Search

To describe the increase in prevalence of ethambutol-induced optic neuropathy (EON) in patients presenting to a single tertiary referral eye care center in India after introduction of weight-based fixed dose combinations and an increase in duration of ethambutol use from 2016 in the Revised National Tuberculosis Control Program.This was a retrospective, observational, referral hospital-based study of 156 patients with a diagnosis of EON presenting to a single tertiary referral eye care center between January 2016 and December 2019. The main outcome measure was to assess the increase in prevalence of EON cases presenting to our tertiary care institute.During the 4-year study period, 156 new patients were diagnosed with EON. A total of 101 patients (64.7%) were males and 55 (35.3%) were females. The most common age group affected was 41-60 years. The significant complaint at presentation was decreased vision in all the patients. A rising trend in the number of patients diagnosed as EON was seen, with the prevalence increasing from 16 cases in 2016, 13 cases in 2017, and 31 cases in 2018 to 96 cases in 2019.The results of this study indicated an alarming increase in the trend of EON cases presenting to our tertiary care institute.

In today’s world, millions of people are using micro-blogging sites for expressing their views. So, a large number of data are available on these micro-blogging sites. Understanding this kind of data helps in determining what a business needs to improve on. Our objective is to identify a better food delivery management system between Swiggy and Zomato, by performing sentiment analysis on Twitter data. Sentiment analysis is a part of AI which analyses the subjective information in a sentence, it can be a perception, appraisals, emotions, or attitude towards a topic, person, or entity. In this paper, we have extracted the Twitter data through Twitter API for performing sentiment analysis on tweets related to Zomato and Swiggy. We have calculated the subjectivity and polarity of a sentence to know whether the reviews are positive, negative, or neutral. This calculation helped us in determining which food delivery system is better among Zomato and Swiggy.

Background: The purpose of this study is to identifying time series analysis and mathematical model fitting on electroencephalography channels that are placed on Amyotrophic Lateral Sclerosis (ALS) patients with P300 based brain-computer interface (BCI). Methods: Amyotrophic Lateral Sclerosis (ALS) or motor neuron diseases are a rapidly progressing neurological disease that attacks and kills neurons responsible for controlling voluntary muscles. There is no cure and treatment effective to reverse, to halt the disease progression. A Brain- Computer Interface enables disable person to communicate & interact with each other and with the environment. To bypass the important motor difficulties present in ALS patient, BCI is useful. An input for BCI system is patient's brain signals and these signals are converted into external operations, for brain signals detection, Electroencephalography (EEG) is normally used. P300 based BCI is used to record the reading of EEG brain signals with the help of non-invasive placement of channels. In EEG, channel analysis Autoregressive (AR) model is a widely used. In the present study, Yule-Walker approach of AR model has been used for channel data fitting. Model fitting as a form of digitization is majorly required for good understanding of the dataset, and also for data prediction. Results: Fourth order of the mathematical curve for this dataset is selected. The reason is the high accuracy obtained in the 4th order of Autoregressive model (97.51±0.64). Conclusion: In proposed Auto Regressive (AR) model has been used for Amyotrophic Lateral Sclerosis (ALS) patient data analysis. The 4th order of Yule Walker auto-regressive model is giving best fitting on this problem.

Objective: the objective of the presented work is to analyse the electroencephalography signal based on brain computer interface by using P300 speller for amyotrophic lateral sclerosis (ALS) patients and perform classification on extracted features to get accuracy. Analysis/methods: amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that involves the degeneration and death of nerve cell in the brain. It affects the process related to speech and loss of motor function in the patient. BCI technology is a communication solution for all amyotrophic lateral sclerosis (ALS) patients. The P300 speller included in the BNCI Horizon 2020 data is an application allows calculating the accuracy of classifier, which is necessary for the user to spell letters or sentences in a BCI speller paradigm. In this paper, we have extracted wavelet and power spectral density features. Association rule mining and ranking method is used for feature selection. For the classification, we have used multiple techniques and different classifiers and out of those, ten best techniques are selected based on their good performance. Finding: as a result, we get maximum 75% accuracy when we used random committee classifier.