International Journal of Computer Networks and Communications Security

Volume 5, Issue 2, February 2016




An Introduction on Separating Gray-Sheep Users in Personalized Recommender Systems Using Clustering Solution

Pages: 14-18 (5) | [Full Text] PDF (166 KB)
S Ghorbani, AH Novin
Department of Computer Engineering, East Azerbaijan Science and Research Branch, Islamic Azad University, Tabriz, Iran
Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Abstract -
Nowadays, regarding the development of the Internet and the uncontrolled increase in the amount of information, users have faced difficulties for retrieving information for webs. To solve retrieving information, Personalized Recommender Systems appeared. These systems provide information appropriate to users needs regarding information obtained from them and their behaviors in searching texts. But, one of the commonest difficulties in Personalized Recommender Systems is the existence of users called grey-sheep users. These users have little similarity with other users; therefore, their presence in these systems along with normal users causes the reduction in the precision of predictions and suggestions for the two groups. An appropriate method for reducing the effect of the presence of grey-sheep users is the use of clustering methods. Therefore, in the present study, clustering methods for separating users and reducing the mean absolute error as well as increasing the precision of clustering methods. To separate grey-sheep users, some suggestions for future research were presented.
Index Terms - Personalized Recommender Systems, Grey-sheep Users, Clustering Methods

Citation - S Ghorbani, AH Novin. "An Introduction on Separating Gray-Sheep Users in Personalized Recommender Systems Using Clustering Solution." International Journal of Computer Science and Software Engineering 5, no. 2 (2016): 14-18.


Clinical Decision Support Systems for Heart Disease Using Data Mining Approach

Pages: 19-23 (5) | [Full Text] PDF (269 KB)
H. Singh, KS Kaswan
P.D.M College of Engineering, Bahadurgarh

Abstract -
Now a days business is growing at a very rapid pace and a lot of information is generated. The more information we have, based on internal experiences or from external sources, the better our decisions would be. Business executives are faced with the same dilemmas when they make decisions. They need the best tools available to help them. Decision support system helps the managers to take better and quick decision by using historical and current data. By combining massive amounts of data with sophisticated analytical models and tools, and by making the system easy to use, they provide a much better source of information to use in the decision-making process. Health care is also one of the domains which get a lot of benefits and researches with the advent and progress in data mining. Data mining in medicine can resolve this problem and can provide promising results. It plays a vital role in extracting useful knowledge and making scientific decision for diagnosis and treatment of disease. Treatment records of millions of patients have been recorded and many tools and algorithms are applied to understand and analyze the data. Heart failure is a common disease which is difficult to diagnose. To aid physicians in diagnosing heart failure, a decision support system has been proposed. A classification based methods in health care is used to diagnose based on certain parameters to diagnosis if the patient have certain disease or not. The purpose is to explore the aspects of Clinical Decision Support Systems and to figure out the most optimal methodology that can be used in Clinical Decision Support Systems to provide the best solutions and diagnosis to medical problems.
Index Terms - Data Mining, Health Care, Heart Disease

Citation - H. Singh, KS Kaswan. "Clinical Decision Support Systems for Heart Disease Using Data Mining Approach." International Journal of Computer Science and Software Engineering 5, no. 2 (2016): 19-23.


An Efficient Searching Algorithm for Data Mining in Bioinformatics

Pages: 24-27 (4) | [Full Text] PDF (516 KB)
MS Hossain, MB Hossain, MM Ali, MD Haque, MA Siddik
Department of Telecommunication and Electronic Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200, Bangladesh

Abstract -
Bioinformatics is the application of computational techniques to analyze the Information associated with bimolecular on a large-scale. Bioinformatics is the science of storing, extracting, organizing, analyzing, interpreting and utilizing Information from biological sequences and molecules. Bioinformatics may be used in Sequence analysis, Genome annotation, Analysis of gene expression, Analysis of mutations in cancer etc .Data Mining (DM) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. It requires intelligent technologies and the willingness to explore the possibility of hidden knowledge that resides in the data. Data mining have some pre processing, processing and post processing activities. In this paper we analyze how data mining may help bio-medical data analysis and outline some research problems that may motivate the further developments of data mining tools for bio-data analysis However, one of the drawback of searching of data mining is its huge time complexity. The time complexity of linear search is linearly increased with increasing input. For binary search the time complexity is increased by logarithmic function. Moreover binary search require the input data to be sorted. So we proposed a new algorithm to solve these problems. In our proposed algorithm the input data is divided first by several time slots, then searching is performed by using the concept of tree. By using this algorithm we see the time complexity is reduced incredibly. For this the Bit Error Rate (BER) of the system is also reduced hence it increase the performance of the system..
Index Terms - Bioinformatics, Data Mining, Searching, BER, Efficient Algorithm etc

Citation - MS Hossain, MB Hossain, MM Ali, MD Haque, MA Siddik. "An Efficient Searching Algorithm for Data Mining in Bioinformatics." International Journal of Computer Science and Software Engineering 5, no. 2 (2016): 24-27.