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

Wireless body area sensor networks signal processing and communication framework: Survey on sensing, communication technologies, delivery and feedback

Problem statement: The Wireless Body Area Sensor Networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements.This study surveys the state-of-the-art on Wireless Body Area Networks, discussing the major components of research in this area including physiological sensing and preprocessing, WBASNs communication techniques and data fusion for gathering data from sensors.In addition, data analysis and feedback will be presented including feature extraction, detection and classification of human related phenomena.Approach: Comparative studies of the technologies and techniques used in such systems will be provided in this study, using qualitative comparisons and use case analysis to give insight on potential uses for different techniques.Results and Conclusion: Wireless Sensor Networks (WSNs) technologies are considered as one of the key of the research areas in computer science and healthcare application industries.Sensor supply chain and communication technologies used within the system and power consumption therein, depend largely on the use case and the characteristics of the application.Authors conclude that Life-saving applications and thorough studies and tests should be conducted before WBANs can be widely applied to humans, particularly to address the challenges related to robust techniques for detection and classification to increase the accuracy and hence the confidence of applying such techniques without physician intervention

Survey on wireless body area sensor networks for healthcare applications: Signal processing, data analysis and feedback

Wireless sensor networks (WSNs) technologies are considered as one of the key of the research areas in computer science and healthcare application industries.The wireless body area sensor networks (WBASNs) is a wireless network used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements.The paper surveys the state-of-the-art on WBASNs discussing the major components of research in this area including physiological sensing, data preprocessing, detection and classification of human related phenomena. We provide comparative studies of the technologies and techniques used in such systems

Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data

Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities.This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity.The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when SNR = 1dB, 84% when SNR = 5dB, and 88% when SNR = 10dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned.NPRP 7-684-1-127, from the Qatar National Research Fund, a member of Qatar Foundation

Structural Equation Model on Factors Affecting Students Satisfaction towards University Library: A Case Study

Library in the university is known as the centre of knowledge. University library offers learning support, research requirement and also teaching materials. The objective of this study is to identify the most (and least) factors that contribute to students satisfaction towards university library. A case study on selected respondents consist of 266 undergraduate students was conducted. Their opinion on the library services has been recorded. Questionnaires were distributed to the respondents. Structural Equation Model was developed to display their responses. The finding concluded that online services and collections have significant relationship with the overall satisfaction towards the library, whereas facilities and library staffs do not have significant contribution to the satisfaction. As a suggestion, the library management should improve their service quality by upgrading the online services such as wireless access, library website service,  and online book renewal process to mentioned a few, which might be effective in improving the students’ satisfaction towards the library