Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..
Automatic recognition of facial expressions is an important component for human-machine interfaces. It has lot of attraction in research area since 1990's.Although humans recognize face without effort or delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user authentication, person identification, video surveillance, information security, data privacy etc. The various approaches for facial recognition are categorized into two namely holistic based facial recognition and feature based facial recognition. Holistic based treat the image data as one entity without isolating different region in the face where as feature based methods identify certain points on the face such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various methods of facial detection,facial feature extraction and classification.
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Outlier detection refers to find patterns that do not fit in to normal behaviour. Outlier detection plays an important role in data mining. Most of real world datasets are using outlier detection. Outlier detection is useful in many fields like Network intrusion, Credit card fraud detection, stock market, and wireless sensor network data. The distance based outlier detection techniques be unsuccessful to will increases the dimensionality of data because in high dimensionality distance between the two points is less. In existing system using Anti-hub method in reverse nearest neighbors. Anti-hubs are few points are regularly comes in K-nearest neighbors list of another points and few points are an irregularly comes in k-nearest neighbors list of different points. In Anti-hub method propose high computational cost and time requirements for finding outliers. To overcome this problem we can use new method in this paper that is the advanced variety of Anti-hub is Anti-hub2.which is for reconsider the outlier score of a data point obtained by the Anti-hub method. The goal of this paper is locate the inconsistent objects in data which has high dimension through reduced computation time, cost and increase the accuracy. We apply logistic regression rule on the results of Anti-hub dataset then obtained combination of data, prevention measures and Anti-hub calculation. It increase the efficiency of remove out irrelevant, redundant feature. Keywords: outlier detection, High dimensional data, Anti-hub, Anti-hub2, Logistic regression I. INTRODUCTIONData Mining means the process of extracting the knowledge for huge data sources. The general objective of the data mining process is to extract information from a data set and transform it into a reasonable structure for further utilize. In the Data Mining have four techniques that are Clustering, Classification, Association, and Outliers. In this paper we discuss the Outlier detection techniques definition of Outlier location (otherwise called inconsistency discovery) is the way toward discovering data objects with practices that are altogether different from desire. Such objects are called outliers or anomalies. Outlier detection is important in many applications such as medical care, public safety and security, industry damage detection, image processing, sensor/video network surveillance, and intrusion detection. In general, outliers is classified into 3 varieties, those are global outliers, conditional outliers, and collective outliers. To discover global outliers, an essential issue is to search for out an acceptable mensuration of deviation with relation to the appliance in question. Global outlier detection is very significant in several applications. Take into account intrusion detection in laptop networks, as an example. If the statement behaviour of a laptop is extremely completely changed from the conventional designs (e.g., an oversized range of packages is broadcast in a very short time), this behaviour is also thought-about as a worldwide outlier and also t...
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