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Articles by Z. Ismail
Total Records ( 12 ) for Z. Ismail
  Z. Ismail , A. Yahya and A. Shabri
  Problem statement: Forecasting is a function in management to assist decision making. It is also described as the process of estimation in unknown future situations. In a more general term it is commonly known as prediction which refers to estimation of time series or longitudinal type data. Gold is a precious yellow commodity once used as money. It was made illegal in USA 41 years ago, but is now once again accepted as a potential currency. The demand for this commodity is on the rise. Approach: Objective of this study was to develop a forecasting model for predicting gold prices based on economic factors such as inflation, currency price movements and others. Following the melt-down of US dollars, investors are putting their money into gold because gold plays an important role as a stabilizing influence for investment portfolios. Due to the increase in demand for gold in Malaysian and other parts of the world, it is necessary to develop a model that reflects the structure and pattern of gold market and forecast movement of gold price. The most appropriate approach to the understanding of gold prices is the Multiple Linear Regression (MLR) model. MLR is a study on the relationship between a single dependent variable and one or more independent variables, as this case with gold price as the single dependent variable. The fitted model of MLR will be used to predict the future gold prices. A naive model known as "forecast-1" was considered to be a benchmark model in order to evaluate the performance of the model. Results: Many factors determine the price of gold and based on "a hunch of experts", several economic factors had been identified to have influence on the gold prices. Variables such as Commodity Research Bureau future index (CRB); USD/Euro Foreign Exchange Rate (EUROUSD); Inflation rate (INF); Money Supply (M1); New York Stock Exchange (NYSE); Standard and Poor 500 (SPX); Treasury Bill (T-BILL) and US Dollar index (USDX) were considered to have influence on the prices. Parameter estimations for the MLR were carried out using Statistical Packages for Social Science package (SPSS) with Mean Square Error (MSE) as the fitness function to determine the forecast accuracy. Conclusion: Two models were considered. The first model considered all possible independent variables. The model appeared to be useful for predicting the price of gold with 85.2% of sample variations in monthly gold prices explained by the model. The second model considered the following four independent variables the (CRB lagged one), (EUROUSD lagged one), (INF lagged two) and (M1 lagged two) to be significant. In terms of prediction, the second model achieved high level of predictive accuracy. The amount of variance explained was about 70% and the regression coefficients also provide a means of assessing the relative importance of individual variables in the overall prediction of gold price.
  Z. Ismail , A. Yahya and K.A. Mahpol
  Problem statement: Forecasting of electricity load demand is an essential activity and an important function in power system planning and development. It is a prerequisite to power system expansion planning as the world of electricity is dominated by substantial lead times between decision making and its implementation. The importance of demand forecasting needs to be emphasized at all level as the consequences of under or over forecasting the demand are serious and will affect all stakeholders in the electricity supply industry. Approach: If under estimated, the result is serious since plant installation cannot easily be advanced, this will affect the economy, business, loss of time and image. If over estimated, the financial penalty for excess capacity (i.e., over-estimated and wasting of resources). Therefore this study aimed to develop new forecasting model for forecasting electricity load demand which will minimize the error of forecasting. In this study, we explored the development of rule-based method for forecasting electricity peak load demand. The rule-based system synergized human reasoning style of fuzzy systems through the use of set of rules consisting of IF-THEN approximators with the learning and connectionist structure. Prior to the implementation of rule-based models, SARIMAT model and Regression time series were used. Results: Modification of the basic regression model and modeled it using Box-Jenkins auto regressive error had produced a satisfactory and adequate model with 2.41% forecasting error. With rule-based based forecasting, one can apply forecaster expertise and domain knowledge that is appropriate to the conditions of time series. Conclusion: This study showed a significant improvement in forecast accuracy when compared with the traditional time series model. Good domain knowledge of the experts had contributed to the increase in forecast accuracy. In general, the improvement will depend on the conditions of the data, the knowledge development and validation. The rule-based forecasting procedure offered many promises and we hoped this study can become a starting point for further research in this field.
  H.B. Sahib , A.F. Aisha , M.F. Yam , M.Z. Asmawi , Z. Ismail , S.M. Salhimi , N.H. Othman and A.M.S. Abdul Majid
  Angiogenesis is a process by which new blood vessels are formed from the pre-existing blood vessel. Orthosiphon stamineus Benth. OS has been used as a medicinal herb for many centuries. Due to the presence of high level of anti-oxidants and phenolic content compounds in OS and the effect of anti-oxidants and phenolic compounds being anti-angiogenic, the perturbation of new blood vessels ability of OS was tested. Dry powdered leaves of the OS plant were extracted with Petroleum Ether (PE), chloroform (CE), methanol (ME) and water (WE) by using sequential cold maceration method. The ME of OS has the highest anti-angiogenic activity (93.28±1.24%) in the rat aortic assay followed by CE (85.55±1.64%), PE (51.54±4.12%) and WE (50.22±1.23%) in descending order of reactivity. The methanol extract was also found to have potent anti-oxidant activity in the1, 1-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging activity assay. The IC50 value was measured to be 0.286 mg mL-1. The total phenolic content of 1 mg mL-1 of methanol extract was equal to 38.27%.
  O.Z. Ameer , I.M. Salman , Md. J.A. Siddiqui , M.F. Yam , R.N. Sriramaneni , A.F. Mutee , A. Sadikun , Z. Ismail and M.Z. Asmawi
  The aim of the current investigation was to examine the vascular responsiveness to different extracts obtained from M. cochinchinensis using isolated Sprague Dawley (SD) rat aortic rings preparations. The fresh aerial parts of the plant were dried, pulverized into powder and sequentially extracted with petroleum ether, chloroform, methanol and water using hot extraction method. The effects of three concentrations (0.5, 1 and 2 mg mL-1) of each extract on rat thoracic aorta were tested using cumulative concentrations of noradrenaline (NA). The data showed that all the extracts had the ability to relax vascular smooth muscle; however, high concentrations of the methanol and water extracts caused the most significant (p<0.05) reduction in NA-induced vasoconstriction as compared to petroleum ether and chloroform extracts. Polyphenolic content, HPLC profiling and IR spectra were indicative of the presence of diterpenoid constituents. The results collectively suggested the presence of some biologically active ingredients of possible diterpenoid nature that have the ability to modulate the action of naturally occurring vasoactive agents such as NA on vascular smooth muscle responses in vitro.
  H.B. Sahib , Z. Ismail , N.H. Othman and A.M.S. Abdul Majid
  Estrogen Receptor (ER+) antagonist, Tamoxifen (TMX), is widely used in the treatment of the hormone responsive breast cancer. However, the common occurrence of resistance after prolonged treatment of TMX hampers its effectiveness. Orthosiphon stamineus Benth. (OS) is a common herb found in South East Asia and is used traditionally to treat various types of ailments. The aim of this study was to determine whether the methanolic extract of Orthosiphon stamineus Benth. (MEOS), that had been proven in previous study to act as anti-angiogenic agents, enhance the anticancer efficacy of ER+ antagonists. In this study methanolic extract of (MEOS) was treated to MCF-7 hormone sensitive breast cancer cell line with the addition of TMX. MEOS showed no significant cytotoxic effect towards MCF-7 when used alone, however when combined with TMX, the anti proliferative activity of the combination increased five fold higher when compared to the anti-proliferative activity of singly treated TMX. The result suggests that MEOS synergistically enhance the activity of TMX against hormone responsive breast cancer cells in vitro and may prove to be useful for the treatment of metastatic breast cancer.
  M.J. Siddiqui , Z. Ismail , A.F.A. Aisha and A.M.S. Abdul Majid
  Catharanthus roseus (Apocynaceae), in Malaysia known as Kemuning cina, well known for being rich in alkaloids was investigated for its cytotoxic activity by using MTT assay against Human Colorectal Carcinoma Cell Line (HCT 116). The preliminary cytotoxicity study demonstrated dose independent cytotoxic activity of the methanol extract of C. roseus when screened against HCT-116 colorectal carcinoma cell line. n-hexane, chloroform and methanol fractions also showed dose independent cytotoxic activity with chloroform fraction showing the highest activity. Water fraction showed a minor cytotoxic activity, vindoline also showed some cytotoxic activity at 200 μg mL-1. Catharanthine showed the most promising activity while dose dependent cytotoxic activity of its IC50 value was found to be at 60 μg mL-1. Simple and facile method has been developed for the isolation of compounds catharanthine and vindoline from this plant.
  N.S. Muslim , K.W. Ng , A. Itam , Z.D. Nassa , Z. Ismail and A.M.S. Abdul Majid
  Strobilanthes crispus (Acanthaceae) is widely used for treatment of cancer in the South Asian region. In this study, validation of GC-TOF mass spectrophotometric methods for quantitative determination of phytoconstituents in methanolic and aqueous extracts of S. crispus was conducted. The cytotoxicity of standardised methanolic and aqueous extracts of S. crispus was assessed against a panel of human cancer cell lines, namely breast carcinoma (MCF7), colon carcinoma (HCT 116), hepatocellular carcinoma (Hep G2), non-small cell lung adenocarcinoma (NCI-H23) and human breast ductal carcinoma (T-47D) cells and one normal colonic fibroblast cell line (CCD-18Co). The cell proliferation assay was performed using tetrazolium (MTT) method. Furthermore, the inhibitory effect of the extracts on angiogenesis was evaluated using ex vivo rat aortic ring assay. Finally, the antioxidant properties of both extracts were studied using DPPH free radical, xanthine oxidase activity and β-carotene-linoleate model system. Aqueous extract was found to be nontoxic towards all cell lines used, while the methanolic extract exhibited cytotoxic response towards the T-47D and MCF7 cells. Both the extracts demonstrated detectable anti-angiogenic activity. The extracts displayed very strong inhibitory activity towards xanthine oxidase enzyme, however, they demonstrated moderate antioxidant properties, which is evidenced by the quenching of DPPH free radical and preventing the bleaching of β-carotene by linoleic acid.
  Z. Ismail and Irhamah
  The primary objective of this study is to solve the Vehicle Routing Problem with Stochastic Demands (VRPSD) under restocking policy by using adaptive Genetic Algorithm (GA). The problem of VRPSD is one of the most important and studied combinatorial optimization problems, which finds its application on wide ranges of logistics and transportation area. It is a variant of a Vehicle Routing Problem (VRP). The algorithms for stochastic VRP are considerably more intricate than deterministic VRP and very time consuming. This has led us to explore the used of metaheuristics focusing on the permutation-based GA. The GA is enhanced by automatically adapting the mutation probability to capture dynamic changing in population. The GA becomes a more effective optimizer where the adaptive schemes are depend on population diversity measure. The proposed algorithm is compared with standard GA on a set of randomly generated problems following some discrete probability distributions inspired by real case of VRPSD in solid waste collection in Malaysia. The performances of several types of adaptive mutation probability were also investigated. Experimental results show performance enhancements when adaptive GA is used.
  A. Shabri , R. Samsudin and Z. Ismail
  Accurate forecasting of the rice yields is very important for the organization to make a better planning and decision making. In this study, a hybrid methodology that combines the individual forecasts based on artificial neural network (CANN) approach for modeling rice yields was investigated. The CANN has several advantages compared with conventional Artificial Neural Network (ANN) model, the statistical the autoregressive integrated moving average (ARIMA) and exponential smoothing (EXPS) model in order to get more effective evaluation. To assess the effectiveness of these models, we used 38 years of time series records for rice yield data in Malaysia from 1971 to 2008. Results show that the CANN model appears to perform reasonably well and hence can be applied to real-life prediction and modeling problems.
  Z. Ismail and Irhamah
  This study considers a version of the stochastic vehicle routing problem where customer demands are random variables with known probability distribution. A new scheme based on a hybrid GA and Tabu Search heuristic is proposed for this problem under a priori approach with preventive restocking. The relative performance of the proposed HGATS is compared to each GA and TS alone, on a set of randomly generated problems following some discrete probability distributions. The problem data are inspired by real case of VRPSD in waste collection. Results from the experiment show the advantages of the proposed algorithm that are its robustness and better solution qualities resulted.
  Z. Ismail and S.L. Loh
  Problem statement: Southern Waste Management environment (SWM environment) is a company responsible for the collection and disposal of solid waste for the city of Johor Bahru, a city with over one million populations. The company is implementing an integrated solid waste management system where it involved in the optimization of resources to ensure the effectiveness of its services. Formulating this real life problem into vehicle routing problem with stochastic demand model and using some designed algorithms to minimize operation cost of solid waste management. Approach: The implementation of Ant Colony Optimization (ACO) for solving solid waste collection problem as a VRPSD model was described. A set of data modified from the well known 50 customers problems were used to find the route such that the expected traveling cost was minimized. The total cost was minimized by adopting a preventive restocking policy which was trading off the extra cost of returning to depot after a stock-out with the cost of returning depot for restocking before a stock-out actually occurs. For comparison purposes, Simulated Annealing (SA) was used to generate the solution under the same condition. Results: For the problem size with 12 customers with vehicle capacity 10 units, both algorithms obtained the same best cost which is 69.4358 units. But the percentage deviations of averages from the associated best cost are 0.1322 and 0.7064 for ACS and SA. The results indicated that for all demand ranges, proposed ACO algorithm showed better performance than SA algorithm. Conclusion: SA was able to obtain good solutions for small ranges especially small size of problem. For ACS, it is always provide good results for all tested ranges and problems sizes of the tested problem.
  Z. Ismail , Suhartono , A. Yahaya and R. Efendi
  Problem statement: It is common in time series data with extreme change in its mean caused by an intervention which comes from external and/or internal factors. This extreme change in mean is known as regime change or structural change. Problems of external factor intervention such as the effect of the Arab oil embargo to consumption level of electricity in United States. The issue of interest here is on the impact of terrorist Bali’s bomb to tourism industry in Indonesia. Approach: A theoretical and empirical studies on the intervention model, particularly pulse function of intervention is carried out focusing on the differential statistics that can be used to determine the order of intervention model. A case study of the first Bali bomb that occurred on October 12th, 2002 is an intervention of external factor that has affected the occupancy level of five star hotels in Bali. Results: The results of this theoretical study were applied to construct a model procedure of intervention model. The empirical study showed that intervention model is used to describe and explain the quantity and the length of the first Bali bomb effect towards the occupancy level of five star hotels in Bali. It shows a decreasing trend in tourist arrival in Bali, Indonesia. Conclusion: This study was focused on the derivation of some effect shapes, i.e., temporary, gradually or permanent on the arrival of tourist into Bali. A new model building procedure with three main iterative steps for determining an intervention model was used for data with extreme change in mean. The results from this theoretical study will give an opportunity for further research related to time series model that contains regime change, caused by intervention of pulse function and/or step function.
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