http://localhost:80/xmlui/handle/123456789/1066 2026-05-13T18:14:34Z 2026-05-13T18:14:34Z Datta, Debabrata Mishra, Swakantik Rajest, S. Suman Sakthivanitha, M. Hanirex, D. Kerana Priscila, S. Silvia http://localhost:80/xmlui/handle/123456789/10944 2026-04-10T05:17:06Z 2024-01-01T00:00:00Z

Title: Modelling solitary wave via numerical solution of Korteweg de Vries and KdV-Burger equation using differential quadrature Authors: Datta, Debabrata; Mishra, Swakantik; Rajest, S. Suman; Sakthivanitha, M.; Hanirex, D. Kerana; Priscila, S. Silvia Abstract: The prime objective of the coastal engineering community is to protect near-shore areas. Because they lessen shock from single waves near coastal locations, artificial structures may protect the near-shore areas. A single wave propagates without changing shape or size. The mathematical description of solitary wave explains that the global peak of solitary wave decays gradually far away from the peak. The solitary wave can be obtained by solving the 'Korteweg de Vries (KdV) ' equation analytically or numerically and also develops a numerical solver of the KdV equation to understand the behaviour of travelling waves. The research is also extended to develop the numerical solution of the KdV-Burger equation to understand the travelling characteristics of heat waves. Traditional finite difference methods can be applied to have the corresponding numerical solutions. However, in this research, the challenge is to develop numerical solutions of KdV and KdV-Burger equations using an innovative numerical method such as differential quadrature. The basic idea of differential quadrature is to approximate partial derivatives of any order as a matrix. L2 norm and L∞ are computed for stability analysis of the outcome of the differential quadrature method.

2024-01-01T00:00:00Z Bhattacharya, Madhubrata Datta, Debabrata http://localhost:80/xmlui/handle/123456789/10943 2026-04-10T05:16:55Z 2024-01-01T00:00:00Z

Title: Intelligent Models for Diabetic Prediction Using Conventional Machine Learning Techniques and Ensemble Learning Algorithms Authors: Bhattacharya, Madhubrata; Datta, Debabrata Abstract: The discovery of knowledge from medical database using machine learning approach is always beneficial as well as challenging task for diagnosis. Diabetes if left undiagnosed can affect many other organs (e.g., kidney and liver) of human body and this particular disease is very common in all ages young to adult. A large number of researches have been already taken place to predict diabetes using traditional machine learning algorithm such as artificial neural network, Naïve Bayes theorem, decision tree, etc. However, improvement of performance measures towards accuracy of identification of diabetes with a certain degree of confidence is a challenging task. Ensemble learning approach of classification of diabetes is one of such techniques in the parlour of machine learning classifier algorithms that provide a research gap for predicting the diabetes. This work presents classification algorithms for the prediction of diabetes based on two conventional machine learning classifiers (Naïve Bayes classifier model and decision tree) and four ensemble classifiers (Random Forest (RF), Bagging, AdaBoosting and Gradient Boosting). Performance measures of these algorithms have been carried out in terms of accuracy score. Dataset for training and testing the algorithms mentioned is retrieved from Pima Indian Database. On the basis of their comparative evaluation, most important feature with respect to identification of diabetic is extracted. This research underscores the significance of ensemble learning in diabetes prediction, comparing its efficiency with traditional classifiers. The study enhances accuracy assessment and identifies key features crucial for diabetes identification. These findings contribute valuable insights, paving the way for advancements in machine learning applications for healthcare diagnostics.

2024-01-01T00:00:00Z Ghosh, Sourav Ranjan Jana, Atish Dipankar http://localhost:80/xmlui/handle/123456789/8636 2023-12-14T06:52:10Z 2023-01-01T00:00:00Z

Title: Electric field-driven up-and-down motion of the flexible tail of Al13+ cluster system—a nano-scale flipper Authors: Ghosh, Sourav Ranjan; Jana, Atish Dipankar Abstract: Dynamic metal nanoclusters have become a hot area of research in the field of nanoscience and nanotechnology due to their potential applications in micro devices. One such dynamic cluster is a quasi-planar ground state (GS) Al13+ cluster which exhibits an electric field driven up and down flipping motion of the flexible tail which oscillates with respect to the mean plane. A Car-Parrinello molecular dynamics (CPMD) simulation has been carried out to understand the nature of dynamics of the cluster. CPMD simulation study reveals that the flexible tail region of the Al13+ isomeric system (two ground states M1, M2 and a transition state TS connecting them) can be engaged in a systematic up down flipping motion by the application of a transverse electric field. A saw tooth electric field of amplitude 5.19 V/nm is sufficient to induce the up-and-down flipping oscillation of the cluster, which has an average oscillation frequency of around 20 THz. AIM, NICS and AdNDP analyses also have been carried out to understand the fluxional nature of the cluster from the electronic structural perspective. Electronic structural analysis of selected optimized intermediate states in the presence of transverse electric field has also been analyzed to correlate the electronic structure with the dynamic nature of the cluster. Description: https://doi.org/10.1007/s00894-023-05781-4

2023-01-01T00:00:00Z Sinha, Rituparna De, Rajat K. http://localhost:80/xmlui/handle/123456789/8635 2023-12-14T06:16:15Z 2023-01-01T00:00:00Z

Title: ENLIGHTENMENT: A Scalable Annotated Database of Genomics and NGS-based Nucleotide Level Profiles Authors: Sinha, Rituparna; De, Rajat K. Abstract: Abstract: The revolution in sequencing technologies has enabled human genomes to be sequenced at a very low cost and time leading to exponential growth in the availability of whole-genome sequences. However, the complete understanding of our genome and its association with cancer is a far way to go. Researchers are striving hard to detect new variants and find their association with diseases, which further gives rise to the need for aggregation of this Big Data into a common standard scalable platform. In this work, a database named Enlightenment has been implemented which makes the availability of genomic data integrated from eight public databases, and DNA sequencing profiles of H . sapiens in a single platform. Annotated results with respect to cancer specific biomarkers, pharmacogenetic biomarkers and its association with variability in drug response, and DNA profiles along with novel copy number variants are computed and stored, which are accessible through a web interface. In order to overcome the challenge of storage and processing of NGS technology-based whole-genome DNA sequences, Enlightenment has been extended and deployed to a flexible and horizontally scalable database HBase, which is distributed over a hadoop cluster, which would enable the integration of other omics data into the database for enlightening the path towards eradication of cancer

2023-01-01T00:00:00Z