Penyelesaian masalah persamaan resapan olakan satu dimensi secara selari dan berjujukan

This article discusses the solution of one-dimensional convection-diffusion equations by using Successive Over-Relaxation Red Black (SORRB) and Gauss-Seidel Red Black (GSRB) method for parallel and sequential. The implementation for this method has been performed on parallel computers for distributed memory systems using Parallel Virtual Machine (PVM) and C which used 18 personal computers with Intel Pentium IV processor. This research successfully results in affirmation of the theories presented by Foster (1995) which proposed that the percentage of efficiency decreases and the speedup factor increases when the numbers of processors are increasing. The research result also shows that the parallelization of GSRB and SORRB method is faster than sequential especially when the number ofprocessors increased. As a conclusion, problem solving by using parallelization will reduce execution time especially in solving large scale problems

Data mining approaches in business intelligence: postgraduate data analytic

Over recent years, there has been tremendous growth of interest in business intelligence (BI) for higher education. BI analysis solutions are operated to extract useful information from a multi-dimensional datasets. However, higher education-based business intelligence is complex to build, maintain and it faces the knowledge constraints. Therefore, data mining techniques provide an effective computational methods for higher educationbased business intelligence. The main purpose of using data mining approaches in business intelligence is to provide decision making solution to higher education management. This paper presents the implementation of data mining approaches in business intelligence using a total of 13508 postgraduates (PG) data. These PG data are to allow the research to identify the postgraduates who Graduate On Time (GOT) via business intelligence process integrating data mining approaches. There are four layers will be discussed in this paper: data source layer (Layer 1), data integration layer (Layer 2), logic layer (Layer 3), and reporting layer (Layer 4). The main scope of this paper is to identify suitable data mining which is to allow decision making on GOT so as to an appropriate analysis to education management on GOT. The results show that Support Vector Machine (SVM) classifier is with better accuracy of 99%. Hence, the contribution of data mining in business intelligence allows an accurate decision making in higher education

LoRaLOFT-A Local Outlier Factor-based Malicious Nodes detection Method on MAC Layer for LoRaWAN

LoRaWAN is one of the network technologies that provide a long-range wireless network at low energy consumption. However, the pure Aloha MAC protocol and the duty-cycle limitation at both end devices and gateway make LoRaWAN very sensitive to malicious behaviors in the MAC layer. Moreover, this kind of sensitivity makes the false-positives problem challenging for malicious behavior detection with simple threshold methods. This study investigates two malicious behaviors - greedy and attack on the MAC layer. Furthermore, by combining the threshold method with a Local Outlier Factor (LOF) model in machine learning, LoRaLOFT is proposed. It is a centralized malicious node detection method. Analytical results show that the proposed method gives high detection accuracy while significantly reducing the false-positive rate in both behaviors

Improving gender classification with feature selection in forensic anthropology

Gender classification has been one of the most vital tasks in a real world problem especially when it comes to death investigations. Developing a biological profile of an individual is a crucial step in forensic anthropology process as for the identification of gender. Forensic anthropologists employ the principle of skeleton remains to produce a biological profile. Different parts of skeleton contains different features that will contribute to gender classification. However, not all the features could contribute to gender classification and affect to a low accuracy of gender classification. Therefore, feature selection method is applied to identify the most significant features for gender classification. This paper presents the implementation of feature selection approaches which are Particle Swarm Optimization (PSO) and Harmony Search (HS) algorithm using three different dataset from Goldman Osteometric Dataset, Osteological Collection and George Murray Black Collection. All three dataset contains 4081 samples of metrics measurement and have gone through the process of classification by using Back Propagation Neural Network (BPNN) and Naïve Bayes classifier. The main scope of this paper is to identify the effect of feature selection towards gender classification. The result shows that the accuracy of gender classification for every dataset increased when feature selection is applied to the dataset. Among all the skeleton parts in this experiment, clavicle part achieved the highest increment of accuracy rate which is from 89.76% to 96.06% for PSO algorithm and 96.32% for HS

An MDP model-based initial strategy prediction method for LoRaWAN

As one of the technologies in the wide-area network category, LoRaWAN provides a wireless network with a large capacity of end devices (ED) in long-range. With a pure Aloha protocol implemented into its MAC layer, LoRaWAN can reduce its power consumption. Besides, some orthogonal transmission parameters give LoRaWAN capability to avoid collision and packet loss. Thus, allocating transmission parameters to increase the network performance becomes a challenging issue for LoRaWAN. Some dynamic Spreading Factor (SF) allocation strategies are studied in this paper. A distributed Markov Decision Process (MDP) model is constructed for the uplink transmission of the class-A device in LoRaWAN. The model is also solved and implemented to the algorithms for the initial strategy prediction. Analytical results show that the MDP model increases the performance of the studied algorithms on the transmission of the packet

Properties and water absorption rate of paraffin wax coated paperboard

Paper and paperboard are the most popular packaging material nowadays. This is due to its low cost of production and biodegradable properties. Paper and paperboard are better than traditional plastics as a packaging material because of its value and sustainability. Coated paper and paperboard can be explained as a paper which is covered by a layer of plastics material or chemical which can improve the surface appearance and the mechanical properties of the paper itself..

Pengkelasan dokumen web menggunakan teknik vector machine (SVM)

Dewasa ini, kebanyakan enjin carian di internet menggunakan sistem pengindeksan subjek berbanding pengkelasan dokumen. Dalam sistem pengindeksan subjek, kosa kata atau kata kunci yang terkawal digunakan untuk menetapkan istilah pengindeksan pada dokumendokumen web. Manakala, pengkelasan dokumen pula akan mengkelaskan dokumen-dokumen web dalam satu struktur hirarki berdasarkan kategori subjek. Pengindeksan berdasarkan kata kunci berkemampuan untuk mencari dokumen-dokumen yang mengandungi kata kunci yang spesifik. Walau bagaimanapunia sukar untuk mengenalpasti dokumen-dokumen yang mempunyai kategori yang sama. Oleh yang demikian, pengkelasan teks secara automatik adalah diperlukan. Ini bertujuan untuk mengkelaskan dokumen-dokumen ke dalam kategoriketegori yang berbeza berdasarkan kandungan teks. Sehubungan dengan itu, kertas kerja ini akan membincangkan tentang kajian pengkelasan teks dengan menggunakan kaedah Support Vector Machine (SVM). Set data yang digunakan dalam kajian ini diperolehi daripada Bank Search Information Consultancy Ltd. dan Jabatan Sains Komputer di University of Reading. Set data ini dipecahkan kepada empat kategori iaitu perbankan dan kewangan, bahasa pengaturcaraan, sains dan sukan. Hasil kajian ini menunjukkan peratus ketepatan pengkelasan dokumen web untuk set data yang digunakan adalah rendah dan kurang memuaskan

Hybrid of hierarchical and partitional clustering algorithm for gene expression data

Microarray analysis able to monitor thousands of gene expression data, however, to elucidate the hidden patterns in the data is a complex process. These gene expression data show its imprecision, noise and vagueness due to its high dimensional properties. There are a handful of clustering algorithms have been proposed to extract the important information from the gene expression data. However, identifying the underlying biological knowledge of the data is still hard. To acknowledge these issues, clustering algorithms are used to reduce the data complexity. In this article, hybrid of agglomerative hierarchical clustering and modified k-medoids (partitional clustering) are proposed. Application of the proposed of clustering algorithms to group the genes that have similar functionality which might assist pre-processing procedures. In order to emphasize the quality of the clustering results, cluster quality index (CQI) is determined. Lung and ovary data sets used and the method retrieved a fair clustering with CQI, 0.37 and 0.48 respectively. This research contributes by avoiding biasness toward genes and provide true sense of clustering output using the advantage of hierarchical and partitional clustering methods

Social Media for Collaborative Learning and Engagement: Adoption Framework in Higher Education Institutions in Malaysia

This paper addresses collaborative learning and engagement via intention towards social media use that have been tackled by some researches in terms of its impacts on students’ academic performance. However, only a few of such studies have been carried out in the area of collaborative and engagement use of social media for enhancing researchers’/students’ performance. The present study attempts to determine the way social media can be utilized to enhance researchers’ performance via collaborative and engagement by applying the theory of technology acceptance model (TAM) along with constructivism theory. According to the results, collaborative learning and engagement positively and significantly impact perceived ease of use (PE), perceived usefulness (PU), and intention to use social media (IU) through the social media use in the context of Universiti Teknologi, Malaysia. DOI: 10.5901/mjss.2015.v6n3s1p24