Data Science Project I and Data Science Project II Presentation Day

On 1st – 2nd February 2023, there are 60 students from the programme of Bachelor of Applied Science in Data Analytics with Honours who have been successfully presented their Data Science Project I

The Study Of Cement Properties With Ceramic Waste Fillers

Due to a continuously expanding global population and the desire to satisfy consumer demands, landfills will continue to receive vast quantities of waste. Changing a substantial quantity of solid waste into an alternative resource can assist conserve diminishing non-renewable material supplies, sustain essential energy, and alleviate environmental and landfill issues. Ceramic waste (CW) has the potential to be employed as an effective supplementary cementitious material (SCM) in cement-based materials due to its high silica-alumina content. Utilizing CW as an alternative concrete ingredient will positively affect the environment. However, there is still a paucity of knowledge about using CW as a raw material in the production of eco-friendly cement. The aim of the research is to characterize the ceramic sample to determine its suitability as partial replacement in Ordinary Portland Cement (OPC) and to figure out the best conditions for synthesizing an eco-friendly cement using CW. The OPC and CW will be characterised using Scanning Electron Microscopy (SEM), X-Ray Fluorescence (XRF), X-Ray Diffraction (XRD) and Particle Size Analysis (PSA) investigations preliminary to sample preparation. The eco-friendly cement is then sintered at various temperatures after being produced to a specific mineralogical composition. The gypsum addition will be added to cement mixtures to delay hydration. Analytical procedures such as XRF, SEM, and PSA will be used to characterise the final sample. Because the primary compounds were fully reacted and hydraulic active chemicals predominated in the products, 1100°C was revealed to be the ideal sintering temperature for eco-friendly cement. These findings were backed up by XRD, XRF, and SEM analysis. As a result, study reveals that ceramic waste can be used as raw materials to make ecologically friendly cements

Graphical Summaries of Circular Data with Outliers Using Python Programming Language

Graph in statistics is used to summarise and visualise the data in pictorial form. Graphical summary enables us to visualise the data in a more simple and meaningful way so that the interpretation will be easier to understand. The graphical summaries of circular data with outliers is discussed in this study. Most of the time, people use linear data in real life applications. Other than linear data, there is another data type that has a direction which refers to circular data and it is different from linear data in many aspects such as in descriptive statistics and statistical modeling. Unfortunately, the availability of statistical software specialises in analysing circular data is very limited. In this study, the graphical summaries of circular data are plotted using the in-demand programming language, Python. The Python code for generating graphical summaries of circular data such as circular dot plot and rose diagram is proposed. The historical circular data is used to illustrate the graphical summaries with the existence of outliers. This study will be helpful for those who are started exploring circular data and choose Python as an analysis tool

A synthetic data generation procedure for univariate circular data with various outliers scenarios using Python programming language

Synthetic data is artificial data that is created based on the statistical properties of the original data. The aim of this study is to generate a synthetic or simulated data for univariate circular data that follow von Mises (VM) distribution with various outliers scenario using Python programming language. The procedure of formulation a synthetic data generation is proposed in this study. The synthetic data is generated from various combinations of seven sample size, n and five concentration parameters, K. Moreover, a synthetic data will be generated by formulating a data generation procedure with different condition of outliers scenarios. Three outliers scenarios are proposed in this study to introduce the outliers in synthetic dataset by placing them away from inliers at a specific distance. The number of outliers planted in the dataset are fixed with three outliers. The synthetic data is randomly generated by using Python library and package which are 'numpy', 'random' and von Mises'. In conclusion, the synthetic data of univariate circular data from von Mises distribution is generated and the outliers are successfully introduced in the dataset with three outliers scenarios using Python. This study will be valuable for those who are interested to study univariate circular data with outliers and choose Python as an analysis tool

The effect of different similarity distance measures in detecting outliers using single-linkage clustering algorithm for univariate circular biological data

Clustering algorithms can be used to create an outlier detection procedure in univariate circular data. The circular distance between each point of angular observation in circular data is used to calculate the similarity measure to appropriately group observations. In this paper, we present a clustering-based procedure for detecting outliers in univariate circular biological data using various similarity distance measures. Three circular similarity distance measures; Satari distance, Di distance and Chang-chien distance were used to detect outliers using a single-linkage clustering algorithm. Satari distance and Di distance are two similarity measures that have similar formulas for univariate circular data. This study aims to develop and demonstrate the effectiveness of the proposed clustering-based procedure with various similarity distance measures in detecting outliers. The circular similarity distance of SL-Satari/Di and other similarity measures, including SL-Chang, were compared at various dendrogram cutting points. It is found that a clustering-based procedure using a single-linkage algorithm with various similarity distances is a practical and promising approach to detect outliers in univariate circular data, particularly for biological data. According to the results, the SL-Satari/Di distance outperformed the SL-Chang distance for certain data conditions

Review on circular-linear regression models

Classical linear statistics method is no longer appropriate when handling circular data since the data is influenced by direction or angle. Considering the possibility of circular data appeared as dependent variable, it has resulted in the remodeling of classic linear regression model into circular-linear regression model over the past few decades. It is important to acknowledge these circular data characteristics as it can affect the descriptive and inference of statistical analysis. With the growing body of literature regarding this issue, this paper will review on circular-linear regression model by highlighting and exploring their benefits and limitations

Determination of the best single imputation algorithm for missing rainfall data treatment

The presence of missing rainfall data is inevitable due to error of recording, meteorological extremes and malfunction of instruments. Consequently, a competent imputation algorithm for missing data treatment algorithm is very much needed. There are several such efficient algorithms which have been introduced in earlier studies. However, the limitations of current algorithms are they are highly dependent on the information and homogeneity of adjoining rainfall stations. Therefore, this study is intended to introduce several single imputation algorithms for missing data treatment, which believed to be more competent in treating missing daily rainfall data without the need to depend on the information of adjoining rainfall stations. The proposed algorithms use descriptive measures of the data, including arithmetric means, geometric means, harmonic means, medians and midranges. These algorithms are tested on hourly rainfall data records from six selected rainfall stations located in the Kuantan River Basin. Based on the analysis, the proposed singular imputation algorithms, which treated missing data by geometric means, harmonic means and medians are more superior compared to the other imputation algorithms, irrespective of missing rates and rainfall stations

Kernel Estimation in Line Transect Sampling for Parametric Model

Line transect sampling is a common method used in ecology to sampling the sample required. It is an important procedure for estimating the population density of objects in interested study area. There are mainly two ways to estimate the population density which a parametric and nonparametric estimation methods. In this paper, we present kernel estimation method to estimate new estimator of the propose population density. Kernel estimation method is used due to avoid the assumption about the shape of the unknown detectable functions. We investigate the performance of the new estimator using simulation study and compared with the existing estimators. Based on the simulation study, the results show that the proposed estimator preforms better than other well-known estimator

Scrutinizing the rigorousness of government interventions in addressing homelessness in Malaysia / Noor Amira Syazwani Abd Rahman...[et. al.]

Homelessness remains a world problem, although a majority of homeless groups survive only in modernized cultures. The occurrence of homelessness is becoming a social problem in Malaysia, especially in city areas such as Kuala Lumpur, Penang and Johor Baharu. It is believed that this group of people has less received much concentration since the current social policies have no direct bearing to the homeless. Hence, this paper analyzes the rigorousness of government interventions in addressing the homelessness in Malaysia. This paper reviews relevant literatures pertaining to programs carried out by the government in helping homeless via preliminary reports, observation and interviews that have been written in previous research. It is hoped that the study will grant to the existing body of knowledge related to homelessness study

Parameter estimate for three-parameter kappa distribution using LH-moments approach

The method of higher-order L-moments (LH-moment) was proposed as a more robust alternative compared to classical L-moments to characterize extreme events. The new derivation will be done for Mielke-Johnson's Kappa and Three-Parameters Kappa Type-II (K3D-II) distributions based on the LHmoments approach. The data of maximum monthly rainfall for Embong station in Terengganu were used as a case study. The analyses were conducted using the classical L-moments method with η = 0 and LHmoments methods with η = 1, η = 2, η = 3 and η = 4 for a complete data series and upper parts of the distributions. The most suitable distributions were determined based on the Mean Absolute Deviation Index (MADI), Mean Square Deviation Index (MSDI), and Correlation (r). Also, L-moment and LHmoment ratio diagrams were used to represent visual proofs of the results. The analysis showed that LH-moments methods at a higher order of K3D-II distribution best fit the data of maximum monthly rainfalls for the Embong station for the upper parts of the distribution compared to L-moments. The results also proved that whenever η increases, LH-moments reflect more and more characteristics of the upper part of the distribution. This seems to suggest that LH-moments estimates for the upper part of the distribution events are superior to L-moments in fitting the data of maximum monthly rainfalls