Nor Shamsidah Amir Hamzah

Algebraic properties of finite switchboard state machine

A finite switchboard state machine is a specialized finite state machine. The algebraic approach of finite switchboard state machine is still lacking in literature. In this paper, we applied the algebraic approach of finite switchboard state machine and study its related properties. Real life examples of the applications are given by discussing the algebraic properties used.

The analysis of morphometric data on rocky mountain wolves and artic wolves using statistical method

Morphometrics is a quantitative analysis depending on the shape and size of several specimens. Morphometric quantitative analyses are commonly used to analyse fossil record, shape and size of specimens and others. The aim of the study is to find the differences between rocky mountain wolves and arctic wolves based on gender. The sample utilised secondary data which included seven variables as independent variables and two dependent variables. Statistical modelling was used in the analysis such was the analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA). The results showed there exist differentiating results between arctic wolves and rocky mountain wolves based on independent factors and gender.

FUZZIFICATION OF QUANTITATIVE DATA TO PREDICT TUMOUR SIZE OF COLORECTAL CANCER

Regression analysis has become more popular among researchers as a standard tool in analyzing data. This paper used fuzzy linear regression model (FLRM) to predict tumour size of colorectal cancer (CRC) data in Malaysia. 180 patients with colorectal cancer received treatment in hospital were recorded by nurses and doctors. Based on the patient records, a triangular fuzzy data will be built toward the size of the tumour. Mean square error (MSE) and root mean square error (RMSE) will be measured as a part of the process for predicting the size of the tumour. The degree of fitting adjusted is set between 0 and 1 in order to find the least error. It was found that the combination of FLRM model with fuzzy data provided a better prediction compared to the FLRM model alone. Hence, this study concluded that the tumour size is directly proportional to several factors such as gender, ethnic, icd 10, TNM staging, diabetes mellitus, Crohn’s disease, ulcerative colitis, polyp, history of cancer, endometrial, small bowel, hepatobiliary, urinary tract, ovarian, other cancer, colorectal, weight loss, diarrhea, blood stool and abdominal.