Pooja Thakar

@vips.edu

Associate Professor and IT
Vivekananda Institute of Professional Studies

https://researchid.co/poojathakar

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science

Scopus Publications

211

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

Machine Learning Model for a Digital Twin for Predictive Maintenance and Optimization in A Fruit Supply Chain
Vania Goel, Anusha Arora, Ritu Rani, Neetu Goel, Pooja Thakar, and Himanshu Arora
IEEE
The statistics released by the Logistics Bureau of US state that the shipment of fruits in a fruit supply chain bears about a 33% loss during transit due to inefficiencies in the supply chain and mismanagement of stock and resources. This is a critical issue for fruit dealers and demands a mechanism to reduce these losses. This research paper focuses on the development of a machine learning model for the implementation of a digital twin to help fruit storage warehouses prolong the shelf-life and the fruit quality of their stock by utilizing predictive maintenance and optimizing storage conditions. The integration of digital twin with predictive analysis using Decision Tree Regressor, Random Forest Classifier, Naïve Bayes and Supported Vector Machine produces overall accuracy of 82%, thus showing tremendous potential in the model thus, better decision making. Digital twin creates a virtual replica of the supply chain, which, on inputting real-time data for temperature, humidity, and other environmental factors, uses predictive analysis to determine the approximate shelf-life, overall spoilage, and degradation of the fruits. This model is quite helpful for warehouse managers to take inform decisions about strategies to rotate stock and manage the produce inventory. By applying advanced analytics, the model can also predict potential rot conditions and maintenance needs. This study provides valuable insights for future applications in the agri-food sector.

Computational intelligence in human-computer interaction–Case study on employability in higher education
Pooja Thakar, Manisha, Anil Mehta, Neetu Goel, and Seema Verma
Elsevier

A unified model of clustering and classification to improve students' employability prediction
Pooja Thakar, , Anil Mehta, and Manisha
MECS Publisher
Data Mining is gaining immense popularity in the field of education due to its predictive capabilities. But, most of the prior effort in this area is only directed towards prediction of performance in academic results only. Nowadays, education has become employment oriented. Very little attempt is made to predict students’ employability. Precise prediction of students’ performance in campus placements at an early stage can identify students, who are at the risk of unemployment and proactive actions can be taken to improve their performance. Existing researches on students’ employability prediction are either based upon only one type of course or on single University/Institute; thus is not scalable from one context to another. With this necessity, the conception of a unified model of clustering and classification is proposed in this paper. With the notion of unification, data of professional courses namely Engineering and Masters in Computer Applications students are collected from various universities and institutions pan India. Data is large, multivariate, incomplete, heterogeneous and unbalanced in nature. To deal with such a data, a unified predictive model is built by integrating clustering and classification techniques. TwoLevel clustering (k-means kernel) with chi-square analysis is applied at the pre-processing stage for the automated selection of relevant attributes and then ensemble vote classification technique with a combination of four classifiers namely k-star, random tree, simple cart and the random forest is applied to predict students’ employability. Proposed framework provides a generalized solution for student employability prediction. Comparative results clearly depict model performance over various classification techniques. Also, when the proposed model is applied up to the level of the state, classification accuracy touches 96.78% and 0.937 kappa value.

RECENT SCHOLAR PUBLICATIONS

Machine Learning Model for a Digital Twin for Predictive Maintenance and Optimization in A Fruit Supply Chain
V Goel, A Arora, R Rani, N Goel, P Thakar, H Arora
2024 International Conference on Artificial Intelligence and Quantum 2024

A Comparative Analysis of Task Scheduling Algorithms for Resource Management in Cloud Environments
N Goel, P Thakar, S Gupta, M Kansara
Empirical Economics Letters 23 (special Issue 1), 131-147 2024

Enhancing Agricultural Decision Support with AIoT: A Research Travelogue
P Thakar, N Goel, Manisha, S Gupta, S Gautam
Empirical Economics Letters 23 (special Issue 1), 115-129 2024

Basics of Python Programming
N Goel, S Gupta, P Thakar
2023

Computational intelligence in human-computer interaction–Case study on employability in higher education
P Thakar, A Mehta, N Goel, S Verma
Innovations in Artificial Intelligence and Human-Computer Interaction in the 2023

Robust Prediction Model for Multidimensional and Unbalanced Datasets
P Thakar, A Mehta, Manisha
https://ssrn.com/abstract=3364973 1 (2) 2019

Unified Prediction Model for Employability in Indian Higher Education System
P Thakar, A Mehta, Manisha
Journal of Advanced Research in Dynamical and Control Systems 10 (02-Special 2018

A Unified Model of Clustering and Classification to Improve Students’ Employability Prediction
P Thakar, A Mehta, Manisha
International Journal of Intelligent Systems and Applications (IJISA) 9 (9 2017

Cluster Model for parsimonious selection of variables and enhancing Students' Employability Prediction
P Thakar, A Mehta, Manisha
International Journal of Computer Science and Information Security 14 (12), 611 2016

Role of Secondary Attributes to Boost the Prediction Accuracy of Students’ Employability Via Data Mining
P Thakar, PDA Mehta, D Manisha
https://arxiv.org/ftp/arxiv/papers/1708/1708.02940.pdf 6 (11), 84-90 2015

Manisha-“Performance Analysis and prediction in educational Data mining: A research travelogue” International Journal of Computer Applications (0975–8887) Volume 110–No. 15
P Thakar, A Mehta
January 2015

Performance analysis and prediction in educational data mining: a research travelogue
P Thakar, A Mehta, Manisha
arXiv preprint arXiv:1509.05176 110 (15), 60-68 2015

Anil Mehta Manisha
P Thakar
Psychology 39, 01-10 2013

Knowledge Management: An Innovative Tool for Business Intelligence
P Thakar
Disha Journal of Management 2011

MOST CITED SCHOLAR PUBLICATIONS

Performance analysis and prediction in educational data mining: a research travelogue
P Thakar, A Mehta, Manisha
arXiv preprint arXiv:1509.05176 110 (15), 60-68 2015
Citations: 140

Robust Prediction Model for Multidimensional and Unbalanced Datasets
P Thakar, A Mehta, Manisha
https://ssrn.com/abstract=3364973 1 (2) 2019
Citations: 3

Unified Prediction Model for Employability in Indian Higher Education System
P Thakar, A Mehta, Manisha
Journal of Advanced Research in Dynamical and Control Systems 10 (02-Special 2018
Citations: 1