Pooja Thakar
@vips.edu
Associate Professor and IT
Vivekananda Institute of Professional Studies
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
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.
Pooja V. Thakar and Hiren Mewada
IEEE
Now days, the satellite navigation system such as GPS is used world widely for many applications which are growing fast creating need of satellite navigation. Regional satellite navigation systems are growing now which uses similar frequency as GPS so it creates the interference to GPS signal. The use of the Binary Offset Carrier (BOC) modulated signal in incoming satellite navigation systems can reduce this interference as it has low PSD at carrier frequency. In the receiver for satellite navigation system, the first main part is the acquisition which gives the coarse value of code phase and frequency to make the transmitter and receiver code align so to get data back so it is very important part of the satellite navigation receiver. The popular algorithms of the code phase and frequency acquisition for GPS like signals are serial search or time domain acquisition algorithm, parallel code phase search or FFT based algorithm, parallel frequency space search acquisition. The same algorithms can work for the BOC modulated satellite navigation signal by making a small change in them. Also to overcome the drawback of the parallel frequency space search acquisition, as a solution the squaring acquisition algorithm is described in this paper. In this paper, these algorithms are implemented and simulated by us in MATLAB for the BOC modulated satellite navigation signal and they are compared.