Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Baseline Wander and Power-Line Interference Removal from ECG Signals Using Fourier Decomposition Method

Abstract: Analysis of electrocardiogram (ECG) signals helps us in detecting various abnormalities and diseases of heart. These signals commonly suffer from the problems of baseline wander and power-line interference. In this paper, we propose a new approach to eliminate such noises from ECG signals using the Fourier decomposition method. Simulation results are presented to show the efficacy of our method over previously used EMD-based methods. The proposed method has been shown to preserve shape characteristics of ECG signals of heart abnormalities.

Seamless Health Monitoring Using 5G NR for Internet of Medical Things

Abstract: Nowadays, information and communication technology grows rapidly. The microelectronics and communication mediums also enhance their reachability of coverage and connectivity. 5G enhances the capacity of the network in terms of lowest communication latency, highest speed, enhanced throughput, minimum E2E delay, and minimizing the packet loss. In this paper, we discuss the working principle and key features of 5G communication technology along with the limitations of existing technologies. Further, we provide the taxonomy of the 5G network. Moreover, we provide a comparison of 5G and 4G LTE in terms of data privacy and security aspects. Further, we propose a four-layer architecture for ehealthcare system, which uses 5G NR (New Radio) architecture incorporating the control plane and user plane. We perform the simulation over the frequency range1 and frequency range2 and calculated the throughput and latency for distinct values of OFDM numerologies. Further, we provide a comparative analysis for 4G and 5G and deduce that 5G facilitates 10 times lower latency than 4G, and 5G can accommodate a much higher number of devices than 4G. In this work, we discuss providing better healthcare facilities electronically using 5G NR. Moreover, the data sharing and diagnosing the disease become faster and easier by using 5G NR.

Privacy preserving efficient digital forensic investigation framework

Abstract: In today's era, efficiency of digital forensic investigation process is the biggest challenge for digital forensic community. The efficiency of investigation depends on the other various factors like storage capacity of digital media, proficiency of investigator and type of investigating case. Moreover, these storage devices do not store only investigating case related data but also stores accused personal and professional information which can lead a breach of privacy of accused. Very little attention has been given to make digital investigation process fully automated. In the Privacy Preserving Efficient Digital Forensic Investigation (PPEDFI) framework, we bridge the gap between efficiency and the privacy issue in digital forensic investigation process. The main contribution of the PPEDFI framework is to make the digital investigation process automated and shorten the turnaround time in evidence extraction process, thus saving the cost and time of digital investigation process. To evaluate the system, we conducted an investigation study namely: Digitized Document Fraud. The proposed framework was able to extract the evidence files and also rank them on the basis of their relevancy for being evidence.

Comparison of Pretrained Embeddings to Identify Hate Speech in Indian Code-Mixed Text

Abstract: Two or more languages used in the same sentence is known as the code-mixed text. The phenomenon is abundant in social media due to multilingualism. It poses a considerable challenge for classic NLP tools trained on monolingual corpora. Automatic hate speech detection in code-mixed text becomes even more challenging due to non-standard variations in the spelling, grammar and writing in foreign scripts. Pre-trained models provide word embedding trained on massive monolingual corpora, which are now ubiquitous forms of word representation to classifying text. In this paper, we compare pretrained models and create an ensemble model for code-mixed data of hate speech classification task on Hindi-English data. We have also experimented with using word embedding for CNN networks and showed that XLNet performs better for hate speech detection in code-mixed text.

Whole genome analysis of more than 10 000 SARS-CoV-2 virus unveils global genetic diversity and target region of NSP6.

Abstract: Whole genome analysis of SARS-CoV-2 is important to identify its genetic diversity. Moreover, accurate detection of SARS-CoV-2 is required for its correct diagnosis. To address these, first we have analysed publicly available 10 664 complete or near-complete SARS-CoV-2 genomes of 73 countries globally to find mutation points in the coding regions as substitution, deletion, insertion and single nucleotide polymorphism (SNP) globally and country wise. In this regard, multiple sequence alignment is performed in the presence of reference sequence from NCBI. Once the alignment is done, a consensus sequence is build to analyse each genomic sequence to identify the unique mutation points as substitutions, deletions, insertions and SNPs globally, thereby resulting in 7209, 11700, 119 and 53 such mutation points respectively. Second, in such categories, unique mutations for individual countries are determined with respect to other 72 countries. In case of India, unique 385, 867, 1 and 11 substitutions, deletions, insertions and SNPs are present in 566 SARS-CoV-2 genomes while 458, 1343, 8 and 52 mutation points in such categories are common with other countries. In majority (above 10%) of virus population, the most frequent and common mutation points between global excluding India and India are L37F, P323L, F506L, S507G, D614G and Q57H in NSP6, RdRp, Exon, Spike and ORF3a respectively. While for India, the other most frequent mutation points are T1198K, A97V, T315N and P13L in NSP3, RdRp, Spike and ORF8 respectively. These mutations are further visualised in protein structures and phylogenetic analysis has been done to show the diversity in virus genomes. Third, a web application is provided for searching mutation points globally and country wise. Finally, we have identified the potential conserved region as target that belongs to the coding region of ORF1ab, specifically to the NSP6 gene. Subsequently, we have provided the primers and probes using that conserved region so that it can be used for detecting SARS-CoV-2. Contact:indrajit@nitttrkol.ac.inSupplementary information: Supplementary data are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-Mutation-10K.