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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.

open access article This research is funded by UniKL/CoRI/str19045. Very great appreciation to UniKl and University of Science and Technology (Yemen) for their unlimited support. Land registration systems are very essential for property ownership management. The exited land registry systems are less efficient and time-consuming and expose to human errors. By using blockchain technology, most of the principles of good governance in land administration such as transparency and efficiency can be fulfilled. However, there is a lack of experience in developing blockchain-based land registry systems. This paper proposes a blockchain-based adoption framework for land registry management in Malaysia. It elaborates more on developing a prototype that fulfills the main functions of current land registration by using smart contract functionalities. Also, this paper illustrates the main challenges of adopting this technology such as expertise shortage of software developers, implementation difficulties due to scalability of the land transactions, data sharing with different types of blockchain and lack of security attacks resistance. Therefore, there is a need to form an agreed-upon blockchain development platform that meet such constraints.

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.

Recently, there has been a renewed interest in smart health systems that aim to deliver high quality healthcare services. Prediction methods are very essential to support these systems. They mainly rely on datasets with assumptions that match the reality. However, one of the greatest challenges to prediction methods is to have datasets which are normally distributed. This paper presents an experimental work to implement SMOTE (Synthetic Minority Oversampling Technique) and bootstrapping methods to normalize datasets. It also measured the impact of both methods in the performance of different prediction methods such as Support vector machine (SVM), Naive Bayes, and neural network(NN) The results showed that bootstrapping with native bays yielded better prediction performance as compared to other prediction methods with SMOTE.

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.

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.