Model-Based Outlier Detection System with Statistical Preprocessing

Reliability, lack of error, and security are important improvements to quality of service. Outlier detection is a process of detecting the erroneous parts or abnormal objects in defined populations, and can contribute to secured and error-free services. Outlier detection approaches can be categorized into four types: statistic-based, unsupervised, supervised, and semi-supervised. A model-based outlier detection system with statistical preprocessing is proposed, taking advantage of the statistical approach to preprocess training data and using unsupervised learning to construct the model. The robustness of the proposed system is evaluated using the performance evaluation metrics sum of squared error (SSE) and time to build model (TBM). The proposed system performs better for detecting outliers regardless of the application domain

A Survey on Big Data and Cloud Computing

In the information age, analyzing and extracting the knowledge from the data is a challenging task since the data are being accumulated massively from various sources and sectors. These massively accumulated data is known as big data since it possess the characteristics such as high volume, different variety and high velocity. Processing these big data using the normal work station is quiet complex since it is saturated with the vertical scalability. Therefore, processing the big data is a challenging task. Hence, the cloud computing arrives for handling massive data for storing and analyzing them to obtain the knowledge and make decisions to improve the productivity and the services. Therefore, conducting the study on the big data and the cloud computing is important to promote the research and development activities in the field of the big data and the cloud computing. Therefore, this paper presents a survey on the big data and cloud computing

Regression Based Sales Data Forecasting for Predicting the Business Performance

Business plays a vital role in day-to-day life to bring the goods and services to the people. The profit ofa business highly depends on the sales. Forecasting thesales in business is essential since the sales forecast predicts the business performance.Moreover, sales forecasting is an estimation of futuresales in a business based on the past sales data. This forecasting to make better managerial decisions allows in business for improving the performance of the business. Furthermore, the sales forecasting helps to increase the revenue, reduce the operating cost, improve the working capital use, and increase the shareholder�s values. Therefore, this paper presents a sales data forecasting to predict the business performance

Salt and Pepper Noise Detection and Removal in Gray Scale Images: An Experimental Analysis

Impulse noise removal is a mechanism for detection and removal of impulse noise from images. Median filters are preferred for removing impulse noise because of their simplicity and less computational complexity. In this paper, impulse noise removal using the standard median filter and its variants are analyzed. Extensive simulations have been carried out on a set of standard gray scale images and the state of the art median filter variants are compared in terms of the well known image quality assessment metrics namely mean square error, peak signal to noise ratio and multiscale structural similarity index