Ranking Causes of Road Accident Occurrence Using Extended Interval Type-2 Fuzzy TOPSIS

Over the past century there has been a dramatic increase in the number of road accidents in Malaysia. Hence, it is necessary to create a decision making method which can consider various preferences and criteria in order to identify the main causes of the accidents. This paper proposes an Interval Type-2 Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IT2FTOPSIS) method which provides a comprehensive valuation from experts. This method is developed based on the aggregation of experts’ opinions on preferred causes of road accidents. The extended IT2FTOPSIS employs a linguistic scales of positive and negative Interval Type-2 Trapezoidal Fuzzy Number (IT2TrFN) and hybrid averaging approach (from an ambiguity and type-reduction methods) to formulate a collective decision environment. Three authorised personnel from three Malaysian Government agencies were interviewed where they were asked to rank the causes. The analysis shows that the linguistic scales of positive and negative Interval Type-2 Trapezoidal Fuzzy Number (IT2TrFN) and hybrid averaging approach are effective in measuring the uncertainties in the interviewees’ responses. Thus this paper concludes that the extended IT2FTOPSIS is more aligned with the users’ decisions compared to the earlier IT2FTOPSIS. Keywords: Multiple criteria decision-making; interval type-2 fuzzy set; IT2FTOPSIS; road accident

A Survey on Building Safety after Completing the Construction Process in Malaysia Using Statistical Approach

Building condition is an important issue in all over the world to enhance safety, health and sustainability of built environment. The objective of this study is to determine the most frequent causes of building failures in order to avoid the building from collapses, cracks and so on. The collection of data has been done among the engineers, workers and public. The questionnaire was distributed among engineers, contractors and public with 100 respondents. This survey focuses on two main parts of the safety which are building design and building management. The building designs are divided into four main criteria which are building structure, service design, building fitting and hazard environment. Meanwhile, the item of building management is focused on the management criteria. Results are analysed using statistical approach. Structural equation modeling (SEM) is used to evaluate the efficiency of the models’ fitness and goodness. The survey shows that all criteria are importantly needed in maintaining the safety of building after completing the contraction process

Assessing the Level of Competence in Automated Trading Among Malaysian Traders

 In today's technology world, financial trading instrument such as stock, currencies, futures, and acts is now done electronically via the internet due to technological improvements. Financial market trading traders apply technical and fundamental analyses to forecast the best price when buying or selling the finance instrument, and they develop their trading tactics and strategies with technical analysis tools. The objective of this research is to figure out the level of competence that Malaysian retail traders have in Automated or Algorithmic Trading (AT) focusing on currency trading. The searches were conducted on a sample of Malaysian retail traders. The viewpoints of retail traders were investigated using a questionnaire designed specifically for this purpose. The data was analyzed using statistical software (SPSS). The findings of this study reveal that Malaysian traders have an excellent comprehension of AT. This study is useful for traders and researchers who want to design their own AT systems in the future

New Implementation of Residual Power Series for Solving Fuzzy Fractional Riccati Equation

This paper reveals a computational method using a Residual Power Series Method (RPSM) for the solution of fuzzy fractional riccati equation under caputo fractional differentiability. An analytical solution of fuzzy fractional riccati equation is obtained as a convergent fractional power series. The procedure produces solutions of high accuracy, and some illustrative examples are solved with a different value of orders to show the efficiency of the RPSM

Achieving Efficient Decision Making Through Hybrid Reduction in Soft Set Theory

The main intention of proposing an alternative technique is to ensure consistency is been upheld besides successfully reducing the file. Of all the reduction techniques available currently, only normal parameter reduction has managed to address the issue of consistency at optimal and suboptimal level. In this paper, we initiated another form of reduction known as hybrid reduction by complementing the normal parameter reduction with object reduction. It has already demonstrated that the proposed hybrid reduction has successfully reduced data by 55% with the sample used, thus proving that it as a good alternative for the process of decision making using less amount of data

An analysis of finding the best strategies of water security for water source areas using an integrated IT2FVIKOR with machine learning

Worldwide, water security is adversely affected by factors such as population growth, rural–urban migration, climate, hydrological conditions, over-abstraction of groundwater, and increased per-capita water use. Water security modeling is one of the key strategies to better manage water safety and develop appropriate policies to improve security. In view of the growing global demand for safe water, intelligent methods and algorithms must be developed. Therefore, this paper proposes an integrated interval type-2 Fuzzy VIseKriterijumska Optimizcija I Kompromisno Resenje (IT2FVIKOR) with unsupervised machine learning (ML). This includes IT2FVIKOR for ranking and selecting a set of alternatives. Unsupervised machine learning includes hierarchical clustering, self-organizing map, and autoencoder for clustering, silhouette analysis and elbow method to find the most optimal cluster count, and finally Adjusted Rank Index (ARI) to find the best comparison within two clusters. This proposed integrated method can be divided into a two-phase fuzzy-machine learning-based framework to select the best water security strategies and categorize the polluted area using the water datasets from the Terengganu River, one of Malaysia’s rivers. Phase 1 focuses on the IT2FVIKOR method to select five different strategies with five different criteria using five decision makers for finding the best water security strategies. Phase 2 continues the unsupervised machine learning where three different clustering algorithms, namely, hierarchical clustering, self-organizing map, and autoencoder, are used to cluster the polluted area in the Terengganu River. Silhouette analysis is applied along with the clustering algorithms to estimate the number of optimal clusters in a dataset. Then, the ARI is applied to find the best comparison within the original data with hierarchical clustering, self-organizing map, and autoencoder. Next, the elbow method is applied to double-confirm the best clusters for each clustering algorithm. Last, lists of polluted areas in each cluster are retrieved. Finally, this 2-phase fuzzy-Machine learning–based framework offers an alternative intelligent model to solve the water security problems and find the most polluted area

River quality classification using different distances in k-nearest neighbors algorithm

The practice of river quality classification usually uses Water Quality Index (WQI) to evaluate the WQI values of the river. However, due to huge data collection on river pollution with uncertain water quality parameter values, need to a different approach to classify the river quality. One of the supervised classification algorithms known as K-Nearest Neighbors (KNN) seems to give new approach for river quality classification where each data points are classified according to the k number or the closest data points neighbors. Therefore, the purpose of this paper is to apply different distances and distance-weighted in KNN for finding the most accurate river quality classification. The accuracy results are compared with Support Vector Machine (SVM) and Decision Tree (DT) algorithms. This KNN algorithm will give a different approach in classify the river quality

A comparison of unsupervised and supervised machine learning algorithms to predict water pollutions

Clean and safe water is vital for our lives and public health. In recent decades, population growth, agriculture, industries, and climate change have worsened freshwater resource depletion and clean water pollution. Several studies have focused on water pollutions risk simulation and prediction in the presence of pollution hotspots. However, the increase and complexity of big data caused by uncertain water quality parameters led to a new efficient algorithm to trace the most accurate pollution hotspots. Therefore, this study proposes to offer different algorithms and comparative studies using Machine Learning (ML) algorithms. Ten different most widely used algorithms, including unsupervised and supervised ML, will be employed to categorize the pollution hotspots for the Terengganu River. Besides, we also validate algorithms' accuracies by improving and changing each parameter in ML algorithms. Our results list all the accurate and efficient ML algorithms for the classification of river pollutions. These results help to facilitate river prediction using efficient and accurate algorithms in various water quality scenario

An extended Interval Type-2 Fuzzy VIKOR technique with equitable linguistic scales and Z-Numbers for solving water security problems in Malaysia

Interval Type-2 Fuzzy VIseKriterijumska Optimizacija I Kompromisno Resenje (IT2FVIKOR) technique is one of the techniques of Interval Type-2 Fuzzy Multi-Criteria Decision Making (IT2FMCDM), which was developed to solve problems involving conflicting and multiple objectives. Most of the IT2FVIKOR methods are created from linguistic variables based on Interval Type-2 Fuzzy Set (IT2FS) and its generalization, such as Interval Type-2 Fuzzy Numbers (IT2FNs). Recent literature suggests that equitable linguistic scales can offer a better alternative, particularly when IT2FSs have some limitations in handling uncertainty and imbalance. This paper proposes the extended IT2FVIKOR with an equitable linguistic scale and Z-Numbers, where its linguistic scale introduces an equitable balance of positive and negative scales added to the restriction and reliability approach. Different from the typical IT2FVIKOR, which directly utilizes IT2FNs with a positive membership, the proposed method introduces positive and negative membership where each side considers a restriction and reliability approach. Besides, this paper also offers objective weights using fuzzy entropy-based IT2FS to calculate the weights of the extended IT2FVIKOR. The obtained solutions would help decision makers (DMs) identify the best solution to enhance water security projects in terms of finding the best strategies for water supply security in Malaysia

Ranking of the Factors Associated with Road Accidents using Correlation Analysis and Fuzzy TOPSIS

Abstract Road accident is one of the major causes of death and injuries in Malaysia. The increase of road accidents is said to be associated with the factors of rapid growth in population, economic development, and motorization. However no specific literature was found to specify weights and subsequently rank the factors. This paper proposes a ranking of three selected factors associated with road accidents using the correlation analysis and Multi Criteria Decision Making, fuzzy TOPSIS. Statistical accident data issued by Royal Malaysian Police and linguistic judgement data collected from three authorised personnel of three Malaysian Government agencies were considered in analysis. The ranks ane be drawn using the strength of correlation coefficients and the magnitude of closeness coefficients in fuzzy TOPSIS. The results from two analyses indicate that registered vehicles yielded the highest ranking followed by population and road length. This ranking gives rise to concerns about the relevance of the factors in reducing accidents rate