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The upstream capital project oil and gas industry is considered a critical sector in Malaysia. Apart from its significant monetary contribution to the country, big data analysis is also applied to the supply chain operation. The prescriptive analysis is based on Artificial intelligence (AI), specifically Machine Learning (ML), which involves algorithms and models that enable computers to make decisions based on mathematical data relationships and patterns. This study aims to identify ML analysis in Malaysia’s upstream capital projects, which may improve business decisions via the use of statistical models and ML algorithms. Incorporating ML algorithms and statistical models will produce better business decision-making by enhancing efficiency and productivity besides fast monetisation and minimising risk and returns. Overall, with the use of mixed analysis elements, it can produce better decision support for stakeholders and company owners before making crucial business decisions.

A variety of applications have been built in recent years with the aim to extract knowledge from Al-Quran. Current knowledge representations of Al-Quran give attention primarily on conceptual ontology models that describe the semantic relations between the Quranic concepts or entities. There seems to be minimal effort towards recognizing the semantic relations between words in Quranic text, which is relatively more complex. This paper aims to present a framework for semantic knowledge representation of Al-Quran using dependency relations between words, in an attempt to boost the retrieval accuracy for Al-Quran. The semantic analysis is performed on Quranic verses according to word dependency relations using dependency parsing. Based on parsed dependencies, a set of rules are formulated to build a semantic graph of Surah Ali Imran of Al-Quran. The efficiency of the semantic representation was tested by developing a prototype question answering system. The framework was evaluated using precision and recall, First Hit Success, First Answer Reciprocal Rank and Total Reciprocal Rank by comparing the retrieved and actual answers. The results indicate that the performance of the proposed framework using word dependencies improves the semantic representation of knowledge.

The needed time to send and receive a message among two nodes in an interconnection network has a fundamental role in determining the performance of this network. Therefore, taking a short period of time to send a packet between a source and destination nodes indicates a good performance network with less congestion and latency. Besides, processing data in short-term help in providing fast solutions for many complex problems. Thus, various designs of hierarchical interconnection networks (HINs) for the massively parallel computer (MPC) systems have been presented recently; the main goal of these networks is to replace the conventional ones which showed poor performance in scaling the network size. A Shifted Completely Connected Network (SCCN) proposed as a new HIN topology. Several basic modules (BMs) interconnected hierarchically to create advanced levels networks based on this topology. The structural design and a proposed routing protocol of SCCN discussed in this paper. However, the foremost focus of this work is to evaluate the time cost-effectiveness factor (TCEF) of SCCN in different levels in order to examine the effect of expanding the size of the network on the TCEF. Therefore, the TCEF for the higher levels of SCCN from level (1) to level (3) will be assessed to examine whether SCCN is an effective network in term of time. In addition, the obtained results from each level will be compared to other networks to prove the preeminence of the proposed topology.

Usage of Industry4.0 enablers such as cloud computing, IoT and data analytics is still not common among SMEs. Many SMEs had failed in adopting digital transformations due to the conflicting values of digitalization with the company’s organizational culture. There is a gap in examining the moderating influence of digital organizational culture and its effect on cloud computing technology adoption within the organization. Thus, we aim to explore, develop, and propose the conceptual framework consisting of constructs from the technology-organization-environment (TOE) framework combined with cloud computing as a mediator and the effect of digital organizational culture as a moderator. The study outcomes should address the gap in the role of cloud computing and digital organizational culture to further enhance the SME’s performance. The research is still ongoing work, with field data will be collected using the structured survey questionnaire with managers of SMEs, including owners, senior executives or supervisors as the target responders. Then an empirical study using Structural Equation Model (SEM) will be proposed to validate the hypotheses of the conceptual framework.

Named Entity Recognition (NER) is an early stage processing of an Information Extraction, which identifies and classify entities in text. The outcomes of the task have become the foundation of building complex Information Extraction applications. With the enormous amount of information available everywhere, the area has gained lots of attention from the research community. Currently, there are two main approaches to perform the Named Entity Recognition; rule-based approach and machine learning approach. In order to improve the accuracy of the classification and performance of the recognizer, some researchers have implemented a hybrid approach, which is the combination of both approaches. Even though many research and works have been done in the Named Entity Recognition, there is still room available for improvement. This paper proposed to increase the accuracy of entities detected by implementing anaphora resolution during the preprocessing phase and a hybrid approach to classify the detected tokens during the classification phase. The hybrid approach is combined the Conditional Random Field (CRF) classifier with a gazetteer and pattern rules to perform classification. The result has shown that the application of anaphora and gazetteer has increased 46% accuracy of the detected entities for the person class.

Contextual retrieval is a critical component for efficient usage of knowledge hidden behind the data. It is also among the most important factors for user satisfaction. It essentially comprise of two equally important parts – the retrieval mechanism and the knowledge base from which the information is retrieved. Despite the importance, context aware knowledge bases have not received much attention and thereby, limiting the efficiency of precise context aware retrieval. Such knowledge bases would not only contain information that has been efficiently stored but the knowledge contained would be context based. In other words, machines would understand the knowledge and its context rather than just storing data. This would help in efficient and context aware retrieval. The current paper proposes rules and methodologies for construction of such context aware knowledge bases. A case study to demonstrate the application of the methodology and test the efficiency of the proposed methodology has also been presented. The results indicate that knowledge bases built on these principles tend to generate more efficient and better context aware retrieval results.

The knowledge behind the data is of utmost importance. It is necessary to develop meaningful, expressive and comprehensive knowledge base for every domain in order to use the knowledge. The repositories need to be designed considering natural language interactions for better performance. A knowledge base with such features seems to be non-existent for Al-Qur'an. The current work aims to develop an efficient and comprehensive knowledge base for Al-Qur'an based on principles of logic, linguistics and semantic networks, etc. The developed knowledge base is expressive, comprehensive, meaningful and efficient, establishing and maintaining semantic relations. The developed knowledge base can be used for varied purposes such as expert systems, etc. contributing to the efficiency of these systems based on its unique design.

The exponential growth of internet access and computing devices, has led to the growth of social media usage and changed the way of our lives in society. Due to the growth of social media usage such as Facebook, Twitter, Instagram, YouTube, etc., there are possibilities that users share information which may pose a threat to the national security. For example, the threats might be propagated by a certain group of terrorists. The threats can be written either in Malay or mixed with other languages; especially English and in a short form basis. The major issue is to extract the 'meaning' of the messages for further processing tasks (e.g. categorizing and identifying the insight of threats). Therefore, a semantic-based grammar approach helps to fill this 'knowledge gap' for threat analysis at the early stage. This research will focus on knowledge extraction (based-on Malay words) through semantic relations between words and their dependents.

It is critical to have a knowledge base model for efficient storage of extracted knowledge. This ensures that the knowledge is stored in a meaningful way to be used for different applications. The efficiency of the knowledge base model depends largely on the rules of construction. Knowledge represented using logico-linguistic techniques and semantic networks lack a consistent rule based knowledge model. The current paper deals with the analysis of text from the knowledge extraction, representation and semantic network phase to formulate rules which would lay foundations of a knowledge model. The developed rules seem to be promising providing a comprehensive coverage of different scenarios. The extensive coverage is an indication that the knowledge model will cater to the entire domain knowledge, thereby laying the foundations of automatic construction of efficient knowledge bases.

A Symmetric Midimew Mesh connected network (SMMN) is a hierarchical interconnection networks (HIN) that capable to interconnect vast number of nodes that could reach up to millions of nodes in the network. SMMN has multiple basic modules of 2D-mesh networks that are recursively interconnected by Midimew network to create higher-level networks. In this paper, we explain the architectural details of SMMN,we also present the a deadlock-free routing algorithm for SMMN using four virtual channels and evaluate the performance of dynamic communication of SMMN network using proposed routing algorithm under uniform traffic pattern .We also evaluate the performance of dynamic communication of TESH network using topaz simulator. It is shown that the SMMN network has high throughput and low latency, which yield higher performance of dynamic communication compare to other Hierarchical networks.

The goal of this study was to predict visual acuity (VA) and contrast sensitivity function (CSF) with tissue redness grading after pterygium surgery.A total of 67 primary pterygium participants were selected from patients who visited an ophthalmology clinic. We developed a semi-automated computer program to measure the pterygium fibrovascular redness from digital pterygium images. The final outcome of this software is a continuous scale grading of 1 (minimum redness) to 3 (maximum redness). The region of interest (ROI) was selected manually using the software. Reliability was determined by repeat grading of all 67 images, and its association with CSF and VA was examined.The mean and standard deviation of redness of the pterygium fibrovascular images was 1.88 ± 0.55. Intra-grader and inter-grader reliability estimates were high with intraclass correlation ranging from 0.97 to 0.98. The new grading was positively associated with CSF (p0.01) and VA (p0.01). The redness grading was able to predict 25% and 23% of the variance in the CSF and the VA, respectively.The new grading of pterygium fibrovascular redness can be reliably measured from digital images and showed a good correlation with CSF and VA. The redness grading can be used in addition to the existing pterygium grading.

This paper discussed the idea of the computer system capable of simulating understanding with respect to reading a text document. The research is concerned with the problem of generating sophisticated knowledge representation for the purpose of understanding the natural language. Due to that, a simplification form of logical-oriented model of knowledge representation called Pragmatic Skolem Clauses (PSC) is proposed to represent the semantic formalism for the computational linguistic. Each set of pragmatic skolem clauses containing at least one skolem constant, which shows the thematic role relationship between clauses. Semantically and pragmatically-accented approach will be discussed in this paper in the context of formal grammar and linguistic semantic.

This paper presents our work on Subject-Action-Object (SAO) extraction as one component needed on patent discovery system development for Islamic Finance and Banking. There are some processes done in order to conduct SAO extraction. They are covering retrieving patents and transforming patents into structured data. We used several patent databases such as USPTO, MyIPO, WIPO, and EPO to collect patents. In patent searching, we used keywords, which were derived from Islamic Finance and Banking phrase, book's title, of which its content is related to Islamic Finance and Banking, and all concepts on Islamic Finance and Banking ontology. Result on SAO extraction will be utilized on the next component on the system i.e. analyzing patents trend using TRIZ evolution trend model.

Hierarchical Interconnection Network (HIN) is indispensable for the practical implementation of future generation massively parallel computer systems which consists of hundred thousands nodes or even millions of nodes. Because it yields good performance with low cost due to reduction of communication links and by exploring the locality in the communication & traffic patterns. A Midimew connected Mesh Network (MMN) is an HIN comprised of numerous basic modules, where the basic modules are 2D-mesh networks and they are hierarchically interconnected using midimew network to construct the higher level networks. In this paper, we present the architecture of a MMN and evaluate the time-cost effective factor of MMN, TESH, mesh, and torus networks. It is found that the proposed MMN yields slightly high time-cost effectiveness factor with small diameter and average distance as compared to other networks. Overall, performance with respect to time-cost effectiveness factor with small diameter and average distance suggests that the proposed MMN will be a indispensable choice for the next generation massively parallel computer systems.

This paper presents our work in designing system architecture for applying Malik Bennabi's ruler on Intellectual Property (IP) of Islamic Finance and Banking. We chose journal article as one form of IP for Islamic Finance and Banking. There are several components in the architecture starting from IP retrieval, structure extraction, structure classification, structure summarization, and Knowledge-based similarity measuring. The architecture will be implemented in order to measure all journal article's content and group them into two categories namely Useful and Harmful.

Objectives: This paper describes work in constructing two models of knowledge representation in order to increase the performance of retrieving information on English Quran domain. Due to many approaches available to construct knowledge representation, this paper only focused on ontology-based and graph database-based knowledge representation. Methods: Quranic Arabic Corpus that available at http://www.corpus.quran.com is used as a source to build the knowledge representation. This paper extracted several data from it which include English token, token location, and token Part Of Speech (POS). Protege is used to construct the ontology and Neo4j is utilized in developing the graph database. Findings: This paper has presented a construction process of two models of Knowledge Representation(KR)which are ontology and graph database based KR. These models will be used in developing of an English Quran question answering system in order to discover which model exhibit the best achievement in increasing question answering system performance. Improvements/Applications: The authors hope there will be useful information gained from the question answering system experiments when using both of those models.

This paper describes a work in constructing two models of knowledge representation (KR) in aiming to do evaluation of their achievement in contributing to increase performance of retrieving information on English Quran domain. Due to many approaches available to construct a KR in providing data for information retrieval process, there is a need to find out in what model the KR could provide a valuable contribution for retrieving information. We focused on ontology-based KR and graph database-based KR. We use Quranic Arabic corpus that available at http://www.corpus.quran.com as a source to build the KR. We extracted several data from it i.e. English token, token location, and token Part of Speech (POS). Protégé is used to construct the ontology and Neo4j is utilized in developing the graph database. Both KR models will be equipped in developing of an English Quran Question Answering system in order to evaluate their benefit.

The knowledge behind the gigantic pool of data remains largely unextracted. Techniques such as ontology design, RDF representations, hpernym extraction, etc. have been used to represent the knowledge. However, the area of logic (FOPL) and linguistics (Semantics) has not been explored in depth for this purpose. Search engines suffer in extraction of specific answers to queries because of the absence of structured domain knowledge. The current paper deals with the design of formalism to extract and represent knowledge from the data in a consistent format. The application of logic and linguistics combined greatly eases and increases the precision of knowledge translation from natural language. The results clearly indicate the effectiveness of the knowledge extraction and representation methodology developed providing intelligence to machines for efficient analysis of data. The methodology helps machines to retrieve precise results in an efficient manner.

Interconnection network topology is a pivotal factor in the performance and reliability of the parallel computer system ranging from multiprocessor to massively parallel computers (MPC). Hierarchical network topology yields better performance with low cost by exploring the locality in the communication and traffic patterns. A Midimew connected Mesh Network (MMN) is a hierarchical interconnection network comprised of numerous basic modules, where the basic module and higher level network of hierarchy is a 2D-mesh and a midimew networks, respectively. In this paper, we present the architecture of MMN and evaluate the packing density and message traffic density of MMN, TESH, mesh, and torus networks. It is revealed that the proposed MMN yields high packing density and low message traffic density which will results efficient VLSI realization and high communication performance of the MMN. Overall, performance with respect to packing and densities reveals that the MMN will be a promising choice for next generation MPC systems.

Architecture of the interconnection network has a great influence on the speed of the multi-core processor design. The main aims of any new architecture are to avoid the latency, and to decrease the cost. In this paper, we proposed new hierarchical interconnection network, in order to build fast parallel computing system. We have evaluated the static network performance of the proposed network such as: node degree, diameter, cost, arc connectivity, bisection width, and wiring complexity. The proposed topology achieved low cost and small diameter comparing to 2D-mesh, and 2D-torus topologies. As well as, it gives good results in the other static parameters. Hence, the proposed network is good solution to improve the performance, and decrease the cost of the interconnection networks for the future generation parallel computing systems.

In this paper, we have proposed a new hierarchical interconnection network called Tori-connected Flattened Butterfly Network (TFBN). It is a hierarchical interconnection network which is able to interconnect millions of nodes in the network to achieve high performance and cost-effective design for massively parallel computing system. TFBN consists of multiple basic modules of flattened butterfly network whereby the basic modules are connected using 2D-torus network. This paper focuses on the architecture of the TFBN and the evaluation of its static network performance such as degree, diameter, cost, average distance, arc connectivity, bisection width and wiring complexity. The comparison of performance shows better performance in terms of distance parameters as well as other parameters.

Morphological analysis is used to study the internal structure words by reducing the number of vocabularies used while retaining the semantic meaning of the knowledge in NLP system. Most of the existing algorithms are focusing on stemmatization instead of lemmatization process. Even with technology advancement, yet none of the available lemmatization algorithms able to produce 100 % accurate result. The base words produced by the current algorithm might be unusable as it alters the overall meaning it tried to represent, which will directly affect the outcome of NLP systems. This paper proposed a new method to handle lemmatization process during the morphological analysis. The method consists three layers of lemmatization process, which incorporate the used of Stanford parser API, WordNet database and adaptive learning technique. The lemmatized words yields from the proposed method are more accurate, thus it will improve the semantic knowledge represented and stored in the knowledge base.

In this era of technology and ICT learners and tutors are moving towards self-learning and self-set goals. Personal Learning Environments (PLE) are systems that enable learners to manage their own learning process for attaining a specific goal. PLEs interconnect and integrate both formal and informal learning with self-regulated learning in tertiary education. Most of the PLEs are adopted at universities for learners to collaborate and share their ideas and content using different tools, communities and services. In this paper, we have created the architecture for a Personal Learning Environment specifically for the Self-help Maqam-based Search System built upon the seven maqams outlined in Al-Ghazali's Minhajul Abidin. This self-help system assesses a person's current state (maqam) of spirituality with an opportunity to learn ways of improving one's spiritual state as defined in Minhajul Abidin. Thus, we developed a pedagogical framework by combining self-regulated learning and metacognitive knowledge first, so that the framework could be used to map the Personal Learning Environment (PLE) into the Self-help Maqam-based Search System mentioned above.

Despite the recent economic slowdown, Malaysia still aspires to move from an industrial to an information economy. Malaysia’s ultimate goal is that, by the year 2020 the country will have become a fully developed nation and have created the Multimedia Super Corridor (MSC) which world class companies can use as a regional hub. One of seven flagship applications created to jumpstart the MSC is that for Smart Schools. With the advent of the information society a new system of education with information technology and multimedia as the enabling forces is needed. This paper describes the Collaborative Courseware Production Model (CCPM) adopted by the Universiti Kebangsaan Malaysia and a commercial firm, Digital Technologies (DTech), in bidding for the Smart School project. Describes the problems faced by the two organizations and how they were overcome. Concludes that this successful collaboration between a business organization and a university can be emulated by other business and academic organizations by leveraging on partnering intelligence or the respective strengths of the organizations.

Integration between the members in complex network of the supply chain group has been an issue for flexible supply chain management. Restructuring of pre-planned activities across the group to react to known deviation may not be possible. The authors decided to introduce an Information Management Architecture (IMA) as the intermediary process which can provide semi-automated assistance for tighter integration. The IMA sits between the RFID infrastructure and the existing supply chain systems and continuously monitors the current activities and wraps services from existing systems to increase the flexibility for immediate process restructuring. A comparison was made between two types of deviations that occurred at the early and final stages of a customization process with and without IMA support. The results showed that the IMA was more stable compared to other supports and significantly performed better in completing joint tasks.

Machine Translation has been defined as the process that utilizes computer software to translate text from one natural language to another. This definition involves accounting for the grammatical structure of each language and using rules, examples and grammars to transfer the grammatical structure of the source language (SL) into the target language (TL). This paper presents English to Arabic approach for translating well-structured English sentences into well-structured Arabic sentences, using a Grammar based and example-translation techniques to handle the problems of ordering and agreement. The proposed methodology is flexible and scalable, the main advantages are: first, a hybrid-based approach combined advantages of rule-based (RBMT) with advantages example-based (EBMT), and second, it can be applied on some other languages with minor modifications. The OAK Parser is used to analyze the input English text to get the part of speech (POS) for each word in the text as a pre-translation process using the C# language, validation rules have been applied in both the database design and the programming code in order to ensure the integrity of data. A major design goal of this system is that it will be used as a stand-alone tool, and can be very well integrated with a general machine translation system for English sentences.

We are in the information intensive environment in which various forms of digital contents have been growing exponentially. In this era of digital data, knowledge representation has been considered as a crucial component of any information retrieval system. It is also considered as a major problem especially in representing the content of unstructured text in an effective way. Although the mission remains impossible to achieve 100% accuracy, many researchers are indulging themselves in documenting these data in many different techniques so that it can be communicated effectively and easily. Indexing is an important element that determines the success of retrieval. Since we are dealing with multiple documents, preprocessing of data is needed before the data gets indexed. Thus, this paper presents an approach on the preprocessing technique. The semantic data which have been represented in skolem clauses will be preprocessed with the help of automatic lexicon generator output and WordNet. This preprocessing plays an important role in getting rid of redundant data before it gets indexed into the semantic matrix. Besides redundancy, it also helps in dealing with common problem that exists in indexing multiple documents in which similar sentences with more or less the same meaning but have been constructed by using different sets of words. As a conclusion, the integration of WordNet and lexicon leads to better result in terms of building effective knowledge representation.

There have been very few studies on the use of conflation algorithms for indexing and retrieval of Malay documents. The two main classes of conflation algorithms are string-similarity algorithms and stemming algorithms. Stemming is used in information retrieval systems to reduce variant word forms to common roots in order to improve retrieval effectiveness. As in other languages, there is a need for an effective stemming algorithm for the indexing and retrieval of Malay documents. Again there are few research on n-gram string-similarity measures done on Malay. We have experimented on the application of stemming and string similarity matching on retrieving of verses from the Al-Quran in order to evaluated their effectiveness. Before retrieval effectiveness can be carried out an experimental data set need to be developed which comprises of a collection of documents, a set of queries, and their relevant judgements. In this paper we will describe the development of the experimental data set and the application of stemming and similarity matching algorithms in retrieving the verses from the Al-Quran. Inherent characteristics of n-grams and several variations of experiments performed on the queries and documents are discussed. The variations are: both non-stemmed queries and documents; stemmed queries and nonstemmed documents; and both stemmed queries and documents. Further experiment are then carried out by removing the most frequently occurring n-gram. The dice-coefficient is used as threshold and weight in ranking the retrieved documents. Beside using dice coefficients to rank documents, inverse document frequency weights are also used. Interpolation technique and standard recall-precision functions are used to evaluate the retrieval effectiveness.

The representation of document content is very important factors in retrieval process. The failure to create a good knowledge representation will definitely lead to failure in terms of its retrieval no matter how good the retrieval engine is. Therefore, this research focused on creating a reliable knowledge representation for our retrieval engine. We are using skolem to capture the information conveyed by multiple text documents and used skolem as an index language. This research also focuses on utilizing the skolem index as its knowledge representation in its question answering system. The system is capable of retrieving the answer as well as states the exact document in which the answer is derived from.

Region growing and edge detection are two popular and common techniques used for image segmentation. Region growing is preferred over edge detection methods because it is more robust against low contrast problems and effectively addresses the connectivity issues faced by edge detectors. Edgebased techniques, on the other hand, can significantly reduce useless information while preserving the important structural properties in an image. Recent studies have shown that combining region growing and edge methods for segmentation will produce much better results. This paper proposed using edge information to automatically select seed pixels and guide the process of region growing in segmenting geometric objects from an image. The geometric objects are songket motifs from songket patterns. Songket motifs are the main elements that decorate songket pattern. The beauty of songket lies in the elaborate design of the patterns and combination of motifs that are intricately woven on the cloth. After experimenting on thirty songket pattern images, the proposed method achieved promising extraction of the songket motifs.

The rapid growth of digital data and users’ information needs have made the demands for automatic indexing to become more important than before. Indexing based on keyword has proven to be unsuccessful to cater for the current needs. Thus, this paper presents a new approach in creating semantic skolem indexing for multiple documents that automatically index all the documents into single knowledge representation. The skolem indexing matrix will then be incorporated in question answering system to retrieve the answer for users query.

The evaluation on the success of E-Government applications through visual relationship measurement model using Structural Equation Modeling (SEM) was conducted on 157 public user. Thus, the objective of this paper is to discuss the implementation of e-government in Malaysia, based on the findings of public user assessment conducted on Malaysia's electronic government applications such as E-services, e-procurement, Generic Office Environment (GOE), Human Resources Management Information System (HRMIS), Project Monitoring System (PMS), Electronic Labor Exchange (ELX), e-tanah, e-pbt (local authority), e-kehakiman and e-syariah. The study adopted a crosssectional survey research approach and a three-round Delphi for ascertaining the definition of information system project success. Findings of the study showed that there was evidence from public user's perspective that the egovernment applications were generally successful. Data were analyzed using exploratory factor analysis via SPSS and confirmatory factor analysis via visual relationship shown through AMOS, to validate the hypothesis model.

Thesaurus has become another valuable structure in any Information Retrieval system. It is a list of terms and concepts that provide a controlled vocabulary of words to use in document indexing, clustering, searching and retrieval. This paper present the results of expanding user's query using Malay thesaurus in the process of searching Malay documents from Malay Hadith retrieval system. The results obtained shows that the retrieval effectiveness improves by four percent when thesaurus is employed in the process of retrieving Malay translated Hadith documents, compared to when single term queries are employed.

Holistic Islamic Banking System (HiCORE) is a virtual multi-tiered system adopting the Services Orientation Architecture (SOA) and parameter driven modules to create new Islamic banking products based on Syariah laws. Islamic banking or Islamic finance reflects banking or financial business that is not contradictory to the principles of Islamic law. This is statutorily mentioned in both the Islamic Banking Act 1983 and the Banking and Financial Institutions Act 1989. In Malaysia, for the creation of Islamic banking products, flexibility is given to practitioners, regulators and Syariah advisors to create and introduce unlimited Islamic banking products to support Islamic banking business. If in the conventional manner, banks may take at least six months for new Islamic products to be created, HiCORE can ensure the possibility of creating an Islamic product at less than a quarter of the time span taken or less (taking into consideration that all policies and processes needed to carry out the transactions are in place). HiCORE went through the normal evaluation process: unit testing, System Integration Test (SIT) and User Acceptance Test (UAT). This paper highlights the latter but unique to the testing of HiCORE, as the UAT is conducted by a Panel of Experts, represented by 15 members from the Islamic Banking industry in the country. Generally, results of the UAT conducted, indicated positive results by the panel of experts. However, there were some specific features and functionalities that needed improvement. In fact one of the features of the system on new product creation which allows users to see the Source of the Dalil (Al-Quran verses or Sunnah), Hukum dan the Syarat during new product creation makes HiCORE novel.

Over the past decades, computer revolution has opened up many possibilities for new field of investigation. With greater accessibility to information and lowering cost of powerful computers, this has spawned new efforts towards understanding complex tasks. Lexicon in particular has long been recognized as interesting and challenging because of its complexness. It is the knowledge of individual words in the language that has been perceived as central component for all types of natural language processing system. In this paper we present an algorithm to create an automatic lexicon generator in order to generate lexicon from an input document by making use of Apple Pie Parser. The lexicon generated managed to reduce significant amount of time and manpower drastically. Psycholinguists as well as computational linguists can benefit from this automatic lexicon construction.

Even though researches in content-based image retrieval are one of the most intensive research areas of information retrieval, they almost certainly focused on retrieving natural images or photographs. Artificially-produced images are stylized form of natural, real-life objects. Research for these images have received less attention, thus this paper attempts to intensify work in this area by implementing quantitative and qualitative performance evaluation of artificially-produced images. Fifty selected images are used as sample queries and they are retrieved based on fusion of geometric shape descriptors. Similarities of these images are measured using Euclidean distance and performances of the retrievals are evaluated using both quantitative and qualitative tests. Quantitative test using recall-precision rate and hit-miss ratio showed that geometric shape descriptors are effective in retrieving the images. However, qualitative test results demonstrate agreement between the system and the interviewed people in the assignment of similarity ranks is at average level.

Computer science is in the more challenging era due to the digital growth and demand that we are facing today. A typical IR system will not go far enough as it uses keyword in order to retrieve the desired information. On the other hand, natural language question answering which is based on logic retrieval have proven to perform better than other methods. The most important component of question answering system is the lexicon. Researchers have identified this as the backbone of any NLP system. In this paper we present our approach on how an automatic lexicon generator has been integrated with question answering system which uses logical inference model. This paper also shows on how the automatic lexicon generator has helped in creating knowledge representation to be used by logic based question answering system. Besides that, the lexicon generated managed to reduce significant amount of time and manpower. It has also helped to make the question answering system to be more robust.

Performance on a question answering system is reflected by the number of correct answers that is produce from a list of alternate choices. However, many answers would appear in documents and passages laden with terms from the query. It becomes more difficult and eventually impossible to make a precise answer to a query. The aim of this study is to determine the keyword of the precise answer to a specific query and illustrate the answer in visual expression. In this paper we propose a method to translate text into visual expression, closest to the correct answer. To achieve a significant illustration capability it is necessary to develop methods of dealing with knowledge base. Firstly, we must consider the real world and the visual expression of key information that can be extracted from the words. The proposed system will analyse and match them using visual semantic simplification description based on logical representation.

The Holistic Islamic Banking System (HiCORE), a banking system suitable for virtual banking environment, created based on universityindustry collaboration initiative between Universiti Kebangsaan Malaysia (UKM) and Fuziq Software Sdn Bhd. HiCORE was modeled on a multitiered Simple - Services Oriented Architecture (S-SOA), using the parameterbased semantic approach. HiCORE's existence is timely as the financial world is looking for a new approach to creating banking and financial products that are interest free or based on the Islamic Syariah principles and jurisprudence. An interest free banking system has currently caught the interest of bankers and financiers all over the world. HiCORE's Parameter-based module houses the Customer-information file (CIF), Deposit and Financing components. The Parameter based module represents the third tier of the multi-tiered Simple SOA approach. This paper highlights the multi-tiered parameter- driven approach to the creation of new Islamiic products based on the `dalil' (Quran), `syarat' (rules) and `rukun' (procedures) as required by the syariah principles and jurisprudence reflected by the semantic ontology embedded in the parameter module of the system.

Support vector machines (SVMs) and related kernel methods have become widely known tools for text mining tasks such as classification and regression. The Arabic part of speech (POS) based support vectors machine is designed and implemented. The NeuroSolutions software is used to adopt and learn the proposed tagger. The radial basis functions (RBFs) is used as a linear function approximator. The experiments has give an evinced that the SVMs tagger is accurate of (99.99%), has low processing time, and use a little a mount of data at training phase.

The Holistic Islamic Banking System: (HiCORE) is a university and industry collaboration initiative between Universiti Kebangsaan Malaysia (UKM) and Fuziq Software Sdn Bhd., sponsored through the Techno Fund Grant of the Ministry of Science, Technology and Innovation, Malaysia. HiCORE, is modeled on a Multi-tiered Services Oriented Architecture (SOA), using the parameter-based Multimedia semantic approach. The research conducted is timely as the financial world is looking for a new approach to banking and financial system. The interest free banking system caught the interest of bankers and financiers all over the world. Today, it is in line with the Islamic banking, financial and related commercial activities, consistent with the syariah principles propagated since the time of the Prophet (p.b.u.h.). This paper highlights the conceptual framework of HiCORE, its arhitecture and the modules designed based on the Cognitive-Intuitive Life Cycle Development model (CI-HiCORE). The paper shall also highlight examples of the DFD or ERD modeling of the Software Requirement Specifications (SRS) and the Software Design Specifications (SDS) of HiCORE.

Machine Translation has been defined as the process that utilizes computer software to translate text from one natural language to another[2].This definition involves accounting for the grammatical structure of each language and using rules and grammars to transfer the grammatical structure of the source language (SL) into the target language (TL). Agreement and Word-ordering play an important role with this structure, as MT should handle agreement between the subject and verb where the number gender, person and features of the subject are important factors in the derivation of the verb as well as the features of the verb itself. Other agreements are required between the adjective and the noun where Arabic adjectives depend on the number, gender and person as well as the definiteness and indefiniteness of the noun. Some other agreements also exist between the numbers and the countable nouns. This paper examines the agreement and word-order rules in tackling Arabic MT systems. Special attention is given to Tarjim by Sakhr Software Company which pioneered a new generation of Arabic Natural Language Processing (NLP) technologies. The approach adopted is flexible and scalable, and the main advantages are: first, it is a rule-based approach, and second, it can be applied on other languages with minor modifications. A major design goal of this system is that it can be used as a stand-alone tool and can be very well integrated with a general machine translation system for English sentence.

Mathematical morphological operations are commonly used as a tool in image processing for extracting image components that are useful in the representation and description of region shape. In this paper, six basic morphological operations are investigated to remove noise and enhance the appearance of binary images. Dilation, erosion, opening, closing, fill and majority operations are tested on twenty-five images and subjectively evaluated based on perceived quality of the enhanced images. Results of the experiments showed that noise can be effectively removed from binary images using combinations of erode-dilate operations. Also, the binary images are significantly enhanced using combinations of majority-close operations.

The idea of the computer system capable of simulating understanding with respect to reading a story or passage and answering questions pertaining to it, has received increased attention within the NLP community as a means to develop and evaluate robust question answering methods. This research is concerned with the problem of generating an automated answer in the context of sophisticated knowledge representation, reasoning, and logical inferential processing. The research focused on Wh types of question to a restricted domain. External knowledge sources i.e. world knowledge (WK) and hypernyms matching procedure (HMP) were introduced. World knowledge led to the refinement of the ability of the system to extract the relevant answers. It provided a solution to the outstanding problem related to the ambiguity enclosed by anaphora and synonym words. Meanwhile, hypernyms refer to a more general or broad keywords, which was able to widen the results of the search. Thus, hypernyms matching procedure was to give a more coherent meaning of words, and eased in the process of extracting the answer based on a given question. This research found that the combined external knowledge sources on word dependencies improved the accuracy of the question answering system with respect to human performance measures.

This paper introduces a logical-linguistic model of document retrieval systems and describes an implementation of a system called SILOL which is based on this model. SILOL uses a shallow semantic translation of natural language texts into a first order predicate representation in performing a document indexing and retrieval process. Some preliminary experiments have been carried out to test the retrieval effectiveness of this system. The results obtained show improvements in the level of retrieval effectiveness, which demonstrate that the approach of using a semantic theory of natural language and logic in document retrieval systems is a valid one.

Songket is a traditional handwoven cloth of the Malays and its beauty lies on the design of the songket motifs intricately woven on the cloth. Even though color is a feature of the motif, shape is more important in determining the type of the motifs. This paper describes and tests the efficiency of retrieving songket motifs images using different combination of five geometric shape descriptors. Even though there are many existing approaches of shape-based image retrievals, the goal of this paper is to take advantage of the faster calculations of the geometric shape descriptors to retrieve the images. Fifty selected songket motifs are used as sample queries in this paper. Similarities of these images are measured using Euclidean distance and performance of the retrieval effectiveness is evaluated using recall and precision rate. Results of the experiment showed that geometric shape descriptors are effective in retrieving the songket motifs.

Exploring virtual world will give a memorable experience to the users. Generally, they are expecting smooth navigation with high quality image drawn into their eyes. Nevertheless, when the virtual world is growing simultaneously with the number of objects, the navigation will be detained. Yet, the frame rates can become unacceptable. In this paper, we present and examine range detection approach for view frustum culling to effectively speed-up the interactive virtual heritage project namely Ancient Malacca Virtual Walkthrough. Normal six plane view frustum culling approach use the plane equation to test whether objects is within the visible region. However, range detection approach is different with normal six plane view frustum. It is based on camera referential point and tests the objects if they are in the viewing range. The results show that this approach is able to increase the rendering performance of the virtual walkthrough without sacrificing the visual quality.