Cryptocurrency is a type of cryptographic e-currency generated by the computational result of mathematical techniques. It is one of the secure online integrated payment systems in B2B financial transactions. Bitcoin is the world's first decentralized digital currency that depends on a decentralized peer-to-peer network that anonymously allows online transactions without relying on third-party institutions or government. Though, its technology is considered as one of the secure online ledgers with strong cryptography integrated payment systems, it is still struggling for its adoption in E-commerce as mode of payment and people are reluctant to its. Substantial research studies on bitcoin's adoption have mainly focused on usability, security, and uncertainties toward its implementation. Such studies also cited its use for illegal transactions and its price volatility. This unwillingness of implementation might either be due to individuals' motivations, confidence, and belief in their knowledge or not. Typically, it is still unclear if bitcoins adoption in E-commerce might rely on people's decisions or the technology itself. To address this gap, this research, therefore, builds on theories of Technological Acceptance Model (TAM), Theory of Reasoned Action (TRA), Delone and McLean Information System Success Model and Self-determination Theory Model (SDT) to examine the factors influencing its adoption with bitcoin in an e-commerce. The study was conducted in Malaysia using both qualitative interviews and a quantitative survey. The study used a sample of 5-five informants (I) for the qualitative study and 402 participants for the quantitative study where nine main hypotheses were formulated and tested using the partial least squares (PLS) path modeling with SPSS. In contrast, thematic analysis was undertaking for the interview data generated. The result shows that the analysis of the quantitative data provided support for all the nine hypotheses and established the following findings: (i) positive relation between Self-Efficacy and Intention to Use bitcoin in e-payment; (ii) positive relation between Resource Appropriateness and Intention to Use bitcoin in e-payment; (iii) positive relation between Perceived Trust and Intention to Use bitcoin as e-payment; (iv) positive association between Perceived Risk and Intention to Use; (v) mediation effect of Perceived Usefulness, Perceived Ease of Use on the relationships between Self-Efficacy, Resource Appropriateness and Intention to Use bitcoin in e-payment. Simultaneously, the qualitative finding reveals that people are still skeptical about adopting bitcoin for e-payment in e-commerce. The thesis discussed several implications for theory within an e-commerce transaction considering the conclusions of the study. While some limitations face conducting the research, specifically related to the use of the single-informant method, and cross-sectional research design, future research may incorporate multiple approach. Results obtained in this study have both academic and practical worth in terms of Information Systems technology adoption for improving the understanding of factors influencing bitcoin adoption in e-transaction.

This research work focuses on examining, analysing and classifying the difficulties of using sensors to improve the working standards of systems that are used in healthcare commonly referred to as Healthcare Information Systems. This constitutes an active research area because the cost of healthcare services is rising and the world population is growing older (population aging) whose need for HIS is paramount. Quite a number of researchers are investigating how to use sensors in order to provide healthcare services efficaciously and how to monitor healthcare systems remotely. A major challenge affecting integrating sensors in healthcare is interoperability, and in order to address this issue in the present environment where devices are expected to interact with each other more smoothly in what is popularly known as ‘the internet of things’, Interoperability as a Property (IaaP) has been proposed as a new paradigm. In this thesis, the enabling factors (or criteria) for Interoperability as a Property of a system (IaaP) were refined and a framework based on IaaP was proposed. To pave the way for further investigation, experimental scenarios for implementing each enabling criteria was examined, where environmental temperature was simulated as the Independent Variable (IV) and blood pressure was simulated as the dependent variable (DV). DEMATEL (DEcision-MAking Trial and Evaluation Laboratory) was used as a multiple-criteria decision analysis method to identify the interdependence among IaaP enabling criteria. Furthermore, DEMATEL was used to categorize IaaP enabling criteria as either being part of the causal or the effect group. Multi-criteria decision making results show that Intelligence Criteria (INT) constitute the most important IaaP enabling criteria followed by Communication Criteria (COM), the least important being Trace Criteria (TRC). Evaluation of results indicates that the IaaP framework constitutes a useful approach for bridging the interoperability gap in HIS. The contribution of the research lies with the effect of the main interoperable criteria for the huge amount of devices that are connected to the internet, it is outrageously difficult for device to smoothly interoperate, but Intelligence Criteria as well as the other criteria have been able to efficiently make interoperability possible. Even though a lot of research has been conducted with the aim of making devices used in HIS more interoperable, interoperability remains a challenge to most healthcare providers. In this research, interoperability as a property of a system is further confirmed to dwells on seven enabling requirements. All the seven requirements are used as building blocks for ‘Interoperability as a Property Framework’ (IaaPF). In addition to the medical sensory data, IaaPF should be able to receive other useful signals from sensors in its vicinity.

Cryptocurrency is a peer-to-peer digital exchange system that generates and distributes tokens using cryptography. Concerning the Islamic view of cryptocurrency, Shari'ah scholars and financial experts have formed varying views, with some finding it permissible (halal) and others prohibiting it (haram). As a result, the aim of this study is to examine the factors of implementing cryptocurrency from an Islamic perspective, to assess the mediating factors of implementing cryptocurrency from a Muslim perspective, and to propose a model of implementing cryptocurrency from a Muslim perspective. Given the lack of an Islamic digital currency model and the importance of applying Islamic law, the study employs the Theory of Reason Action (TRA), the Technology Acceptance Model (TAM), Deindividuation theory, and the Shari'ah compliance e-commerce trust model, as well as a mixed-method research methodology that incorporates both qualitative and quantitative approaches. In this study, the unit of analysis is the individual level, as it is a stakeholder in cryptocurrency and its Islamic perception. In the quantitative research round, 307 people completed a significant number of items on the research questionnaire through web-based and face-to-face methods. For qualitative research, the sample size is nine informants. In both cases, the samples include individuals who are familiar with cryptocurrencies, especially Bitcoin and its Islamic perspective. The following results can be taken from the study of both qualitative and quantitative research: 1) "Emotionality" significantly influences "Behavioral Intention to Use Cryptocurrency (Bitcoin)" as well as mediating "Financial concern", "Shari’ah Compliance" towards Intention to Use Cryptocurrency (Bitcoin). This has been the study's main finding toward a new development of theory. 2) For the Qualitative findings, many experts in the field of Muslim cryptocurrency conclude that cryptocurrency is purely an accounting treatment that brings what is required of transparency and the accuracy of reporting, which is embedded in the hadith of (the) Prophet sallallahu ‘alayhi wa sallam. Moreover, the results revealed that cryptocurrency can be accepted as a commodity or a piece of merchandise just like software. There is no objection to trading it as a commodity for someone who thinks that it will help him if there is a guarantee of not losing his money. The Hanafi and Maliki schools of thought agree that anything used in a financial transaction must be either a commodity, a currency, or a financial asset. it was uncovered that, if this Bitcoin transaction is measured according to the rules of Islamic law, it violates the Holy book of Allah, as well as the Sunnah of our prophet (SAW), unless anything else is established. These were some of the research's practical contributions, this study contributes to the debate over the legitimacy of cryptocurrency transactions from an Islamic perspective by specifying the requirements for considering cryptocurrencies as a legitimate or illegal digital transaction method. The findings would be beneficial for investors and Islamic finance experts.

A medical image is a visual representation of the shape or function of internal body organs or tissues used in clinical analysis or medical diagnosis. Watermarking techniques are used to protect copyright and provide evidence of any attempt to unlawfully manipulate the content of medical images. The watermarking technique was used to mask the data within the medical image without affecting the original visual quality. The challenge is producing an optimal watermarked image for different types of medical images where each with unique characteristics. Currently, there is no watermarking technique that can find the optimal action to use without affecting the visual quality and can resist image attacks. This research aims to investigate the characteristics of the frequency watermarking technique that affect the process of producing an optimal watermarked medical image. In the proposed optimal watermarking technique, watermark data will embed in a constant ratio of the original medical images frequency domain to increase the resistibility of the watermarked medical image to image attacks. The first step in developing an optimal watermarking technique in this research is to construct a theoretical framework and conceptual design model to understand the underlying characteristics of the watermarking technique process. Based on the proposed theoretical framework and conceptual design model, this research then investigates the role of the optimized frequency technique to achieve optimal performance for the watermarking technique. This research conducted an implementation experiment and simulation for the optimized frequency watermarking algorithm. Also, it analysed the effect of using the optimized frequency domain on the performance of the watermarking technique. Finally, the results of the optimized frequency watermarking algorithm are compared to the results of published watermarking algorithms. The proposed watermarking technique used optimization algorithm with frequency technique and Schur decomposition to produce watermarked images able to trade-off between the performance evaluation terms. The imperceptibility performance is high and has the robustness of the JPEG compression, salt and pepper noise, Gaussian noise, and rotation attack image attacks. Where the average PSNR values is 75.44 dB and NCC values for rotation attack is 0.91 and PEG compression is 0.99.

Sepsis is a severe threat to global health. Approximately, the mortality rate of sepsis in the Intensive Care Unit (ICU) is 42%. In 2017, 11 million sepsis-related deaths were reported among 49 million cases, 20% of all-cause of deaths worldwide. Detection and prediction of sepsis in earlier stage allow patients to get earlier care and better results, but sepsis is often unknown until the late stages. Substantial bodies of research studies on sepsis prediction have mainly focused on rules-based severity scores, which are transparent and straightforward; unfortunately, they have imperfect sensitivity and specificity in identifying and predicting sepsis. Typically, various sepsis predictions approach that would allow for predicting in an earlier stage, which can reduce the mortality rate and treatment cost. So, machine learning algorithms can be a choice for predicting sepsis. Therefore, this current thesis identified the features influencing early sepsis prediction and examining the features impelling the clinical severity scores used for the prediction of sepsis. The thesis also developed a hybrid optimal brain surgeon algorithm for sepsis prediction and tested the proposed algorithm's accuracy. The research methodology adopted for this thesis is an experimental simulation. The datasets used in this research were adopted from MIMIC-III, which comes with vast electronic health records. A systematic literature review was performed, and significant features of the MIMIC-III dataset for sepsis prediction were obtained by applying Automatic Backward Elimination (ABE) algorithm, Generalized Linear Model (GLM), and Correlation Matrix (CM). After that, the research built a hybrid-sepsis prediction model using machine learning techniques to train and test with selected features for model selection. Then Optimal Brain Surgeon (OBS) algorithm was used to simplify the architecture of the neural network for making an explainable deep learning-based sepsis prediction model. This is where hybridization has taken effect. The pruning algorithm OBS uses Hessian information and considers the time delay for measuring the saliency. Second-derivative information is used to compromise between the difficulty of the network and the training set error. The thesis's finding revealed that the AUROC of the predictive model was 0.882. The hybrid OBS algorithm pruned network is 80.0% with the same accuracy of the prediction model. This result indicates that the proposed hybrid model is efficient with high prediction accuracy and slight complexity compared with some previous prediction techniques. Early prediction of sepsis can reduce mortality rates and save treatment costs among ICU patients.

At the current time, finding an alternative computing device with extreme computation power became the main concern of the research community. Therefore, building a computer device able to execute extremely difficult calculations in a short time is required. Presently, massively parallel computer (MPC) systems considered the highest computing devices, and the existence of these systems is important to execute many operations in many sectors such as engineering and science. These systems built based on an internal network called interconnection network which has a particular design represented by the network topology. The performance of these networks affected widely by the network topology. Besides, the cost of these networks influenced by the price of the processing elements (PEs) and the communication links. Thus, the design of the interconnection network topology has a crucial impact on the network cost and performance. Many topologies of interconnection networks have been presented to be used as basic modules in building MPC systems. However, the earlier topologies showed a lack of performance in case of increasing the size of the interconnection network. As a result, hierarchical interconnection networks (HINs) proposed to replace these networks. Currently, many HINs introduced to enhance the performance of MPC systems, however, we still lack a good one. In this research, a hierarchical interconnection network proposed as a basic module (BM) to build a complete parallel computer system. This topology is a completely connected network composed of six nodes and called shifted completely connected network (SCCN), also, it connected hierarchically to produce higher-levels leading to a complete system network. A two-dimensional system with multiple levels is built based on SCCN. The two-dimensional levels which composing this system are network-on-chip level, board-level, cabinet-level, and system-level. The static network performance parameters of these levels evaluated by computer simulators and the obtained results compared to multiple conventional and hierarchical interconnection networks. Moreover, in this research, we presented a three-dimensional design of SCCN based on the proposed topology. Therefore, a three-dimensional network-on-chip (3D-NoC) presented to build higher levels of 3D-SCCN. The static network performance parameters of 3D-NoC level and the higher levels assessed by computer simulators. Furthermore, the obtained results compared to other conventional and hierarchical interconnection networks. The purpose of the comparison is to prove the strength of the proposed topology which showed promising results in many aspects.

Nowadays, the need to employ a new technology to update, support education and enhance studying in general but specifically the Holy Quran is of great necessity. One of these new technologies that is often used is mind maps (mind mapping) as it has been used for decades in schools and colleges for note taking and learning. Even though mind maps are beneficial in terms of learning, creating maps after reading and understanding a text takes a long time. Mind maps were used in Holy Quran learning, but the majority of the learners use the traditional method of Quran memorization, which is based on repetition. The use of repetition in the process of memorization makes it difficult to memorize. Individuals require more time and they tend to forget what they have just memorized. In order to avoid these problems mentioned earlier, some researchers worked on creating mind maps of the Holy Quran and discussed the positive impact on using a manual drawn mind map in studying and memorizing the Holy Quran. Creating mind maps of one chapter of the Holy Quran took a long time. Researchers worked on automatic creation of mind maps for the English, Croatian and Indonesian languages but not for Arabic language. Based on the literature review of this research, there is no research emphasizing the creation of an automatic mind map of the Holy Quran. Therefore, the aim of this research is to develop an algorithm that automatically generates the topics based on the mind map of a set of verses in the Holy Quran. The general idea of the algorithm uses the frequency of the Arabic root word to find the topic of the verses. This work is significant because it will enhance the understanding and memorization of the Holy Quran by viewing the concepts in a way that is easy for the brain to perceive. Moreover, it could help extract new knowledge from the Holy Quran from the generated mind maps and facilitate the understanding of Tafsir. This research found the topics of all sets of verses based on the Topical Mushaf and Al-Duwayish mind map of Surat Al-Baqarah. The algorithm results were evaluated with topics of expert scholars and the error rate of the algorithm was 25% with the Topical Mushaf and about 35% with Al-Duwayish mind map topics. This work is limited to extract the topics from the Qur'anic verses as an input for the mind map drawing. This research contributes to the field of automatic generation of mind maps of the Holy Quran and provides the algorithm that automatically returns the topics of a set of Qur'anic verses.

The tremendous growth in the field of data science and the widespread usage of information retrieval techniques has enabled users to retrieve accurate information. The diverse data availability in various Knowledge Base (KB) formats introduces several challenges to deliver concise and precise information corresponding to human queries. This requires a user to be familiar with the structures of KB and use a formal query language for the system to effectively understand the query. Question Answering (QA) systems have been introduced to enable users to post questions in Natural Language (NL) and infer specific answers instead of lists of documents. Such a system requires the capability of both critical analysis on questions and inference on answers selection. NL question analysis module is a fundamental step that impacts the QA system performance. It aims to transform users’ NL questions into representations of a structured format suitable to query across KBs. Literature showed, the major challenge in NL question transformation is language ambiguity that may occur at a lexical-semantic level. Moreover, various challenging questions require handling ambiguities based on a certain condition such as seeking instructions or advice. Therefore, the motivation of this study is to propose a Knowledge-based Sense Disambiguation (KSD) method for resolving the problem of lexical ambiguity associated with NL questions. This algorithm is designed by incorporating question’s metadata (date/GPS), context knowledge, and domain ontology, into a shallow NL processor. It aims at enhancing the accuracy of the word sense disambiguation process in questions analysis module to effectively returns potential answers corresponding to questions posed in QA systems. This work explores the use of the proposed KSD method to support pilgrims in expressive queries to obtain accurate information via a mobile QA application. Therefore, the validity of the proposed solution has been supported by two experiments to evaluate the accuracy performance. First, in vitro experiment was carried out as a standalone task to evaluate the KSD as a word sense disambiguation method in comparison with the baselines WordNet Most Frequent Sense (MFS) and the simplified version of the Lesk method on the same condition and test dataset. Second, in vivo experiment was performed to evaluate the effectiveness of the KSD method in a QA application in comparison to the MFS in the context of a pilgrimage domain. The results obtained from both experiments have revealed the feasibility of the proposed solution to effectively cope with lexical ambiguity in NL questions as well as to contribute to QA system performance improvement. “We don’t need more information. We need more meaning.” Paul Salopek