Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work.
Summary
Sometimes, unverified information is disseminated as if it is true information on social media sites. Most of the times, it goes viral and affects the belief of people and their emotions. Rumors and fake news are the most popular form of false and unconfirmed information. Such news must be identified quickly for preventing its negative impact on society. In the last decade, operational procedures for rumors and false news detection came into existence. This paper provides a holistic view of different web waves from web 1.0 to web 5.0 and their usages. Further, taxonomy describes various malicious information contents at different stages. It discusses features used for classification, publicly available datasets, the rumor detection methods proposed during web 1.0, 2.0, 3.0, 4.0, 5.0 periods, and comprehensive analysis related to various methods and techniques. Numerous research gaps and future directions are illustrated to make online information more trustworthy for knowledge sharing and decision‐making purposes.
The pipelines of approaches for classifying diabetic retinopathy were examined in this study. The effort entails developing appropriate transformations and estimators that can be used to automate the process of diabetic retinopathy detection. The segmentation of the blood vessels was done using a hybrid algorithm that uses Otsu and median filter to get the region of interest. Further, ten classifiers were investigated in order to develop an automated pipeline for diabetic retinopathy detection. The ten classifiers were reviewed based on earlier work in a similar setting and on an exploration of new ways for identifying diabetic retinopathy. To overcome the challenge of low volume of dataset, data argumentation was done so that a generic classifier can be configured. Extensive hyper parameter tuning was performed, and it was shown that the gradient boosting approach is the most stable technique for detecting diabetic retinopathy. This was validated using a 10K fold cross validation method on many metrics (accuracy, recall, precision, and v-measure score). Hyper-parameter tuning helped in achieving accuracy of 0.96.
Through the enhancement of numerous social media sites, the rumor spread more rapidly among society and influences people in a very negative way. Nowadays, more attention is given by researchers to mitigating the threats produced by rumors. Bibliometric analysis is a prevalent and rigorous technique for discovering and investigating large volumes of systematic data. It assists us to identify the important aspects of that particular field. Motivated by earlier research we used this approach for our present study. The present study shows bibliometric analysis through VOSviewer software of 2935 records related to rumor dissemination by collecting the data from the web of science from 1989 to 2021. The bibliometric results have shown publication trends, main journals, most cited articles, most productive country, prominent authors, and institutions. Further net map analysis illustrates the growth of rumor detection in past, present, and future as well. Bibliometric and network analysis results from this research will significantly facilitate understanding the progress and trends in rumor detection.
Background: Optometrist all over India have faced various problems during this period of COVID-19. This study is to find out the challenges faced by Indian optometrists during the Second phase of COVID-19 lockdown.Methods: A self-administrated, cross-sectional survey in English was distributed using Google forms through various professional bodies across optometrists in India. The questionnaire was circulated among optometrists practicing in India through social media, namely WhatsApp, Telegram, and Facebook. The survey aims to find out the challenges faced by Indian optometrists during the COVID-19 lockdown.Results: In this study, a total of 107 optometrists from all over India were enrolled, among which 60 (56%) were males and 47 (44.0%) were females. Informed consent was taken online by all the participants who were included in the study. 102 optometrists (95.3%) approved to participate in the study whereas 5 optometrists (4.8%) did not approve to participate in the study. 70% of optometrists faced problems in approaching the patient considering social distancing. 56% of optometrists had difficulty in dispensing spectacles, whereas 44% of optometrists did not face any difficulty. 81% of optometrists responded to have difficulty in dispensing contact lens.Conclusions: There are no doubt, due to COVID-19 optometrists have faced several challenges across the globe. The major challenge for optometrists was to dispense spectacle and contact lenses with maintaining their safety by maintaining social distancing with the patients. Maintaining social distancing is one of the best ways of reducing the further spread of the disease across the globe.
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