Improvement to the Appointment System for Clinic: A Case Study in UUM Clinic

Success in emerging new technology can be vital for future prosperity; therefore, any clinic wants to provide high efficiency in presenting its services to patients and implementing efficient interaction activities between its department and patients through clinic online system. Patient services have played essential role in the health care for the society. Patient services and patient waiting time are problems that medical institutions centers faced, because desktop appointment system takes long time for the patient to meet the doctors. As a consequence, the aim of this study is to reduce long waiting time of patients. In actuality, doctor's efficiency will be increased in some certain level. Current appointment system normally runs in hospitals or clinic randomly. The patients who are appointed at the later interval will wait much longer time. The purpose of this research is to reduce the long waiting time through internet to make adjustments patients' appointment number and interval these appointments through internet. Consequently, patients can be able to make appointments through Web/WAP-based applications system service, to improve patient accessing, to enhance patient and physician satisfaction, and to increase practice productivity. Moreover, there is even evidence that promote better outcomes and lower overall costs of care

Migration From A Relational Database To A Document-Oriented Database Based On Document-Oriented Data Schema

Big data is a crucial issue that has emerged as one of the most important technologies in the modern world. Most studies have highlighted the inability of a relational database to handle big data. This challenge has led to the presentation of the “not only structured query language (NoSQL) database” as a new concept of database technology. One of the most powerful types of NoSQL databases is the document-oriented database that supports a flexible schema and store data in a semi-structured format. Recently, many researchers have migrated from relational databases to document-oriented databases because of scalability, availability, and performance. However, their migration methods are facing three issues; the first issue is that there are no specifications that can be recognized to define a schema for a document-oriented database, and second, there is no method to normalize or de-normalize data in order to implement the embedded and reference document. The third is the migration from the relational database to a document-oriented database does not consider all the properties of the former, especially on how to handle various types of relationships. This study proposed a methodology to handle the migration issues through three phases: first, design a document-oriented data schema (DODS) based on the entity-relational (ER) model; second, enhance transformation rules to map the entity relational schema to the document-oriented data schema based on normalization and de-normalization data; third, migrate a relational database to a document-oriented database

Machine and deep learning techniques for detecting internet protocol version six attacks: a review

The rapid development of information and communication technologies has increased the demand for internet-facing devices that require publicly accessible internet protocol (IP) addresses, resulting in the depletion of internet protocol version 4 (IPv4) address space. As a result, internet protocol version 6 (IPv6) was designed to address this issue. However, IPv6 is still not widely used because of security concerns. An intrusion detection system (IDS) is one example of a security mechanism used to secure networks. Lately, the use of machine learning (ML) or deep learning (DL) detection models in IDSs is gaining popularity due to their ability to detect threats on IPv6 networks accurately. However, there is an apparent lack of studies that review ML and DL in IDS. Even the existing reviews of ML and DL fail to compare those techniques. Thus, this paper comprehensively elucidates ML and DL techniques and IPv6-based distributed denial of service (DDoS) attacks. Additionally, this paper includes a qualitative comparison with other related works. Moreover, this work also thoroughly reviews the existing ML and DL-based IDSs for detecting IPv6 and IPv4 attacks. Lastly, researchers could use this review as a guide in the future to improve their work on DL and ML-based IDS

Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks

Internet Protocol version six (IPv6) is more secure than its forerunner, Internet Protocol version four (IPv4). IPv6 introduces several new protocols, such as the Internet Control Message Protocol version six (ICMPv6), an essential protocol to the IPv6 networks. However, it exposes IPv6 networks to some security threats since ICMPv6 messages are not verified or authenticated, and they are mandatory messages that cannot be blocked or disabled. One of the threats currently facing IPv6 networks is the exploitation of ICMPv6 messages by malicious actors to execute distributed denial of service (DDoS) attacks. Therefore, this paper proposes a deep-learning-based approach to detect ICMPv6 flooding DDoS attacks on IPv6 networks by introducing an ensemble feature selection technique that utilizes chi-square and information gain ratio methods to select significant features for attack detection with high accuracy. In addition, a long short-term memory (LSTM) is employed to train the detection model on the selected features. The proposed approach was evaluated using a synthetic dataset for false-positive rate (FPR), detection accuracy, F-measure, recall, and precision, achieving 0.55%, 98.41%, 98.39%, 97.3%, and 99.4%, respectively. Additionally, the results reveal that the proposed approach outperforms the existing approaches