ABSTRACT
COVID has made education shift towards online mode. In online mode, instructors have a hard time keeping track of their students. Students' performance in online classes falls considerably below the level of learning due to a lack of attention. This initiative aids in the supervision of students during online classes. Artificial Intelligence (AI) models are being developed to better recognize student activities during online sessions. Many applications rely on determining an individual's mental state. When evaluating which subtask is the most challenging, a quantitative measure of human activity while executing a task can be helpful. Thus, the goal of this research is to create an algorithm that uses EEG data gathered with a Muse headset to measure the amount of cognitive intelligence of students during online classes. The data collected by the Muse headset is multidimensional, and it is pre-processed before being fed into machine learning algorithms. Using feature selection, the dataset's dimension is now reduced. The model's precision and recall were calculated, and a confusion matrix was created. The Support Vector Machine produces better outcomes in the experiment. © 2022 IEEE.
ABSTRACT
Nowadays, the entire world is struggling to adapt and survive the global pandemic. Moreover, most countries had a hard time keeping up with the new mutations of COVID-19. Therefore, taking preventive measures to control the spreading of the virus, including lockdowns, curfews, social distancing, masks, vaccines, is not enough to stop the virus. However, using the new technologies to adapt the prevention measures and enhance the existing ones will be more efficient. Most countries have already developed their non-pharmaceutical interventions measures (NPIs), mainly contacts tracing solutions at the pandemic beginning. Using those mobile applications, the authorities were able to reduce the spreading of the virus. Nevertheless, the virus is evolving, mutating, and becoming more and more dangerous to survive. Therefore, these mobile applications have become less effective in facing the constant changes of the pandemic situation. To that end, the need for enhancing and evolving contact tracing became more urgent. The goal here is to control the spread of the new variants and keep up with the rapid changes happening around the world. In this paper, we will present a detailed view of the new solution built to take contact tracing to a new level, empowered by the Bluetooth Low Energy technology for communication, advanced encryption method for security and data privacy, as well as secured storage and data management to have a system capable of slowing the COVID-19 variants from spreading and save lives. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Nowadays, the entire world is struggling to adapt and survive the global pandemic. Moreover, most countries had a hard time keeping up with the new mutations of COVID-19. Therefore, taking preventive measures to control the spreading of the virus, including lockdowns, curfews, social distancing, masks, vaccines, is not enough to stop the virus. However, using the new technologies to adapt the prevention measures and enhance the existing ones will be more efficient. Most countries have already developed their non-pharmaceutical interventions measures (NPIs), mainly contacts tracing solutions at the pandemic beginning. Using those mobile applications, the authorities were able to reduce the spreading of the virus. Nevertheless, the virus is evolving, mutating, and becoming more and more dangerous to survive. Therefore, these mobile applications have become less effective in facing the constant changes of the pandemic situation. To that end, the need for enhancing and evolving contact tracing became more urgent. The goal here is to control the spread of the new variants and keep up with the rapid changes happening around the world. In this paper, we will present a detailed view of the new solution built to take contact tracing to a new level, empowered by the Bluetooth Low Energy technology for communication, advanced encryption method for security and data privacy, as well as secured storage and data management to have a system capable of slowing the COVID-19 variants from spreading and save lives. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
When cardiovascular issues arise in a cardiac patient, it is essential to diagnose them as soon as possible for monitoring and treatment would be less difficult than in the old. Paediatric cardiologists have a difficult time keeping track of their patients’ cardiovascular condition. To accomplish this, a phonocardiogram (PCG) device was created in combination with a MATLAB software based on artificial intelligence (AI) for automatic diagnosis of heart state classification as normal or pathological. Due to the safety concerns associated with COVID-19, testing on school-aged children is currently being explored. Using PCG analyses and machine learning methods, the goal of this work is to detect a cardiac condition, whilst operating on a limited amount of computing resources. This makes it possible for anybody, including non-medical professionals, to diagnose cardiac issues. To put it simply, the current system consists of a distinct portable electronic stethoscope, headphones linked to the stethoscope, a sound-processing computer, and specifically developed software for capturing and analysing heart sounds. However, this is more difficult and time-consuming, and the accuracy is lowered as a result. According to statistical studies, even expert cardiologists only achieve an accuracy of approximately 80%. Nevertheless, primary care doctors and medical students usually attain a level of accuracy of between 20 and 40%. Due to the nonstationary nature of heart sounds and PCG's superior ability to model and analyse even in the face of noise, PCG sounds provide valuable information regarding heart diseases. Spectral characteristics PCG is used to characterise heart sounds in order to diagnose cardiac conditions. We categorise normal and abnormal sounds using cepstral coefficients, or PCG waves, for fast and effective identification, prompted by cepstral features’ effectiveness in speech signal classification. On the basis of their statistical properties, we suggest a new feature set for cepstral coefficients. The PhysioNet PCG training dataset is used in the experiments. This section compares KNN with SVM classifiers, indicating that KNN is more accurate. Furthermore, the results indicate that statistical features derived from PCG Mel-frequency cepstral coefficients outperform both frequently used wavelet-based features and conventional cepstral coefficients, including MFCCs. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.