Data mining is an important research area in computer science. It is a computational process of determining patterns in large data. Image mining is one of important techniques in data mining, which involved in multiple disciplines. Image Classification Refers the tagging the images into a number of predefined sets. It's also includes image preprocessing, feature extraction, object detection, object classification, object segmentation, object classification and many more techniques. Image classification to produce the accurate prediction results in their target class for each case in the data. It is a very predominant and challenging task in various application domains, including video surveillance, biometry, biomedical imaging, industrial visual inspection, vehicle navigation, remote sensing and robot navigation. The aim of this study compares the some predominant data mining algorithms in image classification. For this review SVM, AdaBoost, CART, KNN, Artificial Neural Network, K-Means, Chaos Genetic Algorithm, EM Algorithm, C4.5 algorithms are taken.

Round Robin (RR) scheduling algorithm is the widely used scheduling algorithm in multitasking. It ensures fairness and starvation free execution of processes. Choosing the time quantum in RR algorithm is very crucial as small time slice results in large number of context switches and large time quantum increases the response time. To overcome these problems of RR scheduling, instead of static time slice dynamic time slice can be used to get optimal performance. The objective of this paper is to modify RR algorithm by adjusting time slices of different rounds depending on the remaining CPU bursts of currently running processes and considering their waiting times until that round in respect of the other processes' waiting times. Experimental analysis reveals that the proposed algorithm produces better average turnaround time, average waiting time and fewer number of context switches than existing algorithms.

Sentiment analysis mainly focuses on subjectivity and polarity detection. Today consumer makes buying decision based on the customer's review that is available in each of the online shopping sites. There are some of the specific websites which discuss about positive and negative facts of those products that comes to market. Hence this type of analysis are socially very needed for sellers to undergo market analysis, branding, product penetration, market segmentation and so on. This paper mainly focuses on difference between systematic and automated methods of determining the positive and negative polarity distribution of Samsung Tablet PC.