Mokhairi Makhtar

Robotic Indoor Path Planning Using Dijkstra's Algorithm with Multi-Layer Dictionaries

Dijkstras algorithm is a classic algorithm for finding the shortest path between two points due to its optimisation capability. The adjacency matrix is the naive storage structure of the algorithm. This storage structure has limited the use of the algorithm as it expands large storage space. A multi-layer dictionary is proposed in this work to enhance the storage structure. Previously, the algorithm was used to optimise single parameter (such as distance, time and fuel) for movement between two places. The path computed using the classic Dijkstras algorithm is the shortest; however, it may not be the most feasible. The algorithm needs to optimise other factors such as energy consumption and the degree of turns a robot need to take. Junction degree of difficulty function is introduced to further improve the output. It is defined as the degree of difficulty of the path between two points in an open environment. The experimental result shows that the proposed path planning method produced the most optimal path between two points when applied to a map of any indoor terrain.

A New Customer Churn Prediction Approach Based on Soft Set Ensemble Pruning

Accurate customer churn prediction is vital in any business organization due to higher cost involved in getting new customers. In telecommunication businesses, companies have used various types of single classifiers to classify customer churn, but the classification accuracy is still relatively low. However, the classification accuracy can be improved by integrating decisions from multiple classifiers through an ensemble method. Despite having the ability of producing the highest classification accuracy, ensemble methods have suffered significantly from their large volume of base classifiers. Thus, in the previous work, we have proposed a novel soft set based method to prune the classifiers from heterogeneous ensemble committee and select the best subsets of the component classifiers prior to the combination process. The results of the previous study demonstrated the ability of our proposed soft set ensemble pruning to reduce a substantial number of classifiers and at the same time producing the highest prediction accuracy. In this paper, we extended our soft set ensemble pruning on the customer churn dataset. The results of this work have proven that our proposed method of soft set ensemble pruning is able to overcome one of the drawbacks of ensemble method. Ensemble pruning based on soft set theory not only reduce the number of members of the ensemble, but able to increase the prediction accuracy of customer churn.

Ant colony optimisation for solving university course timetabling problems

Course timetabling is one of the most important activities faced by any educational institution. Furthermore, the course timetabling process is time-consuming and tiresome as it needs to be prepared for each regular semester. This paper aims to apply the Ant Colony Optimisation (ACO) method to solve the course timetabling problem. This approach is to optimise the properties of the course requirement and minimise various conflicts for the time slot assignation. This method is based on the life of the ant colony in generating automatic timetabling according to the properties (pheromones) such as time, student, lecturer and room, besides satisfying the constraints. The implementation of this method is to find an effective and better solution for university course timetabling. The result and performance evaluation is used to determine whether it is reliable in providing the feasible timetable.

A Decision Tree Based Recommender System for Backpackers Accommodations

Backpackers often travel for a longer period of time, have their own budgets and requirements on accommodations. The existing systems do not offer personalized recommendation criteria and some proposed inefficient recommender system (RS) for users. Moreover, other than information searching from websites and bloggers, only limited systems were specifically designed for backpackers’ accommodations recommender system. An observation and online survey was conducted to get the information from backpackers regarding their preferences while looking for the accommodations. Fifty (50) respondents were involved in the survey and the data have been analyzed and were classified to build a decision tree. The decision tree model then implemented in the Backpackers’ accommodations Recommender System (BRS). BRS offers a convenient way and solution for backpackers by including decision tree technique in the system to suggest best accommodations suit to backpacker’s preferences.

Psychosocial Profiles among Persons with Substance Use Disorder Undergoing Islamic-Based Inabah Program in Kelantan, Malaysia

ABSTRACT Psychosocial profiles among a convenience sample of 37 Malay Muslim participants of Inabah program were measured using the Psychological Measure of Islamic Religiousness (PMIR). Data were analysed using SPSS 23.0. Positive relations with others emerged as the best-scored subscale whereas anger trait and depressed mood were minimal. Single and divorced respondents demonstrated significantly higher score for purpose in life. More favorable social desirability was reported by participants with no previous treatment. Less anger and depression were expressed by those not detained before. Essentially, psychosocial status of persons with substance use disorder undergoing Inabah program was moderate with some influences of sociodemographic factors.

Algorithms for extracting adjacency matrix based on formal concept analysis (FCA)

The desire to achieve a holistic representation of Information Retrieval (IR) with the aim for a human-oriented form of representation has spurred the growth of concept-based IR search techniques such as the Semantic Web technology. However, Semantic Web calls for the use of ontologies for many domains. Although meaningful and important, ontology development presents great challenges to the developers especially in terms of conceptual dynamics.. This paper is based on a study that attempts to provide an alternative to ontology lookup for Semantic information retrieval. However, the focus of the paper is on a method proposed to extract adjacency matrix from concepts obtained from the theory of Formal Concept Analysis (FCA) using two consecutive algorithms called the Relatedness Algorithm and Adjacency Matrix Algorithm. Consequently, the adjacency matrices obtained could be used in a similarity measure process based on graph theory. The proposed method offers an alternative to specific domain ontology look-up where results from the measure can further be used in concept-based IR process.

An Association Rule Mining Approach in Predicting Flood Areas

This study focuses on the application of Association rules mining for the flood data in Terengganu. Flood is one of the natural disasters that happens every year during the monsoon season and causes damage towards people, infrastructure and the environment. This paper aimed to find the correlation between water level and flood area in developing a model to predict flood. Malaysian Drainage and Irrigation Department supplied the dataset which were the flood area, water level and rainfall data. The association rules mining technique will generate the best rules from the dataset by using Apriori algorithm which had been applied to find the frequent itemsets. Consequently, by using the Apriori algorithm, it generated the 10 best rules with 100% confidence level and 40% minimum support after the candidate generation and pruning technique. The results of this research showed the usability of data mining in this field and can help to give early warning towards potential victims and spare some time in saving lives and properties.

The Reconstructed Heterogeneity to Enhance Ensemble Neural Network for Large Data

This paper present an enhanced approach for ensemble multi classifier of Artificial Neural Networks (ANN). The motivation of this study is to enhance the ANN capability and performance using reconstructed heterogeneous if the homogenous classifiers are deployed. The clusters set are partitioned into two sets of cluster; clusters of a same class and clusters of multi class which both of them were using different partition techniques. Each partitions represented by an independent classifier of highly correlated patterns from different classes. Each set of clusters are compared and the final decision is voted by using majority voting. The approach is tested on benchmark large dataset and small dataset. The results show that the proposed approach achieved almost near to 99% of accuracy which is better classification than the existing approach.

A Fuzzy TOPSIS with Z-Numbers Approach for Evaluation on Accident at the Construction Site

The construction industry has been identified as one of the most risky industries where involves fatalities accidents. Identifying the causes that lead to the accidents implicates a lot of uncertain and imprecise cases. Z-numbers involve more uncertainties than Fuzzy Sets (FSs). They provide us with additional degree of freedom to represent the uncertainty and fuzziness of the real situations. In this paper, we introduce a Fuzzy TOPSIS (FTOPSIS) with Z-numbers to handle uncertainty in the construction problems. Five criteria and six alternatives are used to evaluate the causes of workers’ accident at the construction sites. Data in form of linguistic variables were collected from three authorised personnel of three agencies. From the analysis, it shows that the FTOPSIS with Z-numbers provides us with an another useful way to handle Fuzzy Multi-Criteria Decision Making (FMCDM) problems in a more intelligent and flexible manner due to the fact that it uses Z-numbers with FTOPSIS.