Farzana Kabir Ahmad

• PhD
• Professor (Associate) at Northern University of Malaysia

The trend of people in using social media services to share personal thoughts has caused textual data to grow exponentially. However, uncontrolled negative discussions on social media can lead to generating social tension and facilitate public gathering in real life. User generated contents on social media can be explored to reveal insights to the...

Recognition of human emotions is a fascinating research field that motivates many researchers to use various approaches, such as facial expression, speech or gesture of the body. Electroencephalogram (EEG) is another approach of recognizing human emotion through brain signals and has offered promising findings. Although EEG signals provide detail i...

This study aims to explore COVID-19 pandemic sentiment by using clustering approach. The data is obtained by crawling COVID-19 related posts from Twitter. The crawled data is pre-processed, and terms are extracted by using Term Frequency-Inverse Document Frequency (TFIDF) technique. Singular Value Decomposition (SVD) technique is then used to reduc...

Feature Selection (FS) phase is crucial in the Event Detection (ED) model. Several studies have captured the most informative features using various filter and wrapper FS methods. Recently, FS methods based on swarm intelligence algorithms have been employed to determine the relevant features. Nevertheless, ED from sparse and high-dimensional featu...

Wrapper Feature Selection (FS) methods based on the Binary Bat Algorithm (BBA) have recently been employed in a variety of detection applications to determine the most relevant feature subset. Despite the outstanding achievement of BBA in these domains, BBA has never been applied in Event Detection (ED). In our recent work, a novel wrapper FS appro...

Personality is significant in distinguishing the individual differences in the characteristic patterns of human thinking, feeling and behavior. By extension, there is a likelihood that personality can influence the development of self-stigma of getting psychological help among individuals. However, limited research has investigated the effect of pe...

The growth of e-commerce has brought about a growing concern regarding its impact on the environment. Activities such as excessive packaging, delivery, and returns have contributed to increased carbon emissions, resulting in a significant carbon footprint. To promote a sustainable e-commerce environment, a study is needed to assess the carbon footp...

The social networks and news ecosystem provide valuable social information, however, the rise of deceptive content such as fake news generated by social media users, poses an increasing threat to the propagation and diffusion of fake news over the social network and among users. Low-quality news and misinformation spread on social media had negativ...

The feature to learn complex text representations enabled by Deep Neural Networks (DNNs) has revolutionized Natural Language Processing and several other fields. However, DNNs have not developed beyond all challenges. For instance, the vanishing gradient problem remains a major challenge. This challenge hinders the ability of the system to capture...

This systematic literature review synthesized 64 studies from 2017–2023 to analyze recent advances in anomalous text detection methods and applications. The analysis revealed increasing adoption of deep learning methods, especially transformer architectures, given their contextual modeling capabilities. However, fundamental data scarcity and evalua...

Topic modeling is a popular method used to discover latent topics hidden in text corpora. Applied to social media, it offers insights into understanding the contents of social media data. This study aims to model the topics found in two hospitals’ Facebook pages, particularly we used the Latent Dirichlet Allocation (LDA) technique to extract 20 top...

Human knowledge is mostly in the form of unstructured text. Text can be transcribed into various languages such as the Thai language. To extract knowledge from Thai text, natural language tasks such as word segmentation, Elementary Discourse Unit (EDU) segmentation, and anaphora resolution is the needed tasks. Some interesting phenomena such as non...

Bat Algorithm (BA) has been extensively applied as an optimal Feature Selection (FS) technique for solving a wide variety of optimization problems due to its impressive characteristics compared to other swarm intelligence methods. Nevertheless, BA still suffers from several problems such as poor exploration search, falling into local optima, and ha...

Microarray technology provides an enormous opportunity to measure large-scale gene expressions simultaneously. However sparse and high-dimensional feature space posed significant challenges during data analysis, mainly in learning network structure. Hence, feature selection (FS) has become an essential phase in microarray data analysis to obtain si...

Bat Algorithm (BA) has been extensively applied as an optimal Feature Selection (FS) technique for solving a wide variety of optimization problems due to its impressive characteristics compared to other swarm intelligence methods. Nevertheless, BA still suffers from several problems such as poor exploration search, falling into local optima, and ha...

Multi-label classification is a general type of classification that has attracted many researchers in the last two decades due to its applicability to many modern domains, such as scene classification, bioinformatics and text classification, among others. This type of classification allows instances to be associated with more than one class label a...

Text is one of the useful knowledge sources of a human. Each element in a text has to be analyzed to identify the piece of information and knowledge. EDU is important for NLP applications that need a smaller unit to process rather than a sentence such as text summarization, information extraction, and question answering. Therefore, EDU can be more...

New Multi-label Ranking Method

Event detection has wide application especially in the area of news streams analyzing where there is a need to monitor what events are emerging and affecting people's lives. This is crucial for public administrations and policy makers to learn from their previous mistakes to make better decisions in the future. Different researchers have introduced...

Event Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists...

Various studies are in progress to analyze the content created by the users on social media due to its influence and the social ripple effect. The content created on social media has pieces of information and the user's sentiments about social issues. This study aims to analyze people's sentiments about the impact of technology on employment and ad...

Motion trajectory prediction is one of the key areas in behaviour and surveillance studies. Many related successful applications have been reported in the literature. However, most of the studiesare based on sigmoidal neural networks in which some dynamic properties of the data are overlooked due to the absence of spatiotemporal encoding functional...

This study proposes an outlier detection model in text data stream. Text stream is an important variant of data stream clustering. It has many useful implementations such as trend analysis, detection and tracking of topics, recommendation of user, and outlier detection. Outlier detection detects events which are interesting to the user and perhaps...

Multi-Label Classification (MLC) is a general type of classification that has attracted many researchers in the last few years. Two common approaches are being used to solve the problem of MLC: Problem Transformation Methods (PTMs) and Algorithm Adaptation Methods (AAMs). This Paper is more interested in the first approach; since it is more general...

In the last few years, multi label classification has attracted many scholars and researchers; due to the increasing number of modern domains that are applicable to this general type of classification. Recently, it has been believed by many researchers that the best way to handle the problem of multi label classification is by exploiting the correl...

In this paper, a comparative analysis between the three main approaches that are being used to solve the problem of Multi Label Classification (MLC) have been conducted. The goal of doing this comparative analysis is to evaluate the performance of the three main approaches, and decide which approach to use with respect to the characteristics of the...

The area of Event Detection (ED) has attracted researchers' attention over the last few years because of the wide use of social media. Many studies have examined the problem of ED in various social media platforms, like Twitter, Facebook, YouTube, etc. The ED task for social networks involves many issues, including the processing of huge volumes of...

DNA microarray technology is a current innovative tool that has offers a new perspective to look sight into cellular systems and measure a large scale of gene expressions at once. Regardless the novel invention of DNA microarray, most of its results relies on the computational intelligence power, which is used to interpret the large number of data....

Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find optimal feature set to maximize classification per...

Trust between human and robot is one of the crucial issues in robot-based therapy. It is highly important to provide a clearer and richer understanding and also to answer the questions why trust occurs in machines and how it can maintain successful interaction. In this paper, an agent based model for trust dynamics in short-term human-robot interac...

Extracting features from electroencephalogram (EEG) is a challenging task because the signals are complex and chaotic in nature. EEG signals are time varying as human brain produces different frequency bands within different period of time. Due to this reason, several time-frequency methods have been used to extract features, and this includes the...

Canonical form is a notion stating that related idea should have the same meaning representation. It is a notion that greatly simplifies the task by dealing with a single meaning representation for a wide range of expression. The issue in text representation is to generate a formal approach of capturing meaning or semantics in sentences. This issue...

The analyzing and extracting important information from a text document is crucial and has produced interest in the area of text mining and information retrieval. This process is used in order to notice particularly in the text. Furthermore, on view of the readers that people tend to read almost everything in text documents to find some specific in...

Semantically related sentence may not have any word in common. However, identifying the semantic similarity between words at sentence level possess difficult challenges such as polysemy, synonyms, heterogeneity and sparsity of unstructured textual datasets. It is assumed that sentences with similar text or words in common are semantically related....

Multi label classification has become a very important paradigm in the last few years because of the increasing domains that it can be applied to. Many researchers have developed many algorithms to solve the problem of multi label classification. Nerveless, there are still some stuck problems that need to be investigated in depth. The aim of this p...

Quick Response Code offers the capability of storing information without the use of electronic chips. However, the storage capacity of a QR Code is limited. This paper proposes to increase the QR Code data capacity by using multiplexing and multilayering techniques. Experimental results of the data capacity in the proposed QR code are later compare...

This paper presents a temporal dynamic model of anxiety states and traits for an individual. Anxiety is a natural part of life, and most of us experience it from time to time. But for some people, anxiety can be extreme. Based on several personal characteristics, traits, and a representation of events (i.e. psychological and physiological stressors...

Human-computer intelligent interaction (HCII) is a rising field of science that aims to refine and enhance the interaction between computer and human. Since emotion plays a vital role in human daily life, the ability of computer to interpret and response to human emotion is a crucial element for future intelligent system. Accordingly, several studi...

Deoxyribonucleic acid (DNA) microarray technology is the recent invention that provided colossal opportunities to measure a large scale of gene expressions simultaneously. However, interpreting large scale of gene expression data remain a challenging issue due to their innate nature of “high dimensional low sample size”. Microarray data mainly invo...

This article presents a temporal dynamic model of anxiety states and traits for an individual. Anxiety is a natural part of life, and most of us experience it from time to time. But for some people, anxiety can be extreme. Based on several individual characteristics, traits, and a representation of events (i.e. psychological and physiological stres...

We propose a stimulus-stimulus association learning by coupling firing rate and precise spike timing encoding for spatio-temporal neural networks. We simulate a generic recurrent network with random and sparse connectivity consisting of Izhikevich spiking neurons. The magnitude of weight adjustment in learning is dependent on pre- and postsynaptic...

Human emotion recognition is the key step toward innovative human-computer interactions. The advanced in computational algorithms and techniques has recently offered the promising results in recognizing human emotion. Recently, Electroencephalogram (EEG) has been shown as an effective way in identifying human emotion since it records the brain acti...

The goal of an active traffic management is to manage congestion based on current and predicted traffic conditions. This can be achieved by utilizing traffic historical data to forecast the traffic flow which later supports travellers for a better journey planning. In this study, a new method that integrates Firefly algorithm (FA) with Least Square...

The recent trend towards developing a new generation of robots capable of operating in human-centered environments, and participating in and assisting our daily lives has introduced the need for robotic systems capable to communicate and to react to their users in a social and engaging way. This type of robot could play essential roles to help indi...

The recent trend towards developing a new generation of robots capable of operating in human-centered environments, and participating in and assisting our daily lives has introduced the need for robotic systems capable to communicate and to react to their users in a social and engaging way. This type of robot could play essential roles to help indi...

The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in computational biology. Better understanding of the ge...

Microarray technology can measure thousands of genes which are useful for biologist to study and classify the cancer cells. However, this high dimensional data consists of large number of genes to be examined in regard of small samples size. Thus, selection of relevant genes is a challenging issue in microarray data analysis and has been a central...

In Malaysia, ICTs have been identified as a crucial enabler in the knowledge-based economy to facilitate the acquisition, utilization, and dissemination of knowledge towards enhancing the economic and social values of society. Numerous programmes have been organized, developed, designed and executed to optimize the usage of these ICT Public access...

In this paper we propose a lateral inhibitory spiking neural network for reward-based associative learning with correlation in spike patterns for conflicting responses. The network has random and sparse connectivity, and we introduce a lateral inhibition via an anatomical constraint and synapse reinforcement. The spiking dynamic follows the propert...

In this paper, we propose an algorithm that performs stimulus-stimulus association via reinforcement learning. In particular, we develop a recurrent network with dynamic properties of Izhikevich spiking neuron model and train the network to associate a stimulus pair using reward modulated spike-time dependent plasticity. The learning algorithm asso...

Focusing on the use of Semantic Network and Conceptual Graph (CO) representations, this paper presents an easy way in understanding concepts discussed in the Holy Quran. Quran is known as the main source of knowledge and has been a major source reference for ail types of problems. However understanding the issues and the solution from the Quran is...

Gene regulatory network is a model of a network that describes the relationships among genes in a given condition. However, constructing gene regulatory network is a complicated task as high-throughput technologies generate large-scale of data compared to number of sample. In addition, the data involves a substantial amount of noise and false posit...

Breast cancer is a complex and heterogeneous disease due to its diverse morphological features, as well as different clinical outcome. As a result, breast cancer patients may response to different therapeutic options. Currently, difficulties in recognizing the breast cancer types lead to inefficient treatments. Generally, there are two types of bre...

Breast cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. In many patients, microscopic or clinically evident metastases have already occurred by the time the primary tumor is diagnosed. Chemotherapy or hormonal therapy reduces the risk of distant metastasis by one-third, but it is estimated that about 70% t...

Understanding the mechanisms of gene regulation during breast cancer is one of the most difficult problems among oncologists because this regulation is likely comprised of complex genetic interactions. Given this complexity, a computational study using the Bayesian network technique has been employed to construct a gene regulatory network from micr...

Focusing on the use of Semantic Network representation, this paper presents an easy way in understanding concepts discussed in the Holy Quran. Quran is known as the main source of knowledge and has been a major source reference for all types of problems. However understanding the issues and the solution from the Quran is difficult due to lack of un...

Breast cancer patients with the same diagnostic and clinical prognostics profilecan have markedly different clinical outcomes. This difference is possibly causedby the limitation of current breast cancer prognostic indices, which groupmolecularly distinct patients into similar clinical classes based mainly on themorphology of diseases. Traditional...

The invention of DNA microarray technology has modernized the approach of biology research in such a way that scientists can now measure the expression levels of thousands of genes simultaneously in a single experiment. Although this technology has shifted a new era in molecular classification, interpreting microarray data still remain a challengin...

One of the backbones in electronic manufacturing industry is the printed circuit board (PCB). The recent rapid growth in electronics devices, results escalating in the production number of the PCBs. For electronic equipment and appliances which are PCB based, new generations of PCB's are produced to suit the requirements of new products. This devel...

Image segmentation has been an important and challenging issue in the Feld of computer vision over decades. It ploys a critical role for most image analysis tasks, such as object detection and recognition.The aim of this paper is to obtain segmented image of Printed Circuit Board (PCB)'s track using mathematical morphological operation. Morphologic...