http://localhost:80/xmlui/handle/123456789/22 2026-05-07T18:43:18Z 2026-05-07T18:43:18Z Chaudhuri, Subhamita http://localhost:80/xmlui/handle/123456789/1292 2026-04-30T10:42:00Z 2025-06-01T00:00:00Z

Title: Tidal Behaviour and its Relation to Suspended Sediment Concentration in Selected Zones along the Hugli River, South 24 Parganas, West Bengal Authors: Chaudhuri, Subhamita Abstract: Suspended sediment concentration (SSC) of the Hugli is the key factor behind smooth operation of riverside brick kilns which thrive both solely and partly on supply of riverine sediments. The process of sedimentation depends on tidal behaviour of the river. On the left bank of the Hugli, two 72-km apart stations Kulpi in the south and Akra in the north - were selected on the basis of concentration of brick kilns. The study aims at understanding the disposition of tidal asymmetry and SSC induced by it, reflecting on the relation between availability of sediment and brick making. Monitoring of tide, analysis of SSC in water samples and dumpy level survey inside the selected brick kilns at the two stations were conducted in pre-monsoon and monsoon seasons from 2017 to 2019. The pattern of rise and fall of the tides remained unchanged for the studied period at the two stations. Asymmetry in terms of duration and velocity of tides was responsible for spatial difference in sedimentation. In this flood dominated channel flood tide travels at a faster velocity and flood phase is shorter than ebb. The sediments travel upstream with tide, recording high SSC at high tide at Kulpi but the load is unable to reach 72 km upstream at Akra. The riverine flow is much effective around the northern station causing high SSC at low tide. Mean SSC was higher in monsoon at Akra and higher in pre monsoon at Kulpi. Kulpi had higher mean SSC in both seasons and higher rate of sedimentation. Steeper incoming water gradient during incoming tide at Kulpi in both seasons brought in huge sediment load causing higher mean SSC at Kulpi. Sedimentation process was controlled by tide around the southern station but tidal effect was dampened by riverine flow around the northern station.

2025-06-01T00:00:00Z Subhajit Kar Dr. Madhabi Ganguly Saptarshi Das http://localhost:80/xmlui/handle/123456789/1285 2024-07-09T07:49:22Z 2018-12-13T00:00:00Z

Title: USING DIT-FFT ALGORITHM FOR IDENTIFICATION OF PROTEIN CODING REGION IN EUKARYOTIC GENE Authors: Subhajit Kar; Dr. Madhabi Ganguly; Saptarshi Das Abstract: The new research platform on biomedical engineering by Digital Signal Processing (DSP) is playing a vital role in the prediction of protein coding regions (Exons) from genomic sequences with great accuracy. We can determine the protein coding area in DNA sequences with the help of period-3 property. It has been seen that in order to find out the period-3 property, the DFT algorithm is mostly used but in this paper, we have tested FFT algorithm instead of DFT algorithm. DSP is basically concerned with processing numerical sequences. When digital signal processing used in DNA sequences analysis, it requires conversion of base characters sequence to the numerical version. The numerical representation of DNA sequences strongly impacts the biological properties mirrored through the numerical genre. In this work, the proposed technique based on DIT-FFT algorithm has been used to identify the exonic area with the help of integer value representation for transforming the DNA sequences. Digital ̄lters are used to read out period 3 components from the output spectrum and to eliminate the unwanted high frequency noise from DNA sequences. To overcome background noise means to suppress the non-coding regions, i.e., Introns. Proposed algorithm is tested on four nucleotide sequences having single or multiple numbers of exons.

2018-12-13T00:00:00Z Subhajit Kar Dr. Madhabi Ganguly http://localhost:80/xmlui/handle/123456789/1284 2024-07-09T07:48:46Z 2024-01-01T00:00:00Z

Title: Study of effectiveness of FIR and IIR filters in Exon identification: A comparative approach Authors: Subhajit Kar; Dr. Madhabi Ganguly Abstract: In recent years, DSP has been widely used in the area of DNA sequence analysis, detection of protein cod-ing and non-coding regions and also in finding abnormalities present in coding and non-coding regions. Using DSP tool, the detection of protein coding and non-coding regions has been executed with great accuracy and less complexity. In this paper, DNA nucleotide sequence is converted into corresponding EIIP sequence in the first step. In order to make a comparative study between different FIR and IIR filters, the sequence is passed through overall six FIR and IIR filters separately to extract the period three fre-quency components. Finally, Gaussian filter is used to suppress the high frequency noise present in the power spectrum. This study aims to find out most efficient FIR and IIR filters in predicting exons (coding regions) and introns (non-coding regions) based on different evaluating parameters as follows: (i) specificity-sensitivity values (ii) Matthews correlation coefficient (iii) Miss rate and wrong rate (iv)Discriminating factor (v) ROC.

2024-01-01T00:00:00Z Subhajit Kar Dr. Madhabi Ganguly http://localhost:80/xmlui/handle/123456789/1283 2024-07-09T07:48:00Z 2023-12-10T00:00:00Z

Title: Application of genomic signal processing as a tool for high- performance classification of SARS-CoV-2 variants: a machine learning- based approach Authors: Subhajit Kar; Dr. Madhabi Ganguly Abstract: From the beginning of COVID-19 pandemic, numerous mutants of SARS-CoV-2 have since been evolved owing to high transmissibility and virulence. Due to the limited effectiveness of previously imposed vaccines and preventive therapies, these strains are still causing concern. This paper proposes comparative evaluation of three novel genomic signal pro-cessing-based methods employing discrete wavelet decomposition with lifting (DWT), discrete Fourier transform (DFT), and singular value decomposition (SVD) for the classification of emerging SARS-CoV-2 variants utilizing feature extraction from collected SARS-CoV-2 variants acquired from the NCBI virus database. The efficiency and accuracy of the proposed alignment-free algorithms have been tested using three Coronavirus datasets including human Coronavirus (HCoV), SARS-CoV-2 variants (CoV-Variants and Omicron). The viral nucleotide sequences which are converted into numerical representation leveraging purine-pyrimidine mapping, DNA walk & Z-curve are fed into DWT, SVD, & DFT processors, respectively. In the approach with DWT, the second-generation wavelet transform employs two best wavelet bases Daubechies (Db) and Biorthogonal (Bior) based on the validation of the HCoV dataset for the feature extraction of the CoV-Variants dataset. Various machine learning algorithms, such as Support Vector Machine, K-nearest neighbors, and ensemble, are used to classify the virus strains and evaluate the efficacy of the algorithm. Finally, hyper-parametric tuning is done utilizing the Bayesian optimization technique to select the best fit model for KNN and SVM. The proposed algorithm has successfully classified the CoV-Variants dataset with an average accuracy of 98.76% utilizing the DWT, DFT, and SVD, while the best-achieved accuracy for this dataset is 98.9% using the DWT technique employing purine–pyrimidine mapping. The best-achieved accuracy rate for predicting Omicron is 99.8% using SVD-based technique. The best-obtained accuracy for HCoV dataset is 100% resulted in all three methods.

2023-12-10T00:00:00Z