Rozlini Mohamed

Logical method for logical operations based on evidential reasoning

The purpose of this paper is to employ Dempster-Shafer theory in problems which are defined in hierarchical and logical structures such as a fault tree. One of main differences of the Dempster-Shafer theory from Bayesian one is that the Dempster-Shafer method enables us to take the lack of knowledge or information into consideration in its analyses. A focal set of hypotheses must be mutual exclusive and exhaustive in the Dempster-Shafer theory. In this paper, we define state patterns in order to satisfy the mutual exclusiveness and exhaustiveness. The method using state patterns requires us much computational time due to the huge number of combinatorials to solve the evidential reasoning. In order to resolve this issue, this paper provides the improved method based on logical relations in the hierarchical structure.

An evidential reasoning based LSA approach to document classification for knowledge acquisition

Web is one of major information sources. Failure in proper management of knowledge leads to incorrect results returned by search engines. Therefore, the web should have an effective information retrieval system to improve the correctness of retrieval results. This study provides a method to assign a new document to the fittest category out of predefined categories, where latent semantic analysis (LSA) is used to evaluate each term in documents, the similarity between terms and documents as well as the one between terms and categories. The objective of our method is to fuse evidential reasoning method with LSA which can assign a new document to a predefined category. The method provides better results in performance of classification comparing to the fusion of an evidential reasoning approach with term frequency inverse document frequency (TFIDF).

Benchmark of feature selection techniques with machine learning algorithms for cancer datasets

Classification is a technique based on machine learning used to classify each item in a set of data into a set of predefined classes or group. It is widely used in medical field to classify the medical data. In producing better classification result, feature selection been applied in many of the classification work as part of preprocessing step, where a subset of feature been used rather than the whole features from particular dataset. Feature selection eliminates irrelevant attribute to obtain high quality features that may contribute in enhancing classification process and producing better classification results. This study is conducted with the intention to focus on feature selection techniques as a method that helps classifiers producing better classification performance with the most significant features. During the experiments, a comparison between benchmark feature selection methods based on three cancer datasets and four well recognized machine learning algorithms has been made. This paper then analyzes the performance of all classifiers with and without feature selection in term of ROC and F-Measure. The study found that although there are no single feature selection method can satisfy all datasets, the results still effectively support the fact that feature selection helps in increasing the classifier performance with existence of minimum number of features.

A Framework to Cluster Temporal Data Using Personalised Modelling Approach

This research paper is focused on the framework design of temporal data by using personalised modelling approach in order to cluster the temporal data. Real world problem on flood occurrences is used as a case study focusing only in Malaysia region. The data are designed according to the criteria needed for temporal data clustering, tested with three clustering techniques including K-means, X-means, and K-medoids. Rapid Miner is used for conducting the clustering processes. Finally, the result from each clustering method is compared to conclude and justify the best clustering approach for clustering temporal data.

M-DCocoa: M-Agriculture Expert System for Diagnosing Cocoa Plant Diseases

Major technological advancements were experienced including mobile applications in the various domain. The advancement in mobile applications not only used for our daily life and chores but it leads to more specific and technical purposes such as in medical, engineering, agriculture and education domain. This paper aims to study the implementation of mobile systems in agriculture and proposes a development of M-Agriculture that help in diagnosing cocoa plant diseases named as M-DCocoa. This application enables a user to recognize cocoa diseases afflict by the plant and provide user appropriate advice or treatments in shorter time period. The user will answer the questions based on cocoa plant condition or symptoms and the application generates the answer in form of disease and treatments. A rule-based and forward chaining inference engine has been used as part of the system development. With this application, it helps and allows the user to recognize cocoa diseases with useful treatments suggestion.

The effectiveness of Bat algorithm for data handling in various applications

Feature selection is a technique used to reduce irrelevant data and finding the most relevant features that would increase classification accuracy. It is widely used in various applications such as medical, agriculture and Information Technology. In producing better classification result, feature selection been applied in many of the classification works as part of preprocessing step; where only a subset of feature been used rather than the whole features from a particular dataset. This research is conducted with the intention to find the appropriate data types according to the percentage of attributes reduction and classification performance. During the experiments, the effectiveness of data handling for Bat algorithm is tested via type of data and size of attributes in generic dataset. 10 datasets from UCI repository from various applications are used. The selected features are selected using Bat algorithm and measured by three classifiers; k-Nearest Neighbor (kNN), Naïve Bayes (NB) and Decision Tree (DT). This paper then analyzes the performance of all classifiers with and without feature selection in term of accuracy, sensitivity, F-Measure and ROC. The research found that although the percentage of reduction is high, it produces lowest result in classification performance since the type of data and number of attribute are not appropriate.

Adoption of hierarchical structure for web document analysis in knowledge management system

The objective of this paper is to analyze a web structure by means of using evidential reasoning to logical hierarchy structure. During the searching on the web, the search engine will return a set of web documents. But some web documents do not fit what we are looking for. The targeted documents are called relevant document, and the rests are irrelevant documents. Our focus is placed on the web document structure and link analysis. The web documents are grouped in an appropriate label and organized in logical hierarchy structure. The theorems proposed by Watada will employed to analyze the value of concepts or events in logical hierarchy structure according to belief and plausibility functions. From these values “influence events” can be determining when an irrelevant document is included in the web document about Tourism Management.