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Telemedicine is an emerging industry with the potential to revolutionize the delivery of healthcare for the benefit of consumers, providers and payors. In general, telemedicine refers to the use of information and telecommunication technologies to distribute both information and expertise necessary for healthcare service provision, collaboration or delivery among geographically separated participants — physicians and patients. In short, it is a generic term which is used to define various aspects of healthcare at a distance. Telemedicine has been heralded as one of several possible solutions to some of the medical dilemmas that are faced by many developing countries. In this paper, we will discuss the current state of telemedicine in developing countries in South-East Asia (SEA) in general, with Malaysia and Indonesia in particular.

We aimed to investigate the effect of basic fitness function according to whole-body vibration (WBV) stimulus with slope during deadlift in adults. A total of 15 subjects performed deadlift exercise with a sound-wave vibrator. The subjects consisted of three groups: no slope and WBV group (control), WBV only group (group 1), and slope and WBV group (group 2). Slope was set at $5^{\circ }$, and the frequency and amplitude of WBV were 10Hz and 5mm, respectively. The participants performed Romanian deadlift 2 days a week for 4 weeks, including 10 trials per set and five sets a day. We measured basic fitness function factor including the isokinetic muscle contraction test using biodex system3. All groups showed an increase in strength of approximately $>20$%. Group 2 showed the highest increase. Moreover, maximal peak torque of the lumbar joint showed an increase trend similar to that of back muscle strength. An increase of 15.72%, 24.86%, and 51.44% was noted in the control, group 1, and group 2, respectively. The findings indicate that WBV exercise with slope is the most efficient exercise protocol for improving muscle function of the trunk. WBV with slope could help stimulate trunk muscles more and efficiently, could result in a more positive effect on muscle function compared with WBV only, and could be included in an exercise program for efficient patient rehabilitation.

Software-defined network (SDN) is a new network structure, which has the characteristics of centralized management and programmable, and is widely used in the field of Internet of things. Distributed denial of service (DDoS) attack is one of the most threatening attacks in SDN network. How to effectively detect DDoS attacks has become a research hotspot in the field of SDN security management. Aiming at the above problems, this paper proposes a DDoS attack detection method based on Deep belief network (DBN) in SDN network architecture. By extracting the characteristics of OpenFlow switch flow table entries, DBN algorithm is trained to detect whether there are DDoS attacks. The experimental results show that the method is better than the other algorithms in accuracy, precision and recall.

In recent years, with the rapid development of Internet and computer technology, network education has developed rapidly. With the rapid development of learning technology, online education has been widely popularized. Especially in 2020, novel coronavirus pneumonia suddenly came into being. Online education based on Internet technology has played a great role in the crisis control period. It has also enriched teaching forms and teaching methods. The blended teaching under online and offline integration has increased the availability of students’ learning data. Therefore, more and more scholars begin to pay attention to the research of learning early warning based on educational data mining or learning analysis. However, most early warning studies use traditional machine learning algorithms, and there are still deficiencies in the granularity of data collection, technical implementation mechanism, early warning state recognition and so on. With the success of deep learning in artificial intelligence and other fields, scholars began to study the application of deep learning to solve the problems in the field of learning early warning. Combining variational self-coding (LVAE) and deep neural network, this paper proposes a scheme (LVAEpre) which can solve the problem of unbalanced distribution of educational data sets. This paper determines the weight value of each dimension and index by adjusting the weight parameters of the model, and obtains the threshold value of the early warning line, and empirically tests its effectiveness. Finally, the paper designs a learning early warning model and builds a learning early warning platform based on process data. The results show that the early warning effect is good. The proposal of the learning early warning model based on process data and the application of the learning early warning platform have greatly improved the teaching quality, reduced the risk of students failing to attend the course, and effectively realized the early warning function. The experimental results show that the framework improves the prediction ability of identifying risk learners as soon as possible, timely intervene and guide risk learners, improves learning efficiency, and provides effective guidance strategies for the development of network education.

In the traditional city for healthcare in IoT, it has been proposed to replace traditional yield models with mathematical models that do not require the assumption of defect density functions. The selection of input parameters in these models is very important, and all the variation factors on the wafer must be included as far as possible. The factors of clustering are usually described by clustering indicators, but some specific clustering patterns will cause the clustering indicators to misjudge the clustering degree, resulting in the yield estimation error becoming larger. In view of this, the proposed study has classified the defect patterns on the wafer into four types: random distribution, regional concentrated distribution, linear distribution and circular distribution, by means of three pattern characteristics analysis. A comparison is made only using cluster indicators to describe cluster phenomena and a model that uses cluster indicators and cluster graphs to describe cluster phenomena. The research results show that when constructing the yield model, the clustering pattern and the clustering index are used to describe the clustering phenomenon in smart city via Internet of things, which is preferred to solely considering the clustering index, as the consequent degree of accuracy far exceeds the improvement of changing the “number of effective grains” in relation to the clustering index. Therefore, the yield rate can be estimated more accurately by using clustering graphs with clustering indicators; the estimated yield in the yield model, with the clustering pattern parameter, is indeed closer to the actual yield than the yield model without the clustering pattern parameter.

This study aimed to create new experiences for the healthcare environment using virtual reality (VR) animation game technology by reviewing the advantages and disadvantages of VR from the literature. Using Aesop’s fables as the background of the game story, a VR animation game was created. The game design incorporated nine factors, and the five related immersion technologies were leveraged to design the game to enhance the state of flow and immersion. Experts tested the game using the present measurement and game experience questionnaires. The game experience was tested based on the following factors: Task fluency, degree of sensory feedback, degree of interactive experience, degree of immersion, and avoidance of virtual side effects. The results are as follows. (1) Providing relevant icons and guiding elements help players take risks on their own. (2) Audio feedback helps players with their virtual environment perception and enhances visual and tactile recognition. (3) Adding character elements, other than the protagonist, provides adventure information, which can improve the follow-up force of the game. (4) Immersion varies according to age, gender, play time, and virtual real-world experiencers that are different. (5) Motion sickness caused by the conflict between vision and perception should be avoided. The developed motion simulator allows players to detect the walking direction and speed on large equipment using a static floor or by wearing joint sensors. These results can be used as a reference for the development of VR animation games for the healthcare environment.

With the increasing engagement of human beings in the pursuit of healthcare, running as a sport has become a fashionable and healthcare first choice. This research uses artificial intelligence technology to carry out intelligent analysis when conducting running training. Artificial intelligence technology can accurately analyze and predict the application requirements of sports training postures. We proposed an analysis of sports posture and a prediction system, which uses running training data in the form of a heart rate, recorded on a GPS smart sports watch, as well as using the recurrent neural network (RNN), long and short-term memory (LSTM) and the gate recursive unit (GRU). These three types of neural network methods can predict which method is best suited for a road race and can confirm that it will be completed within the scheduled finish time; these models will also perform an intelligent analysis of physical fitness (heart rate, pace) and running technology (cadence, pace). The training and test data are collected from the running training records (running distance, time, heart rate, stride frequency, stride length, pace, calories, altitude and other characteristic values) as input parameters, to test and compare the running completion time trends of the RNN, LSTM and GRU neural network methods in the exercise table, so as to evaluate their predictive abilities. The results show that the GRU method has the best predictive accuracy, and the least accurate is the LSTM method. After the hidden layers are added to the three predictive methods, the RNN is slightly regressive, the LSTM indicates a trend of significant improvement and the GRU exhibits less obvious changes.