The present study analyses the magnetohydrodynamic (MHD) flow of a double stratified micropolar fluid across a vertical stretching/shrinking sheet in the presence of suction, chemical reaction, and heat source effects. The governing equations in the form of partial differential equations are transitioned into coupled nonlinear ordinary differential equations by means of similarity transformation. The numerical solutions are obtained with the aid of the boundary value problem bvp4c solver in the MATLAB software. Numerical results have been confirmed with the previous results for a certain case and the comparison is found to be in an excellent agreement. Results for related profiles and heat transfer characteristics are displayed through plots and tabulated for the governing parameters involved. It is found that the reduced skin friction coefficient and the local Nusselt number increase with the increasing chemical reaction and heat source parameters. The rising values of the chemical reaction parameter have increased the magnitude of the local Sherwood number. In contrary, the heat source parameter has the tendency to decrease the magnitude of the local Sherwood number.

Abstract-This study presents a detailed analysis of Iterative Self Organizing Data Analysis (ISODATA) clustering for multispectral data classification. ISODATA is an unsupervised classification method which assumes that each class obeys a multivariate normal distribution, hence requires the class means and covariance matrices for each class. In this study, we use ISODATA to classify a diverse tropical land covers recorded from Landsat 5 TM satellite. The classification is carefully examined using visual analysis, classification accuracy, band correlation and decision boundary. The results show that ISODATA is able to detect eight classes from the study area with 93% agreement with the reference map. The behavior of mean and standard deviation of the classes in the decision space is believed to be one of the main factors that enable ISODATA to classify the land covers with relatively good accuracy. Keywords-ISODATA, Lendsat, ChssifieationStudies on classification of renlote sensing data have long been carried out by numerous researchers worldwide, with more efforts made regionally than globally. Many regional studies have been carried out in places such as Europe and America [I] due to having an up-to-date remote sensing facilities as well as ground truth information. There is also an increasing interest to carry out such studies in climate-affected regions such as Africa [2] and highly populated regions such as Tndia and China r31. Nonetheless. not much effort has beenexpended in Tropical countries such as Malaysia 141, 151 despite their recent promising developments in renlote sensing capabilities [6]. Two types of methods that are commonly used a ; supervised and unsupervised classification. Supervised classification classifies pixels based on known properties of each cover type; therefore it requires representative of land cover information, in terms of training pixels. On the other hand, in unsupervised classification, the clustering process produces clusteis that are statistically separable, giving a hatural grouping of the pixels. This approach is useful when reliable training data are either limited or expensive, and when there is insufficient a priori information about the data 171. Two types of commonly used unsupervised classification are K-means and ISODATA. K-means is a simple clustering procedure that attempts to find the cluster centres in the data, then aims to cluster the full set of pixels into K clusters. The main disadvantage is that K-means requires the number of clusters is known a priori [8]. The main advantage of ISODATA over K-means algorithm is that ISODATA allows different numbers of clusters (ranging from a minimum to a maximum number of clusters) to be specified [S]; therefore is more adaptable and flexible than K-means. This study presents a detailed analysis of ISODATA clustering for Malaysian land covers using Thematic Mapper (TM), a medium resolution multispectral sensor on board Landsat 5 satellite. This makes use qualitative and quantitative approaches. Hopefully, this analysis,...

Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN.

Humans need to eat a good and balanced nutritious diet that provides calories for energy requirements and nutrients for proper growth, repair and maintenance of the body tissues. Moreover, it is essential for resisting and preventing diseases and infection that may lead to problems such as anemia, scurvy and rickets. In recent studies, medical researchers have discovered that good nutrition can help to reduce the risks of coronary heart disease and certain types of cancer. Menu and diet planners face tremendous challenges and difficulties in order to improve human health. Serving healthier meals is a major step towards achieving that objective. However, constructing and planning a nutritious and balanced menu manually is complicated, inefficient and time-consuming. The aim of this study is to develop a mathematical model for diet planning that meets the necessary nutrient intake for the secondary school student as well as minimizing a budget. The data were collected from various boarding schools and also from the Ministry of Education. Nutrition planning is a well-known optimization problem and the goal is to find the best possible optimal solution. Therefore the model was solved by using optimization method along with Integer Programming. This model can be adopted to solve other diet planning problems such as for the military, hospitals nursing home and universities.

Pneumonia is one of the serious illnesses, which involves lung infection specifically alveoli. Nearly 40,000 to 70,000 people die each year in United State because of pneumonia. Therefore, it is not a surprise that pneumonia is one of the most critical illnesses for children under 12 years old in many parts of the world, including Malaysia and particularly in Tawau, Sabah, Malaysia. The objectives of this study are: to develop a summary on the prevalence of pneumonia in Tawau General Hospital, to analyze the best practice to prevent this illness and lastly to determine an overview of which area that is widely affected by pneumonia. The results can assist doctors and the government to take major precautions and preventive measures efficiently to the full extent. This paper presents a descriptive analysis of the data, which are retrieved from the medical reports at the Tawau General Hospital. Through the findings, pneumonia is widely spread among young children under 12 years old. There are more than one major factor that leads to this critical illness, such as family background, genetic and environment. Therefore, the government, doctors and parents should take major steps to prevent children suffering from pneumonia.