for ultra-wideband applications

Abstract: In this communication, a compact two-element ultra-wideband (UWB) wearable multiple-input multiple-output (MIMO) antenna with high port isolation is presented. The proposed structure is composed of jeans material in which an `8' shaped stub is placed on the middle position of the antenna backside and connected to the partially suppressed ground structure to improve the port isolation characteristics. The antenna covers the frequency range from 2.74 to 12.33 GHz (about 127.27%) with the port isolation of >26 dB over the entire UWB frequency range. The envelope correlation co-efficient is found to be 9.9) throughout the complete operating band. The channel capacity loss for the proposed MIMO antenna is <;0.13 bit/s/Hz. The imprinted optimised UWB MIMO antenna covers the area size of 55 × 35 mm 2 . The performances of the proposed antenna by the simulation and experimentation equally designated it a blameless candidate for the UWB applications.

ELDC: An Artificial Neural Network Based Energy-Efficient and Robust Routing Scheme for Pollution Monitoring in WSNs

Abstract: The range of applications of Wireless Sensor Networks (WSNs) is increasing continuously despite of their serious constraints of the sensor nodes’ resources such as storage, processing capacity, communication range and energy. The main issues in WSN are the energy consumption and the delay in relaying data to the Sink node. This becomes extremely important when deploying a big number of nodes, like the case of industry pollution monitoring. We propose an artificial neural network based energy-efficient and robust routing scheme for WSNs called ELDC. In this technique, the network is trained on huge data set containing almost all scenarios to make the network more reliable and adaptive to the environment. Additionally, it uses group based methodology to increase the life-span of the overall network, where groups may have different sizes. An artificial neural network provides an efficient threshold values for the selection of a group's CN and a cluster head based on back propagation technique and allows intelligent, efficient, and robust group organization. Thus, our proposed technique is highly energy-efficient capable to increase sensor nodes’ lifetime. Simulation results show that it outperforms LEACH protocol by 42 percent, and other state-of-the-art protocols by more than 30 percent.