Nurfadhlina Bt Mohd Sharef

Universiti Putra Malaysia | UPM · Department of Computer Science

Diabetic disease classification requires a precise understanding of the clinical inputs and their intensity as observed through different stages. Automated and machine‐centric classification requires validated data handling and non‐converging inputs. For improving the classification precision impacted due by complex computations, this article intro...

Diabetes is a chronic disease characterized by a decrease in pancreatic insulin production. The immune system will be harmed due to this condition, which will raise blood sugar levels. However, early detection of diabetes enables patients to begin treatment on time, therefore reducing or eliminating the risk of severe consequences. One of the most...

Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed outstandingly well. However, when training such mode...

The continuous development of new technology and rapid advancement of Artificial Intelligence (AI) contribute to the improvement and enrichment of the teaching and learning process. AI technology promotes a flexible, customized, and effective learning environment, as well as improves other educational competencies via personalized learning. To crea...

Most existing recommendation systems (RSs) are primarily concerned about the accuracy of rating prediction and only recommending popular items. However, other non-accuracy metrics such as novelty and diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering and combined with evolutionary algorithms to ha...

Many objective optimizations (MaOO) algorithms that intends to solve problems with many objectives (MaOP) (i.e., the problem with more than three objectives) are widely used in various areas such as industrial manufacturing, transportation, sustainability, and even in the medical sector. Various approaches of MaOO algorithms are available and emplo...

Question-answering chatbots have tremendous potential to complement humans in various fields. They are implemented using either rule-based or machine learning-based systems. Unlike the former, machine learning-based chatbots are more scalable. Sequence-to-sequence (Seq2Seq) learning is one of the most popular approaches in machine learning-based ch...

Dalam usaha menarik minat pelajar semasa mengikuti pembelajaran dan pemudahcaraan (PdPc) Bahasa Melayu, para pendidik boleh memberikan pertimbangan terhadap pengggunaan teknologi Reality Maya (Virtual Reality) dan Aplikasi ThingLink. Aplikasi ThingLink dapat dintegrasikan dengan peranti kotak VR berupaya menghadirkan pengalaman baharu kepada bakal...

Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately deter...

Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. Generally, clustering is difficult and complex phenomenon, where the appropriate numbers of clusters are always unknown, comes with a l...

There is a great interest shown by academic researchers to continuously improve the sequence-to-sequence (Seq2Seq) model for natural answer generation (NAG) in chatbots. The Seq2Seq model shows a weakness whereby the model tends to generate answers that are generic, meaningless and inconsistent with the questions. However, a comprehensive literatur...

Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intel-ligence based. However, unlike the ruled-based chatbot...

This article intends to inform people on understanding the process of learning via Talaqqi approach and how Talaqqi framework plays a role in ensuring learning transpired. Certainly, our thinking and approach to effective learning have been influenced by the concept of Talaqqi presented in this article. We listed five pillars in constructing effect...

The complexities and tangles of Arabic dialect in orthography and morphology typically make the sentimental analysis quite challenging. Moreover, most of the classification approaches have addressed this problem based on the hand-crafted features. Since Arabic language has multi-dialects and the language has no word-based order, the extraction proc...

The crucial role of customers’ positive experience and their subsequent word-of-mouth have been highlighted by both scholars and practitioners for all industry sectors. In response to an increasing concern of environmental sustainability and sensitivity of consumers for deteriorating environment, eco-friendly (green) products and services gained tr...

Collaborative filtering that relies on overall ratings has been widely accepted due to the ability to generate satisfactory recommendations. However, the most challenging difficulty of this approach is the lack of sufficient ratings or the so-called data sparsity. Moreover, sometimes these ratings alone are not sufficient to precisely understand us...

Integration of heterogeneous data sources is a crucial step in big data analytics, although it creates ambiguity issues during mapping between the sources due to the variation in the query terms, data structure and granularity conflicts. However, there are limited researches on effective big data integration to address the ambiguity issue for big d...

Most existing Collaborative Filtering (CF) approach relies on single overall ratings assigned to items. However, to precisely understand users' behaviours, sometimes this rating alone is not adequate. A user may show his/her overall preferences on an item through the overall ratings but at the same time, they may not satisfy with certain aspects of...

The most important task in aspect-based sentiment analysis (ABSA) is the aspect and sentiment word extraction. It is a challenge to identify and extract each aspect and it specific associated sentiment word correctly in the review sentence that consists of multiple aspects with various polarities expressed for multiple sentiments. By exploiting the...

Stock price prediction has been an attractive research domain for both investors and computer scientists for more than a decade. Reaction prediction to the stock market, especially based on released financial news articles and published stock prices, still poses a great challenge to researchers because the prediction accuracy is relatively low. For...

With the help of technology, people nowadays can easily express their opinions on services or products that they received. Sentiment analysis is an evolving field of study that deals directly with the online expressions posted by user via the internet. The main objective of sentiment analysis is to automate the process of mining opinions into valua...

The rating matrix of a recommendation system contains a high percentage of data sparsity which lowers the prediction accuracy of the collaborative filtering technique (CF). Recently, the temporal based factorization approaches have been used to solve the sparsity problem, but these approaches have a weakness in terms of learning the popularity deca...

Twitter sentiment analysis according to five points scales has attracted research interest due to its potential use in commercial and public social media application. A multi-point scale classification is a popular way used by many companies to evaluate the sentiment of product reviews (e.g. Alibaba, Amazon and eBay). Most of the classification app...

Collaborative filtering (CF) is one of the most popular techniques of the personalized recommendations, where CF generates personalized predictions in the rating matrix. The rating matrix typically contains a high percentage of unknown rating scores which is called the sparsity problem. The matrix factorization approach through temporal approaches...

Analyzing students’ feedback and their expressed emotions toward any subjects could help lecturers to understand their students’ learning behaviour. Several platforms are used by students to express their feelings such as through social networking sites, blogs, discussion forums and the university survey systems. However, the feedbacks typically co...

With the ever increasing of internet applications and social networking sites, people nowadays can easily express their feelings towards any products and services. These online reviews act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like sentimen...

The temporal recommendation system (TRS) is designed for providing users with an accurate prediction based on the history of their behaviour during a precise time. Most TRS approaches use matrix factorization and collaborative filtering, which are primarily based on the distribution of the user preferences. Recently, TRS has gained significant atte...

Information security has been identified by organizations as part of internal operations that need to be well implemented and protected. This is because each day the organizations face a high probability of increase of threats to their networks and services that will lead to information security issues. Thus, effective information security manageme...

User generated content as such online reviews plays an important role in customer's purchase decisions. Many works have focused on identifying satisfaction of the reviewer in social media through the study of sentiment analysis (SA) and opinion mining. The large amount of potential application and the increasing number of opinions expresses on the...

The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clus...

A recommender system provides users with personalized suggestions for items based on the user's behaviour history. This system often uses the collaborative filtering for analysing the rating scores of users for items in the scoring matrix. The scoring matrix of a recommendation system contains a high percentage of data sparsity which lowers the qua...

Current approaches in aspect-based sentiment analysis ignore or neutralize unhandled issues emerging from the lexicon-based scoring (i.e., SentiWordNet), whereby lexical sentiment analysis only classifies text based on affect word presence and word count are limited to these surface features. This is coupled with considerably low detection rate amo...

A number of ontology engineering methodologies have been proposed to date. Distinct methodologies rely on different techniques and activities for developing ontologies. The concept mapping technique has been recently used for developing ontologies related to different domains. However, the existing approaches using concept mapping do not make use o...

A number of ontology engineering methodologies have been proposed to date. Distinct methodologies rely on different techniques and activities for developing ontologies. The concept mapping technique has been recently used for developing ontologies related to different domains. However, the existing approaches using concept mapping do not make use o...

Background: Spatial trajectories suffer from noise that may be caused by poor signal of GPS devices, sometime the noise is acceptable few meters from its true location. In different situations, the noise is too big that dramatically change the information derive from trajectory segments such as speed, thus filtering of noise is needed before starti...

Ontology information extraction has gain popularity due to the increasing amount of ontologies developed over the years. World Wide Web Consortium (W3C) has introduced SPARQL query language to extract information. However SPARQL query language follow a specific pattern in order to find the triple through subgraph matching. The keywords used in the...

Sentiment classification of financial news deals with the identification of positive and negative news so that they can be applied in decision support systems for stock trend predictions. This paper explores several types of feature spaces as different data spaces for sentiment classification of the news article. Experiments are conducted using N-g...

Ambiguity is a potential problem in any semantic question answering (SQA) system due to the nature of idiosyncrasy in composing natural language (NL) question and semantic resources. Thus, disambiguation of SQA systems is a field of ongoing research. Ambiguity occurs in SQA because a word or a sentence can have more than one meaning or multiple wor...

The rating matrix of a personalized recommendation system contains a high percentage of unknown rating scores which lowers the quality of the prediction. Besides, during data streaming into memory, some rating scores are misplaced from its appropriate cell in the rating matrix which also decrease the quality of the prediction. The singular value de...

The ability to exploit public sentiment in social media is increasingly considered as an important tool for market understanding, customer segmentation and stock price prediction for strategic marketing planning and manoeuvring. This evolution of technology adoption is energised by the healthy growth in big data framework, which caused applications...

The growing of Web 2.0 has led to huge information is available. The analysis of this information can be very useful in various fields. In this regards, opinion mining and sentiment analysis are one of the most interesting task that many researchers have paid attention for two last decades. However, this task involves to some challenges that a very...

Sentiment analysis classification has been typically performed by combining features that represent the dataset at hand. Existing works have employed various features individually such as the syntactical, lexical and machine learning, and some have hybridized to reach optimistic results. Since the debate of the best combination is still active, thi...

Semantic question answering (SQA) demands different processing compared to the common information retrieval method because the semantic knowledge base is stored in the triples form. However, manipulating the knowledge requires understanding of its structure and proficiency in semantic query language such as SPARQL. Natural language interface (NLI)...

By rapid growth of the Internet, finding desirable information would be a challenging and time consuming task. In order to tackle this issue, focused crawlers, as the ideal solution, through mining of the Web, help us to find web pages closely relevant to the desired information. For this purpose, a variety of methods are devised and implemented. N...

The sentiment mining approaches can typically be divided into lexicon and machine learning approaches. Recently there are an increasing number of approaches which combine both to improve the performance when used separately. However, this still lacks contextual understanding which led to the introduction of deep learning approaches which allows for...

Text mining refers to the activity of identifying useful information from natural language text. This is one of the criteria practiced in automated text categorization. Machine learning (ML) based methods are the popular solution for this problem. However, the developed models typically provide low expressivity and lacking in human-understandable r...

The effectiveness of opinion mining relies on the availability of credible opinion for sentiment analysis. Often, there is a need to filter out deceptive opinion from the spammer, therefore several studies are done to detect spam reviews. It is also problematic to test the validity of spam detection techniques due to lack of available annotated dat...

The Quranic oath is God’s emphasizing the importance or truthfulness of a concept. Oaths are multifaceted, rich expressions, in which a single oath contains a line of meaning and a variety of aspects. This study proposes a new stylometric model for detecting apparent and narrative oaths. Toward this end, two types of application-specific features f...

The proliferation of microblogging services present a new collection of obstacles and opportunities to automatically extract and analyze the content to understand the expressed sentiments. Several approaches are introduced ranging from traditional information extraction techniques, incorporation of machine learning and utilization of lexicons. Howe...

Several methods have been studied in text categorization and mostly are inspired by the statistical distribution features in the texts, such as the implementation of Machine Learning (ML) methods. However, there is no work available that investigates the performance of ML-based methods against the text expression-based method, especially for incide...

Semantic technology improves classical information retrieval by representing the information in a structured form and with notion of understanding. The querying process however requires formal language which could be challenging to novice users. The natural language interface research addresses this issue by translating the user's question into the...