Journal of Computer Sciences and Applications

Web Usage Mining (WUM) is a kind of data mining method that can be used to discover user access patterns from Web log data. A lot of research has been done already about this area and the obtained results are used in different applications such as recommending the Web usage patterns, personalization, system improvement and business intelligence. WUM includes three phases that are called preprocessing, pattern discovery and pattern analysis. There are different techniques for WUM that have their own advantages and disadvantages. This paper presents a survey on some of the existing WUM techniques and it is shown that how WUM can be applied to Web server logs.

Journal of Computer Sciences and Applications. 2013, 1(3), 39-45. DOI: 10.12691/jcsa-1-3-3
Pub. Date: May 17, 2013

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26509 Views10239 Downloads36 Likes6 Citations

Application of Decision Support Systems and Its Impact on Human Resources Output: A Study of Selected Universities in Zimbabwe

by Bongani Ngwenya

Original Research

The increased usage of computer based decision support systems is perceived as transferring decision authority to software programs. This has led to the notion that decision making power has been somewhat transferred from human decision makers and over centralized on the machine in the decision loop. The question is, has this enhanced the effectiveness of the decision support system in selected Universities in Zimbabwe? The findings of this study are that, to a great extent decision support systems are effective in assisting decision making in organisations. Responses from management were concentrated on the higher positive side showing that they agreed that most of the human resource elements were improved by use of the decision support systems. It was recognized that the older the respondent the quicker they solve problems and accurate data collection was found to increase the decision scope.

Journal of Computer Sciences and Applications. 2013, 1(3), 46-54. DOI: 10.12691/jcsa-1-3-4
Pub. Date: May 18, 2013

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Microcomputer Application for Instrumentation Development in Drip Irrigation System

by Mohd. Hudzari Razali, Mohd Noorman Masrek and Syazili Roslan

Original Research

Drip irrigation is the most water efficient system available to water garden. Some systems are so efficient that they only allow a few liters of water per hour to fall into the soil and the best way to water the plants at ground level where they need it the most. Installing an irrigation system such as sprinkler or drip system without automation has always been a difficult and frustrating problem for homeowners, contractors and growers alike. An ideal choice would be to use an irrigation controller, which can be the greatest help in conserving water and time. With the right controller, the right amount of water will be able to apply, at the right time, for optimum plant growth. In this study, the amounts of water that produce from microcontrollers and from different water pumps are measure. The result show the average water produced is similar. Microcomputer or controller can manage the flow of water through the irrigation system. Simple controllers such as a mechanical tap timer only need to you set the watering time required. More complex controllers are capable of watering different garden zones at different times and even on different days. It is an essential tool to apply water in the necessary quantity and at the right time to sustain agricultural production and to achieve high levels of efficiency in water, energy and chemical uses.

Journal of Computer Sciences and Applications. 2013, 1(3), 39-42. DOI: 10.12691/jcsa-1-3-2
Pub. Date: May 10, 2013

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Improving the Intrusion Detection Systems' Performance by Correlation as a Sample Selection Method

by Rahimeh Rouhi, Farshid Keynia and Mehran Amiri

Original Research

Due to a growing number of the computer networks in recent years, there has been an increasing interest in the intrusion detection systems (IDSs). In this paper we have proposed a method applied to the instance selection from KDD CUP 99 dataset which is used for evaluating the anomaly detection techniques. In order to determine the performance of proposed method in the dataset reduction, a feed forward neural network was trained by a reduced dataset to classify normal or attack records in the dataset. The most obvious finding resulted from this study is a considerable increase in the accuracy rate obtained from the neural network.

Journal of Computer Sciences and Applications. 2013, 1(3), 33-38. DOI: 10.12691/jcsa-1-3-1
Pub. Date: May 09, 2013

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26575 Views9661 Downloads33 Likes5 Citations