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.

Background Virtual reality is utilised in exergames to help patients with disabilities improve on the movement of their limbs. Exergame settings, such as the game difficulty, play important roles in the rehabilitation outcome. Similarly, suboptimal exergames’ settings may adversely affect the accuracy of the results obtained. As such, the improvement in patients’ movement performances falls below the desired expectations. In this paper, a recommender system is incorporated to suggest the most preferred movement setting for each patient, based on the movement history of the patient. Method The proposed recommender system (ResComS) suggests the most suitable setting necessary to optimally improve patients’ rehabilitation performances. In the course of developing the recommender system, three methods are proposed and compared: ReComS (K-nearest neighbours and collaborative filtering algorithms), ReComS+ (k-means, K-nearest neighbours, and collaborative filtering algorithms) and ReComS++ (bacterial foraging optimisation, k-means, K-nearest neighbours, and collaborative filtering algorithms). The experimental datasets are collected using the Medical Interactive Recovery Assistant (MIRA) software platform. Result Experimental results, validated by the patients’ exergame performances, reveal that the ReComS++ approach predicts the best exergame settings for patients with 85.76% accuracy.

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.

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.

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.