Informatics Publishing Limited

Shelly Gupta ¹, Dharminder Kumar ², Anand Sharma ²

Affiliations
1 AIM & ACT, Banasthali University, Banasthali, IN
2 Department of CSE, GJUS&T, Hisar, IN

Abstract

Health care data includes patient centric data, their treatment data and resource management data. It is very massive and information rich. Valuable knowledge i.e. hidden relationships and trends in data can be discovered from the application of data mining techniques on healthcare data. Data mining techniques have been used in healthcare research and known to be effective. The present study aimed to do the performance analysis of several data mining classification techniques using three different machine learning tools over the healthcare datasets. In this study, different data mining classification techniques have been tested on four different healthcare datasets. The standards used are percentage of accuracy and error rate of every applied classification technique. The experiments are done using the 10 fold cross validation method. A suitable technique for a particular dataset is chosen based on highest classification accuracy and least error rate.

Keywords

KDD, Data Mining, Classification, Healthcare Datasets and Machine Learning Tools.

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Dharminder Kumar ¹, Vinay Chopra ¹

Affiliations
1 Department of Computer Science & Engg, D.A.V. I.E.T., Jalandhar, Punjab, IN

Abstract

Bacteria Foraging Optimization a nature-inspired optimization has drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains. Color image quantization is an important process of representing true color images using a small number of colors. Existing color reduction techniques tend to alter image color structure and distribution. Thus the researchers are always finding alternative strategies for color quantization. In cylindrical color spaces like HSI, color is represented by hue, saturation and intensity. These components are closer to the way human perceives and describes color. Hue, saturation and intensity can also reveal image features that are not so obvious in other color spaces. The objective of this research work, is to design an algorithm for Image Quantization using HSI color space based on Bacteria Foraging Optimization. To implement and test the proposed algorithm. To compare the designed algorithm with other quantization techniques. The conducted experiments indicate that proposed algorithm generally results in a significant improvement of image quality compared to other well-known approaches.

Keywords

Color Reduction, Bacteria Foraging Optimization, HSI Color Space, Euclidean Distance, Swarm Intelligence.

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Tripti Mishra ¹, Dharminder Kumar ², Sangeeta Gupta ³

Affiliations
1 MewarUniversity, Chittorgarh – 312901, Rajasthan, IN
2 Department of Computer Science, G. J. University, Hisar – 125001, Haryana, IN
3 Guru Nanak Institute of Management, West Punjabi Bagh – 110026, Delhi, IN

Abstract

Objective: To systematically review the work done in the field of academic performance prediction and employability prediction of students in higher education. Methods: The survey first explain show higher education has become an exciting field of research and why the prediction of academic performance and employability is beneficial for the institutions. We also explain briefly in how many ways higher education is being provided world-wide. Then we discuss the work done in both the areas of prediction. Findings: The survey explores existing research highlights and finds that prediction of academic performance has progressed a lot but employability prediction is yet to mature. Application: It further suggests few parameters that have not been considered so far in predicting the performance or employability.

Keywords

Academic Performance, Data Mining, Employability, Higher Education, Prediction, Survey

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