Efficient information relay in multi-UAV networks is critical for time-sensitive applications. This paper proposes an optimized power control and bandwidth allocation strategy for joint sensing and communication in a multi-UAV system to minimize sensing and transmission delays under power and bandwidth constraints. The system comprises n UAVs, each monitoring a specific target area, and a flying communication hub that aggregates data from the UAVs and relays it to a data relief center. By dynamically managing power and bandwidth of each UAV, using convex optimization and Lagrangian duality, the system ensures efficient operation, reducing both sensing and communication delays by up to $47.2 \%$ compared to traditional fixed-resource allocation methods. The proposed approach minimizes total system delay, represented as the sum of sensing delay and communication transmission delay, improving data flow and reliability. This adaptive framework addresses critical limitations in UAV-based systems, offering a scalable and robust solution for applications requiring efficient resource utilization in dynamic and time-sensitive scenarios.

Floods are considered one of Malaysia’s most detrimental natural hazards in terms of frequency, extent, duration, damage, and population affected. Consequently, flood risk reduction has become a popular research topic in recent years. Despite various efforts made, flood mitigation strategies within the Malaysian government sectors remain ineffective in alleviating the negative impacts of flooding. As a result, this paper aims to examine the current challenges faced by Malaysian disaster management agencies and their recommendations for reducing the disastrous effects of floods. To achieve this, a qualitative approach was employed through nine focus group discussions comprising a total of 48 participants. Inductive thematic analysis was used to categorize these challenges into several groups under different phases of the disaster management cycle. The obtained results revealed that most challenges faced by Malaysian government agencies were from the pre-disaster phase. Several recommendations were suggested to overcome these challenges and to obtain an effective disaster management capability. In addition, the findings also indicated that although Artificial Intelligence (AI) has great potential in flood risk reduction, AI integration into flood disaster management is still at an early stage in Malaysia. Outcomes from this study are expected to provide valuable insights to support decision-makers in handling flood events more effectively, ultimately saving lives and reducing the damaging impacts of floods in Malaysia.

Magnetic Resonance Imaging (MRI) education often suffers from limited access to physical scanners and the complexity of MRI parameter interdependencies. This paper presents a web-based MRI simulator integrated with a knowledge-based AI assistant to enhance medical and radiography training to bridge the gap between theoretical learning and practical experience in MRI procedures. The simulator offers medical and radiography students an intelligent, interactive platform accessible via any web browser. The simulator enables interactive MRI parameter adjustments and real-time imaging feedback, while the AI assistant employs structured knowledge representation and rule-based reasoning to provide personalized recommendations on parameter optimization, artifact reduction, and protocol selection. The system was developed using modern web technologies including Node.js as a backend solution, JavaScript-driven frontend to scalable with smooth navigation and MongoDB for database. The system’s intelligent assistant leverages domain-specific ontologies and rule-based reasoning to deliver personalized recommendations on parameter optimization, artifact reduction, and protocol selection. The usability testing with 30 medical, system development students and instructors demonstrated high satisfaction, achieving average usability and learning effectiveness scores of 85.1% and 84.7%, respectively. These results indicate the system’s potential to bridge theoretical learning and practical skills in MRI education. Future work will expand anatomical coverage and integrate deep learning to further enhance the AI assistant’s adaptability.

This paper presents the design, implementation, and validation of the X-Band Radar Information System (XRIS), an on-premises, web-based platform enabling real-time ingestion, processing, visualization, and secure dissemination of rainfall measurements from an X-Band Multiparameter Radar (XMPR). Developed at Universiti Teknologi Malaysia (UTM) Pagoh, XRIS modernizes previously fragmented and manual radar data workflows by automating data ingestion and providing centralized, role-based access for researchers, disaster-response teams, and government agencies. Built using Agile methodology and a modular architecture based on Django, Celery, PostgreSQL, Redis, and RabbitMQ, XRIS reliably processes radar data every two minutes, offering responsive real-time visualizations and secure public access through Cloudflare Tunnel. Comprehensive testing and stakeholder validation demonstrate XRIS as a scalable, sustainable platform that bridges the gap between academic research and operational meteorology. By automating and centralizing legacy workflows, XRIS enhances the accessibility and usability of radar data, supporting national disaster preparedness and advancing hydrometeorological research.

This paper presents an intelligent digital concordance system powered by Natural Language Processing (NLP) to advance the study of Arabic scripts and support the development of faith-based intelligent applications. It addresses the limitations of traditional concordance methods by applying advanced computational techniques to analyze the linguistic and thematic structures of Arabic texts. A comprehensive NLP framework was developed, incorporating Arabic morphological analysis, semantic similarity detection, thematic clustering, and cross-referencing algorithms. The system processes the full Arabic corpus (comprising 6,236 verses across 114 chapters of the Quran) using transformer-based models fine-tuned for Classical Arabic, alongside traditional linguistic tools. The proposed framework enables automated indexing, semantic search, and bilingual alignment between Arabic and English texts. Experimental evaluations show strong results, with over 94.7% classification accuracy, 89.3% clustering precision, and a 92% effectiveness rating by domain experts. This research highlights the effective integration of modern NLP techniques for sacred text analysis. By combining linguistic integrity with computational intelligence, the framework offers a robust foundation for faith-aware AI systems and provides scalable, context-sensitive, and semantically rich access to religious knowledge which enhancing academic research and digital scholarship in Arabic script studies.

The selection between Deep Learning (DL) approaches is not easy for URL phishing due to the variety of attacks and scammers. There are various DL techniques to detect phishing URLs, and choosing the suitable algorithm and affecting the model formed is very important. Wrong-choosing DL techniques might lead to low maturity and produce bias. The trained model’s performance and accuracy would also be unsatisfactory if the wrong algorithms and methods were used. It often happens when the attackers change their phishing strategies frequently to target the system’s weaknesses and users’ naivety. The robust characteristics of DL algorithms have led the researchers to develop several URL phishing mitigation strategies. The techniques have been used to detect phishing attacks by using various URL features like URL length, URL domain, and other known features and further incorporating the new features. From the perspective of the Natural Language Processing (NLP) technique perspective, transformers are models designed to handle sequential text, such as summarizing and translating. One of the well-known transformers, called Keras Embedding, has a good application in detecting spam emails. As the transformers proved their usage in URL phishing detection, it is further hypothesized that the URLs can directly parse out the contextual meaning of the string and identify whether the website is benign or phishing. Therefore, this paper provides a URL phishing detection model with a combination of deep learning and natural language processing methods. As shown in the experiments, the result produces and improves with high performance and accuracy for URL phishing detection. We also examined and compared the findings of the proposed solution with deep learning only and NLP-only URL phishing detection approaches.

One of the challenging issues in detecting the malware is that modern stealthy malware prefers to stay hidden during their attacks on our devices and be obfuscated. They can evade antivirus scanners or other malware analysis tools and might attempt to thwart modern detection, including altering the file attributes or performing the action under the pretense of authorized services. Therefore, it’s crucial to understand and analyze how malware implements obfuscation techniques to curb these concerns. This paper is dedicated to presenting an analysis of anti-obfuscation techniques for malware detection. Furthermore, an empirical analysis of the performance evaluation of malware detection using machine learning algorithms and the obfuscation techniques was conducted to address the associated issues that might help researchers plan and generate an efficient algorithm for malware detection.

Worm-like ransomware strains spread quickly to critical systems such as IoMT without human interaction. Therefore, detecting different worm-like ransomware attacks during their spread is vital. Nevertheless, the low detection rate due to the imbalanced ransomware data and the detection systems’ disability for multiclass simultaneous detection are two apparent problems. In this work, we proposed a new approach for multi-classifying ransomware using preprocessing, resampling, and different classifiers. The proposed system uses network traffic NetFlow data, which is privacy-friendly and not heavy. In the first phase, preprocessing techniques were used on the collected and aggregated ransomware traffic, and then an optimized Synthetic Minority Oversampling Technique (SMOTE) was used for resampling the low-class samples. After that, four classifiers were applied, namely, Bayes Net, Hoeffding Tree, K-Nearest Neighbor, and a lightweight Multi-Layered Perceptron (MLP). The experimental results showed that the efficient preprocessing ensured accurate and simultaneous ransomware detection while the resampling technique improved the detection rate, F1, and PRC curve.

There exist numerous solutions to detect malicious URLs based on Natural Language Processing and machine learning technologies. However, there is a lack of comparative analysis among approaches using distributed representation and deep learning. To solve this problem, this paper performs a comparative study on phishing URL detection based on text embedding and deep learning algorithms. Specifically, character-level and word-level embedding were combined to learn the feature representations from the webpage URLs. In addition, three deep learning models, including Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM), were constructed for effective classification of phishing websites. Several experiments were conducted and various evaluation metrics were used to assess the performance of these deep learning models. The findings obtained from the experiments indicated that the combination of the character-level and word-level embedding approach produced better results than the individual text representation methods. Also, the CNN-based model outperformed the other two deep learning algorithms in terms of both detection accuracy and execution time.

Data-driven plays an important role in determining the quality of services in institutions of higher learning (HEIs). Increasingly data in education is encouraging institutions to find ways to improve student academic performance. By using machine learning with visual analytics, data can be predicted based on valuable information and presented with interactive visualizations for institutions to improve decision making. Therefore, predicting students’ academic performance is critical to identifying students at risk of failing a course. In this paper, we propose two approaches, such as (i) a prediction model for predicting students’ final grade based on machine learning that interacts with computational models; (ii) visual analytics to visualize predictive models and insightful data for educators. The data were tested using student achievement records collected from one of the Malaysian Polytechnic databases. The data set used in this study involved 489 first semester students in Computer System Architecture (CSA) course from 2016 to 2019. The decision tree algorithms (J48), Random Tree (RT), Random Forest (RF), and REPTree) was used on the student data set to produce the best predictions of the model. Experimental results show that J48 returns the highest accuracy with 99.8 %, among other algorithms. The findings of this study can help educators predict student success or failure for a particular course at the end of the semester and help educators make informed decisions to improve student academic performance at Polytechnic Malaysia.

This paper presents and discusses a comparative study of three major clustering categories namely Hierarchical-based, Iterative mode-based and Partition-based in analyzing and prioritizing Mobile Augmented reality (MAR) Learning (MAR-learning) usability data. This paper first discusses the related works in usability and clustering before moving on to the identification of gaps that can be addressed through experimentation. This paper will then propose a research methodology to measure four common clustering techniques on MAR-learning usability data. The paper will then discourse comparative results showing how Mini-batch K-means to be an ideal technique within the experimental setup. The paper will then present important research highlights, discussion, conclusion and future works.

Student performance is the most factor that can be beneficial for many parties, including students, parents, instructors, and administrators. Early prediction is needed to give the early monitor by the responsible person in charge of developing a better person for the nation. In this paper, the improvement of Bagged Tree to predict student performance based on four main classes, which are distinction, pass, fail, and withdrawn. The accuracy is used as an evaluation parameter for this prediction technique. The Bagged Tree with the addition of Bag, AdaBoost, RUSBoost learners helps to predict the student performance with the massive datasets. The use of the RUSBoost algorithm proved that it is very suitable for the imbalance datasets as the accuracy is 98.6% after implementing the feature selection and 99.1% without feature selection compared to other learner types even though the data is more than 30,000 datasets.

BACKGROUND: In biomedicine, infrared thermography is the most promising technique among other conventional methods for revealing the differences in skin temperature, resulting from the irregular temperature dispersion, which is the significant signaling of diseases and disorders in human body. Given the process of detecting emitted thermal radiation of human body temperature by infrared imaging, we, in this study, present the current utility of thermal camera models namely FLIR and SEEK in biomedical applications as an extension of our previous article. RESULTS: The most significant result is the differences between image qualities of the thermograms captured by thermal camera models. In other words, the image quality of the thermal images in FLIR One is higher than SEEK Compact PRO. However, the thermal images of FLIR One are noisier than SEEK Compact PRO since the thermal resolution of FLIR One is 160 × 120 while it is 320 × 240 in SEEK Compact PRO. CONCLUSION: Detecting and revealing the inhomogeneous temperature distribution on the injured toe of the subject, we, in this paper, analyzed the imaging results of two different smartphone-based thermal camera models by making comparison among various thermograms. Utilizing the feasibility of the proposed method for faster and comparative diagnosis in biomedical problems is the main contribution of this study.

Parameter selection or attribute selection is one of the crucial tasks in the data analysis process. Incorrect selection of the important attribute might generate imprecise or event for a wrong decision. It is an advantage if the decision-maker could select and apply the best model that helps in identifying the best-optimized attribute set — in the decision analysis process. Recently, many data scientists from various application areas are attracted to investigate and analyze the advantages and disadvantages of big data. One of the issues is, analyzing large volumes and variety of data in a big data environment is very challenging to the data scientists when there is a lack of a suitable model or no appropriate model to be implemented and used as a guideline. Hence, this paper proposes an alternative parameterization model that is able to generate the most optimized attribute set without requiring a high cost to learn, to use, and to maintain. The model is based on two integrated models that are combined with correlation-based feature selection, best-first search algorithm, soft set, and rough set theories which were compliments to each other as a parameter selection method. Experimental have shown that the proposed model has significantly shown as an alternative model in a big data analysis process.

This paper presents and discusses an empirical work of using machine learning K-means clustering algorithm in analyzing and processing Mobile Augmented Reality (MAR) learning usability data. This paper first discusses the issues within usability and machine learning spectrum, then explain in detail a proposed methodology approaching the experiments conducted in this research. This contributes in providing empirical evidence on the feasibility of K-means algorithm through the discreet display of preliminary outcomes and performance results. This paper also proposes a new usability prioritization technique that can be quantified objectively through the calculation of negative differences between cluster centroids. Towards the end, this paper will discourse important research insights, impartial discussions and future works.

This article presents the experimental work of comparing the performances of two machine learning approaches, namely Hierarchical Agglomerative clustering and K-means clustering on Mobile Augmented Reality Usability datasets. The datasets comprises of 2 separate categories of data, namely performance and self-reported, which are completely different in nature, techniques and affiliated biases. This research will first present the background and related literature before presenting initial findings of identified problems and objectives. This paper will the present in detail the proposed methodology before presenting the evidences and discussion of comparing this two widely used machine learning approach on usability data. This paper contributes in presenting evidences showing K-means as the better performing clustering algorithm when compared to Hierarchical Agglomerative when implemented on the usability datasets. The results shown has contradicted with some recent studies claiming otherwise, and the findings have created more research gaps pertaining the combined utilization of machine learning and usability analysis.

This paper presents fractions of research outcome from a bigger project involving machine learning, Hierarchical Agglomerative Clustering (HAC) Algorithms on usability data gathered through performance and self-reported data. This paper highlights the common problems in usability studies where the conventional analysis was frequently utilized while prioritizing usability issues. The utilization of clustering techniques is limited in the area of this study. A previous publication has shown how HAC was used in clustering usability problems in Mobile Augmented Reality (MAR) learning applications. However, there has not been a triangulation effort to confirm the first gathered results due to small datasets. This research presents a methodology adopted from previous studies in confirming earlier usability analysis results. The experiments found consistent evidence approving the feasibility of HAC in clustering and prioritizing Usability performance and self-reported data.

Handling the missing values play important step in the preprocessing phase of hydrological modeling analysis. One of the challenges in preprocessing phase is to deal with the problems of missing data with good consideration on the pattern and approaches of the missing data. Hence, this paper presents a study on Feedforward neural network algorithm (FFNN) and Elman neural network (ENN) imputation algorithm in estimating missing rainfall data at different percentages of missingness. Reliable rainfall data series from nearest neighbor gauging stations were used as inputs to predict the missing rainfall data for an output station. The selected study area is Sungai Merang, East Malaysia. The study revealed that ENN method demonstrated a superior prediction of the missing daily rainfall data than FFNN method. It is also observed that the ENN model-infilling method could be highly beneficial in reducing the data gaps for continuous hydrological modelling analysis.

In higher education institutions, the most significant issue is to improve the students' performance and retention rate. Massive numbers of students' data are used to gain new hidden knowledge from students' learning behaviour, particularly to discover the initial symptom of at-risk students by using Educational Data Mining techniques. However, data with noises, outliers and irrelevant information might cause an inaccurate result. This study aims to develop a robust students' performance prediction model for higher education institution by identifying features of students' data that have the potential to increase performance prediction results, comparing and identifying the most suitable ensemble learning technique after preprocessing the data and optimizing the hyperparameters. Data are collected from 2 different systems, which are: student information system and e-learning system of undergraduate students from the Faculty of Engineering in one of Malaysia's public university. 4413 students' instances are used for this study. The process follows 6 different data mining phases namely: data collection, data integration, data pre-processing (such as cleaning, normalization, and transformation), feature selection, patterns extraction and finally model optimization and evaluation. Machine learning techniques used to build prediction model are Decision Tree, Support Vector Machine and Artificial Neural Network, while for ensemble learning: Random Forest, Bagging, Stacking, Majority Vote and 2 variants of Boosting techniques are AdaBoost and XGBoost. Hyperparameters for ensemble learning techniques are optimized to gain better performance and optimum result. The result shows that the combination of features of students' behaviour from e-learning and students information system using Majority Vote produced better result compared to other ensemble methods.

Botnets are the most deadly threat in the network due to the capability of exploiting resources within a network as an army to launch huge attacks such as Denial-Distributed-of-Service (DDOS) or spam emails. Network Intrusion Detection System (NIDS) that designed based on the behavior of botnets in network traffic is seen as the promising technique in detecting botnets that are hiding by using encryption technique or any hiding techniques. This paper proposes on K-means clustering algorithm as the first phase of botnet's behaviour detection model that extracts data from network traffic. The criterion highlighted for our behaviour detection model is that it should be able to detect botnet in encrypted packets(hiding techniques), structure-independent (centralized and peer-to-peer), requiring minimal computing resources and minimal time processing. Other than that, by representing the real-time of network traffics, the detection model also must be resistant to noise and able to identify the anomaly of botnets behavior among a huge number of normal traffic. We are using the botnet benchmark dataset and normal traffic from Malware Capture Facility Project and comparing our proposed method using K-means algorithm with Expectation Maximization algorithm that proposed by the previous researcher in clustering the similar pattern of botnet behavior. The result shows that the K-means algorithm producing much higher accuracy, 94% and lower false negative rate, 0.1413. While, average accuracy for Expectation Maximization algorithm is 88% and False Negative Rate, 0.2245 with the insertion of uncertain data from real network traffic.

This paper compares two metaheuristic neural networks (ANNs) models, Bat algorithm neural network (BANN) and Bat optimisation neural network (BatNN) for spatial downscaling of long term precipitation. For BANN, model parameters for both pulse rate (R) and loudness (A) are fixed as 0.5. Whilst, R and A parameters for BatNN will dynamically self-adapt in searching the optimal configuration during the training process. Hidden node (HN), iteration number (IN) and learning rate (LR) for both models are predetermined to be 100, 1000 and 1 respectively for comparison. Investigations were carried out with different population (b), maximum pulse frequency (fmax) and velocity factor (α). Models performance will be measured with Square Root of Correlation of Determination (r), Root Mean Square Errors (RMSE), Mean Absolute Error (MAE) and Nash and Sutcliffe coefficient (E). Data from 1961 to 1990 are used for training, whilst validation data are from 1991 to 2010. Predictors of three climate models including HadCM3, ECHAM5 and HadGEM3-RA cum collected precipitation data from Kuching Airport Rainfall Station are input into the models. Model output is the forecasted precipitation. Results showed BatNN is more robust than BANN with its average r=0.96, average RMSE=1.69, average MAE=1.4 and average E=0.84 across the three climate models; while BANN achieved average r=0.95, average RMSE=1.91, average MAE=1.75 and average E=0.82 across the three climate models. The higher accuracy of BatNN can be attributed to the modifications done where dynamical parameters R and A are used in place of static parameter to allow BatNN to self-adapt during the training process.

Online media are well-known to be suitable for conveying hate speech. Hateful wording as such involves communications that unlawfully demean any group or person based on certain characteristics, including colour, race, gender, ethnicity, sexual orientation, religion, or nationality. The continuing rise of internet social platforms, including micro blogging services like Twitter, has compelled the need for more immediate analyses of hatreds and other antagonistic responses to various trigger events. This study aims to investigate the details using aspect-based inspections of sentiments. Content analysis of such tweets along with the associations between them is key. Nevertheless, due to the large data volumes involved, it can oftentimes be burdensome if not infeasible to conduct these types of analyses manually. The main problems of prior methods involve data sparsity, classification accuracy, and sarcastic content identification. for the techniques incorrectly categorise tweets as neutral. For content analysis, three dissimilar schemes were suggested, with all proposing to surmount the above-mentioned problems. The research results show that the proposed strategy has achieved correspondingly increased accuracies of some 75%, 71.43%, and 92.86%.

Zero-day ransomware still threaten users' and enterprises' survival in the cyber-space by disturbing electronic amenities, damaging information systems, and causing data and money losses. Established anti-ransomware techniques are trying to mitigate this security issue, however they are lacking to identify ransomware families effectively without real-time performance overhead. Thus, this paper provides a multi-tier anti-ransomware tool (RANDS) performs via windows platform through three tiers: ransomware analysis tier, learning tier and detection tier. RANDS hybridizes the decisive functions of two machine learning algorithms (Naïve Bays and Decision Tree) to holistically analyze ransomware traits, and accurately classify ransomware families. The prototype implementation of RANDS shows its classification capability against ransomwares with (96.27%) as average accuracy rate and (1.32%) of average mistake rate throughout real-time assessment.

The enhancement of English language proficiency is a clear aim in many educational institutions around the world. One of the latest technology that has been adopted in education recently is Augmented Reality (AR) but still needs more consideration and investigation to insure its effectiveness in English language learning ELL. This paper highlights the most AR technologies that have been employed in ELL and views to what extent they have been useful and beneficial. Moreover, it points out to the limitations that would slowdown the adoption of AR in Education generally and English language learning particularly.

This paper highlights the current literatures in usability studies, performance metrics, self-reported metrics and hierarchical agglomerative clustering algorithms. A literature review is done in these three areas of studies to find a research gap that can be explored further. The paper will then propose a research methodology to study comparatively feature selection based on performance and self-reported usability data. This paper will highlight methods used to compare the feasibility and performance of hierarchical agglomerative clustering algorithms on both performance and self-reported data. The results of the experiment will then be presented and discussed before proceeding to the conclusion and future works of this study.

During disaster the communication behavior between society and crisis management authorities significantly changed due to technological evolutions and social media modernizations. The crowdsourcing and its platforms have gained importance for information exchange during and after disaster events. Social media and mobile apps are powerful and effective crowdsourcing platforms for collection of the data from various sources to collaborate and disseminate the processed information during emergency. However, data collection, integration and processing of unstructured data from diverse platforms, reliability and validity of data as well as privacy and security issues are the challenging part of crowdsourcing. The purpose of this study is to identify the various challenges through the systematic literature review during disaster management by using the crowdsourcing. Fifteen challenges have been highlighted and prioritized on the basis of their frequency of occurrence. Crowdsourcing has an important aspect of collecting and sharing of information during the disaster situation and aiming to reduce the disaster effects. It is recommended to address the identified crowdsourcing challenges for providing relief to affected community.

Fog Computing is a part of edge computing that define as intermediate layer between “Things” and the Cloud. Fog Health is an implementation of fog Computing's concept in health care and its related area. The purpose of this study is to extract and analyze the concept and application of Fog Health. The goal of this study are to identify the trend or patterns in Fog Health publications, to identify the application domain of Fog Health and to identify the research gap and future direction of implementing Fog Computing in health care related areas. Search term with relevant keywords were used to identify primary study related to the topic. About 53 of primary study were identified and selected. 46% (the largest portion) of the selected paper were journal articles. 47% of the publications were published by IEEE and 25 of the publications were published in 2017. We have found that the most three major issues that mostly discussed in fog Computing literatureas are related to the implementation of real-time system with minimum delay, the issues related to the performance on complex data processing that not affecting the system performance and the security & privacy issues related to the Fog Health implementations in the medical related facilities.

Cybersecurity has a lot of challenging problems, from intrusion to illegal actions and destruction. These challenges have attracted so many interests from researchers and practitioners in providing sustainable solutions. The presence of big data has increased the hope in curbing the aforementioned challenges due to its advantage of providing the platform for improved technology-based advancements. With the rapid development in the adoption of cloud-based services and migration of data to the cloud, there is a genuine need for advanced protection and prediction techniques. With this regard, Granular Computing is introduced as a new paradigm capable of providing solution to myriad of problems among which are related to cybersecurity. In this study, by taking the advantage of Granular Computing and Big Data, an improved k-means information granulation framework that incorporate similarity measure from the pre-clustering stage as well as segregation technique is proposed based on Granular Computing to identify threats from a time series dataset. The experiments on public available dataset shows that the method have good recognition performance better than other known predictive analysis classifiers – kNN and naive bayes. This study also provides research direction towards enhancing data granulation techniques in handling uncertainties.

The classification process is a decision-making task. In order to obtain a good decision, the classification process needs to be conducted by following a standard framework or approach. The selection of a good parameterization method plays an important role in executing an effective parameterization process. The parameterization process will generate an optimized parameter reduction set that helps the classifier in generating a significant result. However, the size and characteristics of the dataset might also influence the generation of results in the classification process. The process of parameterization becomes more complex when the dataset is big and consists of uncertain and inconsistent data. Therefore, these problems need to be considered during the decision-making process. Many solutions have been provided recently by researchers, but most of the research works did not consider the highlighted issues as problems that should be solved together through the use of a single framework. In this paper, an alternative framework was proposed for decision-makers in conducting the decision-making process. The framework was demonstrated by the use of a two-tier hybrid parameterization phase that involving two main processes for identifying the most optimized parameter set for uncertain and inconsistent big datasets. The results showed that the proposed framework can be implemented to significantly increase the classification performance by returning an accuracy rate of more than 70% for all datasets.

Given the rapid development of Internet, social media opened up new opportunity for medical and health communications. Numbers of previous research conducted showed that social media shape up new chances to ensure an effective distribution of information in healthcare. Looking from the perspective of medical tourism, this study aimed to identify the availability of social media link in the medical tourism hospitals websites from Malaysia and evaluate their performance on every social media platform namely Facebook, Instagram, Twitter and YouTube. A list of 70 medical tourism hospitals gathered from the websites of Malaysian Health Travel Council (MHTC) http://www.mhtc.org.my. Each URL of the hospital identified and visited. The social media link availability on the website was observed and results showed that Facebook is the most preferable social media among the hospitals which is 51.4% followed by Instagram recorded as 14.3%. The Twitter recorded as 12.9% and YouTube only 11.4% hospitals. Most hospitals only provide one link to social media directly from their website. Astonishingly, almost half of the total medical tourism hospitals in Malaysia that is 44.2% does not have link to any social media on their websites. The top ten-hospital performance in Facebook rank gain more than 15000 “like” and “follows” at their page. Therefore, it was highly recommended for the medical tourism hospitals to actively engage with the social media and promote the link on their websites so that they can lead their current and prospective patients towards various information resources for a better distribution of medical and health information.

In this paper, a neural network is proposed to analyse Twitter sentiment classification for the Twitter domain. The study examines and evaluates the performance of neural networks with word embedding features in Twitter sentiment classification. Four benchmark datasets were used to represent different domains. The results indicated that the proposed method significantly improves the accuracy of the neural network classifier compared to existing works in aspect-based sentiment classification, especially for the highly imbalanced dataset.

Knowledge about relations plays a crucial role in human’s knowledge. Different methods for representing this type of knowledge have been proposed. However, due to the lack of theoretical foundations, these methods cannot guarantee criteria in knowledge representation such as formality, universality, usability and practicality. They are not adequate to represent the knowledge domains in practice which have many components. Based on formal ontology approach, a knowledge model about relations, called Rela-model, is presented in this paper. It has the components such as concepts, relations between concepts, and rules. The concepts in this model consist of attributes, facts and rules of itself. Each object in a concept has also equipped its behavior to solve problems on it. The methods for solving problems based on Rela-model are also studied. The general problems on this model are the following: Given some objects and facts on them, determine the closure of set of attributes and facts on the objects or determine an object or consider a relation between the objects. The algorithms to solve problems are designed and their properties, such as finiteness, effectiveness, have also been proved. Besides the solid mathematical foundation, Rela-model also has a simple specification language which can effectively represent the knowledge, thus it can be used in many real situations. Our approach is also applied to build two systems: the intelligent problem solver about solid geometry in high school mathematics, and the expert system to diagnose diseases in diabetic microvascular complication.

This paper highlights the current literatures in usability studies, performance metrics and machine learning algorithm. A literature review is done in these three areas of studies to find a research gap that can be explored further. The paper will then propose a research methodology to attend to the issues of machine learning and usability. An experiment is proposed to compare the efficiency results in between data consistency, correlation between performance metrics and self-reported metrics of a Mobile Augmented Reality learning application. The methodology proposes hierarchical agglomerative clustering technique as a solution in differentiating usability issues according to priority in order to help with usability re-engineering decisions. This paper proposes two objectives through the proposed framework and present evidence on how to achieve them. Lastly, this paper will discuss the results, conclusion and future works of the proposed study.

Systematic Literature Review (SLR) is a structured way of conducting a review that can assist the researcher in analyzing the progress of a specific stream of research. Although there are a number of SLR guidelines proposed by existing researchers, there is still too little attention paid to the approach taken and the tools that could be applied throughout the SLR process. Therefore, this paper attempted to fill the gap by presenting the application of Data Analytics approach in the SLR with the utilization of data mining and text mining technique. It proposed a step-by-step procedure using data analytics approach, and recommended tools to be used in the SLR process. To demonstrate the applicability of the approach, the authors selected a Master Data Management research domain as a case study. Based on this particular application, it can be concluded that this approach is a replicable, effective and output-based approach in conducting an SLR. It is hoped that other researchers would be able to replicate this approach in doing SLR for other research domains such as software engineering and information technology fields of study.

Decision-making involves several processes such as data pre-processing, data reduction and data selection. In order to assure a valuable solution is made, each of these processes needs to be successfully conducted. When dealing with complex data, parameter reduction is one of the essential processes that the decision-makers should take into account. It helps to reduce the processing time, computational memory and data dimensionality in the decision-making process. However, some of the parameter reduction methods were unable to generate a sub-optimal value during the parameter reduction process. This problem could affect the performance of the classification process. Soft set theory is one of the parameter reduction methods that faces this kind of problem. As a result of the study, to enhance the capability of soft set parameter reduction method, an integration between soft set and rough set theories as a parameter reduction method had been proposed. It was based on the efficiency of these two theories in processing complex and uncertain data problems. These two methods were sequentially applied to simplify the initial parameters in order to improve the performance of the classification process. The experimental work had returned positive classification results and successfully assisted the standard soft set parameter reduction method in generating sub-optimal reduction set and also the classifier in the classification process.

With the benefits of context-aware and the smartphone's participatory sensing potential, individual activity recognition (IAR) has proven to be enormously importance for stampede prediction in a crowd using a geographical location and global positioning system (GPS) data. In the case of an unforeseen incident and in an emergency situations whether in a small or large gathering. The research effort used Kalman filter to remove uncertainty through sensor fusion to create room for a reliable measurement for abnormality prediction. This paper, addressed the following questions. (i) How to determine the flow direction and the velocity of peoples' movement in a crowd to know when stampede will occur? (ii) What is the role of sensor fusion in a crowd scenario? Two scenarios experimented on IAR with accelerometer, GPS, and digital compass sensors to determine the flow pattern of participants' movement in a crowd using the flow velocity Vsi and flow direction Dsi, in the proposed stampede prediction approach. The experimental results show the effect of Vsi and Dsi for different group locations and serve as a pointer to reduce risk towards mitigation of crowd disaster and enhanced the existing context-aware framework to save human lives in our society if used in crowd scenarios.

Recent research regarding hate speech is in the domain of social sciences and psychology. From these trends, the dissemination of hate speech and antagonistic content in social media has not been extensively studies from the perspective of sentiment analysis. In this paper, the main studies concerned about aspect-based sentiment analysis through twitter as the most popular social media communication in the world as they have 313 million active users worldwide. This paper initiate to address the shortcomings of implied aspects specific for hate crime domain. The expected beneficial hate aspects can be extracted from twitter based on combination of both analysis. The evaluation with researcher's own Hate Crime Twitter Sentiment (HCTS) dataset and also Hate Speech Twitter Datasoft (HSTD) was shown that the proposed approach is effective and produces significantly better results than baselines method.

Researchers create larger networks of contacts through research collaboration, known as collaboration networks of researchers. In order to promote and to have effective collaboration among researchers, the collaboration patterns need to be accessed and analysed to elevate research and publication (R&P) performance. However, the collaboration patterns have not been accessed in the context of Universiti Teknologi Malaysia (UTM) researchers. Thus, it brings the aim of this research to analyse research collaboration patterns among UTM researchers using Social Network Analysis (SNA). Researchers from Faculty of Electrical Engineering (FKE) were chosen for our analysis. We have 1446 cleaned publications and 168 researchers to construct collaboration network of FKE and its departments. Our findings show that FKE researchers have fewer inter-departments collaboration. Department of Control and Mechatronic Engineering (CMED) has denser network compared to other FKE departments. Besides that, the most influential and reliable researchers were identified based on SNA measures. The findings derived from this study are very helpful to strategic decision makers in UTM to have a strategic plan in empowering research collaboration efforts. It gives an alternative to existing methods in evaluating R&P performance of researchers in UTM and promotes researchers to conduct research collaboration.

Agent privacy in social networking systems is directly affected by many factors, mainly agent interaction and social activities such as creating relationship links and content sharing between agents. Based on these activities, agent privacy in social networking systems can be protected at three access control levels that are at individual, social and content levels. However, an agent may unintentionally violate its privacy during runtime by overexposing or oversharing its personal, relationship or content information due to the dynamic of the agent privacy preferences and social behavior. Hence, a solution to monitor the level of agent privacy during runtime is needed. In this research, a solution called Runtime Verification and Quality Assessment (RVQA) is proposed to improve agent privacy violations detection during the execution of agents social activities by combining the verification and assessment process from individual, social and content levels. New agent privacy requirements, parameters, checking rules and algorithms to detect agent privacy violations are presented. The effectiveness of the proposed solution is evaluated by implementing RVQA within agent-based social networking system model.

Software prioritization is the act of ranking requirements with respect to their perceived relative importance in order to plan for software releases. This is a crucial task in software development because requirements could be ambiguous or over proportioned if appropriate techniques are not utilized in analyzing and prioritizing them. Consequently, to curb the possibility of delivering poor quality systems developed from vague requirements, a fuzzy-TOPSIS based model that is capable of comparing sets of elicited requirements is presented. To achieve the research aim, normalized fuzzy weights are computed for each criterion that makes up a requirement and a confidence function is determined to ascertain the prioritized requirements. An empirical case scenario is described to illustrate the adaptability processes of the proposed approach.

A process model generally specifies the set of stages in which a project should be divided into, the order in which the stages should be executed and constraints or conditions that influences the execution of these stages. It is either a descriptive or prescriptive characterization of how software is developed. Existing models demonstrate powerful capabilities, but they have many ambiguous characteristics. Many surveys carried out by researchers show that, although different organizations use some process models for software development, a number of industries do not still use any model or formal development method at all. This means that, the existing models lack suitability. Hence, there is need to improve the suitability of existing models by developing a generic one that can be applicable to varieties of software development projects. To achieve the aim of this research, the proposed model used the concept of Object Oriented Analysis and Design (OOAD) to ensure support for different project complexities.

This paper presents a framework for web-based application, which aspires to maintain learning resources, for purposes of learning courses or organizations. The goal is to create resource-rich hierarchical learning environment, which supports collaborative building of learning resources for specific domain. This paper presents basic model with essential user activities, proposal of information architecture and implementation. Presented version of the learning management system is based on principle of shared hierarchy, user contribution and moderated improvement of learning resources. This includes possibility of entry customization for active students as well as creating new entries and custom groups. Teachers can then evaluate new custom entries, which – if approved – they can add to shared hierarchy, accessible for other learners as well. This continuous process can successfully lead to collaborative building of learning resources and thus enrich communities of teachers and active students, interested in one specific domain.

E-learning adoption in universities is a very challenging field of Information Systems that consist of the adoption challenges at two levels – user level and institutional level. Various higher institutions of learning have come to terms with the eminent need for change in order to allow for a successful integration and impact of technology in education. The ability to cope with these changes, right from pre-to-post e-learning adoption, has been a major challenge for management of various higher education institutions (HEIs). This paper aims at revisiting the various Information Systems (IS) approaches previously employed by several studies for investigating the challenges in adopting these e-learning technologies from the user level to the institutional level in Universities, with the aim of identifying the gap between theory and practice in addressing these challenges. The review considered 13 international conference academic papers and 52 journal articles sourced from high ranked international journals in the relevant field of study, validated through a multi-step manual cross-checking based on carefully selected extraction and quality criteria. The results indicate that 37% of the reviewed papers employed the use of quantitative approach while 29% used qualitative method in their study on the current challenges faced in e-learning adoption by universities. This implies that there is a growing interest in the use of qualitative approach for research in the field of IS even though quantitative methods are still slightly dominant in this IS domain. Based on the review findings, there is a need for both practitioners and researchers to appreciate the differences between methodologies and their varying applicability. The study concludes that IS practitioners and researchers should come together to clearly redefine the scope of IS methodologies.

Disastrous circumstances such as radiation and nuclear meltdown require a large and complex scale of emergency health and social care capacity planning framework. Devastating issues and challenges of incompleteness, inconsistency, and the infeasibility of provided requirements of the suggested planning framework might create unnecessary conflicts during the system development. In this paper, we proposed requirements engineering of an emergency preparedness and response simulation model (EPRM) for Malaysia Radiation and Nuclear (MRN) emergency plan simulator. This simulator is expected to plan and manage emergencies and disasters included the risk involved when the nuclear reactor is malfunctioning due to natural disasters such as floods, earthquake, and cyber-attacks. We identify the social and technical problems of implementing the emergencies and disasters policies and also the critical and safety of software systems use to run the nuclear plants. We simulate the proposed emergency preparedness and response model (EPRM) through the simulation software. Finally, we measure the effectiveness of the proposed model using thematic synthesis and qualitative regression analysis provided outstandingly sociotechnical aspects of the affected junctures under investigation. From the testing of the proposed model, we found that if the organization is not able to define and identify, the disaster coordinator roles and responsibility, resources and equipment may contribute 65.63% of emergency plan disorder and severe calamities towards first responders, operators, workers, patients and community at large. Also, we found that those themes were to bone up the main simulation workflow diagram. This finding supported and correlated independently towards Information Systems Development (ISD) key concepts by 13.30 (T value). Those themes were confirmed and reliable coherently (average 89% degree) rather than by chance. Finally, the proposed emergency preparedness and response model (EPRM) that is the theory building methods integrated technical and analytical procedures in a sociotechnical outlook. These methods established on developing emergency response perquisites rather than intervention principles alone. Most likely, this approach was significantly useful to justify a mixture of tacit and explicit knowledge among the emergency plan expertise. Consequently, a strategized, simplified and prevailing RANEP simulator can be achieved, though it is certainly as complex.

In this paper, a maintainability prediction model for an object-oriented software system based on type-2 fuzzy logic system is presented. With the proliferation of object-oriented software systems, it has become very essential for concerned organizations to maintain those systems appropriately and effectively. However, it is pathetic to note that just very few number of maintainability prediction models are currently available for object oriented software systems. In this work, maintainability prediction model based on type-2 fuzzy logic systems is developed for an object-oriented software system. Earlier published object-oriented metric dataset was used in building the proposed model. Comparative studies involving the prediction accuracy of the proposed model was carried out in relation to the earlier used models on the same datasets. Empirical results from experiments carried out indicates that the proposed type-2 fuzzy logic system produced better and interesting results in terms of prediction accuracy measures authorized in object oriented software maintainability literatures. In fact, the proposed method satisfies the three major conditions stated in the literatures as basis to determining a good maintainability prediction model.

One of the major challenges and concerns of researchers is an early detection and diagnosis of thin-cap-fibro-atheroma or vulnerable plaque to prevent the sudden heart events. Recently, Virtual Histology (VH) as a new approach based on spectral analysis of Intravascular Ultrasound (IVUS) provides color coded of coronary tissue maps. In IVUS-VH image, plaque's components can be discriminated based on echogenicity. Nonetheless; available methods do not provide clinical relevant information about the pattern of plaque structure, plaque composition, and geometric position of each components, location or distribution of plaque components toward the lumen border. In this paper, we proposed a new method of feature extraction that plays a decisive role in vulnerable plaque detection including NCCL (Necrotic Core in Contact with the Lumen), DCCL (Dense Calcium in Contact with the Lumen), Confluent NC and Confluent DC.

The nature of ontologies supports their usage as interoperability support components between information systems. However the need to make ontologies themselves interoperable remains high. To address this situation, there is need to study the semantic heterogenity between the ontologies. This paper specifically looks at domain ontologies and how to measure the interoperability degree between them to establish the extent to which they can replace each other. Different interoperability operations between ontologies are discussed together with the measures that define semantic distance and lexical similarity between the ontologies. A method based on model management theory that enables use of algebraic operations such as match on the ontology models is proposed to measure lexical and structural dimensions of domain ontologies to give a value for their degree of interoperabilty. An example of how to compute the degree of interoperability between two domain ontologies using the proposed approach is given with an explanation of how the identified gaps can be addressed.

Ontologies are widely considered to be the backbone of the Semantic Web. Its importance is being recognized in a multiplicity of research fields and application areas. Ontology building is crucial for the aforementioned issues. The main goal of this research is to investigate an effective methodology for collaborative ontology building. A trust-based consensus is proposed to support an efficient solution for conflicts among different viewpoints of participants in the collaborative ontology (CoO) building process. In every cycle of the iterative collaborative process, the ontology is refined and evolved by reaching a trust-based consensus among the participants’ viewpoints. The proposed method is applied for collaborative Vietnamese WordNet building. The result is significant in comparison with previous approaches.

Thesaurus is used with many Information Retrieval (IR) models such as data integration, data warehousing, semantic query processing and classifiers. Considering the existence of various thesauri for a particular domain of knowledge, output quality of an IR model when using different thesauri in the same domain is not predictable. In this paper, we propose a methodology to study the performance of thesaurus in solving schema matching as a case study of IR models. The paper also presents initial results of experiment conducted using different thesauri. Precision, recall, and F-measure were calculated to show that the quality of matching was changed according to the used thesaurus.

Path planning is applied in a variety of ways, including transportation, telecommunications, etc. Path planning to direct vehicles to their destination in a dynamic traffic situation, with the aim of reducing the motoring time and to ensure and efficient use of available road resources is the main challenge in route guidance system. In this paper we propose a fast path algorithm for finding the best shortest paths in the road network. This is poised to minimize costs between the origin and destination nodes. The proposed algorithm was compared with the Dijkstra algorithm in order to find the best and shortest paths using a sample of Tehran city road network. Three cases were tested through simulation using the proposed algorithm. The results show that the efficiency of proposed algorithm and could reduce the cost of vehicle routing on the path planning problems.

In this paper, an improved web page classification method (IWPCM) using neural networks to identify the illicit contents of web pages is proposed. The proposed IWPCM approach is based on the improvement of feature selection of the web pages using class based feature vectors (CPBF). The CPBF feature selection approach has been calculated by considering the important term's weight for illicit web documents and reduce the dependency of the less important term's weight for normal web documents. The IWPCM approach has been examined using the modified term-weighting scheme by comparing it with several traditional term-weighting schemes for non-illicit and illicit web contents available from the web. The precision, recall, and F1 measures have been used to evaluate the effectiveness of the proposed IWPCM approach. The experimental results have shown that the proposed improved term-weighting scheme has been able to identify the non-illicit and illicit web contents available from the experimental datasets.

Language identification is the process identifying predefined language in a document automatically; we focused on the Web documents in this paper. Initially, we have applied the letter frequency as features combine with neural networks in Arabic script language identification. However, reliability of selected letters of the features is a major issue to be overcome. Therefore, we propose an improved letter weighting feature selection in order to enhance the effectiveness of language identification. It is based on the concept letter frequency document frequency. From the experiments, we have found that the improved letter weighting feature selection achieve the highest accuracy 99.75% on Arabic script language identification.

These days, when we want to get the relevant and useful information on political issues from the web, it is important for the Internet users to understand the current political situations in the country. Most of Internet users are using applications like web mining system in order to help them in finding all relevant information available in the political weblog. Based on this problem, we have developed a web mining system called WMAIS (web mining using Artificial Immune System). The objective of the WMAIS is to recommend the relevant and interesting information on political issues in Malaysia through political weblogs to the users. From the experiment that have been done using statistic tool called Student T-test, it shows that by using the term frequency scheme in WMAIS, it has managed to recommend relevant and interesting information through political weblogs to the user.

In this paper, we proposed hybrid-KNN methods on the Arabic script web page language identification. One of the crucial tasks in the text-based language identification that utilizes the same script is how to produce reliable features and how to deal with the huge number of languages in the world. Specifically, it has involved the issue of feature representation, feature selection, identification performance, retrieval performance, and noise tolerance performance. Therefore, there are a number of methods that have been evaluated in this work; k-nearest neighbor (KNN), support vector machine (SVM), backpropagation neural networks (BPNN), hybrid KNN-SVM, and KNN-BPNN, in order to justify the capability of the state-of-the-art methods. KNN is prominent in data clustering or data filtering, SVM and BPNN are well known in supervised classification, and we have proposed hybrid-KNN for noise removal on web page language identification. We have used the standard measurements which are accuracy, precision, recall and F1 measurements to evaluate the effectiveness of the proposed hybrid-KNN. From the experiment, we have observed that BPNN is able to produce precise identification if the data set given is clean. However, when increasing the level of noise in the training data, KNN-SVM performs better than KNN-BPNN against the misclassification data, even on the level of 50% noise. Therefore, it is proven that KNN-SVM produce promising identification performance, in which KNN is able to reduce the noise in the data set and SVM is reliable in the language identification.

This paper investigates Loss of Self-similarity (LoSS) detection performance using Exact and Asymptotic Second Order Self-Similarity (ESOSS and ASOSS) models. Previous works on LoSS detection have used ESOSS model with fixed sampling that we believe is insufficient to reveal LoSS detection efficiently. In this work, we study two variables known as sampling level and correlation lag in order to improve LoSS detection accuracy. This is important when ESOSS and ASOSS models are considered concurrently in the self-similarity parameter estimation method. We used the Optimization Method (OM) to estimate the self-similarity parameter value since it was proven faster and more accurate compared to known methods in the literature. Our simulation results show that normal traffic behavior is not influenced by the sampling parameter. For abnormal traffic, however, LoSS detection accuracy is very much affected by the value of sampling level and correlation lag used in the estimation.

Automatic categorization is the only viable method to deal with the scaling problem of the World Wide Web (WWW). In this paper, we propose a news web page classification method (WPCM). The WPCM uses a neural network with inputs obtained by both the principal components and class profile-based features (CPBF). Each news web page is represented by the term-weighting scheme. As the number of unique words in the collection set is big, the principal component analysis (PCA) has been used to select the most relevant features for the classification. Then the final output of the PCA is augmented with the feature vectors from the class-profile which contains the most regular words in each class before feeding them to the neural networks. We have manually selected the most regular words that exist in each class and weighted them using an entropy weighting scheme. The fixed number of regular words from each class will be used as a feature vectors together with the reduced principal components from the PCA. These feature vectors are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the WPCM method provides acceptable classification accuracy with the sports news datasets.