Naomie Salim

Technology University Of Malaysia, COMPUTER, Faculty Member

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Papers by Naomie Salim

Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals

Computer Methods and Programs in Biomedicine, 2016

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an ele... more Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed,: non-ectopic beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support vector machine and radial basis function method.

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Adapting Document Similarity Measures for Ligand-Based Virtual Screening

Molecules, 2016

Quantifying the similarity of molecules is considered one of the major tasks in virtual screening... more Quantifying the similarity of molecules is considered one of the major tasks in virtual screening. There are many similarity measures that have been proposed for this purpose, some of which have been derived from document and text retrieving areas as most often these similarity methods give good results in document retrieval and can achieve good results in virtual screening. In this work, we propose a similarity measure for ligand-based virtual screening, which has been derived from a text processing similarity measure. It has been adopted to be suitable for virtual screening; we called this proposed measure the Adapted Similarity Measure of Text Processing (ASMTP). For evaluating and testing the proposed ASMTP we conducted several experiments on two different benchmark datasets: the Maximum Unbiased Validation (MUV) and the MDL Drug Data Report (MDDR). The experiments have been conducted by choosing 10 reference structures from each class randomly as queries and evaluate them in the recall of cutoffs at 1% and 5%. The overall obtained results are compared with some similarity methods including the Tanimoto coefficient, which are considered to be the conventional and standard similarity coefficients for fingerprint-based similarity calculations. The achieved results show that the performance of ligand-based virtual screening is better and outperforms the Tanimoto coefficients and other methods.

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Features Reweighting and Selection in ligand-based Virtual Screening for Molecular Similarity Searching Based on Deep Belief Networks

Advances in Data Science and Adaptive Analysis

Virtual screening (VS) is defined as the use of a compilation of computational procedures to grad... more Virtual screening (VS) is defined as the use of a compilation of computational procedures to grade, score and/or sort several chemical formations. The purpose of VS is to identify the molecules holding the greatest prior probabilities of activity. Many of the conventional similarity methods assume that molecular features that do not relate to the biological activity carry the same weight as the important ones. For this reason, the researchers on this paper investigated that some features are being more important than others through the chemist structure diagrams and the weight for each fragment should be taken into consideration by giving more weight to those fragments that are more important. In this paper, a deep learning method specifically known as Deep Belief Networks (DBN) has been used to reweight the molecule features and based on this new weigh, the reconstruction feature error has been calculated for all the features. Based on the reconstruction feature error values, Princ...

Abstractive Text Summarization based on Improved Semantic Graph Approach

International Journal of Parallel Programming

Survey of Text Plagiarism Detection

Computer Engineering and Applications Journal

In this paper we are going to review and list the advantages and limitations of the significant e... more In this paper we are going to review and list the advantages and limitations of the significant effective techniques employed or developed in text plagiarism detection.Â It was found that many of the proposed methods for plagiarism detection have a weakness and lacking for detecting some types of plagiarized text. This paper discussed several important issues in plagiarism detection such as; plagiarism detection Tasks, plagiarism detection process and some of the current plagiarism detection techniques.DOI:Â 10.18495/comengapp.11.037045

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On the Magnitudes of Coefficient Values in the Calculation of Chemical Similarity and Dissimilarity

ACS Symposium Series

Quantum probability ranking principle for ligand-based virtual screening

Journal of computer-aided molecular design, Jan 20, 2017

Chemical libraries contain thousands of compounds that need screening, which increases the need f... more Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation...

Towards Context-Sensitive Domain of Islamic Knowledge Ontology Extraction

International Journal for Infonomics, 2010

Ontology is one of the essential topics in the scope of an important area of current computer sci... more Ontology is one of the essential topics in the scope of an important area of current computer science and Semantic Web. Ontologies present well defined, straightforward and standardized form of the repositories (vast and reliable knowledge) where it can be interoperable and machine understandable. There are many possible utilization of ontologies from automatic annotation of web resources to domain representation and reasoning task. Ontology is an effective conceptualism used for the semantic web. However there is none of the research try to construct an ontology from Islamic knowledge which consist of Holy Quran, Hadiths and etc. Therefore as a first stage, in this paper we try to propose a simple methodology in order to extract a concept based on Al-Quran. Finally, we discuss about the experiment that have been conducted.

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Obstacles and challenges of opinion mining of customers reviews

Molecular Similarity Searching Method Based on Adaptive IR Technique

American Chemical Science Journal, 2014

Swarm Based Features Selection for Text Summarization

The features are the main entries in text summarization. Treating all features equally causes poo... more The features are the main entries in text summarization. Treating all features equally causes poor summary generation. In this paper, we investigate the effect of the feature structure on the features selection using particle swarm optimization. The particle swarm optimization is trained using DUC 2002 data to learn the weight of each feature. The features used are different in terms of the structure, where some features were formed as combination of more than one feature while others as simple or individual feature. Therefore the determining of the effectiveness of each type of features could lead to mechanism to differentiate between the features having high importance and those having low importance. We assume that the combined features have higher priority of getting selection more than the simple features. In each iteration, the particle swarm optimization selects some features, then corresponding weights of those features are used to score the sentences and the top ranking sentences are selected as summary. The selected features of each best summary are used in calculation of the final features weights. The experimental results shown that the simple features are less effective than the combined features.

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Independent component analysis and rough fuzzy based approach to web usage mining

Proceedings of the 24th Iasted International Conference on Artificial Intelligence and Applications, 2006

Web Usage Mining is that area of Web Mining which deals with the extraction of interesting knowle... more Web Usage Mining is that area of Web Mining which deals with the extraction of interesting knowledge from logging information produced by Web servers. A challenge in web classification is how to deal with the high dimensionality of the feature space. In this paper we present Independent Component Analysis (ICA) for feature selection and using Rough Fuzzy for clustering web user sessions. It aims at discovery of trends and regularities in web users' access patterns. ICA is a very general-purpose statistical technique in which observed random data are linearly transformed into components that are maximally independent from each other, and simultaneously have "interesting" distributions. Our experiments indicate can improve the predictive performance when the original feature set for representing web log is large and can handling the different groups of uncertainties/impreciseness accuracy.

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Swarm Diversity Based Text Summarization

Iconip, 2009

Automatic text summarization systems aim to make their created summaries closer to human summarie... more

Leveraging post level quality indicators in online forum thread retrieval

Plagiarism detection techniques

Hak cipta terpelihara. Tiada dibenarkan mengeluar ulang mana-mana bahagian artikel, ilustrasi, da... more Hak cipta terpelihara. Tiada dibenarkan mengeluar ulang mana-mana bahagian artikel, ilustrasi, dan isi kandungan buku ini dalam apa juga bentuk dan cara apa jua sama ada dengan cara elektronik, fotokopi, mekanik, atau cara lain sebelum mendapat izin bertulis daripada Timbalan Naib Canselor (Penyelidikan dan Inovasi), Universiti Teknologi Malaysia, 81310 Skudai, Johor Darul Ta'zim, Malaysia. Perundingan tertakluk kepada perkiraan royalti atau honorarium.

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Fuzzy Semantic-Based String Similarity for Extrinsic Plagiarism Detection - Lab Report for PAN at CLEF 2010

Clef, 2010

Abstract. This report explains our plagiarism detection method using fuzzy semantic-based string ... more

Quality biased thread retrieval using the voting model

ABSTRACT

Using graph-based consensus clustering for combining multiple K-Means clusterings of heterogeneous chemical structures

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A knowledge diagnostic system for product defects

The need to fulfill customer satisfaction and increase product quality has motivated many manufac... more The need to fulfill customer satisfaction and increase product quality has motivated many manufacturing firms to investigate and diagnose their product failure. To gain a correct and accurate diagnostic, the entire processing root must be recorded and controlled in every step of the manufacturing process. In this research, a prototype system has been developed for a tile manufacturing company to diagnose tile defects and to recommend actions for improvement. This system consists of two main components, the knowledge base and inference engine. The knowledge base has been developed by capturing data and information that are related to tile defects, such as symptoms, probable causes, types of defects, processes, sub processes, tile classifications, etc. On the other hand, the inference engine has been built by implementing the forward chaining and depth first searching methods to search for the causes of defects. The analysis proves that this system can help the workers in the company to diagnose tile defects and solve the problems. Besides this, the system can also help to share and transfer knowledge among the knowledge workers in the company.

Virtual document for peer-to-peer database clustering

Naomie Salim

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