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Journal of Information and Communication Technology (JICT) Vol. 15, No. 2, Dec 2016

 
REQUIREMENTS ENGINEERING FOR CLOUD COMPUTING ADAPTIVE MODEL
Kridanto Surendro, Aradea & Iping Supriana
School of Electrical Engineering and Informatics
Bandung Institute of Technology, Indonesia
endro@informatika.org; aradea.informatika@gmail.com; iping@ informatika.org
 
ABSTRACT Ɩ FULL TEXT
The easy access and flexibility provided by cloud computing have made various types of organizations use this technology as an alternative solution. However, the decision to use the cloud technology sometimes ignores the basic aspects related to the understanding of its compliance with the characteristics of the organization. Thus, many advantages of the adoption of the technology do not align with the needs. This paper discusses the requirements engineering of the alignment of the organizational characteristics and the cloud computing technology in order to create the adaptive ability to guide strategic-business driven organizations. The model proposed in this article is formulated based on three views: architectural, alignment, and adaptive, by employing the Goal-Oriented Requirements Engineering. As a case study, the model is applied to cloud computing for university. The architectural view can translate the environmental characteristics of organizations through goal decomposition into detailed needs. Meanwhile, the alignment view can meet the implementation, so the dynamic adaptive ability can be realized through the adaptivity view.The mechanism of our proposed model can cover the adaptation needs before the migration (pre-migration), during the migration, and after the migration (post-migration).
 
Keywords: requirements engineering, adaptive model, cloud computing, goal-oriented, capability maturity model.
 

 
CONTROL OF BIPEDAL LOCOMOTION WITH A NEURAL OSCILLATOR-BASED BRAIN–COMPUTER INTERFACE
¹Ryo Ikeda & ²Ryota Horie
¹Epson Avasys Corporation, Japan
Shibaura Institute of Technology, Japan
ma14010@shibaura-it.ac.jp; horie@shibaura-it.ac.jp
 
ABSTRACT Ɩ FULL TEXT
This study proposes a neural oscillator-based brain–computer interface (BCI) that controls a bipedal neuromusculoskeletal (NMS) model by inputting electroencephalogram (EEG) signals. In this BCI system, while the bipedal NMS system realizes bipedal locomotion through internal entrainment among neural oscillators and a musculoskeletal system, the locomotion of the system is controlled via external entrainment of the neural oscillators to the external input of EEG signals. As the first step in developing the neural oscillator-based BCI controlling a bipedal NMS model, exploratory numerical simulations were conducted to investigate the behavior of the proposed BCI when sinusoidal waves and alpha waves were inputted. The following tendencies were observed: (a) inputting sinusoidal waves with small amplitudes and high frequencies did not affect the natural walking behavior of the bipedal NMS model that was generated by including only offset values in the external input, (b) inputting sinusoidal waves with small amplitudes and low frequencies disturbed and decelerated the walking behavior, (c) inputting sinusoidal waves with large amplitudes accelerated the walking behavior, (d) inputting sinusoidal waves with large amplitudes and a particular frequency changed walking behavior to running behavior, (e) changing the external input of alpha waves between an eyes-open condition and an eyes-closed condition successfully changed the walking behavior. The eyes-open condition led to faster walking compared with the eyes-closed condition.
 
Keywords: bipedal locomotion, brain–computer interface, bipedal neuromusculoskeletal model, entrainment, neural oscillator.
 

 
THROUGHPUT OPTIMIZATION IN MULTI-RADIO MULTI-CHANNEL WIRELESS NETWORKS USING NETWORK CODING
Saleh Dadpour, Reza Ghazizadeh & Mohammad Sadeghian Kerdabadi
University of Birjand, Birjand, Iran
sdadpour@birjand.ac.ir;rghazizade@birjand.ac.ir;mohammad.sadeghian@ birjand.ac.ir
 
ABSTRACT Ɩ FULL TEXT
One major problem of wireless mesh networks is low throughput and on the other hand, network coding (NC) is a reliable solution to alleviate this problem. In this paper, we evaluate the through­put gain of various intersession wireless NC schemes, includ­ing signal level (analog) NC and packet level NC, which may include non-duplex flows, over the traditional non-NC schemes in multi-radio, multi-channel and multi-hop networks. We also propose a routing approach in order to increase NC opportuni­ties and evaluate its performance in wireless ad-hoc networks in terms of network throughput.
 
Keywords: ad-hoc network, throughput gain, network coding, routing, multi-radio multi-channel.
 

 
EVALUATION ON RAPID PROFILING WITH CLUSTERING ALGORITHMS FOR PLANTATION STOCKS ON BURSA MALAYSIA
Keng-Hoong Ng & Kok-Chin Khor
Multimedia University, Malaysia
khng@mmu.edu.my; kckhor@mmu.edu.my
 
ABSTRACT Ɩ FULL TEXT
Building a stock portfolio often requires extensive financial knowledge and Herculean efforts looking at the amount of financial data to analyse. In this study, we utilized Expectation Maximization (EM), K-Means (KM), and Hierarchical Clustering (HC) algorithms to cluster the 38 plantation stocks listed on Bursa Malaysia using 14 financial ratios derived from the fundamental analysis. The clustering allows investors to profile each resulted cluster statistically and assists them in selecting stocks for their stock portfolios rapidly. The performance of each cluster was then assessed using 1-year stock price movement. The result showed that a cluster resulted from EM had a better profile and obtained a higher average capital gain as compared with the other clusters.
 
Keywords: Stock profiling, stock portfolio, financial ratios, expectation maximization, K-means, hierarchical clustering.
 

 
INTELLIGENT COOPERATIVE WEB CACHING POLICIES FOR MEDIA OBJECTS BASED ON J48 DECISION TREE AND NAÏVE BAYES SUPERVISED MACHINE LEARNING ALGORITHMS IN STRUCTURED PEER-TO-PEER SYSTEMS
Hamidah Ibrahim, Waheed Yasin, Nur Izura Udzir & Nor Asilah Wati Abdul Hamid
Universiti Putra Malaysia, Malaysia
hamidah.ibrahim@upm.edu.my;asila@upm.edu.my;izura@upm.edu. my;waheedos80@yahoo.com
 
ABSTRACT Ɩ FULL TEXT
Web caching plays a key role in delivering web items to end users in World Wide Web (WWW). On the other hand, cache size is considered as a limitation of web caching. Furthermore, retrieving the same media object from the origin server many times consumes the network bandwidth. Furthermore, full caching for media objects is not a practical solution and consumes cache storage in keeping few media objects because of its limited capacity. Moreover, traditional web caching policies such as Least Recently Used (LRU), Least Frequently Used (LFU), and Greedy Dual Size (GDS) suffer from caching pollution (i.e. media objects that are stored in the cache are not frequently visited which negatively affects on the performance of web proxy caching). In this work, intelligent cooperative web caching approaches based on J48 decision tree and Naïve Bayes (NB) supervised machine learning algorithms are presented. The proposed approaches take the advantages of structured peer-to-peer systems where the contents of peers’ caches are shared using Distributed Hash Table (DHT) in order to enhance the performance of the web caching policy. The performance of the proposed approaches is evaluated by running a trace-driven simulation on a dataset that is collected from IRCache network. The results demonstrate that the new proposed policies improve the performance of traditional web caching policies that are LRU, LFU, and GDS in terms of Hit Ratio (HR) and Byte Hit Ratio (BHR). Moreover, the results are compared to the most relevant and state-of-the-art web proxy caching policies.
 
Keywords: Web Caching, Machine Learning Algorithms, Peer-to-Peer Systems
 

 
PREDICTION OF FOREX TREND MOVEMENT USING LINEAR REGRESSION LINE, TWO-STAGE OF MULTI-LAYER PERCEPTRON AND DYNAMIC TIME WARPING ALGORITHMS
Leslie Tiong Ching Ow, David Chek Ling Ngo & Yunli Lee
Sunway University, Malaysia
10008027@imail.sunway.edu.my; dngo@sunway.edu.my; yunlil@sunway.edu.my
 
ABSTRACT Ɩ FULL TEXT
Foreign Exchange Currency prediction has become a challeng­ing task since the late 1970s due to uncertainty movement of exchange rates. However, most researchers in this area were neglecting to analyse trend patterns from historical Forex data as input features. Thus, this motivates us to investigate possi­bility of repeated trend patterns from historical Forex data. This paper aims to investigate the repeated trend patterns as features from historical Forex data, which proposes new combination techniques - Linear Regression Line, two-stage of Multi-Layer Perceptron and Dynamic Time Warping algorithms in order to improve the performance of prediction significantly, thus achiev­ing greater accuracy.
 
Keywords: Foreign exchange currency prediction; forex trend movement; multi-layer perceptron; linear regression line; dynamic time warping.
 

 
MODELING RESERVOIR WATER RELEASE DECISION USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM
¹Suriyati Abdul Mokhtar, ²Wan Hussain Wan Ishak & ³Norita Md Norwawi
¹&²Universiti Utara Malaysia, Malaysia
Universiti Sains Islam Malaysia, Malaysia
suriyati87@gmail.com; hussain@uum.edu.my; norita@usim.edu.my
 
ABSTRACT Ɩ FULL TEXT
Reservoir water release decision is one of the critical actions in determining the quantity of water to be retained or released from the reservoir. Typically, the decision is influenced by the reservoir inflow that can be estimated based on the rainfall recorded at the reservoir’s upstream areas. Since the rainfall is recorded at several different locations, the use of temporal pattern alone may not be appropriate. Hence, in this study a spatial temporal pattern was used to retain the spatial information of the rainfall’s location. In addition, rainfall recorded at different locations may cause fuzziness in the data representation. Therefore, a hybrid computational intelligence approach, namely the Adaptive Neuro Fuzzy Inference System (ANFIS), was used to develop a reservoir water release decision model. ANFIS integrates both the neural network and fuzzy logic principles in order to deal with the fuzziness and complexity of the spatial temporal pattern of rainfall. In this study, the Timah Tasoh reservoir and rainfall from five upstream gauging stations were used as a case study. Two ANFIS models were developed and their performances were compared based on the lowest square error achieved from the simulation conducted. Both models utilized the spatial temporal pattern of the rainfall as input. The first model considered the current reservoir water level as an additional input, while the second model retained the existing input. The result indicated that the application of ANFIS could be used successfully for modeling reservoir water release decision. The first model with the additional input showed better performance with the lowest square error compared to the second model.
 
Keywords: ANFIS, decision modeling, fuzzy logic, hybrid computational in­telligence, neural network, reservoir operation.
 

 
DESEASONALISED FORECASTING MODEL OF RAINFALL DISTRIBUTION USING FUZZY TIME SERIES
¹ Mahmod Othman & ²Siti Nor Fathihah Azahari
¹Universiti Teknologi PETRONAS, Malaysia
²Universiti Teknologi MARA, Malaysia
mahmod.othman@petronas.com.my
 
ABSTRACT Ɩ FULL TEXT
Flood is a frequent occurrence which has a high calamity impact on human lifestyle, environment and economics. Although, there are various methods in the vast literature to predict rainfall distributions so as to prevent flood occurrences, the accuracy of these methods still remain a huge concern. Therefore, this study explores the application of the fuzzy time series method in order to obtain more accurate rainfall distribution predictions. Data for the study were collected from the Drainage and Irrigation Department Perlis (DID) of Malaysia. The data were analysed and validated using the mean square error (MSE) and the root mean squared error (RMSE). The result of the validation was compared with selected results in previous methods. The validation analysis depicts that this method has a higher forecasting accuracy than the previous methods.
 
Keywords: fuzzy time series, rainfall distribution, deseasonalising, rainfall forecasting.
 

 
DEVELOPING CONCEPTUAL GOVERNANCE MODEL FOR COLLABORATIVE KNOWLEDGE MANAGEMENT SYSTEM IN PUBLIC SECTOR ORGANISATIONS
¹Azlina Ali, ²Rozi Nor Haizan Nor, ³Rusli Abdullah & Masrah Azrifah Azmi Murad
Universiti Putra Malaysia, Malaysia
imiazlina@yahoo.com; rozinor@upm.edu.my; rusli@upm.edu.my; masrah@upm.edu.my
 
ABSTRACT Ɩ FULL TEXT
Nowadays most of the public sector organisations are implementing a knowledge management system (KMS) to assist the systematic creation and sharing of their knowledge resources. The recent technological needs, collaboration and cooperation among public sector organisations (PSO) serve a vital role in the development of knowledge management systems (KMS). However, in order to establish a successful and standardised collaborative KMS amongst Malaysian PSO (MPSO), proper governance needs to be in place. The aim of this study is to identify governance components for collaborative KMS (CKMS). Accordingly, the journal articles published within the period 1998-2013 were identified and analysed. The articles were sourced mainly from Knowledge Management Research & Practice, Science Direct, Emerald, MIS Quarterly, as well as from some other academic databases. Keywords used during the literature search were “governance”, “knowledge management system”, “collaboration”, “collaborative knowledge management system”, “knowledge sharing” and “knowledge management governance”. Moreover, expert opinion from the public sector expert teams and academic experts was also acquired from several consultation sessions to ensure that the components obtained from the readings meet the requirements of KMS collaborations. Based on the analysis and consultation, a conceptual model for governance of CKMS is constructed.
 
Keywords: governance, knowledge management system, collaborative knowledge management system.
 

 
EVALUATING ACCESSIBILITY OF MALAYSIAN PUBLIC UNIVERSITIES WEBSITES USING ACHECKER AND WAVE
Aidi Ahmi & Rosli Mohamad
Universiti Utara Malaysia, Malaysia
aidi@uum.edu.my; roslim@uum.edu.my
 
ABSTRACT Ɩ FULL TEXT
Websites become essential means for most universities to communicate, exchange of relevant information and enable transactions among their stakeholders. Therefore, website accessibility accessible website is crucial to students to ensure equal access to of the university’s information regardless of their physical disabilities and other possible limitations. This study reports the web accessibility of 20 Malaysian public universities based on AChecker and WAVE. The results suggest a relatively low level of compliance to the guidelines as specified in WCAG 2.0 and Section 508. Among the aspects that deserve immediate attention are the provision of text alternatives for any non-text contents, keyboard accessibility and colour contrast. Other concerns such as navigation, adaptability, input assistance, compatibility, empty link and empty heading can be further improved. Regardless of low conformance, most websites extensively integrated some of the accessibility features as set out by Section 508. Overall, this study offers meaningful insights, particularly to web developers for better compliance with the standards while designing their websites.
 
Keywords: automated accessibility tools, Malaysian public university, Section 508, WCAG 2.0, web accessibility.
 

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