Analyzing preventive precautions to limit spread of COVID-19.

With the global spread of COVID-19, the governments advised the public for adopting safety precautions to limit its spread. The virus spreads from people, contaminated places, and nozzle droplets that necessitate strict precautionary measures. Consequently, different safety precautions have been implemented to fight COVID-19 such as wearing a facemask, restriction of social gatherings, keeping 6 feet distance, etc. Despite the warnings, highlighted need for such measures, and the increasing severity of the pandemic situation, the expected number of people adopting these precautions is low. This study aims at assessing and understanding the public perception of COVID-19 safety precautions, especially the use of facemask. A unified framework of sentiment lexicon with the proposed ensemble EB-DT is devised to analyze sentiments regarding safety precautions. Extensive experiments are performed with a large dataset collected from Twitter. In addition, the factors leading to a negative perception of safety precautions are analyzed by performing topic analysis using the Latent Dirichlet allocation algorithm. The experimental results reveal that 12% of the tweets correspond to negative sentiments towards facemask precaution mainly by its discomfort. Analysis of change in peoples' sentiment over time indicates a gradual increase in the positive sentiments regarding COVID-19 restrictions.

A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.

A novel method for automatically locating the pylorus in the wireless capsule endoscopy.

Wireless capsule endoscopy (WCE) is a non-invasive technique used to examine the interiors of digestive tracts. Generally, the digestive tract can be divided into four segments: the entrance; stomach; small intestine; and large intestine. The stomach and the small intestine have a higher risk of infections than the other segments. In order to locate the diseased organ, an appropriate classification of the WCE images is necessary. In this article, a novel method is proposed for automatically locating the pylorus in WCE. The location of the pylorus is determined on two levels: rough-level and refined-level. In the rough-level, a short-term color change at the boundary between stomach and intestine can help us to find approximately 70-150 positions. In the refined-level, an improved Weber local descriptor (WLD) feature extraction method is designed for gray-scale images. Compared to the original WLD calculation method, the method for calculating the differential excitation is improved to give a higher level of robustness. A K-nearest neighbor (KNN) classifier is incorporated to segment these images around the approximate position into different regions. The proposed algorithm locates three most probable positions of the pylorus that were marked by the clinician. The experimental results indicate that the proposed method is effective.