Abstract. In this paper, support vector machine (SVM) is used to classify satellite remotely sensed multispectral data. The data are recorded from a Landsat-5 TM satellite with resolution of 30x30m. SVM finds the optimal separating hyperplane between classes by focusing on the training cases. The study area of Klang Valley has more than 10 land covers and classification using SVM has been done successfully without any pixel being unclassified. The training area is determined carefully by visual interpretation and with the aid of the reference map of the study area. The result obtained is then analysed for the accuracy and visual performance. Accuracy assessment is done by determination and discussion of Kappa coefficient value, overall and producer accuracy for each class (in pixels and percentage). While, visual analysis is done by comparing the classification data with the reference map. Overall the study shows that SVM is able to classify the land covers within the study area with a high accuracy. IntroductionLand cover classification is one of the most important remote sensing applications. It has been widely used in many fields such as town planning, studies of environmental change, land resource planning, and geological mapping. Generally, a good classifier should be able to classify pixels into desirable land covers. The factors taken into consideration when selecting a classification method include accuracy, speed and practicality. Among the frequently used methods in classification are maximum likelihood classification (MLC) and artificial neural network (ANN). However, there are drawbacks to these classifications; ANN has been associated with over fitting and local minima problems [5], while MLC needs large training area and assumption that the data are normally distributed. In recent years, there have been an effort to develop better reliable classification methods; support vector machine (SVM) is one of them [10]. SVM is characterised by an efficient hyperplane searching technique that uses minimal training area and therefore consumes less processing time. The method is able to avoid over fitting problem and requires no assumption on data type. Although non-parametric, the method is capable of developing efficient decision boundaries and therefore can minimise misclassification. This is done through finding of optimal separating hyperplanes between classes by focusing on the training cases (support vectors) that lie at the edge of the class distributions, with the other training cases being excluded [13]. This study aims to carry out SVM classification on land covers over Selangor, Malaysia.Originally, SVM is a binary classifier that works by identifying the optimal hyperplane and correctly divides the data points into two classes. There will be an infinite number of hypeplanes and SVM will select the hyperplane with maximum margin. The margin indicates the distance between the classifier and the training points (support vector). Figure 1 illustrates the basic idea of support vector machine [13]. A n...

Recommender systems are essential engines to deliver product recommendations for e-commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers’ activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF-IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF-IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e-commerce recommender systems with sparse data issues.

Objectives: The objective of this study was to identify the factors influencing workarounds to the Hospital Information System (HIS) in Malaysian government hospitals. Methods: Semi-structured interviews were conducted among 31 medical doctors in three Malaysian government hospitals on the implementation of the Total Hospital Information System (THIS) between March and May 2015. A thematic qualitative analysis was performed on the resultant data to deduce the relevant themes. Results: Five themes emerged as the factors influencing workarounds to the HIS: (a) typing skills, (b) system usability, (c) computer resources, (d) workload, and (e) time. Conclusions: This study provided the key factors as to why doctors were involved in workarounds during the implementation of the HIS. It is important to understand these factors in order to help mitigate work practices that can pose a threat to patient safety.

This study identifies land use changes in the metropolitan region of Klang-Langat Valley focusing on urban sprawl and green space. A technique called Normalized Difference Vegetation Index (NDVI) is used to quantify temporal urban green space dynamics. All districts in the valley recorded a marked increase in urban area, but decreased in agriculture and forest areas. Result of vegetation index analysis showed that NDVI increases for water body, bare soil, and built-up area category for as much as 16.98% from 1998 to 2001, but during the same period vegetation experience a decrease of 22.25%.

The Combinatorial Optimization Problem (COPs) is one of the branches of applied mathematics and computer sciences, which is accompanied by many problems such as Facility Layout Problem (FLP), Vehicle Routing Problem (VRP), etc. Even though the use of several mathematical formulations is employed for FLP, Quadratic Assignment Problem (QAP) is one of the most commonly used. One of the major problems of Combinatorial NP-hard Optimization Problem is QAP mathematical model. Consequently, many approaches have been introduced to solve this problem, and these approaches are classified as Approximate and Exact methods. With QAP, each facility is allocated to just one location, thereby reducing cost in terms of aggregate distances weighted by flow values. The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark. The performance of the proposed approach has been tested on several sets of instances from the data set of QAP and the results obtained have shown the effective performance of the proposed algorithm in improving several solutions of QAP in reasonable time. Afterwards, the proposed approach is compared with other recent methods in the literature review. Based on the computation results, the proposed hybrid approach outperforms the other methods.