Predicting COVID-19 cases in South Korea with all K-edited nearest neighbors noise filter and machine learning techniques

The application of machine learning techniques to the epidemiology of COVID-19 is a necessary measure that can be exploited to curtail the further spread of this endemic. Conventional techniques used to determine the epidemiology of COVID-19 are slow and costly, and data are scarce. We investigate the effects of noise filters on the performance of machine learning algorithms on the COVID-19 epidemiology dataset. Noise filter algorithms are used to remove noise from the datasets utilized in this study. We applied nine machine learning techniques to classify the epidemiology of COVID-19, which are bagging, boosting, support vector machine, bidirectional long short-term memory, decision tree, naïve Bayes, k-nearest neighbor, random forest, and multinomial logistic regression. Data from patients who contracted coronavirus disease were collected from the Kaggle database between 23 January 2020 and 24 June 2020. Noisy and filtered data were used in our experiments. As a result of denoising, machine learning models have produced high results for the prediction of COVID-19 cases in South Korea. For isolated cases after performing noise filtering operations, machine learning techniques achieved an accuracy between 98–100%. The results indicate that filtering noise from the dataset can improve the accuracy of COVID-19 case prediction algorithms.publishedVersio

Comprehensive study: machine learning approaches for COVID-19 diagnosis

Coronavirus disease 2019 (COVID-19) is caused a large number of death since has declared as an international pandemic in December 2019, and it is spreading all over the world (more than 200 countries). This situation puts the health organizations in an aberrant demand for urgent needs to develop significant early detection and monitoring smart solutions. Therefore, that new system or solution might be capable to identify COVID-19 quickly and accurately. Nowadays, the science of artificial intelligence (AI), and internet of things (IoT) techniques have an extensive range of applications, it can be initiated a possible solution for early detection and accurate decisions. We believe, combine both of the IoT revolution and machine learning (ML) methods are expected to reshape healthcare treatment strategies to provide smart (diagnosis, treatments, monitoring, and hospitals). This work aims to overview the recent solutions that have been used for early detection, and to provide the researchers a comprehensive summary that contribute to the pandemic control such AI, IoT, cloud, fog, algorithms, and all the dataset and their sources that recently published. In addition, all models, frameworks, monitoring systems, devices, and ideas (in four sections) have been sufficiently presented with all clarifications and justifications. Also, we propose a new vision for early detection based on IoT sensors data entry using 1 million patients-data to verify three proposed methods

Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

There is an ever-increasing need to optimise bearing lifetime and maintenance cost through detecting faults at earlier stages. This can be achieved through improving diagnosis and prognosis of bearing faults to better determine bearing remaining useful life (RUL). Until now there has been limited research into the prognosis of bearing life in rotating machines. Towards the development of improved approaches to prognosis of bearing faults a review of fault diagnosis and health management systems research is presented. Traditional time and frequency domain extraction techniques together with machine learning algorithms, both traditional and deep learning, are considered as novel approaches for the development of new prognosis techniques. Different approaches make use of the advantages of each technique while overcoming the disadvantages towards the development of intelligent systems to determine the RUL of bearings. The review shows that while there are numerous approaches to diagnosis and prognosis, they are suitable for certain cases or are domain specific and cannot be generalised

Classifying dangerous species of mosquito using machine learning

This thesis begins by presenting the performance of modern Time Series Classification (TSC) approaches, including HIVE-COTEv2 & InceptionTime, on 4 new insect wingbeat datasets. The experiments throughout this thesis endeavour to explore whether it is possible to classify flying insects into their respective species and into group based on their sex. Furthermore, it is hypothesised that a hierarchical approach to classifying flying insects is possible via filtering “easy” cases using cheap to obtain features, reducing the number of times processing intensive approaches are utilised. Experiments are undertaken on 3 representations of the data: Harmonic Spectral Product (HSP), the raw data and spectral data. HSP is a method of extracting the fundamental frequency of a signal. It represents a logical benchmark for comparison and, is easy and quick to extract. In one dataset, InsectSounds, species are separated into sex. Evaluation of the results achieved with the HSP representation showed that despite a relatively poor overall accuracy this feature produces a low type II error with respect to female mosquitoes. It is shown that classes of mosquitoes species that are female were more likely to be miss-classified as other female mosquito classes and, where fly classes are miss-classified as mosquito classes, they are typically classified as male mosquitoes. Previous work had shown that transformation into the frequency domain has a positive effect on performance. Audio data is typically recorded at a high sample rate, which results in high spectral resolution. As a result, approaches from the literature have used truncation of high and low frequency data to reduce runtime. It is hypothesised that inclusion of low frequency data will aid classification. This is because low frequency data is likely caused by the body of the mosquito and morphological differences, such as size, are strongly correlated to sex. The results show that the performance of all approaches was improved by the use of spectral data. The results also showed that spectral data that included low frequency information resulted in a higher overall accuracy than transformations that discarded it. Formative experiments showed that HIVE-COTEv1 was the most accurate approach at classifying flying insects. HIVE-COTEv1 is a heterogeneous approach that consists of 4 modules, Random Interval Spectral Ensemble (RISE), Bag Of SFA Symbols (BOSS), Shapelet Transform Classifier (STC) and Time Series Forest (TSF). The predictive power of these modules are combined via Cross-validation Accuracy Weighted Probabilistic Ensemble (CAWPE). The RISE approach was chosen as the spectral component as it was “best in class” at the inception of HIVE-COTEv1. It is suggested that a significant improvement to the usability and accuracy of RISE, would translate as an improvement in the performance of HIVE-COTEv1. The introduction of contracting provided a method through witch the training time of RISE could be effectively controlled, improving its usability. A review of the interval selection procedure led to improvements that had a significant positive effect on accuracy. A review of spectral transforms and the method of combining them led to a further improvement to accuracy, and an architecture in which multiple transformations are applied. In order for smart traps to be effective they are required to work for extended periods in rural locations. Implementations of hierarchical approaches show that two expert features, HSP and time of flight (TOF) are effective in reducing test time and therefore the amount of processing required. This is achieved via first classifying the test case using simple approaches, such as BayesNet, and only if the confidence in the prediction does not meet a parameterised threshold using a more powerful approach. In an evaluation of several methods of combination, the most efficient of these is shown to increase classification accuracy by 0.6%, increase the TPR of female mosquitoes by 48/10,000, decrease the FNR of female mosquitoes by 83/15,000 and reduce test time by 1.5 hours over 25,000 instances, when compared to the single best approach InceptionTime. Furthermore, a cumulative approach to combining the expert features with the InceptionTime approach resulted in a 4.14% increase in accuracy, an increase in the TPR of female mosquitoes of 139/10,000 and a decrease in the FNR of female mosquitoes of 45/15,000