Pritheega Magalingam

The COVID-19 lockdown has sparked educational approach all around the world and transformed the traditional learning to online engaging learning. Even so, this transformation has demonstrated the need to implement interactive e-learning technologies on many occasions, and this allows lecturers to help students flexibly develop their soft and technical skills required by the course they are attending. As a result, more online learning, like ODL and using a blended learning method, emerged. Therefore, it is important to understand students' satisfaction of using the interactive online technology into their learning be it as either a means for learning the online lectures or group task meeting assistant. This study piloted a survey on fifty Universiti Teknologi Malaysia, Kuala Lumpur (UTMKL) postgraduate students who attended online courses. This study aims to explore the clarity, reliability and validity of the questionnaire before a full-scale study takes place. An evaluation of the instrument's reliability was conducted using Cronbach's Alpha, alongside an evaluation of indicator reliability through outer loadings. The study revealed that all factors namely perceived usefulness, perceived ease of use, perceived ease of learning, perceived interaction, perceived motivation and perceived satisfaction are reliable to be postulated as the hypotheses in developing a model to explore how perceived usability factors influence postgraduate student satisfaction with the use of the digital whiteboard at UTMKL. The results of this pilot research offer important information about possible factors from usability perspectives to measure satisfaction of using interactive learning tools in teaching and learning. To create a thorough model of satisfaction factors, future studies will increase the sample size and apply inferential statistical methods. Promoting interactive learning at UTMKL and other comparable environments can be explored further by this study, which also may add the expanding corpus of research on the use of technology in education.

The study focuses on exploring the Barents Sea food-web meta-web, a larger network comprising multiple food webs within an ecological community. The research utilizes a social network analysis (SNA) approach to uncover insights into the interconnections and trophic dynamics of species in this unique marine ecosystem. The study aims to identify key trophospecies, understand their significance across trophic groups, detect communities within the food web, and apply centrality measures to gain insights for conservation and management strategies. The data analysis and visualization are performed using Gephi software and R programming. The findings reveal the importance of GAD_MOR trophospecies from the Zooplankton group in shaping the food web dynamics. However, this species is absent in higher trophic groups due to trophic transfer efficiency and selective feeding behavior. The study also identifies three distinct communities within the food web, indicating interconnectedness and species interactions. Moreover, the application of centrality measures provides insights into the structural and functional roles of species, assisting in conservation and management strategies. The results highlight the complex nature of the Barents Sea ecosystem and emphasize the significance of interdependencies for effective ecological preservation and sustainable management.

Heart disease is one of the leading causes of death globally, which takes 17.9 million lives each year. The existing heart disease prediction techniques have a gap that does not consider the smoking attributes from the heart disease data. So, the accuracy is based on the limited number of medical data and the deep learning model. The existing deep learning models which use the Recurrent Neural Network (RNN) for heart disease prediction consume more processing and analysing time, mainly due to the delay of data retrieval. This delay causes the prediction process to become slower and leads to a moderate prediction only. The backpropagation of the RNN with an update gate and internal memory to carry the updated data cause a minor data glitch that leads to lower accuracy. Therefore, an efficient heart disease prediction model is very crucial to provide early detection among patients. This research proposes a Heart Disease Risk Prediction Model (HDRPM) with an enhanced RNN to improve the prediction accuracy using Framingham heart disease datasets. The specificity and sensitivity are imposed to improve the quality of the predictions. Sensitivity measure is used for detecting patients with heart disease perfectly and specificity measure is used for detecting patients without the disease perfectly. Besides the accuracy and quality of the prediction problem, the imbalance of minority classes in the dataset occurred in most deep learning prediction fields. This research aims to improve the quality of imbalanced Framingham datasets using Synthetic Minority Over-sampling Technique (SMOTe), which will synthetic instances in a small class to be equalized. The existing RNN model faces vanishing gradients that impede the learning of long data sequences. These gradients that carry information in the RNN cells will become smaller gradually till it minimises the parameter updates and leads to poor learning. For this purpose, the presence of multiple Gated Recurrent Unit (GRU) is used to overcome the vanishing gradients and ensure the hidden layers. The neurons of RNN rapidly cater for the essential information during the training and validation phase of the HDRPM. The integration of multiple GRU with the RNN, operating on the Tensorflow as back-end and Keras as the core for the neural network library has increased the performance of the proposed model. The proposed model provides up to 98.78%, the highest accuracy achieved compared to related previous work, which is a quantum neural network model with 98.57. This HDRPM is expected to significantly contribute to early detection of heart disease patients.

Vulnerability assessment in industrial IoT networks is critical due to the evolving nature of the domain and the increasing complexity of security threats. This study aims to address the existing gaps in the literature by conducting a comprehensive survey on the use of attack graphs for vulnerability assessment in IoT networks. Attack graphs serve as a valuable cybersecurity tool for modeling and analyzing potential attack scenarios on systems, networks, or applications. The survey covers the research conducted between 2016 and 2021(34 peer-reviewed journal articles and 28 conference papers), identifying and categorizing the main methodologies and technologies employed in generating and analyzing attack graphs. In this review, core modeling techniques for IoT vulnerability assessment are highlighted, such as Markov Decision Processes (MDP), Feature Pyramid Networks (FPN), K-means clustering, and logistic regression models, along with other techniques involving genetic algorithms like fast-forward (FF), contingent fast-forwards (CFF), advanced reinforcement-learning algorithms, and HARMs models. The evaluation of the performance of these attack graph models using IoT networks or devices as case studies is also emphasized. This survey provides valuable insights into the state-of-the-art in attack graph techniques for IoT network vulnerability assessment, identifying various applications, performances, research opportunities, and challenges. As a reference source, it serves to inform academicians and practitioners interested in leveraging attack graphs for IoT network vulnerability assessment and guides future research directions in this area.

As the trend to use the Internet of Things (IoT) applications and devices increases, security and privacy have become key concerns. IoT application adoption has increased significantly over time, with sensitive data frequently gathered by IoT devices, accidentally or consciously. According to recent research, numerous types of IoT devices have substantial vulnerabilities, and in many cases, no security procedures are in place to secure them. The focus of this study was the security issues of Internet protocol (IP) cameras. The vulnerabilities of IP cameras were investigated in more depth, as well as their influence on security and privacy at the user level, in order to assist companies and security experts in predicting attacker behavior and securing the systems. The objective of this study was to research and uncover the security and privacy vulnerabilities associated with an IP camera. This was accomplished by performing a direct inspection of the camera. A real-world test was carried out with a VAVA Outdoor Wireless IP Security Cam, which was employed as a home security camera. Information was gathered from numerous sources on the Internet for this purpose, and then the software and hardware were used to examine the security features of this device. The findings of this study revealed that the IP camera contains security faults and vulnerabilities that put user safety at risk.

Penetration testing (pen-testing) is one of the most effective approaches to increase the security level of information systems. Although pen-testing is a very popular approach, the process requires a significant investment of time and extensive financial resources. Existing pen-testing models have focused heavily on handling IoT security breaches and improving attack path analysis. However, they have their limitations as they are too general and unable to detect multi-stage, multi-host, or zero-day vulnerabilities in IoT devices. In this proposed study, an algorithm is developed to generate all attack paths for vulnerable IoT devices. The techniques to optimize the attack paths are defined. A second algorithm is developed to identify the criticality of the paths, nodes, and vulnerabilities before it is used to optimize the target graphs. The developed model is expected to be an end-to-end, accurate, flexible, and automatic pen-testing model using attack graphs for detecting all possible paths that can be used by an attacker to penetrate the target system.

Purpose: This study examines the impact of social media information sharing and usage on consumer beliefs particularly in the credibility of the information provided by e-commerce vendors, and consumer trust formation. Design/methodology/approach: Drawing on trust through social media usage and surface credibility, the authors have proposed a research model to investigate consumers satisfaction on food and beverage (F&B) products. Empirical support for the research model was provided by using structural equation modelling using survey data drawn from Malaysian consumers with an account with Facebook. Findings: Results indicate that consumer participation in social media communities support higher levels of consumer trust and ratings of the surface credibility of information provided by an F&B vendor, and surface credibility also helped to develop consumer trust. Trust in the vendor also exerted a positive influence on consumer satisfaction with F&B product offerings. Results suggest that F&B that provide credible and transparent information regarding their branded products, enjoy increased levels of consumer trust, leading to higher levels of consumer satisfaction with their F&B consumption experience. Originality/value: The result of this research contributes to social commerce branch of literature and has practical implications for practitioners in the F&B industry as a means to survival strategies to embrace critical and challenging period during an endemic, particularly. As such, this study analyses the relationship between social media usage, surface credibility, trust and satisfaction for developing consumer trust while managing enterprise social media.