Detection of covid-19 from chest x-ray and ct scan images using improved stacked sparse autoencoder

The novel Coronavirus 2019 (COVID-19) has spread rapidly and has become a pandemic around the world. So far, about 44 million cases have been registered, causing more than one million deaths worldwide. COVID-19 has had a devastating impact on every nation, particularly the economic sector. To identify the infected human being and prevent the virus from spreading further, easy, and precise screening is required. COVID-19 can be potentially detected by using Chest X-ray and computed tomography (CT) images, as these images contain essential information of lung infection. This radiology image is usually examined by the expert to detect the presence of COVID-19 symptom. In this study, the improved stacked sparse autoencoder is used to examine the radiology images. According to the result, the proposed deep learning model was able to achieve a classification accuracy of 96.6% and 83.0% for chest X-ray and chest CT-scan images, respectively

Local Authority planning provision for Event Management in Ireland: A Socio-Cultural Perspective

The increasing popularity of the event sector in Ireland has resulted in many community events being developed and marketed to international and domestic tourists alike. This growth has had an effect on host communities in a positive and negative manner. This paper assesses the current levels of Local Authority socio-cultural planning provision and guidelines for event management in Ireland. To achieve this, a content analysis approach was used to illustrate which Local Authorities in the Republic of Ireland employed socio-cultural tools and indicators for event management. Accordingly, analysis revealed a lack of Local Authority socio-cultural planning guidelines or policies for event management. However, this offers and opportunity to be improved by implementing and applying best practice indicators in socio-cultural policies and guidelines for event management in Ireland

Remaining useful life prediction using an integrated Laplacian-LST< network on machinery components

Accurate remaining useful life (RUL) analysis of a machinery system is of great importance. Such systems work in long-term operations in which unexpected failures often occur. Due to the rapid development of computer technology, the deep learning model has supplanted physical-based RUL analysis. The data-driven approach using the deep learning model is capable of providing an accurate RUL analysis. However, an accurate analysis using deep learning comes with challenges and costs. In current RUL analysis practice, the deep learning hyperparameters are manually selected, which hinders the deep network from reaching the local optima. Additionally, current practice uses powerful signal processing methods with complicated prediction indicators. Therefore, a novel methodological step is proposed to tackle this problem by integrating the Laplacian score (LS), random search optimisation and long short-term memory (LSTM). The proposed system, called integrated Laplacian-LSTM, produced accurate RUL analyses on the IEEE PHM 2012 Competition and IMS bearing datasets, showing significant improvement in prediction accuracy. This system increases prediction accuracy by 18% compared to other available RUL methods in similar studies

A novel hybrid extreme learning machine-whale optimization algorithm for bearing fault diagnosis

In order to avoid fatalities and ensure safe operation, a good and accurate diagnosis method is required. A diagnosis method based on extreme learning machine (ELM) has attracted much attention and the ELM method had been applied in various field of study. The advantages of the ELM method which are rapid learning rate, better generalization performance and ease of implementation makes the ELM method suitable to be used in various field including fault diagnosis fields. However, the performance of the ELM method becomes inefficient due to incorrect selection of neurons number and randomness of input weight and hidden layer bias. Hence, this paper aims to propose a novel hybrid fault diagnosis method based on ELM and whale optimization algorithm (WOA), known as ELM-WOA for bearing fault diagnosis. Four different types of bearing datasets from Case Western Reserve University Bearing Data Centre were used in this paper in order to present the performance of the proposed method. Based on the result, the performance of the proposed method was able to surpass the performance of the conventional ELM

Bearing fault diagnosis using deep sparse autoencoder

Rolling element bearing is an important component in various machinery. Faulty on bearing cause severe equipment damage that lead to high maintenance cost. The development of deep learning has been paid a considerable amount of attention to fault diagnosis on rolling element bearing. Traditional machine learning such as Artificial Neural Network and Support Vector Machine have problems of lacking expression capacity, existing the curse of dimensionality, require manual feature extraction and require an additional feature selection. Deep learning model has the ability to effectively mine the high dimensional features and accurately recognize the health condition. In consequence, deep learning model has turned into an innovative and promising research in bearing fault diagnosis field. Thus, this paper tends to proposed Deep Sparse Autoencoder (DSAE) with Teager Kaiser Energy Operator (TKEO) to diagnose the bearing condition. DSAE is one of deep learning model which uses the architecture of neural network. During the analysis, the hyperparameter of DSAE model was optimized by Ant Lion Optimization. The analysis results show that the proposed TKEO-DSAE achieved 99.5% accuracy of the fault diagnosis. The comparative study between proposed model and ANN proved that deep learning model outperform traditional machine learning model on bearing fault diagnosis

A review on signal processing techniques for bearing diagnostics

Bearing is the most widely used component in many applications such as home appliances, industrial applications and military applications. Bearing works continuously in harsh environment especially for industrial use where the environment factor may affect the bearing conditions. Conditions of bearing require a proper monitoring to prevent sudden failure which will cause financial loss and threaten human life. Thus, a proper maintenance is introduced called Condition-based Maintenance (CBM). CBM is a maintenance strategy that provide a guideline to monitor the asset condition based on the information collected. In CBM, there are several important steps for monitoring the asset condition which one of them is signal processing. Signal Processing is important due to data acquire from the sensor is heavily masked by the background noise (machine sound and environment sound). Therefore, a robust signal processing technique is required to eliminate the noise and provide good features for decision making. This paper tends to review on the signal processing utilised for bearing fault diagnosis from the previous researcher

A review of assembled polyacrylonitrile-based carbon nanofiber prepared electrospinning process

Electrospinning is a very simple and versatile process by which polymer nanofibers with diameters ranging from a few nanometers to several micrometers can be produced using an electrostatically driven jet of polymer solution (or polymer melt). Significant progress has been made in this process throughout the last decade and the resultant nanostructures have been exploited to a wide range of applications. An important feature of the electrospinning process is that electrospinning nanofibers are produced in atmospheric air and at room temperature. This paper reviews the assembled polyacrylonitrile (PAN)-based carbon nanofibers with various processing parameters such as electrical potential, distance between capillary and collector drum, solution flow rate (dope extrusion rate), and concentration of polymer solution. The average fiber diameter would increase with increasing concentration of the polymer solution and the flow rate. Therefore, the screen distance could also increase but the average electrical potential of the fibers diameter decreases. Electrospinning process can be conducted at higher electrical potentials, lower flow rate, nearer screen distance, and higher concentrations of dope

Structure and properties relationships for aromatic polyimides and their derived carbon membranes: Experimental and simulation approaches

10.1021/jp050177lJournal of Physical Chemistry B1094018741-18748JPCB