Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.

Numerous epidemic lung diseases such as COVID-19, tuberculosis (TB), and pneumonia have spread over the world, killing millions of people. Medical specialists have experienced challenges in correctly identifying these diseases due to their subtle differences in Chest X-ray images (CXR). To assist the medical experts, this study proposed a computer-aided lung illness identification method based on the CXR images. For the first time, 17 different forms of lung disorders were considered and the study was divided into six trials with each containing two, two, three, four, fourteen, and seventeen different forms of lung disorders. The proposed framework combined robust feature extraction capabilities of a lightweight parallel convolutional neural network (CNN) with the classification abilities of the extreme learning machine algorithm named CNN-ELM. An optimistic accuracy of 90.92% and an area under the curve (AUC) of 96.93% was achieved when 17 classes were classified side by side. It also accurately identified COVID-19 and TB with 99.37% and 99.98% accuracy, respectively, in 0.996 microseconds for a single image. Additionally, the current results also demonstrated that the framework could outperform the existing state-of-the-art (SOTA) models. On top of that, a secondary conclusion drawn from this study was that the prospective framework retained its effectiveness over a range of real-world environments, including balanced-unbalanced or large-small datasets, large multiclass or simple binary class, and high- or low-resolution images. A prototype Android App was also developed to establish the potential of the framework in real-life implementation.

Knowledge, Attitude, and Practices Towards COVID-19 Among Social Workers of Bangladesh

Abstract: This study intended to evaluate the knowledge, attitude, and practices (KAP) toward the pandemic among the social workers of Bangladesh. Approximately 94% of them faced challenges working during COVID-19. They lacked knowledge regarding COVID-19 (0.62 ± 0.25). They also did not show enough practice. Attitudes were better than knowledge and practices. Respondents' gender, location, having vulnerable people at the home, educational attainment, and experiencing COVID-19 symptoms before were significantly associated with the overall KAP. In general, COVID-19 preparedness and response campaigns, and effective education, are required to ensure the competence of pandemic responses among this vital group. [ABSTRACT FROM AUTHOR] Copyright of Public Organization Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Towards assisting visually impaired individuals: A review on current status and future prospects

Visually impaired people require support with regular tasks including navigating, detecting obstacles, and maintaining safety, especially in both indoor and outdoor environments. As a result of the advancement of assistive technology, their lives have become substantially more convenient. Here, cutting-edge assistive devices and technologies for the visually impaired are reviewed, along with a chronology of their evolution. These methodologies are classified according to their intended applications. The taxonomy is combined with a description of the tests and experiments that can be used to examine the characteristics and assessments of assistive technology. In addition, the algorithms used in assistive devices are examined. This paper looks at solar industry innovations and promotes using renewable energy sources to create assistive devices, as well as, addresses the sudden advent of COVID-19 and the shift in the development of assistive devices. This review can serve as a stepping stone for further research on the topic.

Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach.

Recently the most infectious disease is the novel Coronavirus disease (COVID 19) creates a devastating effect on public health in more than 200 countries in the world. Since the detection of COVID19 using reverse transcription-polymerase chain reaction (RT-PCR) is time-consuming and error-prone, the alternative solution of detection is Computed Tomography (CT) images. In this paper, Contrast Limited Histogram Equalization (CLAHE) was applied to CT images as a preprocessing step for enhancing the quality of the images. After that, we developed a novel Convolutional Neural Network (CNN) model that extracted 100 prominent features from a total of 2482 CT scan images. These extracted features were then deployed to various machine learning algorithms - Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Finally, we proposed an ensemble model for the COVID19 CT image classification. We also showed various performance comparisons with the state-of-art methods. Our proposed model outperforms the state-of-art models and achieved an accuracy, precision, and recall score of 99.73%, 99.46%, and 100%, respectively.

HAS COUNTRYWIDE LOCKDOWN WORKED AS A FEASIBLE MEASURE IN BENDING THE COVID-19 CURVE IN DEVELOPING COUNTRIES? (preprint)

In the absence of any effective vaccine and clinically proven treatment, experts thought that strict lockdown measures could be an effective way to slow down the spread of novel coronavirus. Despite the strict lockdown measures in several developing countries, the number of newly infected cases is getting unbridled as time progresses. This anomaly ignites questions about the effectiveness of the prolonged strict confinement measures. In light of the above view, with an aim to find the answer to this question, trends of four epidemiological parameters: growth factor of daily reported COVID-19 cases, daily incidence proportion, daily cumulative index and effective reproduction number in five developing countries named Bangladesh, Brazil, Chile, Pakistan and South Africa have been analysed meticulously considering the different phases of their national lockdowns. Any compelling evidence has not been found in favor of country-wide lockdown effectiveness in the above-mentioned countries. Numerical results illustrate that stringent nationwide lockdown measures have failed bringing the epidemic threshold (Re) of COVID-19 under unity. In addition, citizens of the aforementioned countries have been struggling with catastrophic socio-economic consequences due to prolonged confinement measures. Our study suggests that a new policy should be proposed for developing countries to battle against future disease outbreaks ensuring a perfect balance between saving lives and confirming livelihoods.