Informatics Publishing Limited

N. Nandhini ¹, Vinoth Chakkaravarthy ², G. Deepa Priya ¹, K. Santha Sheela ¹

Affiliations
1 Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai, IN
2 Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai, IN

Abstract

God gifted sense to human being which is an important aspect in our life is vision. With the help of our eyes we are able to see the beauty of nature and also the things which happen in day-to-day life. But there are some people who lack this ability of visualizing these things. They face many difficulties to move on with their daily life. The problem gets worse when they move to an unfamiliar location. Visually impaired people face many challenges when moving in unfamiliar public places. Only few of the navigation systems for visually impaired people can provide dynamic interactions. None of these systems work perfectly both indoors and outdoors.  Available navigation device for the blind people in the market focus on travelling of the blind people from one location to another. This paper focuses on surveying the different technology used for talking assistive walking stick about location finding for blind people.

Keywords

RFID, GPS, GSM, Sonar Senor.

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K. Thangadurai ¹, N. Nandhini ²

Affiliations
1 Department of Computer Science, Karur Arts and Science College, IN
2 Department of Computer Science, Periyar University, IN

Abstract

Diabetic diagnosis is an important research in health care domain to analyze relevant microorganisms at an earlier stage. Due to large growth in world’s population, feature subset selection model receives a great deal in any domain of research and also a reliable tool for diabetic diagnosis. Several data mining techniques have been developed to evaluate the significant causes of diabetes with least sets of risk factors. These minimum set is selected without considering the potential significance of the risk factors and optimal feature subset selection, hence it failed to diagnosis the pattern of diabetes accurately. In order to improve the feature subset selection, an Integration of Fuzzy Rough Set Theory and Optimized Genetic algorithm (IFRST-OGA) is introduced. The main objective of the IFRST-OGA is to find optimal risk factors for efficient pattern recognition on diabetes healthcare data. Initially, feature selection is performed using Fuzzy Rough Set Theory (FRST) for diagnosing the diabetes. After that, the Optimized Genetic Algorithm (OGA) is applied which mainly searches for an optimal feature subset through the selection, crossover, and mutation operations to diagnose the disease at an earlier stage. This helps to identify the risk factor and diagnosing the diabetes disease efficiently. Experimental results show that the proposed IFRST-OGA increases the performance in terms of true positive rate, computation time and diabetes diagnosing accuracy.

Keywords

Diabetic Diagnosis, Risk Factors Analysis, Rough Set Theory, Feature Selection, Optimized Genetic Algorithm, Selection, Crossover, Mutation, Optimal Feature Subset Selection.

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N. Nandhini ¹, J. Gowri Shankar ¹

Affiliations
1 Department of Computer Science and Engineering, Knowledge Institute of Technology, IN

Abstract

The presence of plant species in the wrong place and time is identified as plant weeds. The loss of yield could result from interference with plant crop weed species. To classify the plant weeds among the seeds, seed classification is carried out in this paper. Here the image of seeds and datasets of sample seed are input. In pre-processing stage the seed image is given as input. An unwanted seeds are removed by comparing the features of seed with sample seeds features by using ID3 algorithm. One of the reasons for failure in crop yield production is selecting suitable soil for crop. As sample dataset contains the detail of growth of crop in soil, it will help for selecting the suitable soil for seeds. The features of sample dataset are compared with the features extracted from the affected crop and predict the disease and prevention measures taken place. In this method prediction is done only after the growth of crop which leads to decrease in quality of crop growth. In order to overcome these issues, diseases can be predicted using seed features by comparing the features with sample dataset. Using Support Vector Machine algorithm the seeds are classified based on the growth and predicting the diseases of crop. This is done by training the dataset by comparing the features extracted from new seeds and features of sample seeds and predicting the crop growth and diseases. Based on prediction of crop growth and crop diseases, preventive measures takes place.

Keywords

Data Mining, Big Data, Feature Extraction, Crop Growth

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