This research aims to enhance the achievement of the students on their study plan. The problem of the students in the university is that some students cannot design the efficient study plan, and this can cause the failure of studying. Machine Learning techniques are very powerful technique, and they can be adopted to solve this problem. Therefore, we developed our techniques and analyzed data from 300 samples by obtaining their grades of students from subjects in the curriculum of Computer Science, Faculty of Science and Technology, Sakon Nakhon Rajabhat University. In this research, we deployed CGPA prediction models and K-means models on 3rd-year and 4th-year students. The results of the experiment show high performance of these models. 37 students as representative samples were classified for their clusters and were predicted for CGPA. After sample classification, samples can inspect all vectors in their clusters as feasible study plans for next semesters. Samples can select a study plan and predict to achieve their desired CGPA. The result shows that the samples have significant improvement in CGPA by applying self-adaptive learning according to selected study plan.

While the mobile game industry is growing with each passing day with the popularization of 3G smart devices, the creation of successful games, which may interest users, become quite important in terms of the survival of the designed game. Clustering, which has many application fields, is a successful method and its implementation in the field of mobile games is inevitable. In this study, classical ball blasting game was carried out based on clustering. In the game, clustering the color codes with K-means, Iterative K-means, Iterative Multi K-means and K-medoids methods and blasting the balls of colors located in the same cluster by bringing them together were proposed. As a result of the experiments, the suitability of clustering methods for mobile based ball blasting game was shown. At the same time, the clustering methods were compared to produce the more successful clusters and because of obtaining more accurate results and stability, the use of K-medoids method has been chosen for this game.

Cloud computing is transforming the way IT is owned and utilized in the present day business scenario. Several predictions by the researchers and analytical enterprises have predicted unprecedented growth for this emerging paradigm. This work is an attempt to analyze the cloud future based on the various reports and predictions published recently. We have explored the various opportunities that will drive the cloud growth. We have also highlighted the effect of cloud in Indian and US market. Significance of the study is validated by conducting the Strength, weakness, opportunity, and Threat (SWOT) analysis. Based on the findings, we have identified the intensity of challenges faced by the various types of cloud deployment model. Correspondingly, we have recommended the critical challenges that need to be addressed first, in order to facilitate the cloud in gaining further momentum.

The increasing amount of data and a need to analyze the given data in a timely manner for multiple purposes has created a serious barrier in the big data analysis process. This article describes the challenges that big data creates at each step of the big data analysis process. These problems include typical analytical problems as well as the most uncommon challenges that are futuristic for the big data only. The article breaks down problems for each step of the big data analysis process and discusses these problems separately at each stage. It also offers some simplistic ways to solve these problems.

In this paper, we have tried to identify the prominent factors of Rice production of all the three seasons of the year (Aus, Aman, and Boro) by applying K-Means clustering on climate and soil variables' data warehoused using Fact Constellation schema. For the clustering, the popular machine-learning tool Weka was used whose visualization feature was principally useful to determine the patterns, dependencies, and relationships of rice yield on different climate and soil factors of rice production.

The amount and the complexity of malicious activity increasing and evolving day by day. Typical static code analysis is futile when challenged by diverse variants. The prolog of new malware samples every day is not uncommon and the malware designed by the attackers have the ability to change as they propagate. Thus, automated dynamic malware analysis becomes a widely preferred technique for the identification of unknown malware.
In this paper, an automated malware detection system is presented based on dynamic malware analysis approach. The behavior of malware is observed in the controlled environment of the popular malware analysis system. It uses the clustering and classification of embedded malware behavior reports to identify the presence of malicious behavior. Based on the experimentation and evaluation it is evident that the proposed system is able to achieve better F-measures, FPR, FNR, TPR and TNR values resulting in accurate classification leading to more efficient detection of unknown malware compared to the traditional hierarchical classification approach.

Many datasets are published on the Web using semantic Web technologies. These datasets contain data that represent links to similar resources. If these datasets are linked together by properly constructed links, users can easily query the data through a uniform interface, as if they were querying a single dataset. In this paper we propose an approach to discover (semi) automatically links between RDF data based on the description models that appear around the resources. Our approach also includes a (semi) automatic process to maintain links when a data-change occurs.