Categories leaf healthiness using RGB spectrum and fuzzy logic

In this paper, a general approach is to classify of the green leaf healthiness.Fuzzy logic tool (FuzzyLite 3.2 software) and color features (RGB Spectrum) are used in this experiment.Mean values of primary colors (Red, Green and Blue) channels as input to FIS (Fuzzy Inference System).FIS gives decision whether this part of leaf is healthy, unhealthy or dying.Experimentation is conducted on our own dataset for determining knowledge base, consisting of 40 images of leaves for each category; 20 for training and 20 for testing.The experiment has 4 phases which were data preparation, features extraction, features selection and classification.The experimental results indicate that proposed model achieves a good average classification accuracy which are 85% healthy,95% unhealthy and 100% dying

Data mining reduction methods and performances of rules

In data mining the accuracy of models are associated with the strength of the rules.However, most machine learning techniques produce a large number of rules.The consequence is with large number of rules generated,processing time is much longer. This study examines rules of different lengths of attributes in terms of performance based on percentage of accuracy. The research adopts the Knowledge Discovery in Databases “KDD” methodology for analysis and applies various data mining techniques in the experiments.Data of 50 hardware dataset companies which, contains 31 attributes and 400 records have been used. In summary, results show that in terms of performance of rules, Genetic Algorithm has produced the highest number of rules followed by Johnson’s Algorithm and Holte’s 1R.The best classifier for extracting rules in this study is VOT (Voting of Object Tracking).In terms of performance of rules, best results comes from rules with 30 attributes, followed by rules with 1 intersection attribute and lastly rules with 3 intersection attributes. Among the three sets of attributes, the 3 intersection attributes are considered as the attributes that can be used as predictor attributes

Peranan, Impak dan Keberkesanan Teknologi Maklumat terhadap Organisasi Perbankan: Suatu Kajian Kes Bank Perdagangan Malaysia

Kajian ini bertujuan melihat pengaruh ‘teknologi maklumat'(TM) dengan keuntungan syarikat. Sampel kajian merupakan 10 buah organisasi bank perdagangan tempatan yang bertaraf sepuluh keatas yang disenaraikan di dalam Pertukaran Saham Kuala Lumpur (KLSE). Bank-bank tersebut adalah Bank Commerce, Bank Utama, Bank Development & Commercial, Bank Hock Hua, Bank Malayan Banking, Bank Malaysian French, Bank Perwira Habib, Bank Public, Bank Pacific dan Bank Southern. Pembolehubah-pembolehubah yang bekaitan dengan TM seperti pelaburan TM, nisbah TM/ekuiti serta perbelanjaan selain TM dianalisa dengan menggunakan “OLS estimates” dan “Multiple Regression”. Kajian mendapati pelaburan TM mempunyai korelasi positif dengan keuntungan syarikat. Secara empirikal kajian mendapati pertambahan kepada pelaburan TM dapat meningkatkan keuntungan syarikat

Identification of suitable web application development methods for small software firms

Many development methods have been proposed for developing web application in small software firms.However, these methods have some limitations.This paper aims to identify the suitable development methods for building high quality web application.In order to achieve this objective, a comparative study was conducted on several current development methods.Comparisons were made according to five criteria that include fitted to 10-50 size, simplicity, flexible to change, customer collaboration and quality assurance used measurement program(QAMP).The findings of this paper will be used as a baseline for building a new development methodology for small software firms that emphasize on monitoring

Agile development methods for developing web application in small software firms

Small software firms that involved with developing web application are lacked of well defined development process. Many development methods have been proposed for developing web application in these firms. However, these methods have some limitations.This paper aims to identify the agile development methods that are suitable for small development teams and determine the enhancements needed to get high quality web application.In order to achieve these objectives a comparative study was conducted on the suitable agile development methods that have been selected. Comparisons were made according to a set of criteria that include development process, project management, requirement, testing and design.The findings of this paper will be used as baseline for building a new measurable web application development methodology for small software firms

Ensemble classifier and resampling for imbalanced multiclass learning

An ensemble classifier called DECIML has previously reported that the classifier is able to perform on benchmark data compared to several single classifiers and ensemble classifiers such as AdaBoost, Bagging and Random Forest.The implementation of the ensemble using sampling was carried out in order to investigate if there are any improvements in the classification performances of the DECIML.Random sampling with replacement (SWR) method is applied to minority class in the imbalanced multiclass data. Results show that the SWR is able to increase the average performance of the ensemble classifie

Improving the identification and classification of Malaysian medicinal leaf images using ensemble method

Malaysia has abundant natural resources especially plants which can be used for medicinal or herbal purposes. However, there is less research to preserve the knowledge of these resources to be utilized by the community in identifying useful medicinal plants using computing tools. This paper presents the implementation of digital opportunities for Malaysian medicinal plants via leaf image identification and classification. Of late, experts in traditional medicine and herbs have become few and the younger generation are mostly unknowledgeable about the medicinal and herbal properties of the plants. Therefore, this work is important in assisting the community (rural and urban) to identify and possibly share the knowledge of Malaysian medicinal plants with the future generation. The focus of this paper is to prepare the identification phase before the actual system is developed. Thus, the implementation of such a system is vital in order to enable the community to preserve these important resources

Indicator selection based on Rough Set Theory

A method for indicator selection is proposed in this paper.The method, which adopts the General Methodology and Design Research approach, consists of four steps: Problem Identification, Requirement Gathering, Indicator Extraction, and Evaluation. Rough Set approach also has been applied in the Indicator Extraction phase.This phase consists of 5 steps: Data selection, Data Preprocessing, Discretization, Split Data, Reduction, and Classification.A dataset of 427 records have been used for experimentation.The datasets which contains financial information from several companies consists of 30 dependant indicators and one independent indicator.The selection of indicators is based on rough set theory where sets of reducts are computed from a dataset.Based on the sets of reducts, indicators have been ranked and selected based on certain set of criteria.Indicators have been ranked through computation of frequencies in reduct sets.The major contribution of this work is the extraction method for identifying reduced indicators.Results obtained have shown competitive accuracies in classifying new cases, thus showing that the quality of knowledge is maintained through the use of a reduced set of indicators