Disasters whether natural or human-made, leave a lasting impact on human lives and require mitigation measures. In the past, millions of human beings lost their lives and properties in disasters. Information and Communication Technology provides many solutions. The issue of so far developed DMSs is their inefficiency in semantics that causes failure in producing dynamic inferences. Here comes the role of semantic web technology that helps to retrieve useful information. Semantic web-based intelligent and self-administered framework utilizes XML, RDF, and ontologies for a semantic presentation of data. The ontology establishes fundamental rules for data searching from the unstructured world, i.e., the World Wide Web. Afterward, these rules are utilized for data extraction and reasoning purposes. Many disaster-related ontologies have been studied; however, none conceptualizes the domain comprehensively. Some of the domain ontologies intend for the precise end goal like the disaster plans. Others have been developed for the emergency operation center or the recognition and characterization of the objects in a calamity scene. A few ontologies depend on upper ontologies that are excessively abstract and are exceptionally difficult to grasp by the individuals who are not conversant with theories of the upper ontologies. The present developed semantic web-based disaster trail management ontology almost covers all vital facets of disasters like disaster type, disaster location, disaster time, misfortunes including the causalities and the infrastructure loss, services, service providers, relief items, and so forth. The objectives of this research were to identify the requirements of a disaster ontology, to construct the ontology, and to evaluate the ontology developed for Disaster Trail Management. The ontology editor applied by this research is Protégé version 5.2.0, which utilizes OWL as ontology language. The ontology consists of 6969 axioms, 84 concepts, 103 properties, and 726 individuals. The ontology was assessed efficaciously via competency questions; externally by the domain experts and internally with the help of SPARQL queries. The ontology was assessed by a software tool and found 100% accurate concerning its structure and overall 97% perfect as evaluated by the domain experts.

The rapid expansion of online learning has highlighted the need for robust quality assurance frameworks prioritizing user experience and pedagogical effectiveness. While many existing frameworks focus on technical functionalities, they often neglect essential user-cantered aspects such as engagement, interactivity, and accessibility. This study proposes an Enhanced User-Cantered Quality Assurance Framework for Cooperative Online Learning in Jordanian universities, addressing the critical need for a structured approach that integrates instructor and learner perspectives. Using a mixed methods research design, the study incorporates qualitative and quantitative methodologies, including focus group discussions, individual interviews, and surveys. The research follows a three-phase process: needs assessment, participatory design, and evaluation. The participatory design approach ensures that both instructors and learners contribute to shaping the framework, fostering engagement and practical implementation. The study also examines the role of Artificial Intelligence (AI) in enhancing online learning by evaluating its impact on system quality, information quality, and service quality. The findings reveal that AI-driven features significantly improve user satisfaction and learning outcomes, yet challenges remain regarding usability, accessibility, and instructor readiness. The study’s results confirm that a user-centred quality assurance framework can bridge existing gaps in online learning by aligning technological advancements with the specific needs of users. The framework includes real-time analytics, continuous feedback loops, and adaptive learning tools to enhance engagement and effectiveness. The research contributes to the growing field of digital education by demonstrating the transformative potential of participatory design in developing quality assurance mechanisms that are both scalable and adaptable to evolving educational landscapes. This study presents a structured approach to improving cooperative online learning in Jordanian universities by addressing the deficiencies in current online learning platforms. The proposed framework enhances student engagement and instructional effectiveness and sets a foundation for future research in AI-driven quality assurance strategies. The findings underscore the necessity of integrating user-driven insights into digital education policies to ensure sustainable and inclusive online learning environments.

The holy Qur' an is the first fundamental resource of legislation and law in the Muslim community. The Qur'anic text has been covered by Islamic scholars to offer Qur'anic knowledge quickly and systematically, such as digital Qur'an, and Qur'anic computing. This is performed using the techniques of text mining to automate the Qur'anic text. The classification of Qur' anic verses is one of the focal points in many research, which is conducted through automatic Qur' anic classification. The purpose of Qur' anic classification is to assign the most appropriate topics that are predefined to a specified Qur'anic verse according to its content. However, some properties in the Qur'anic topics such as imbalanced classes could weaken the perfonnance of classification when these classes are classified using traditional classification. Imbalanced classes occur when the sample number of classes in the dataset is not equal. As noticed in the dictionary used in this research, many Qur'anic topics are unequal in the number of verses, which means the problem of imbalanced classes will occur when these topics are classified together using traditional classification. The main problem that this study tries to solve is obtaining equal accuracies for all classes of Qur'anic topics during the classification process. Therefore, this study attempts to explore a new approach to categorise the Qur' anic topics based on imbalanced learning and a genetic algorithm that is called optimisation learning. The technique of imbalanced classification was applied to solve the problem of imbalanced classes existing in the Qur' anic topics. The genetic algorithm was used as an optimisation objective before the implementation of classification. This optimisation was performed for the samples of Qur' anic text to adjust the convergence and spacing between the samples, whether in the same class or among the classes. This adjusting leads to improve the performance of Quranic topics classification. Three cases of optimisation were experimented in this study using the proposed techniques: partial optimisation with oversampling, full optimisation without oversampling, and full optimisation with oversampling. These cases were implemented by using three new oversampling methods, Genetic Oversampling (GOS) and Harmonised Oversampling method based on Genetic Algorithm (HOGA-I and HOGA- 2). In conclusion, the third case of optimisation achieved the best results. Meanwhile, all proposed methods outperformed significantly the other famous methods that have been used widely to classify imbalanced datasets, which are Synthetic Minority Oversampling Technique (SMOTE), Random Undersampling (RUS), and Random Oversampling (ROS). According to the experiment results, GOS method outperformed SMOTE and ROS methods, which were the second best methods among the other previous methods in Specificity metric by I 2% using the validation technique of I 0- fold cross-validation. Meanwhile, HOGA-I method outperfonned the closest method in Matthews Correlation Coefficient (MCC) metric by 7% using the validation technique of training-testing. HOGA-2 method, which was the best among all proposed oversampling methods, outperfonned all closest methods in Sensitivity/Recall, Balanced Accuracy, and Geometric Mean (G-Mean) metrics by I 0% using the validation technique of I 0-fold cross-validation.

The purpose of this study is to identify cultural mismatches between users in the operation of learning spaces to improve understanding of user preferences in physical learning environments in the context of current educational trends in Malaysia's physical academic library learning spaces in the era of Education 4.0. The research has examined the relationships between the learning space attributes of users' activities, sociability, and comfort image, and the user preferences, noise management practices, users' behavior, and noise levels of 40-55 decibels (dB), as it is believed that these relationships will contribute to the highest level of the hierarchy of library users' needs, which is community as the library. This study uses an explanatory sequential mixed design research methodology, collecting data through a quantitative phase, followed by qualitative data collection, with the final phase involving linking the data obtained from these two strands of research. A true experimental and questionnaire methods study was conducted to collect quantitative data from 384 library users from higher education institutions using simple random sampling. The data was then analyzed using SPSS to test hypotheses using paired samples t-tests, one-way ANOVA, two-way repeated measures ANOVA, and descriptive analyses. Qualitative data were obtained from eight academic librarians in Malaysia using snowball sampling and semi-structured Zoom interviews. The data were then analyzed using Atlas.ti for coding analysis. Quantitative findings revealed that noise levels below 50 decibels had minimal impact on user learning. The interplay between user preferences, automated noise control efficacy, and collaborative study desirability in common areas was evident. Over half expressed a preference for and frequent use of these spaces, with 57% planning to recommend them for collaborative activities. Automated noise control systems were deemed effective for reducing noise by 65% of respondents. A significant majority (67.4%) believed that the library required service transformation. A notable interaction between modern learning design and noise detection machines was observed, influencing user learning abilities. These quantitative findings served as a catalyst for qualitative research with librarians, resulting in five pivotal insights. First, it emphasized the connection between Education 4.0 and contemporary collaborative learning environments. Additionally, the study suggested the creation of more learning spaces in common areas with noise levels of below than 50dB in collaborative activity areas to fulfill the strong need for collaborative learning spaces among students. Furthermore, the study found that the use of comfort and noise detector machines was positively correlated with improved learning outcomes. The findings also revealed a connection between users' preference to quantify noise levels and reduce human situational control in learning environments. Finally, the study emphasized the importance of creating welcoming and accessible spaces for students, as evidenced by the relationship between library image and user behaviour. In conclusion, this study has provided valuable insights into the changing patterns of user behaviour in physical academic library learning spaces. The findings of this study have implications for the design and management of these spaces in order to meet the needs of users and facilitate effective learning.

Heart failure is a medical disorder characterized by mortality as one of its ultimate consequences. It is characterized by a multitude of signs and symptoms that often overlap with those observed in various other medical disorders and diseases. The collective manifestation of these signs and symptoms has the potential to result in the mortality of the patient. The capacity to anticipate the risk of mortality in individuals with heart failure enables healthcare professionals to allocate resources more effectively to mitigate or avert potential fatalities. A large dataset is recommended for creating machine learning models that can be accurately generalized. Medical data is generally difficult to obtain, and studies use data that is either collected firsthand or conduct secondary analyses on data that was collected by others. To this end, this study uses a curated dataset of a cohort of heart failure patients obtained from the MIMIC-III database, and the results were compared with previous studies that used a distinct, but much smaller dataset of heart failure patients. This has the added advantage of the research being reproducible. Within the context of the using machine learning to predict mortality risk in heart failure patients, this study examines many signs and symptoms associated with heart failure to ascertain the characteristics that can effectively forecast the risk of death. Additionally, the study aims to evaluate the extent to which these findings can be applied to heart failure patients as a whole. In the evaluation of serum creatinine, ejection fraction, and binned age as features, the experimental analysis findings provide evidence of the potential efficacy of machine learning in appropriately categorizing heart failure patients according to their risk of mortality. This information has the potential to enable doctors to take preemptive measures and enhance treatment procedures, ultimately resulting in enhanced patient outcomes and allocation of resources. This study investigates a range of machine learning techniques, including logistic regression, random forests, and gradient boosting, to determine the optimal method for predicting mortality risk. The research demonstrates the effectiveness of employing machine learning methods to leverage extensive clinical datasets such as MIMIC-III for the purpose of improving the accuracy of mortality risk prediction in patients with heart failure. The present study significantly contributes to the expanding field of predictive analytics in healthcare, providing vital insights for physicians and academics who seek to enhance heart failure therapy and improve patient care.

Cardiovascular diseases (CVD) have a significant impact on increasing the mortality rate in the Middle East and The United Arab Emirates (UAE) has one of the highest age-standardized death rates for cardiovascular disease (CVD). Recently, based on the Assessment Risk Tools for Cardiovascular Diseases (CVD), World Health Organization (WHO) reported that 40% of all fatalities in the UAE are attributed to CVD, which has been linked to the main Risk Factors (RF) advances as obesity, hypertension, tobacco, and high cholesterol. In most cases, angiography is a reliable method for the diagnosis and treatment of cardiovascular diseases. However, it is a costly approach associated with various complications. The significant increase in the prevalence of cardiovascular diseases and the subsequent complications and treatment costs have urged researchers to plan for the better examination, prevention, early detection, and effective treatment of these conditions The present study aimed to detect the patterns for the overlapped symptoms of cardiovascular diseases using integrated Deep Learning classification techniques for analyzing the data of internal medicine patients who are at the risk of heart failure with 2621 samples and 40 characteristics. Selecting the characteristics and evaluating the influential factors are essential to the development of classifiers and increasing their accuracy. The proposed work suggested a model based on Gini-Entropy-Regression Model (GERM). The objective is to predict future risk with a certain probability and compare its performance with Deep Learning MLP Model. Statistical analysis and methods were used in this research to detect the symptoms of CVD that overlapped and to accurately identify a specific heart condition. The dataset utilized to train the computer consists of medical records from more than 14 hospitals in UAE which were collected based on four main categories such as basic information, symptoms, inducement and history, and physical sign and assistant examination. The suggested model consisted of four levels, level 1: Preprocessing data, Level 2: Feature Extraction, Level 3: Feature Selection, Level 4: Feature Detection. The results of the suggested model were as follows: the result was 84.4% when the symptoms of (CVD) is overlapping DSYP and CHEP. When Accuracy measured with combination DSYP, CHEP, and CYAN it has been increased up to 88.9%. DSYP, CHEP, CYAN, showing values of 89.8%. in 5th Neural Network (NN) the combinations were DSYP, CHEP, CYAN, DBPH, WFAT, EMPT showing ideal value of accuracy measured up to 90.6% and with Fever this combination of Neural Network has been showing accuracy = 91%. From the findings the previous seven predictors (Risk Factors) give the best overlapping and diagnosis for CVD.

Infectious disease crisis management relies heavily on contact tracing applications to curb transmission. With advancements in technology, smartphones, and mobile applications have revolutionized the process, enabling rapid identification and notification of individuals exposed to infectious diseases. These contact tracing applications, such as Malaysia's MySejahtera, provide real-time data to health authorities, facilitating targeted interventions and reducing disease transmission. Understanding user sentiments toward these applications is critical to improving their effectiveness and acceptance. However, there remains a significant research gap regarding sentiment analysis, influencing factors, and usability models specific to Malaysia. This research investigates user sentiment polarity toward contact tracing applications, identifies factors driving positive and negative sentiments, and proposes a usability model tailored to these factors. The methodology involves sentiment analysis using supervised binary classification algorithms and thematic analysis of user reviews of MySejahtera. Key factors influencing sentiment are identified and integrated into a usability model, developed deductively from Hoehle and Venkatesh’s (2015) framework and relevant literature. The findings indicate that the K-Nearest Neighbour (K-NN) algorithm achieves the best performance with an F-measure of 83.99%. The majority of users express positive sentiment, attributed to factors such as User Interface Input, Application Utility, and Application Design. Negative sentiment, however, is also linked to Application Utility and Design. The proposed usability model comprises seven higher-order constructs and fourteen lower-order constructs, which were validated statistically to confirm its robustness. This research contributes significantly to the fields of sentiment analysis and usability by providing actionable insights for developers and public health authorities. The usability model offers a comprehensive framework for improving contact tracing applications, addressing user concerns, and optimizing usability to enhance public health outcomes.

This study investigates the factors influencing the business process management (BPM) under the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) paradigm in the agricultural sector of FELCRA Berhad. The aim of the study is to identify the key elements that affect the acceptance and effective implementation of BPM technology inside the organization. FELCRA Berhad uses these technologies to monitor crops, control soil conditions, and make better decisions based on data collected across the organization. Unmanned Aerial Vehicles (UAVs) provide real-time aerial data on crop growth, pest detection, and field conditions. Unmanned Ground Vehicles (UGVs) automate tasks like planting and harvesting, reducing the need for manual labour and increasing accuracy. Internet of Things (IoT) weather stations and soil monitoring systems continuously track environmental factors like temperature, humidity, and soil moisture. All this data is integrated into the Enterprise Resource Planning (ERP) system for better management and strategic planning. However, despite these advancements, much of the data collection and integration at FELCRA Berhad is still done manually or through disconnected systems, slowing down decision-making. Without a fully automated and connected digital system in FELCRA Berhad Business Process Management (BPM), the technology’s full potential to boost productivity is limited. A mixed-methods approach was employed to gather data from a representative sample of FELCRA Berhad personnel, integrating semi-structured interviews with qualitative inquiries and quantitative surveys. A researcher-administered questionnaire survey was disseminated across the five regions of FELCRA Berhad, targeting 500 respondents to attain a final sample size of roughly 500 useable responses. The Statistical Package for Social Sciences (SPSS) was initially employed to conduct descriptive statistical analysis to understand the preliminary patterns and demographic characteristics in the data. Subsequently, multiple regression analysis was employed to investigate the relationships among variables and assess the hypotheses. This method was selected for its capacity to simultaneously analyze the impact of several independent variables on a single dependent variable, rendering it appropriate for evaluating the theoretical linkages posited in the UTAUT2 model. The results indicate important insights into the interaction among individual, organizational, and technology elements that influence BPM acceptability. The study highlights "familiarity" as a significant moderating component, a previously overlooked gap in the literature. This was revealed through semi-structured interviews that underscored the obstacles and issues of BPM acceptability inside FELCRA Berhad. The results indicate that the primary factors affecting user acceptability are performance expectancy (r = .089) and social influence (r = .195), but hedonic motivation (r = .055), habit (r = .059) and familiarity also exert considerable influence. By applying the UTAUT2 model to the context of BPM inside a Malaysian government-linked enterprise. This study established a modified UTAUT2 model to forecast the adoption of BPMagriculture technology in enterprises. According to the study's findings, FELCRA Berhad, a leading Malaysian agricultural firm, is recommended to implement this customized model to improve its comprehension of the determinants affecting BPM technology adoption.