A near-optimal centroids initialization in K-means algorithm using bees algorithm

The K-mean algorithm is one of the popular clustering techniques.The algorithm requires user to state and initialize centroid values of each group in advance. This creates problem for novice users especially to those who have no or little knowledge on the data.Trial-error attempt might be one of the possible preference to deal with this issue.In this paper, an optimization algorithm inspired from the bees foraging activities is used to locate near-optimal centroid of a given data set.Result shows that propose approached prove it robustness and competence in finding a near optimal centroid on both synthetic and real data sets

Parking space: A design of WLAN mobile phone application in urban area

The common problem with the public parking space using automatic machine is that the customers unable to obtain enough information concerning parking lot and wasting customer’s time of finding the vacant parking space especially in the parking in the urban area. The idea behind this application is to make the drivers easily access all related parking space information and to match customer’s booking needs.This on-going research is also rest on an integrated architecture comprising a WAP cellular phone or standard internet access and free parking spaces interfaced with parking space provider's reservation system

Knowledge and integrated data management model for personalized intercropping in rubber plantation

Selection and allocation of space for intercropping in rubber plantations to maximize yield and minimum costs for individual farmers involves Multi-Criteria Decision Making (MCDM) and several conditions.  The problem is that the information is scattered in many related agencies, there are separate stores and some data is redundant. In addition, the format of the data varies depending on the purpose of the data. The knowledge of selecting plants to grow in the rubber plantation is the tacit knowledge acquired from the experience of successful farmers in rubber plantations and from agricultural experts. Therefore, this research involves an Integrated Ontology-based knowledge and Multi-Objective Optimization model for intercropping Decision Support Systems (DSS). This article presents the knowledge and integrated data management model for developing the Intercropping in Rubber Plantations Ontology by using the Triangulation in the method to verify the accuracy of the data and results.  Moreover, propose ways to create recommendation rules that are easy to rule update and maintenance.  Using an ontology for DSS helps to recommended plants according to the appropriate environment of the farmer area by rule-based inference to represent logical reasoning.  It could also be applied to another domain that requires Intelligent DSS for MCDM

Classifying Firms’ Performance Using Data Mining Approaches

Superior prediction and classification in determining company’s performance are major concern for practitioners and academic research in providing useful or important information to the shareholders and potential investors for investment decision. Generally, the normal practice to analysed firm’s performance are based on financial indicators reported in the company’s annual report including the balance sheet, income and cash flow statements. In this work, a few popular and important benchmarking machine learning techniques for the data mining including neural networks, support vector machine, rough set theory, discriminant analysis, logistic regression, decision table, sequential minimal optimization and decision tree have been tested as to classify firm’s performance. The data mining techniques produce high classification rate that is more than 92%. This work also has reduced total number of ratios to be evaluated due to long processing time and large processing resources. Finally, the CA/TA, S/TA, E/TA, GM, FC, PBT/TA, and EPS have been considered for of the final reduced financial ratios. The results show that the 7 reduced ratios are comparable as the common 24 ratios. And to the still produce high classification rate and able classify the firm’s performance

Behavior usage model to manage the best practice of e-learning

This study aims to find e-Learning users’ behavior model that use data mining techniques to predict the successful learning behavior in utilizing e-Learning systems and to develop appropriate e-Learning users’ behavior models that could be used broadly in other higher institutions. Due to the lack of suitable e-Learning user’s behavior model for open source e-Learning system (Moodle) that could not be able to make a prediction for learning outcomes or performances.In this case, it is not useful enough for improving learners’ performance which may cause failure in learning.Therefore, this research is conducted upon three main phases, which are data preparation, data extraction and model verification for generating a verification pattern. This pattern could be used as a direction for creating a more appropriate e-Learning users’ behavior model

A conceptual model of knowledge work productivity for software development process: Quality issues

Knowledge is considered as the main competitive asset of the organization.Work on the knowledge work productivity has barely begun, but the most important contribution that management needs to construct in the 21st century is not only to increase the productivity of knowledge work and knowledge workers in the new century.The quality of knowledge work productivity are becomes pivotal in the context of software development today.Software development is a knowledge-intensive activity and its success depends heavily on the developers’ knowledge and experience. A conceptual model will be proposed on a way describing organization to improve quality of knowledge work productivity. The methodology begins with a reviewing a theoretical foundation and expert review that provides the scientific basis for knowledge work productivity specifically for software development. A questionnaire will be constructing in order to investigate the relationship between factors of knowledge work and quality of productivity on knowledge work. The respondents are software developers from Small Manufacturing Enterprise(SME). The data will be analyzed using Structural Equation Modeling (SEM) to identify the significant direct relationship effect among the factors. The proposed model will be helpful for the software developers to understand the determinant factors for knowledge works productivity

Conditional hybrid approach for intrusion detection

Background and Objective: Inspecting all packets to detect intrusions faces challenges when coping with a high volume of traffic.Packet-based detection processes every payload on the wire, which degrades the performance of intrusion-detection systems.This issue requires the introduction of a flow-based IDS approach that reduces the amount of data to be processed by examining aggregated information of related packets in the form of flow.However, flow-based detection still suffers from the generation of false positive alerts due to lack of completed data input.This study proposed a model to improve packet-based performance and reduce flow-based false positive rate by combining flow-based with packet-based detection to compensate for their mutual shortcomings.This proposed model is named as conditional hybrid intrusion detection.Materials and Methods: In this model, only malicious flows marked by flow-based must be further analyzed by packet-based detection.For packet-based detection to communicate with flow-based detection, input framework approach was used.To evaluate the proposed detection methods, public datasets were replayed in different traffic rates into both the proposed method and default Bro implementations in a testbed controlled environment.Results: Experimental evaluation shows that the proposed approach was able to detect all infected hosts reported and corresponding datasets.At 200 Mbps rate, proposed approach can save 50.6% of memory and 18.1% of CPU usage compared with default Bro packet-based detection. Experiments demonstrated that the default Bro packet-based can handle bandwidth up to 100 Mbps without packets drop, while 200 Mbps in the proposed approach. Conclusion: Experimental evaluation showed that the proposed model gains a significant performance improvement, in term of resource consumption and packet drop rate compared with a default Bro packet-based detection implementation.The proposed approach can mitigate the false positive rate of flow-based detection and reduce the resource consumption of packet-based detection, while preserving detection accuracy.This study can be considered as skeleton model to be applied for intrusion or monitoring detection systems

Empirical analysis of classifiers and feature selection techniques on mobile phone data activities

Mobile phones nowadays become ubiquitous device and not only a device to facilitate communication, with some addition feature of hardware and software.There are many activities can be captured using mobile phone with many of features.However, not all of these features could benefit to the in processing and analyzer.The large number of features, in some cases, gives less accuracy influence the result. In the same time, a large feature takes requires longer time to build model. This paper aims to analyze accuracy impact of selected feature selection techniques and classifiers that taken on mobile phone activity data and evaluate the method. Furthermore, with use feature selection and discussed emphasis on accuracy impact on classified data of respective classifier, usage of features can be determined. To find the suitable combination between the classifier and the feature selection sometime is crucial. A series of tests conducted in Weka on the accuracy on feature selection shows a consistency on the results although with different order of features.The result found that combination of K* algorithm and correlation feature selection is the best combination with high accuracy rate and in the same time produce less feature subset

Flow-based approach on bro intrusion detection

Packet-based or Deep Packet Inspection (DPI) intrusion detection systems (IDSs) face challenges when coping with high volume of traffic. Processing every payload on the wire degrades the performance of intrusion detection. This paper aims to develop a model for reducing the amount of data to be processed by intrusion detection using flow-based approach. We investigated the detection accuracy of this approach via implementation of this model using Bro IDS. Bro was used to generate malicious features from several recent labeled datasets. Then, the model made use the machine learning classification algorithms for attribute evaluation and Bro policy scripts for detecting malicious flows. Based on our experiments, the findings showed that flow-based detection was able to identify the presence of all malicious activities. This verifies the capability of this approach to detect malicious flows with high accuracy. However, this approach generated a significant number of false positive alarms. This indicates that for detection purpose, it is difficult to make a complete behavior of the malicious activities from only limited data and flow-level

Student marketability: enhancing software skills

The government of Malaysia emphasizes content creation for the purposes of nation growth to fill the existing infrastructure. A number of initiatives have been emphasized in latest Malaysian plans. At university levels, efforts were also put to equip students with interests and abilities in the areas of content creation. This paper describes about a training program, as part of student’s skill enhancement. The program which was more like a camp was fully conducted by a key player in animation industry. The paper starts with an introduction to the camp. The model of the camp is described, followed with the implementation of the camp. Two assessment methods of the camp are explained, followed with the camp outcome. It was found that the camp was successful in making trainees feel more interested and confident to be animators, and involved in animation productions