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Analysis study on R-Eclat algorithm in infrequent itemsets mining Mustafa Man; Julaily Aida Jusoh; Syarilla Iryani Ahmad Saany; Wan Aezwani Wan Abu Bakar; Mohd Hafizuddin Ibrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

There are rising interests in developing techniques for data mining. One of the important subfield in data mining is itemset mining, which consists of discovering appealing and useful patterns in transaction databases. In a big data environment, the problem of mining infrequent itemsets becomes more complicated when dealing with a huge dataset. Infrequent itemsets mining may provide valuable information in the knowledge mining process. The current basic algorithms that widely implemented in infrequent itemset mining are derived from Apriori and FP-Growth. The use of Eclat-based in infrequent itemset mining has not yet been extensively exploited. This paper addresses the discovery of infrequent itemsets mining from the transactional database based on Eclat algorithm. To address this issue, the minimum support measure is defined as a weighted frequency of occurrence of an itemsets in the analysed data. Preliminary experimental results illustrate that Eclat-based algorithm is more efficient in mining dense data as compared to sparse data.

Postdiffset Algorithm in Rare Pattern: An Implementation via Benchmark Case Study Mustafa Man; Wan Aezwani Wan Abu Bakar; Masita Masila Abd Jalil; Julalily Aida Jusoh
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (316.046 KB)

Frequent and infrequent itemset mining are trending in data mining techniques. The pattern of Association Rule (AR) generated will help decision maker or business policy maker to project for the next intended items across a wide variety of applications. While frequent itemsets are dealing with items that are most purchased or used, infrequent items are those items that are infrequently occur or also called rare items. The AR mining still remains as one of the most prominent areas in data mining that aims to extract interesting correlations, patterns, association or casual structures among set of items in the transaction databases or other data repositories. The design of database structure in association rules mining algorithms are based upon horizontal or vertical data formats. These two data formats have been widely discussed by showing few examples of algorithm of each data formats. The efforts on horizontal format suffers in huge candidate generation and multiple database scans which resulting in higher memory consumptions. To overcome the issue, the solutions on vertical approaches are proposed. One of the established algorithms in vertical data format is Eclat.ECLAT or Equivalence Class Transformation algorithm is one example solution that lies in vertical database format. Because of its, fast intersection‟, in this paper, we analyze the fundamental Eclat and Eclatvariants such asdiffsetand sortdiffset. In response to vertical data format and as a continuity to Eclat extension, we propose a postdiffset algorithm as a new member in Eclat variants that use tidset format in the first looping and diffset in the later looping. In this paper, we present the performance of Postdiffset algorithm prior to implementation in mining of infrequent or rare itemset.Postdiffset algorithm outperforms 23% and 84% to diffset and sortdiffset in mushroom and 94% and 99% to diffset and sortdiffset in retail dataset.

A performance of comparative study for semi-structured web data extraction model Ily Amalina Ahmad Sabri; Mustafa Man
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

The extraction of information from multi-sources of web is an essential yet complicated step for data analysis in multiple domains. In this paper, we present a data extraction model based on visual segmentation, DOM tree and JSON approach which is known as Wrapper Extraction of Image using DOM and JSON (WEIDJ) for extracting semi-structured data from biodiversity web. The large number of information from multiple sources of web which is image’s information will be extracted using three different approach; Document Object Model (DOM), Wrapper image using Hybrid DOM and JSON (WHDJ) and Wrapper Extraction of Image using DOM and JSON (WEIDJ). Experiments were conducted on several biodiversity website. The experiment results show that WEIDJ approach promising results with respect to time analysis values. WEIDJ wrapper has successfully extracted greater than 100 images of data from the multi-source web biodiversity of over 15 different websites.

Formal Specification for Spatial Information Databases Integration Framework (SIDIF) Mustafa Man; Julaily Aida Jusuh; Mohd Shafry Mohd Rahim; Mohammad Zaidi Zakaria
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 9, No 1: April 2011
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v9i1.672

This paper discusses the formal validation for spatial information databases integration framework (SIDIF). A SIDIF database is a large, organized body of persistent data, usually associated with computerized software designed to update, query, and retrieve components of the data stored within the system. One of the common difficulties faced by the developer is in designing a robust database system. Even so, in order to solve this matter, developers have to focus their efforts on the formal specifications. The formal specification is supposed to reduce the overall development time. Formal specifications can be used to provide an unambiguous and precise supplement to natural language descriptions. Besides, it can be rigorously validated and verified leading to the early detection of specification errors. Consequently, to validate this problem formally, we specify the SIDIF database framework using Z language and prove by using Z/EVES theorem proven tool. By using this kind of tools, it may help to reduce time, energy and mistake compared to manual theorem proving which can be error task and tedious.

WEIDJ: Development of a new algorithm for semi-structured web data extraction Ily Amalina Ahmad Sabri; Mustafa Man
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.16205

In the era of industrial digitalization, people are increasingly investing in solutions that allow their process for data collection, data analysis and performance improvement. In this paper, advancing web scale knowledge extraction and alignment by integrating few sources by exploring different methods of aggregation and attention is considered in order focusing on image information. The main aim of data extraction with regards to semi-structured data is to retrieve beneficial information from the web. The data from web also known as deep web is retrievable but it requires request through form submission because it cannot be performed by any search engines. As the HTML documents start to grow larger, it has been found that the process of data extraction has been plagued with lengthy processing time. In this research work, we propose an improved model namely wrapper extraction of image using document object model (DOM) and JavaScript object notation data (JSON) (WEIDJ) in response to the promising results of mining in a higher volume of image from a various type of format. To observe the efficiency of WEIDJ, we compare the performance of data extraction by different level of page extraction with VIBS, MDR, DEPTA and VIDE. It has yielded the best results in Precision with 100, Recall with 97.93103 and F-measure with 98.9547.

i-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining Wan Aezwani Wan Abu Bakar; Mustafa Man; Mahadi Man; Zailani Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.13497

One example of the state-of-the-art vertical rule mining technique is called equivalence class transformation (Eclat) algorithm. Neither horizontal nor vertical data format, both are still suffering from the huge memory consumption. In response to the promising results of mining in a higher volume of data from a vertical format, and taking consideration of dynamic transaction of data in a database, the research proposes a performance enhancement of Eclat algorithm that relies on incremental approach called an Incremental-Eclat (i-Eclat) algorithm. Motivated from the fast intersection in Eclat, this algorithm of performance enhancement adopts via my structured query language (MySQL) database management system (DBMS) as its platform. It serves as the association rule mining database engine in testing benchmark frequent itemset mining (FIMI) datasets from online repository. The MySQL DBMS is chosen in order to reduce the preprocessing stages of datasets. The experimental results indicate that the proposed algorithm outperforms the traditional Eclat with 17% both in chess and T10I4D100K, 69% in mushroom, 5% and 8% in pumsb_star and retail datasets. Thus, among five (5) dense and sparse datasets, the average performance of i-Eclat is concluded to be 23% better than Eclat.

A deep web data extraction model for web mining: a review Ily Amalina Ahmad Sabri; Mustafa Man
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp519-528

The World Wide Web has become a large pool of information. Extracting structured data from a published web pages has drawn attention in the last decade. The process of web data extraction (WDE) has many challenges, dueto variety of web data and the unstructured data from hypertext mark up language (HTML) files. The aim of this paper is to provide a comprehensive overview of current web data extraction techniques, in termsof extracted quality data. This paper focuses on study for data extraction using wrapper approaches and compares each other to identify the best approach to extract data from online sites. To observe the efficiency of the proposed model, we compare the performance of data extraction by single web page extraction with different models such as document object model (DOM), wrapper using hybrid dom and json (WHDJ), wrapper extraction of image using DOM and JSON (WEIDJ) and WEIDJ (no-rules). Finally, the experimentations proved that WEIDJ can extract data fastest and low time consuming compared to other proposed method.

Feature extraction and classification for multiple species of Gyrodactylus ectoparasite Rozniza Ali; Amir Hussain; Mustafa Man
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Active Shape Models (ASM) are applied to the attachment hooks of several species of Gyrodactylus, including the notifiable pathogen G. salaris, to assign each species to its truespecies type. Linear (i.e. LDA and K-NN) andnon-linear (i.e. MLP and SVM) models are used to classify Gyrodactylus species. Speciesof Gyrodactylus, ectoparasitic monogenetic flukes of fish, are difficult to discriminate andidentify according to morphology alone and their speciation currently requires taxonomicexpertise. The current exercise sets out to confidently classify species, which in this example includes a species which is a notifiable pathogen of Atlantic salmon, to their true classwith a high degree of accuracy. The findings from the current exercise demonstrates thatimport of ASM data into a MLP classifier, outperforms several other methods of classification (i.e. LDA, K-NN and SVM) that were assessed, with an average classification accuracyof 98.72%. DOI: http://dx.doi.org/10.11591/telkomnika.v13i3.7096

Improving Performance of DOM in Semi-structured Data Extraction using WEIDJ Model Ily Amalina Ahmad Sabri; Mustafa Man
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 3: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i3.pp752-763

Web data extraction is the process of extracting user required information from web page. The information consists of semi-structured data not in structured format. The extraction data involves the web documents in html format. Nowadays, most people uses web data extractors because the extraction involve large information which makes the process of manual information extraction takes time and complicated. We present in this paper WEIDJ approach to extract images from the web, whose goal is to harvest images as object from template-based html pages. The WEIDJ (Web Extraction Image using DOM (Document Object Model) and JSON (JavaScript Object Notation)) applies DOM theory in order to build the structure and JSON as environment of programming. The extraction process leverages both the input of web address and the structure of extraction. Then, WEIDJ splits DOM tree into small subtrees and applies searching algorithm by visual blocks for each web page to find images. Our approach focus on three level of extraction; single web page, multiple web page and the whole web page. Extensive experiments on several biodiversity web pages has been done to show the comparison time performance between image extraction using DOM, JSON and WEIDJ for single web page. The experimental results advocate via our model, WEIDJ image extraction can be done fast and effectively.

A new modification CNN using VGG19 and ResNet50V2 for classification of COVID-19 from X-ray radiograph images Usman Haruna; Rozniza Ali; Mustafa Man
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp369-377

Coronavirus often called COVID-19 is a deadly viral disease that causes as a result of severe acute respiratory syndrome coronavirus-2 that needs to be identified especially at its early stages, and failure of which can lead to the further spread of the virus. Despite with the huge success recorded towards the use of the original convolutional neural networks (CNN) of deep learning models. However, their architecture needs to be modified to design their modified versions that can have more powerful feature layer extractors to improve their classification performance. This research is aimed at designing a modified CNN of a deep learning model that can be applied to interpret X-rays to classify COVID-19 cases with improved performance. Therefore, we proposed a modified convolutional neural network (shortened as modification CNN) approach that uses X-rays to classify a COVID-19 case by combining VGG19 and ResNet50V2 along with putting additional dense layers to the combined feature layer extractors. The proposed modified CNN achieved 99.24%, 98.89%, 98.90%, 99.58%, and 99.23% of the overall accuracy, precision, specificity, sensitivity, and F1-Score, respectively. This demonstrates that the results of the proposed approach show a promising classification performance in the classification of COVID-19 cases.

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