In this paper, classification of brainwave using real world data from Parkinson’s patients is presented. Emotional model is produced from the classification of brainwave. Electroencephalograph (EEG) signal is recorded on eleven Parkinson’s patients. This paper aim to find the “best” classification for the emotional model in brainwave patterns for the Parkinson’s disease. The work performed based on the two method phases which are using the raw data and pre-processing data. In each of the method, we performed for steps in the sum of the hertz and divided by total hertz. In the pre-processing data we are using statistic mean and standard deviation. We used WEKA Application for the classification with 11 fold validation. As a results, implecart from the classification tree performed the “best” classification for the emotional model for Parkinson Patients. The Simplecart classification result is 84.42% accuracy.

Coronavirus disease 2019 (COVID-19) is a global pandemic. Clinical studies have shown that there was an association between COVID-19 and cardiovascular disease. The virus can directly induce myocardial injury, arrhythmia, acute coronary syndrome, and venous thromboembolism. In Malaysia, students will come back to University soon. The screening techniques framework is required to reduce the pandemic Covid-19 transmission among the students. In this manuscript, we present a new screening technique framework which is consists of temperature and heart rate measurements, movement tracking and risk assessment. Students will be given a questionnaire to stratify their risk into high, medium, and low risk. The temperature will be measured by using an infrared thermometer. The heart rate will be monitored only in those in high and lowrisk categories by using a smart bracelet. The students’ movement will be tracked by using a Wi-Fi based location technique. To avoid any privacy concerns, the location data will be extracted only if the student shared the location with the confirmed COVID-19 case. Lastly, the risk assessment is required in reporting if the infection occurs among students.

This project focuses mainly on System Development Life Cycle Methodology on how to develop software that using the principles in the System Development Life Cycle. In stage of system analysis, we use the Unified Modelling Language (UML). This system provides additional functions like keeping a historical database for future and using GSM Modem as interface to the system for fast communication between the support and the help desk coordinator. Through implementing the system that records the usual problem and links to GSM Modem technologies for “SMS” that will improve the services, reduce cost and educate the users to solve their problem themselves. The support will be resolve as soon as possible since all the problems is been recorded and circulate to the support. The method of information gathering has been selected such as interview and research on the GSM Modem Product. We use the product GSM Module Siemens MC35 for the communication device for “SMS” Functions.

The aim of this study is to investigate the effectiveness of annotations in nlultimedia on-screen texts using two presentational modes in enhancing learners comprehension level as well as vocabulary level. The subjects involved are 44 first year Biotechnology students of USIM. The methodology employed is experimental comparison of the performance of two groups of subjects under two different conditions: one using multimedia on-screen text with the annotations of 'text+animation' and the other using multimedia onscreen text with the annotations of 'voice+animation'. The instruments used in this study are the multimedia texts on "transgenesis method' which focuses on the processes involved in the "Protoplast Fusion' as well as the "Agro bacterium mediated transfusion' and a Comprehension Test that assessed the subjects understanding of the reading texts assigned. The neth hod of data analysis used is of descriptive statistics which uses the frequency count of the average (mean) value scores as well as the highest (max) and lowest score (min) in identifying the effectiveness of the multimedia annotations in facilitating the students comprehension level using the two presentational modes. Generally, the overall scores indicate that there is no significance difference in the use of annotations via the two presentational mode - 'text+animation7 and 'voice+animation' as the difference that exist in the scores are small. However, the findings do indicate that the subjects are found to emphasise on the use of their visual sense of modality in facilitating their reading tasks. Subsequently, this has resulted the students that are exposed to the presentational mode of 'text+animation' to do fairly better than the latter group 'voice+animation'. Finally, it is important to highlight here that the annotations (animations) found in the multimedia on-screen text, do to a certain extent, facilitate the students in understanding a science based text due to its ability to build referential connections between the two mental representations in short term memory hence, resulting in better performance in the tasks assigned

The provision of Storage as a Service (STaaS) in many geo-distributed datacenters by several Cloud Storage Providers (CSPs) has made online cloud storage a great choice for replicating and distributing objects that are accessed worldwide. Online Social Networks (OSN) such as Facebook and Twitter have billions of active users worldwide accessing shared objects. These users expect to access these objects within a tolerable time. To minimize users’ access latency time of these objects, OSN service providers must host several replicas of objects in many datacenters. However, this replication process produces a higher monetary cost. This paper addresses crucial issues, including how many replicas are required to fulfil the expected workload of the object and the optimal datacenters to host these replicas to reduce latency time for users and monetary costs for OSN service providers. Two online algorithms are proposed to determine the suitable number of replicas for each object and the optimal placement of these replicas. The DTS algorithm establishes the replication and placement of objects using deterministic time slots, while the RTS algorithm is based on randomized time slots. Experimental results show the effectiveness of the proposed algorithms for producing latency time below certain thresholds and reducing the monetary cost.

Database era has been appear during Prophet Muhammad S.A.W. We are using the same concept but only deployed in the medical environment with security features which is related to aqli and naqli. The development of a system not only covered the ability of the system to be functioned and execute very well but a good and efficient system must be able to communicate with the users and become a user friendly system. This research is about developing Hospital Patient Information System developed for Hematology Ward and Hemato-oncology Laboratory at the Hospital UniversitiSains Malaysia (HUSM) based on the aqli and naqli. It lacks of security in term of data integrity of the system. It will be enhanced in its security features which will be implemented with the data integrity feature and also password encryption to overcome the problems. As a result, an automated calculation of blood series and graph production as for data analysis will be implemented in this system.An approach of agile method is the chosen process to be used in realizing this research. This system will come out with the ability of protection in data integrity, password encryption and automatic calculation in order to reduce the fraud and human errors.

In this article, we present a dataset that comprises different physical rehabilitation movements. The dataset was captured as part of a research project intended to provide automatic feedback on the execution of rehabilitation exercises, even in the absence of a physiotherapist. A Kinect motion sensor camera was used to record gestures. The dataset contains repetitions of nine gestures performed by 29 subjects, out of which 15 were patients and 14 were healthy controls. The data are presented in an easily accessible format, provided as 3D coordinates of 25 body joints along with the corresponding depth map for each frame. Each movement was annotated with the gesture type, the position of the person performing the gesture (sitting or standing) as well as a correctness label. The data are publicly available and were released with to provide a comprehensive dataset that can be used for assessing the performance of different patients while performing simple movements in a rehabilitation setting and for comparing these movements with a control group of healthy individuals

Credit scoring models are used in decision-making processes to produce an accurate prediction of an applicant’s creditworthiness. A five-step credit scoring model for personal loans was developed using the seven-step credit scoring model by Siddiqi. It uses real data provided by a bank. This study aims to remove the unnecessary complexity of the credit scoring process. The five-step credit scoring model consists of data massaging, factor analysis, data mining modelling, credit scoring and post-modelling. To ensure accuracy, factors that were significant in determining the creditworthiness of applicants were used in the model, which are the type of installment, age, monthly expenses, job sector, payment method and income-to-finance ratio. Furthermore, by presenting a systematic and structured step for developing a credit scoring model, this study contributed to the research on credit scoring. Based on the findings of this study, banks may use this model to create their own credit scoring model to assess the creditworthiness of personal loan applicants. By managing risks with this model, banks can create a long-term solution for credit system management and aid in the decision-making process

In social media, a huge number of worldwide data objects are posted every day. The contents of these data objects include text, links, images, audio, and videos which could be small, medium, or large and accessed across the world. Moving these data objects into a single cloud service provider (CSP) is risky and results in four-fold obstacles: vendor lock-in, service availability, cost-ineffective use, and increasing latency. Using multiple CSPs to replicate and distribute the data object solves such obstacles. However, replicating data objects among multiple CSPs increases the cost of creating and maintaining this replication. This study focuses on three issues of Online Social Network (OSN) which include: (1) determining the appropriate number of replicas of each data object based on its popularity on the OSN, (2) identifying the suitable datacenters that host the replicas according to latency time of different regions, and (3) deciding the suitable storage class for the data object at a specific time of its lifetime. Two algorithms are proposed to adapt the replication and placement of the data object according to its popularity in the OSN. The first algorithm is Dynamic Fixed Time (DFT) which uses fixed time periods to adapt replication and placement. The second algorithm is Dynamic Exponential Time (DET) which determines the data object replication and placement based on exponential time periods. A simulation using a synthesized workload generated based on a real Facebook statistic dataset shows that the proposed algorithms produce a monetary cost savings of more than 23% compared to the Static Replication and Local Placement (SRLP) algorithm.

In the telerehabilitation system, the statistical data of the patients’ movement are stored in the temporary storage and synchronised to the storage service of online cloud data. Application providers faced a problem in reducing the monetary cost of the whole cloud service and reducing the footprint of the main memory space. In addition, users encounter long latency when the required data need to be read from the cloud via the internet and the hard disk drive (HDD) of the cloud servers. To solve this problem, an optimal data access framework is presented to cache the statistical data of the patients in the application server. The main memory database and cache use internal tracking in the main memory to track records that are not accessed by transferring the data to the disk. This mechanism retains the keys and all indexed fields of evicted records in the main memory which prevents potential memory space savings for the application that have many keys and secondary indexes. Therefore, to overcome the mentioned problems, the cloud database is categorised into three partitions (hot, warm, cold). In addition, a cache memory image in the application server is provided for the hot partition of the cloud database. The use of cache memory image reduces the number of reading operations from the cloud and saves the space of the main memory. The experimental results showed that the proposed framework can produce good quality solutions by utilising the main memory space and reducing the latency and read operations from the cloud that lead to reducing the monetary costs.

Background: Heartbeat playing the main roles in our life. With the heartbeat, the anxiety level can be known. Most of the heartbeat is used in the exercise. Heart rate measurement is unique and uncontrollable by any human being. Objective: This research aims to learn student’s actions by monitoring the heart rate. In this paper, we are measuring the student reaction and action in classroom can give impact on teacher’s way of delivery when in the teaching session. In monitoring, student’s behavior may give feedback whether the teaching session have positive or negative outcome. Methods: The method we use is K-Means algorithm. Firstly, we need to know the student’s normal heartbeat as benchmark. We used Hexiware for collecting data from students’ hear beat. We perform the classification where K is benchmark students’ heartbeat. K-Means algorithm performs classification of the heart rate measurement of students. Results: We did the testing for five students in different subjects. It shows that all students have anxiety during the testing and presentation. Its consistency because we tested 5 students with mixes activities in the classroom, where the student has quiz, presentation and only teaching.Conclusion: Heart rate during studying in the classroom can change the education world in improving the efficiency of knowledge transfer between student and teacher. This research may act as basic way in monitoring student behavior in the classroom. We have tested for 5 students. Three students have their anxiety in classroom during the exam, presentation, and question. Two students have normal rate during the seminar and lecturer. The drawback, Hexiware is capturing average of ten minutes and tested in different classes and students. In future, we need just measure one student for all the subjects and Hexiware need to configure in one minute.

In today’s Information and Communication Technology (ICT) environment and with current global economics, Business Continuity Management (BCM) becomes a crucial requirement to the organization. BCM is a managerial activity that identifies potential impacts to the organization caused by the threats. However, BCM is only gets the attention when it is demanded by the regulatory compliance or the stakeholders or customers. The implementation of BCM not only involves the information technology (IT) department, but also the business areas that use the IT services. Therefore, this paper aims to explore and identify the success factors on the execution of business continuity management in the organization particularly in Malaysia. In this study, quantitative analysis has been used to explore the organization employee’s view and the importance of these factors towards the successful execution of BCM to the organizations.

Advancements in internet, cloud, and business intelligence technologies, as well as the emergence of big data, have caused massive traffic in online transaction processing (OLTP) systems. All these impose the needs for effective and efficient data storage and processing which is not available in conventional Relational Database Management Systems (RDBMSs). In the year 2000, a new generation of database management systems (DBMSs) called Not Only Structured Query Language (NoSQL) has emerged to address the scalability of OLTP workloads in a way that was impossible for conventional relational database management systems (RDBMSs). Despite adequately addressing issues linked to scalability and producing better read-write performance than RDBMS, NoSQL DBMSs appear to lack in providing definite assurance of data consistency. Hence, a newer DBMS alternative called NewSQL has emerged. NewSQL DBMS has combined the advantages of both conventional RDBMS and NoSQL. They have the scalable performance of NoSQL DBMSs and the data consistency of traditional RDBMS. To date, there are tens of existing NewSQL-DBMSs, so it is difficult to understand the difference between them, and it is also quite challenging to decide the best solution for a specific task. Hence, this paper presents a comprehensive review concerning NewSQL DBMSs by emphasizing the following purposes:(1) providing researchers and practitioners guidance that may assist in selecting the most appropriate NewSQL-DBMS, (2) classifying NewSQL-DBMSs based on internal implantation and number of tiers, (3) providing comparisons and analyses of query capabilities, technical characteristics, and security attributes of the prominent NewSQL-DBMSs, and finally, (4) identifying issues and challenges raised with the emergence of NewSQL-DBMSs. In conclusion, NewSQL DBMSs can offers solutions for fault tolerance, horizontal scalability features.

Objectives: To examine the adoption of telerehabilitation services from the stakeholders’ perspective and to investigate recent advances and future challenges. Data Sources: A systematic review of English articles indexed by PubMed, Thomson Institute of Scientific Information’s Web of Science, and Elsevier’s Scopus between 1998 and 2020. Study Selection: The first author (N.N.) screened all titles and abstracts based on the eligibility criteria. Experimental and empirical articles such as randomized and nonrandomized controlled trials, pre-experimental studies, case studies, surveys, feasibility studies, qualitative descriptive studies, and cohort studies were all included in this review. Data Extraction: The first, second, and fourth authors (N.N., W.I., B.N.) independently extracted data using data fields predefined by the third author (M.B.). The data extracted through this review included study objective, study design, purpose of telerehabilitation, telerehabilitation equipment, patient/sample, age, disease, data collection methods, theory/framework, and adoption themes. Data Synthesis: A telerehabilitation adoption process model was proposed to highlight the significance of the readiness stage and to classify the primary studies. The articles were classified based on 6 adoption themes, namely users’ perception, perspective, and experience; users’ satisfaction; users’ acceptance and adherence; TeleRehab usability; individual readiness; and users’ motivation and awareness. Results: A total of 133 of 914 articles met the eligibility criteria. The majority of papers were randomized controlled trials (27%), followed by surveys (15%). Almost 49% of the papers examined the use of telerehabilitation technology in patients with nervous system problems, 23% examined physical disability disorders, 10% examined cardiovascular diseases, and 8% inspected pulmonary diseases. Conclusion: Research on the adoption of telerehabilitation is still in its infancy and needs further attention from researchers working in health care, especially in resource-limited countries. Indeed, studies on the adoption of telerehabilitation are essential to minimize implementation failure, as these studies will help to inform health care personnel and clients about successful adoption strategies.

Motivation is one of the vital keys for special needs patients to feel sustain with their performances during rehabilitation therapies. as technology grows, serious games have become one of the assistive technology tools that used in rehabilitation field to help special needs patients to undergo their exercise. In this regard, the adaptation of special needs patients in using serious games for rehabilitation have attracted scholars to conduct number of relevant studies. Accordingly, a systematic review was conducted to identify the motivation attributes that influence special needs patients while playing serious games for rehabilitation. After the identification and screening processes, the use of the ARCS motivation model is to classify the motivation attributes into four main factors: attention, relevance, confidence, and satisfaction. A total number of 29 motivation attributes have been found from the previous studies that vigorously important in determine the rehabilitation performance of special needs patients. Each motivation attribute which repeatedly used in the recent studies from the year of 2011 to 2019 has been grouped as one. As results, the mostly used attribute in the previous research is motivation. The essential of motivation in ensuring confidence level and satisfaction among special needs patients while undergoing rehabilitation therapies is very important and has been successfully proven on the results. In other words, the previous studies have shown a positive experience of special needs patients in gaining and sustaining their motivation through serious games for rehabilitation by using the ARCS model.

As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010–2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.