Department of Information Technology
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Item SYNTHETIC MICRODATA GENERATION IN PRIVACY PRESERVING DATA MINING(VELS university, Chennai., 2011-03-11) Vijayarani S; Nithya SData mining is the process of extracting the previously unknown patterns from large amount of data. Privacy preserving data mining is one of the reserve areas in data mining. It is used to provide the privacy for personally identifiable information in data mining. It also provides security to protect data. Privacy preserving Association Rule Mining, Privacy Preserving Clustering, Privacy Preserving classification, and etc., are some of the privacy preserving algorithms. The various techniques used in the privacy preserving data mining are statistical disclosure control, randomization, k-anonymity and I-diversity. In this paper, we havediscussed about the synthetic data generation concept, its techniques and methods which are used for protecting sensitive data in statistical disclosure control.Item WIRELESS SENSOR NETWORK IN AGRICULTURE(Michael Job College Of Arts and Science For Women, 2020-02-19) Nithya S; Yuvashree R; Sowmiya PWireless network sensor(WSN) is a self configurable to monitor physical or environment condition. It contain many node is equipped with sensing and computing devices. In this paper we discussed about how WSN used in agriculture it employ as a part of agriculture for many reasons. India is 2nd in agriculture activities. The agriculture production process is affected by different factor such as temperature ,light, soil moisture . It provide accurate information about environmental condition to formers. It helps to increase the production of crops , low power consumption and gather distributed data. The wireless network technologies are increasingly being implemented for modern precision agriculture monitoring. WSN is also used in soil and water condition management .Item ARTIFICIAL INTELLIGENCE (AI) IN AGRICULTURE(Our Heritage, 2020-01-19) Nithya S; Nivetha V; Varshini SAgriculture and farming is one of the oldest and most important professions in the world. Humanity has come a long way over the millennia in how we farm and grow crops with the introduction of various technologies. As the world population continues to grow and land becomes scarcer, people have needed to get creative and become more efficient. AI is most common in growing sectors, now AI is breaking into agriculture sector too (Fig .1). Agriculture plays a huge role in developing our economy but it has certain arising problems like lack of labours, water scarcity etc, the solution for this is use of artificial intelligent agriculture. AI sensors can detect and target weeds and then decide which herbicides to apply within the right buffer zone. In addition to ground data, farmers are also taking to the sky to monitor the farm.Item GENETIC BASED SUPPORT VECTOR MACHINE CLASSIFIER FOR HEART DISEASE CLASSIFICATION(Journal of Emerging Technologies and Innovative Research (JETIR), 2019-06) Nithya SClassification is one among the hot research topic in the field of data mining. Classically classification task represents the data to be categorized based on its features or characteristics. This proposed research work aims in developing genetic based support vector machine classifier. Support vector machine is a type of supervised machine learning technique and once when the dataset is given as input it performs the classification task by itself. The proposed classifier aims in improving the classification accuracy of the support vector machine by making use genetic algorithm. Genetic algorithm is used in order to perform fuzzy association rule extraction, candidate rule pre-screening, rule selection and lateral tuning. The proposed classifier has been tested onnamely PIMA Indian diabetes to classify the occurrence of heart disease among the patients. Performance metrics classification accuracy are taken for comparison of the proposed genetic based support vector machine classifier (GSVM) with SVM classification algorithm. Results showed that the proposed GSVM classifier gives better classification accuracy than that of support vector machineItem BAT IMPERIALIST COMPETITIVE ALGORITHM (BICA) BASED FEATURE SELECTION AND GENETIC FUZZY BASED IMPROVED KERNEL SUPPORT VECTOR MACHINE(GF-IKSVM) CLASSIFIER FOR DIAGNOSIS OF CARDIOVASCULAR HEART DISEASE(Biomedical Research 2018; Special Issue: S95-S104, 2018) Nithya S; Suresh Gnana Dhas CNowadays rate of death is increased due to the rapid growth of cardiovascular diseases. Due to the above reason, diagnosing the cardiovascular heart disease becomes very important in medical field. The subset features which are considered as vital role in disease diagnosis are identified for the same disease in modern medicine. Currently, many data mining techniques related to different types of heart disease diagnosis were presented by several authors. Existing methods mainly concentrated on high accuracy and less time consumption and it uses many different types of data mining techniques. This work consists of three major steps such as missing data imputation, high dimensionality reduction or feature selection and classification. The above steps are performed using a dataset called cardiovascular heart disease dataset with 500 patients and 14 features and it utilizes several effective features. Because of incomplete data collections Real time datasets often reveal unaware missing feature’s patterns. First step consists of the Expectation Maximization (EM) algorithm which fits an independent component model of the data. This increases the possibility of performing Modified Independent Component Analysis (MICA) on imperfect observations. Bat Imperialist Competitive Algorithm (BICA) based feature selection method is proposed to improve the dataset. BICA is an evolutionary algorithm which is based on the development of human's socio-political. In this algorithm an m number of features and the N number of cardiovascular heart disease observations are used as initial population which is called as countries. A classification approach is introduced with Genetic Fuzzy based Improved Kernel Support Vector machine (GF-IKSVM) classifier and a BICA based feature selection for the classification of cardiovascular heart disease dataset. BICA is used for feature selection methods to reduce number of features which indirectly decreases the important diagnosis tests required to the patients. The proposed method achieves 94.4% accuracy, which is higher than the methods used in the literature. This GFIKSVM classifier is well-organized and provides good accuracy results for cardiovascular heart diseasediagnosis.Item SURVEY ON COMPUTER PROGRAMS AND METHODS FOR HEART DISEASES PREDICTION AND CLASSIFICATION(ARPN Journal of Engineering and Applied Sciences, 2015-07) Nithya S; Suresh Gnana Dhas CThis paper presents several approaches carried out for the prediction, risk assessment of heart diseases such as Coronary Artery Disease (CAD), Congestive Heart Failure, Myocardial Infarction (MI). Researchers of applied soft W computing, image processing, data mining has taken strenuous efforts in prediction, risk assessment and classification of cardiac diseases. The paper thoroughly reviewed their contribution and several cross functional research dimensions.Item AN EFFICIENT CLUSTERING ALGORITHM FOR OUTLIER DETECTION(International Journal of Computer Applications, 2011-10) Vijayarani S; Nithya SWith the help of data mining, an important and valuable knowledge is extracted from the large massive collection of data. There are s several techniques and algorithms are used for extracting the hidden patterns from the large data sets and finding the relationships between them. Clustering is one of the important techniques in data mining. Clustering algorithms are used for grouping the data items based on their similarity. Outlier Detection is a very important research problem in data mining. Clustering algorithms are used for detecting the outliers efficiently. In this research paper, we focused on outlier detection in health data sets such as Pima Indians Diabetes data set and Breast Cancer Wisconsin data set using partitioning clustering algorithms. The algorithms used in this research work are PAM, CLARA AND CLARANS and a new clustering algorithm ECLARANS is proposed for detecting outliers. In order to find the best clustering algorithm for outlier detection several performance measures are used. The experimental results show that the outlier detection accuracy is very good in the proposed ECLARANS clustering algorithm compared to the existing algorithms.Item SENSITIVE OUTLIER PROTECTION IN PRIVACY PRESERVING DATA MINING(International Journal of Computer Applications, 2011-11) Vijayarani S; Nithya SData mining is the extraction of hidden predictive information from large databases and also a powerful new technology with great potential to analyze important information in their data warehouses. Privacy preserving data mining is a latest research area in the field of data mining which generally deals with the side effects of the data mining techniques. Privacy is defined as “protecting individual’s information”. Protection of privacy has become an important issue in data mining research. Sensitive outlier protection is novel research in the data mining research field. Clustering is a division of data into groups of similar objects. One of the main tasks in data mining research is Outlier Detection. In data mining, clustering algorithms are used for detecting the outliers efficiently. In this paper we have used four clustering algorithms to detect outliers and also proposed a new privacy technique GAUSSIAN PERTURBATION RANDOM METHOD to protect the sensitive outliers in health data sets.