Two stages hybrid model of fuzzy linear regression with support vector machines for colorectal cancer

Fuzzy linear regression analysis has become popular among researchers and standard model in analyzing data in vagueness phenomena. However, the factor and symptoms to predict tumor size of colorectal cancer still ambiguous and not clear. The problem in using a linear regression will arise when uncertain data and not precise data were presented. Since the fuzzy set theory‟s concept can deal with data not to a precise point value (uncertainty data), fuzzy linear regression was applied. In this study, two new models for hybrid model namely the multiple linear regression clustering with support vector machine model (MLRCSVM) and fuzzy linear regression with symmetric parameter with support vector machine (FLRWSPCSVM) were proposed to analyze colorectal cancer data. Other than that, the parameter, error and explanation of the five procedures to both new models were included. These models applying five statistical models such as multiple linear regression, fuzzy linear regression, fuzzy linear regression with symmetric parameter, fuzzy linear regression with asymmetric parameter and support vector machine model. At first, the proposed models were applied to the 1000 simulated data. Furthermore, secondary data of 180 colorectal cancer patients who received treatment in general hospital with twenty five independent variables with different combination of variable types were considered to find the best models to predict the tumor size of CRC. The main objective of this study is to determine the best model to predicting the tumor size of CRC and to identify the factors and symptoms that contribute to the size of CRC. The comparisons among all the models were carried out to find the best model by using statistical measurements of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results showed that the FLRWSPCSVM was found to be the best model, having the lowest MSE, RMSE, MAE and MAPE value by 100.605, 10.030, 7.556 and 14.769. Hence, the size of colorectal cancer could be predicted by managing twenty five independent variables

A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction

This study proposed the new hybrid model of Multiple Linear Regression Clustering (MLRC) combined with Support Vector Machine (SVM) to predict tumor size of colorectal cancer (CRC). Three models: Multiple Linear Regression (MLR), MLRC and hybrid MLRC with SVM model were compared to get the best model in predicting tumor size of colorectal cancer using two measurement statistical errors. The proposed model of hybrid MLRC with SVM have found two significant clusters whereby, each clusters contained 15 and three significant variables for cluster 1 and 2, respectively. The experiments found that the proposed model tend to be the best model with least value of Mean Square Error (MSE) and Root Mean Square Error (RMSE). This finding has shed light to health practitioner in determining the factors that contribute to colorectal cancer

A time series analysis for sales of chicken based food product

This study provides a time series analysis and interpretation of the output for forecast sales of chicken based food product of weekly sales data. These data were collected directly from the outlet shop of one factory in Malacca started from January 2015 to December 2016. Methods of forecasting include autoregressive (AR) method and simple exponential smoothing (SES) method. The accuracy for both methods will be compared using mean squared error (MSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD). There will be 1 period ahead of predictions for AR method and 1 period ahead for SES method. This analysis found that AR method with AR (1) model is more accurate than SES method and can be used for the future prediction of chicken based food product of weekly sales data. Recommendations for future study is trying out other method to analyse this sales of chicken based food product and using R software to analyse the dataset

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

Analytic hierarchy process on criteria selection on the ideal partner among university students

. This study investigates the criteria of an ideal partner among university student by using Analytic Hierarchy Process (AHP) in Universiti Tun Hussein Onn Malaysia (UTHM). A questionnaire survey has been distributed to respondents in nine faculties in UTHM with the total of 11 criteria of an ideal woman and 11 criteria of an ideal man have been listed for respondents to rank based on their priorities. The objective of the study is to determine the priority criteria of man and woman that most students considered before getting marriage. Some comparison of selection criteria for an ideal partner between Malay and non-Malay has been analysed. Result shows the main priority criteria for an ideal man are religious followed by responsible, loyal, honest, loving, mature, hygiene, ambitious, rich, intelligent and good looking. Meanwhile, the main priority criteria for an ideal woman are religious followed by honest, motherly, patient, beautiful, polite, cooking, friendly, cheerful, intelligent and independent. The comparison between non-Malay present the main criteria of an ideal man and ideal woman are responsible and beautiful. One of the significant findings throughout the research was majority of the respondents agreed that religious become the main criteria in selecting a right partner and highly significant difference in selection criteria of an ideal partner between Malay and non-Malay

Application of fuzzy linear regression models for predicting tumor size of colorectal cancer in Malaysia's Hospital

Fuzzy linear regression analysis has become popular among researchers and standard model in analysing data vagueness phenomena. These models were represented by five statistical models such as multiple linear regression, fuzzy linear regression (Tanaka), fuzzy linear regression (Ni), extended fuzzy linear regression under benchmarking model (Chung) and fuzzy linear regression with symmetric parameter (Zolfaghari). A case study in colorectal cancer (CRC) data at the general hospital in Kuala Lumpur was carried out using the five models as mention above. Secondary data of 180 colorectal cancer patients who received treatment in general hospital were recorded by nurses and doctors. Twenty five independent variables with different combination of variable types were considered to find the best models to predict the size of tumor colorectal cancer. The quality of life among CRC patients which is to detect the early CRC stage is still very poor, not implemented and divulged as a nationwide programme. The main objective of this study is to determine the best model by predicting the size of tumor of CRC. Moreover, this study wants to identify the factors and symptoms that contribute the size of tumor. The comparisons among the five models were carried out to find the best model by using statistical measurements of mean square error (MSE) and root mean square error (RMSE). The results showed that the fuzzy linear regression with symmetric parameter (Zolfaghari) was found to be the best model, having the lowest MSE and RMSE value by 98.21 and 9.91. Hence, the size of tumor could be predicted by managing twenty five independent variables

The Effect of Work Environment on Employee Productivity: A Case Study of Manufacturing Company

The work environment plays an important role in employee productivity. Due to the work environment is a set of relationships that exist between employees and the environment where they work. In addition, we can see that most of the common problems that affect the work environment and productivity of employees in manufacturing companies in Batu Pahat is that the workplace perspective of most companies is unhealthy and dangerous. Therefore, this study focuses on the effect of the work environment on employee productivity in a Batu Pahat manufacturing company. In this study, the researcher aims to identify the effect of the work environment on employee productivity in a Batu Pahat manufacturing company. Quantitative methods are used in this research. Questionnaires were distributed to 384 respondents in manufacturing companies in Batu Pahat. The response rate was 70.31% and data was collected and analyzed using the Statistical Package for Social Science (SPSS). The results of the descriptive analysis show that working conditions are the main effect of employee productivity in manufacturing companies in Batu Pahat. The physical work environment, working conditions and workplace layout have a significant relationship between employee productivity. This research will help research to know more about the effects of the work environment. This finding may be a reference for companies in manufacturing to know the work environment that can affect employee productivity

Time series analysis on mackerel (scombridae) landings in Malaysia

Mackerel fish is one type of pelagic fish that live in the surface of the ocean. It is also have benefits in terms of protein which also has high demand in Asian and others countries and helps gaining profits in fisheries industries. This study aims to predict mackerel landings in Malaysia in one year advance which is 2018. The data of 132 monthly of mackerel landings from year 2007 until 2017 is used to make a prediction of mackerels landing by using four methods which are Seasonal Autoregressive Integrated Moving Average (SARIMA) method, Multiplicative Holt- Winters, Additive Holt-Winters Method and Simple Exponential Smoothing method. The aim is to compare the performance among four methods by measuring the accuracy of each method. The result shows that Additive Holt-Winters method is the best method used to forecast mackerel landings in 2018 with the lowest value of Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE). In conclusion, the potential result from this study could be used by fish farmers in their annual planning of supplying fish in Malaysia

The effects of industrial training on students’ generic skills development

This conceptual paper reviews the underpinning theories and past literatures on generic skills in relation to industrial training attended by students in higher learning institutions. Subsequently, a number of hypotheses and a framework of generic skills development are proposed. Four generic skills discussed are communication skill, teamwork skill, critical thinking and problem solving and moral and professional ethics. The paper proposes that the development of these generic skills is influenced by or have some relationships with students’ demographic and motivation, as well as organizational characteristics and culture. The methodology of conducting this study is also illustrated

Comparative study of indoor air contaminants in different stages of new building occupancy: work environment assessment

Indoor Air Quality (IAQ) is a part of Building Environment. Nowadays, the construction of new building took place over the world. Upon new building occupancy, a lot of indoors material was used without IAQ concern. This study has been conducted in a new constructed building of the National Institute of Occupational Safety and Health (NIOSH) Malaysia. The goal of the study is to monitor on the level of IAQ parameters including chemical and physical parameters within four consequent stages which are before furniture install, after furniture install and during one and three month occupancy. The indoor parameters have been measured consist of nine parameters including of Carbon Dioxide (CO2), Carbon Monoxide (CO), Total Volatile Organic Compounds (TVOC), Formaldehyde, Respirable Particulates (PM10), Ozone, Relative Humidity (RH), Temperature and Air Movement. The interaction between Malaysian and international standard was referred and utilized in collecting the data and analyzing of the findings. There was a significant correlation between the high values of RH, Formaldehyde and PM10 where (r 0.324, p <0.05), (r 0.344, p <0.05) and (r 0.319, p<0.05) with extension of phases of new building occupancy respectively. This study established significant different on Formaldehyde and Particulate Matter (PM10) concentration level as go along with the building occupancy. These finding indicated that furniture and fittings, indoor materials and human population has a potential sources of indoor air contaminants. It is recommended that the management should be aware to their indoor air status to protect the occupant from the risk of unwanted exposure especially during the early stage of building occupancy. Finally this research has fully supported the Malaysian need to formulate of future guideline on IAQ commissioning and maintenance of new building occupancy