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Most nations across the world are actively pursuing equal access to healthcare services. Teleconsultation technology is a substantial improvement in terms of an effective framework for the provision of healthcare services. However, a lack of understanding of people's willingness towards the use of this technology has been observed. The goal of this study is to investigate the factors affecting the post-acceptance of teleconsultation services in Malaysia. This study developed a theoretical model which involves the combination of the second generation of Unified Theory of Acceptance and Use of Technology (UTAUT2) and Expectation Confirmation Theory (ECT), with the inclusion of several other constructs. An online survey was used to collect data from 154 university students and partial least squares (PLS) approach was used for data analysis. The research findings indicate that confirmation, performance, effort expectancy, usefulness, and satisfaction were the key factors that affect the post-acceptance of teleconsultation services. Furthermore, actual use, ease of use, technology readiness, and facilitating conditions did not impact participants' post intention in the continuous usage of teleconsultation facilities. © 2021 J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved.

Malaysian organizations are following different methods for cybersecurity risk assessment such as Control Objectives for Information and Related Technologies (COBIT) and International Organization for Standardization (ISO) 27001. The higher education institutions in Malaysia are facing the same difficulties as different standards and approaches are used to evaluate the cybersecurity risk of their institutions. So, there is a lack of a cybersecurity risk assessment approach that takes Malaysian and international standards into consideration. This paper aims to develop a cybersecurity risk assessment approach for higher education institutions in Malaysia. The methodology of conducting this research is qualitative research using a case study. The framework has considered international cybersecurity standards such as the Holistic cybersecurity maturity assessment framework (HCYMAF) and local cybersecurity standards such as National Cyber Security Agency (NACSA) and the Malaysia Cyber Security Strategy (MCSS). In addition, a measurable instrument to assess cybersecurity risk has been proposed. The subject-matter expert can apply this approach to assess their organization's cybersecurity maturity and risk. The findings of this study shall be useful in overcoming the drawbacks of employing non-standard procedures in such evaluations, resulting in more accurate and reliable evaluation outcomes.

The detection performance of tree crowns in forest environment has not been satisfactory compared to common objects, especially using aerial RGB imagery. Previous methods regarding Individual Tree Crown Detection (ITCD) utilizes different data sources to improve the detection rate due to the noisy image. Image enhancement methods such as super-resolution provide a solution to the noisy image by reconstructing the image using the low-resolution image. Generative Adversarial Network (GAN)-based model has shown success in super-resolution techniques. However, the GAN-based model created artefacts that may hinder the accuracy of the detection. In this paper, a noise-cancelling GAN-based model is proposed by averaging the weights of a compressed image and non-compressed image. The proposed method forces the network to discriminate the noise to generate a more photorealistic image. This method is inspired by super-resolution GAN (SRGAN) architecture with Residual Dense Network as the generator network. A two-stage object detection RetinaNet model is then used to detect the individual tree crowns in a sequential fashion. Extensive experiments have been conducted on a self-assembled tree crown dataset which showed the proposed model is more superior than a non-enhanced model with 0.6017 and 0.5908 respectively. Based on the results of the proposed method, the super-resolution technique can be used in conjunction with object detection algorithm to improve the detection in ITCD to improve the detection rate.

Internet of Things (IoT) networks are in danger of being attacked due to their heterogeneity and low power capability. There is a necessity for a more efficient method to protect IoT networks from potential attacks. Machine learning plays a vital role to explore and investigate the malicious behavior of the attacks. However, abnormal distribution of the training datasets among classes causes instability of the classification performance. This paper proposes a framework for IoT botnet attack detection with proper handling of imbalanced classes. It employed a combination of deep neural networks and Synthetic Minority Over-Sampling Technique (SMOTE). The proposed framework consists of training, testing, and evaluation phases. In the proposed framework, standard UCI benchmark datasets were used. The experiment results have demonstrated the effectiveness of the proposed framework in differentiating between normal and malicious traffic attacks.

Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.

Digital games are widely used in several applications such as education , entertainment and health care. Gradually, people are becoming addicted to some types of games. The ooverplaying of digital games leads to some behavioral disorders such as lack of sleep, depression, and social relationship challenges. Telemedicine technology is a promised way of delivering clinical services to patients remotely. This technology is widely used in different domains. However , there is a lack of understanding on how people would continue to use this technology as a way to receive health care services. Therefore, this study aims to explore people’s attitude towards adopting and continued usage of this technology in digital games addiction treatment. This paper proposes a theoretical model based on the modified technology acceptance model (TAM), the unified theory of acceptance and use of technology (UTAUT), and Original post-acceptance models and with additional factors. The studied population contains 112 digital gamers between 10 and 60 years old in Malaysia. Thematic content analysis approach is used to investigate the participants’ views on the main factors that have impacted on adopting and continuity of the usage of ttelemedicine technology. The results showed the proposed model which partially explains the participants’ attitudes toward accepting and continuously using telemedicine technology.

Applications for face detection use algorithms that rely on identifying human faces in broader photos that may include environments, artifacts, and other sections of a person's physique. This work proposes a real-time identification system, based on modern image processing capabilities of open source API like OpenCV and due to the solution requirements, a study on the performance analysis of such solution compared to available commercial framework like SPID from NEC is intended. However, here, the study is available with the results of various experiments on the developed system. A systematic approach is followed to produce such outputs and have been measured using software codes. By using IP camera and a Raspberry Pi, the solution developed is simple in nature. This study relies on face detection and identification functionalities for human faces but not limited to live faces only but mix of faces from still images as well.

In this era, the Internet represents the main tool of data searching and exchanging. By using Internet many apps in different locations, can be connected to each other remotely via web server connectivity. This connectivity is exposed to the internet vulnerabilities such as flooding attacks. Flooding attack is an example of an attack that causes harmful action against the web server resources or network bandwidth. Multivariate Correlation Analysis based Detection approach (MADM) is one of the statistical based detection (NIDS) approaches used to detect such attacks. However, MADM use predefined features that contain some intercorrelated features that have direct impact on classification performance. In this paper, a new extension on MADM architecture is proposed by adding SVM based feature selection method. So the mathematical formulation is explained. The proposed features selection method will be contributing in minimizing the false positive alarm rate and classification error rate.

The development of the binary algorithm for MyRA big data analysis and management is discussed in this paper. The algorithm is used in database group at the Universiti of Kuala Lumpur (UniKL) and that is a central data base server service aimed at domain scientists around the all 14 UniKL campuses. The MyRA database project has two main objectives. The first is to create an engine that is mature enough, fast and easy to use that domain researchers can use. The second goal is to create a platform to test new database research concepts inspired by our users' needs. To create the system, we have created a binary algorithm that has the freedom to determine all components of the system design as expected. So, we highlight the binary algorithm for selecting and innovating MyRA Big Data key design and reporting on our experience with the use of binary algorithms in the use of various data sciences.

This paper presents the real-time environment development in a virtual environment (VE) by developing a new algorithm. The development process has been done by examining the novel algorithm. The algorithm has metaphors as the basis for facilitating social interaction, which explores space by adjusting the trajectories of individual vectors, referring to those that are considered points of departure in extra-dimensional virtual space. This algorithm will adjust the turn-around time, burst time and waiting time when compared with the current algorithm..

Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.

Dengue is a critical communicable and vector-borne disease and is becoming a serious concern in Malaysia. It is important to have an early detection system that could provide immediate action, such as the control of dengue transmission at a specific location. However, the available strategy and action may give long-term effects to the community since inaccurate decision making or prediction may lead to other circumstances. Moreover, the need to have a system that can detect the outbreak in a reasonable amount of time is critical. In this study, a nature-inspired computing technique, the artificial immune system (AIS), is used for dengue outbreak detection. One of the variants of the AIS algorithms, called the negative selection algorithm (NSA), has been widely applied in anomaly detection and fault detection. This study aims to employ the NSA for dengue outbreak detection.

This paper discusses on the detection of outliers by hybriding Rough_Outlier Algorithm with Negative Association Rules. An optimization algorithm named Binary Particle Swarm Optimization is used to improve the computation of Non_Reduct in order to detect outliers. By using Binary PSO algorithm, the rules generated from Rough_Outliers algorithm is optimized, giving significant outliers object detected. The detection of outliers process is then enhanced by hybriding it with Negative Association Rules. Frequent and Infrequent item sets from outlier rules are generated. Results show that the hybrid Rough_Negative algorithm is able to uncover meaningful knowledge of outliers from the frequent and infrequent item sets. These knowledge can then be used by experts in their field of domain for better decision making.

Public health surveillance system focuses on outbreak detection and data sources used. Variation or aberration in the frequency distribution of health data, compared to historical data is often used to detect outbreaks. It is important that new techniques be developed to improve the detection rate, thereby reducing wastage of resources in public health. Thus, the objective is to developed technique by applying frequent mining and outlier mining techniques in outbreak detection. 14 datasets from the UCI were tested on the proposed technique. The performance of the effectiveness for each technique was measured by t-test. The overall performance shows that DTK can be used to detect outlier within frequent dataset. In conclusion the outbreak detection technique using anomaly-based on frequent-outlier technique can be used to identify the outlier within frequent dataset.

Today, the objective of public health surveillance system is to reduce the impact of outbreaks by enabling appropriate intervention. Commonly used techniques are based on the changes or aberration in health events when compared with normal history to detect an outbreak. The main problem encountered in outbreaks is high rates of false alarm. High false alarm rates can lead to unnecessary interventions, and falsely detected outbreaks will lead to costly investigation. In this chapter, the authors review data mining techniques focusing on frequent and outlier mining to develop generic outbreak detection process model, named as “Frequent-outlier” model. The process model was tested against the real dengue dataset obtained from FSK, UKM, and also tested on the synthetic respiratory dataset obtained from AUTON LAB. The ROC was run to analyze the overall performance of “frequent-outlier” with CUSUM and Moving Average (MA). The results were promising and were evaluated using detection rate, false positive rate, and overall performance. An important outcome of this study is the knowledge rules derived from the notification of the outbreak cases to be used in counter measure assessment for outbreak preparedness.

Data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. While Association Rules Mining (ARM) algorithm especially the Apriori algorithm has been an active research work in recent years. Diverse improvement varies in term of producing more frequent items and also generating further k-length. The idea is to produce better pattern and more interesting rules. In this paper, we propose new approach for ARM based on Multiple Attribute Value within the non-binary search spaces. The proposed algorithm improves the existing frequent pattern mining by generating the most frequent values (item) within the attribute and generate candidate based on the frequent attribute value. The main idea of our work is to discover more meaningful frequent items and maximum k-length items. The experimental results show that our proposed MAV frequent pattern mining enhance the impact in generating more frequents items and maximum length

Dengue fever (DF) and dengue hemorrhagic fever (DHF) are vector borne disease which is notifiable diseases in Malaysia since 1974. Early notification is essential for control measures as delayed notification will lead to further occurrences of outbreak cases. In this study we identify the number of attributes to be used in determining outbreaks rather than using only case counts. The experiment is conducted using multiple attribute value based on Apriori concept. The outcomes are promising when we can identify more than one attributes showing similar graph in vector-borne diseases outbreaks. Our methods also outperform in term of detection rate, false positive rate and overall performance. We prove through our experiment that more than one attributes can be used to better detect outbreaks.