National University of Malaysia

About: National University of Malaysia is a education organization based out in Kuala Lumpur, Malaysia. It is known for research contribution in the topics: Population & Heat transfer. The organization has 26593 authors who have published 41270 publications receiving 552683 citations. The organization is also known as: NUM & Universiti Kebangsaan Malaysia.

Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)

Abstract: Reservoir planning and management are critical to the development of the hydrological field and necessary to Integrated Water Resources Management. The growth of forecasting models has resulted in an excellent model known as the Support Vector Machine (SVM). This model uses linearly separable patterns based on an optimal hyperplane, which are extended to non-linearly separable patterns by transforming the raw data to map into a new space. SVM can find a global optimal solution equipped with Kernel functions. These Kernel functions have high flexibility in the forecasting computation, enabling data to be mapped at a higher and infinite-dimensional space in an implicit manner. This paper presents a new solution to the expert system, using SVM to forecast the daily dam water level of the Klang gate. Four categories are identified to determine the best model: the input scenario, the type of SVM regression, the number of V-fold cross-validation and the time lag. The best input scenario employs both the rainfall R(t-i) and the dam water level L(t-i). Type 2 SVM regression is selected as the best regression type, and 5-fold cross-validation produces the most accurate results. The results are compared with those obtained using ANFIS: all the RMSE, MAE and MAPE values prove that SVM is a superior model to ANFIS. Finally, all the results are combined to determine the best time lag, resulting in R(t-2) L(t-2) for the best model with only 1.64 % error.

Application of artificial neural networks for water quality prediction

Abstract: The term “water quality” is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.

Implementation of waste management and minimisation in the construction industry of Malaysia

Abstract: Construction waste has a major impact on the environment. With the demands in implementing major infrastructure projects in Malaysia, together with many commercial building and housing development programmes, a large amount of construction waste is being produced by the construction sector. Thus, waste minimisation is an important area of concern in the implementation of the construction waste management in the construction industry of Malaysia. This study identified 12 waste minimisation factors (WMF) in implementing construction waste management in the construction industry. This study provides empirical evidence on the significant level of contribution and the level of practice among the waste minimisation factors by following the model of weighted average and minimisation and practiced index value. The results of the analysis indicate the most significant, least significant and significant factors that contribute to waste minimisation and the most practiced, least practiced and practiced waste minimisation factors in the Malaysian construction industry. The findings will assist in the formulation of appropriate policy interventions in addressing the construction waste management problem in Malaysia and indirectly improving the quality of construction in the country.

A randomized double-blind placebo-controlled trial of probiotics in post-surgical colorectal cancer

Abstract: Our study aimed to determine the effect of probiotic consumption containing six viable microorganisms of 30 × 1010 cfu Lactobacillus and Bifidobacteria strains for six months on clinical outcomes and inflammatory cytokines (TNF-α, IFN-γ, IL-6, IL-10, IL-12, IL-17A, IL-17C and IL-22) in patients with colorectal cancer. Fifty-two patients with colorectal cancer were randomized at four weeks after surgery to receive either a placebo (n = 25) or 30 billion colony-forming unit (CFU) of a mixture of six viable strains including 107 mg of Lactobacillus acidophilus BCMC® 12,130, Lactobacillus lactis BCMC® 12,451, Lactobacillus casei subsp BCMC® 12,313, Bifidobacterium longum BCMC® 02120, Bifidobacterium bifidum BCMC® 02290 and Bifidobacterium infantis BCMC® 02129 (n = 27). Patients were instructed to take the product orally twice daily for six months. Infection status, diarrhea or hospital admission were recorded throughout the study. Blood was taken pre- and post-intervention to measure TNF-α, IFN-γ, IL-6, IL-10, IL-12, IL-17A, IL-17C and IL-22 using ELISA multiplex kit. The majority of cases (~ 70%) were in Duke’s C colorectal cancer for both groups. No surgical infection occurred and no antibiotics were required. Chemotherapy induced diarrhea was observed in both groups. Significant reduction in the level of pro-inflammatory cytokine, TNF-α, IL-6, IL-10, IL-12, IL-17A, IL-17C and IL-22 were observed in CRC patients who received probiotics as compared to pre-treatment level (P < 0.05). However, there was no significant difference in the IFN-γ in both groups. We have shown that probiotics containing six viable microorganisms of Lactobacillus and Bifidobacteria strains are safe to be consumed at four weeks after surgery in colorectal cancer patients and have reduced pro-inflammatory cytokines (except for IFN-gamma). Probiotic may modify intestinal microenvironment resulting in a decline in pro-inflammatory cytokines. NCT03782428; retrospectively registered on 20th December 2018.