Unapparent information revelation for counterterrorism: Visualizing associations using a hybrid graph-based approach

Unapparent Information Revelation refers to the task in the text mining of a document collection of revealing interesting information other than that which is explicitly stated. It focuses on detecting possible links between concepts across multiple text documents by generating a graph that matches the evidence trail found in the documents. A Concept Chain Graph is a statistical technique to find links in snippets of information where singularly each small piece appears to be unconnected.In relation to algorithm performance, Latent Semantic Indexing and the Contextual Network Graph are found to be comparable to the Concept Chain Graph.These aspects are explored and discussed.In this paper,a review is performed on these three similarly grounded approaches. The Concept Chain Graph is proposed as being suited to extracting interesting relations among concepts that co-occur within text collections due to its prominent ability to construct a directed graph, representing the evidence trail. It is the baseline study for our hybrid Concept Chain Graph approac

ISO 9126 Quality Model For Evaluating of Student Portal: Al-Madinah International University (Case study)

E-learning is a teaching system that involves electronic resources such as computers and the Internet, and the student portal is an essential tool that supports learning in universities. However, there is a limited evaluation model for educational websites. Therefore, a framework is required to guide the creation of such a model. The study conducted evaluates the quality of the student portal at Al-Madinah International University based on ISO 9126 quality model from the student's perspective, and the results show a good quality portal. Nonetheless, students suggest improvements to enhance its effectiveness, ease of use, and learning process. Keywords: ISO 9126; Al-Madinah International University; Quality Model; Student Portal. eISSN: 2398-4287 © 2023. The Authors. Published for AMER and cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under the responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), and cE-Bs (Centre for Environment-Behaviour Studies), College of Built Environment, Universiti Teknologi MARA, Malaysia DOI:  https://doi.org/10.21834/e-bpj.v8iSI15.510

Projecting named entity tags from a resource rich language to a resource poor language

Named Entities (NE) are the prominent entities appearing in textual documents.Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc.This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism.A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism.The English corpus is the translated version of the Malay corpus.Hence, these two corpora are treated as parallel corpora. The method computes the string similarity between the English words and the list of available lexemes in a pre-built lexicon that approximates the best NE mapping.The algorithm has been effectively evaluated using our own terrorism tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.An evaluation of the selected open source NER tool for English is also presented

CROSS-LINGUAL ANNOTATION PROJECTION FOR THE DEVELOPMENT OF MALAY CORPUS

Cross-lingual annotation projection methods can benefit from rich-resourced languages to improve the performance of Natural Language Processing (NLP) tasks in less-resourced languages. In this research, Malay is experimented as the lessresourced language and English is experimented as the rich-resourced language. The research is proposed to reduce the deadlock in Malay computational linguistic research due to the shortage of Malay tools and annotated corpus by exploiting stateof- the-art English tools. The aim of the research is to investigate a suitable crosslingual annotation projection based on word alignment of two languages with syntactical differences. A word alignment method known as MEW A (Malay-J;nglish Word Aligner) that integrates a Dice Coefficient and bigram string similarity measure with little supervision is proposed

PROJECTING NAMED ENTITY TAGS FROM A RESOURCE RICH LANGUAGE TO A RESOURCE POOR LANGUAGE

Named Entities (NE) are the prominent entities appearing in textual documents. Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc. This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism. A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism. The English corpus is the translated version of the Malay corpus. Hence, these two corpora are treated as parallel corpora. The method computes the string similarity between the English words and the list of available lexemes in a pre-built lexicon that approximates the best NE mapping. The algorithm has been effectively evaluated using our own terrorism tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure. An evaluation of the selected open source NER tool for English is also presented.

Influences on perceived stress among undergraduates during COVID-19-induced transition to open distance learning:a multiple linear regression analysis

Due to the COVID-19 pandemic, numerous institutions, encompassing both public and private universities, have shifted their focus towards open and distance learning (ODL) as a replacement method of teaching. This rapid evolution of the education system has brought to the forefront a concerning increase in students' mental health issues. Therefore, this study endeavors to identify the factors that contribute to the perceived stress levels experienced by undergraduate students during ODL. A total of 630 undergraduate students were involved in this research, chosen using a convenience sampling approach. The survey instrument encompassed inquiries about demographic characteristics and employed the Perceived Stress Scale-10 to gauge stress levels. Multiple Linear Regression analysis was employed to scrutinize the data in pursuit of our research objective. The findings underscore the significance of several factors in influencing perceived stress among undergraduate students in the ODL context. Specifically, network condition, level of semester, and gender emerged as noteworthy contributors to the perceived stress experienced by undergraduate students

A Review of Circularly Polarized Dielectric Resonator Antennas: Recent Developments and Applications

A comprehensive review on recent developments and applications of circularly polarized (CP) dielectric resonator antennas (DRAs) is proposed in this paper. DRAs have received more considerations in various applications due to their advantages such as wide bandwidth, high gain, high efficiency, low losses, and low profile. A broad justification for circular polarization and DRAs is stated at the beginning of the review. Various techniques such as single feed, dual, or multiple feeds used by different researchers for generating circular polarization in DRAs are briefly studied in this paper. Multiple-input-multiple-output (MIMO) CP DRAs, which can increase channel capacity, link reliability, and data rate, have also been analyzed. Additionally, innovative design solutions for broadening the circular polarization bandwidth and reducing mutual coupling are studied. Several applications of DRA are also discussed comprehensively. This paper finishes with concluding remarks

Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS–BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS–BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS–BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample