Mustafa Man

Decision support for web-based prequalification tender management system in construction projects

Tendering processes need some improvements as they being used widely by government and private sector. One of the improvements attempts to apply is to use Web services technology as a medium to process all the tenders that submitted by the interested contractors. Integration of Web-based application and decision support concepts are employed in order to develop an efficient Web application for prequalification tendering processes. PreQTender is used as a tool to help decision maker (DM) to select the best contractors in Malaysia. PreQTender processes all tender documents and it generates the short listed of the qualified contractors. Those eligible contractors will be considered to be evaluated in the next phase which is evaluation phase.

PreQTender: Web-based prequalification for tender management system in construction projects

Tendering processes need some improvements as they being used widely by government and private sector. The improvements that attempts to apply is to used Web services technology as a medium to process all the tenders that submitted by the interested contractors. This study is to develop an efficient Web application for prequalification tendering processes. Decision Support System (DSS) is used as a tool to help decision making (DMs) for selecting contractors. This work will process tender and it will generate the short listed of the qualified contractors those eligible contractors into the evaluation process.

Spatial information databases integration model

The integration process of the various information in various database types require a thorough understanding to carry out data extraction process. The data extraction process here is in terms of the scheme and the structure for scattered and distributed locations. The Spatial Information Databases Integration Model (SIDIM) is a model which covered the integration processes such as pre-integration, scheme comparison, algorithm development process and intermediary software (middleware) and post-integration. Emphasis are administered in algorithm development by using hybrid approach based on CLARANS approach’s, combination with abstract visualization and Catch Per Unit Effort (CPUE) to enable to achieve processed data or information that is located in different database type in order to obtain data in quick, trusted and reliable manner. SIDIM will become a new engine to process information in various database types without changing any of existing organization system. To verify the model credibility, case studies which are related to fishing industry in Malaysia and artificial reef project are being made as a foundation for SIDIM efficiency testing.

WEIDJ: An improvised algorithm for image extraction from web pages

World wide web (www) is a huge information repository and rapidly growing as source of information. Web pages is known as semi-structured data and it contains variety of information such as text, images, audio, video and other various format. The process of extracting information from the web pages is time consuming and requires correct approach and this paper presents an improvised algorithm in extracting images from the web pages efficiently. In this paper, we study the problem of extracting images in efficient manner. This paper presents an improvised algorithm using Document Object Model (DOM) and JavaScript Object Notation (JSON) that accepts web address as input and extracted images information as the output. The experimental evaluation on webpage of real input has been discussed to prove the limitation of existing method. An experiment was conducted on same website using different approach JSON and DOM to show the comparison of time performance.

Classification of Fish Ectoparasite Genus Gyrodactylus SEM Images Using ASM and Complex Network Model

Active Shape Models and Complex Network method are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to classify each species to their true species type. ASM is used as a feature extraction tool to select information from hook images that can be used as input data into trained classifiers. Linear (i.e. LDA and K-NN) and non-linear (i.e. MLP and SVM) models are used to classify Gyrodactylus species. Species of Gyrodactylus, ectoparasitic monogenetic flukes of fish, are difficult to discriminate and identify on morphology alone and their speciation currently requires taxonomic expertise. The current exercise sets out to confidently classify species, which in this example includes a species which is notifiable pathogen of Atlantic salmon, to their true class with a high degree of accuracy. The results show that Multi-Layer Perceptron (MLP) is the best classifier for performing the initial classification of Gyrodactylus species, with an average of 98.36%. Using MLP classifier, only one species has been misallocated. It is essential, therefore, to employ a method that does not generate type I or type II misclassifications where G. salaris is concerned. In comparison, only K-NN classifier has managed to to achieve full classification on the G. salaris.

Integration Model for Multiple Types of Spatial and Non Spatial Databases

Integration process of a various information in various database types requires a thorough understanding to carry out data extraction process in terms of its scheme and the structure. Due to this, a new model should be developed to resolve the integration process of this heterogeneous information in various database types and in various scattered and distributed locations. SIDIM is a model which covered processes such as pre-integration, scheme comparison, algorithm and intermediary software (middleware) development process and as well as post-integration. Emphasis are administered in algorithm development by using hybrid approach based on CLARANS approach’s combination, abstract visualization and Catch Per Unit Effort (CPUE) to enable to achieve the required processed data or information in a quick, trusted and reliable manner. SIDIM will become a new engine to process information in various database types without changing any of the existing (legacy) organization system. To verify this model credibility, the case study related to fishing industry in Malaysia and artificial reef project are being made as a foundation for SIDIM efficiency testing.

Multiple Types of Semi-structured Data Extraction Using Wrapper for Extraction of Image Using DOM (WEID)

The amount of all kinds of data that available electronically has increased dramatically in recent years. The data resides in different types, either in structured (SD), semi-structured (SSD) or unstructured data (USD). Data integration for multiple types of data can be defined as the problem of combining data from heterogeneous sources to one unified structure. A user is unable to view it as a single entity irrespective of the origination or its data type. It involves combining data coming from different sources and providing users with a unified view of these data. In this paper, we propose a diagrammatic representation of a wrapper for multiple types of SSD data extraction using Document Object Model (DOM). We have implemented the automated web extractor, Wrapper for Extraction of Image using DOM (WEID) using the PHP programming language that can extract images from a web page. Experimental results on a web page are encouraging and confirm the feasibility of the approach in extracting images successfully. Our approach is less labour-intensive, and we believe via our technique that automatic extraction of images can be done fast and effectively.

Improving Performance of DOM in Semi-structured Data Extraction using WEIDJ Model

Received Nov 21, 2017 Revised Jan 29, 2018 Accepted Feb 17, 2018 For continuous target following under complex scene, an objective following calculation in light of multi-shading joint likelihood investigation model was introduced. The calculation embraced shading histogram to speak to the actual factual trademark with Camshaft standard and completed exploratory research in such angles as multichannel joint shading highlights measurements, projection delineate weighted preparing, the following window size and position ascertaining, calculation handling component of course. It utilised red, green, blue, tint, luminance channel shading as the objective watched attributes, and planned the computation technique given the likelihood measurement to recognise any shading focus from the compound scene. It likewise settled the counting method for following window size and position which adjusted the multi-shading model. Utilizing weighting projection outline strategy, the foundation obstruction around the objective potential territory was dispensed with. Finally, more reasonable joining judgment and the calculation cycle tenets were advanced. After the test accreditation, the ongoing execution and recognition proportion introduce a decent outcome.

A Framework for Collaborative Multi-Institution MOOC Environment

In e-Learning web applications, users interact directly via the web platforms with other users. The learning management system (LMS) is one web platform that has been used to manage the online teaching and learning (T&L). Moodle is an open-sourced LMS that has a widespread adoption in several universities as a virtual learning environment. However, each university does not have any connection with another. Thus, it is difficult for students in one university to enroll in any readily available courses from another. The Malaysian Government has taken the lead to embark on the Massive Open Online Courseware (MOOC) for the Malaysian Public Universities (MPU). This will enable any student from any university to enroll in any courses available in any university. This paper describes a framework called ArmadaNet for a multi-institution collaborative MOOC platform. It covers technical and non-technical issues related to the MOOC implementation. The Moodle LMS has been chosen as the web platform to support this multi-institution MOOC collaboration. The development of ArmadaNet as the model for the collaboration will be given. It is a hub that connects and displays courses hosted in the MOOC. The progress of the implementation is given.

Incremental-Eclat Model: An Implementation via Benchmark Case Study

Association Rule Mining (ARM) is one of the most prominent areas in detecting pattern analysis especially for crucial business decision making. With the aims to extract interesting correlations, frequent patterns, association or casual structures among set of items in the transaction databases or other data repositories, the end product of association rule mining is the analysis of pattern that could be a major contributor especially in managerial decision making. Most of previous frequent mining techniques are dealing with horizontal format of their data repositories. However, the current and emerging trend exists where some of the research works are focusing on dealing with vertical data format and the rule mining results are quite promising. One example of vertical rule mining technique is called Eclat which is the abbreviation of Equivalence Class Transformation. In response to the promising results of the vertical format and mining in a higher volume of data, in this study we propose a new model called an Incremental-Eclat adopting via relational database management system, MySQL (My Structured Query Language) that serves as our association rule mining database engine in testing benchmark Frequent Itemset Mining (FIMI) datasets from online repository. The experimental results of our proposed model outperform the traditional Eclat with certain order of magnitude.