CNN supported automated recognition of Covid-19 infection in chest X-ray images.

Automatic recognition of lung system is use to identify normal and covid infected lungs from chest X-ray images of the people. In the year 2020, the coronavirus forcefully pushed the entire world into a freakish situation, the foremost challenge is to diagnosis the coronavirus. We have got standard diagnosis test called PCR test which is complex and costlier to check the patient's sample at initial stage. Keeping this in mind, we developed a work to recognize the chest X-ray image automatically and label it as Covid or normal lungs. For this work, we collected the dataset from open-source data repository and then pre-process each X-ray images from each category such as covid X-ray images and non-covid X-ray images using various techniques such as filtering, edge detection, segmentation, etc., and then the pre-processed X-ray images are trained using CNN-Resnet18 network. Using PyTorch python package, the resnet-18 network layer is created which gives more accuracy than any other algorithm. From the acquired knowledge the model is correctly classifies the testing X-ray images. Then the performance of the model is calculated and analyzed with various algorithms and hence gives that the resnet-18 network improves our model performance in terms of specificity and sensitivity with more than 90%.

A hybrid approach of Weighted Fine-Tuned BERT extraction with deep Siamese Bi - LSTM model for semantic text similarity identification.

The conventional semantic text-similarity methods requires high amount of trained labeled data and also human interventions. Generally, it neglects the contextual-information and word-orders information resulted in data sparseness problem and latitudinal-explosion issue. Recently, deep-learning methods are used for determining text-similarity. Hence, this study investigates NLP application tasks usage in detecting text-similarity of question pairs or documents and explores the similarity score predictions. A new hybridized approach using Weighted Fine-Tuned BERT Feature extraction with Siamese Bi-LSTM model is implemented. The technique is employed for determining question pair sets using Semantic-text-similarity from Quora dataset. The text features are extracted using BERT process, followed by words embedding with weights. The features along with weight values, are represented as embedded vectors, are subjected to various layers of Siamese Networks. The embedded vectors of input text features were trained by using Deep Siamese Bi-LSTM model, in various layers. Finally, similarity scores are determined for each sentence, and the semantic text-similarity is learned. The performance evaluation of proposed-framework is established with respect to accuracy rate, precision value, F1 score data and Recall values parameters compared with other existing text-similarity detection methods. The proposed-framework exhibited higher efficiency rate with 91% in accuracy level in determining semantic-text-similarity compared with other existing algorithms.

Decadal changes in surface ozone at the tropical station Thiruvananthapuram (8.542° N, 76.858° E), India: effects of anthropogenic activities and meteorological variability.

This paper presents the first observational results from an Indian station on the long-term changes in surface ozone (O3)-a major environmental pollutant and green house gas-over a period of about 40 years. It is based on the in situ measurements carried out during 1973-1975, 1983-1985, 1997-1998 and 2004-2014 at the tropical coastal station, Thiruvananthapuram. From 1973 to 1997, surface O3 shows a slow increase of ~ 0.1 ppb year-1 and a faster increase of 0.4 ppb year-1 afterwards till 2009 after which it showed a levelling off till 2012 followed by a minor decrease. The highest rate of increase is observed during 2005 to 2009 (2 ppb year-1), and the overall increase from 1973 to 2012 is ~ 10 ppb. The increase in day time O3 (peak O3) is estimated as 0.42 ppb year-1 during 1997-2012 and 2.93 ppb year-1 during 2006-2012. Interestingly, the long-term trend in O3 showed seasonal dependence which is more pronounced during O3 peaking seasons (winter/summer). The observed trends were analysed in the light of the changes in NO2, a major outcome of anthropogenic activities and methane which has both natural and anthropogenic sources and also meteorological parameters. Surface O3 and NO x exhibited positive association, but with varying rate of increase of O3 for NO x < 4 and > 4 ppb. Methane, a precursor of O3 also showed increase in tune with O3. Unlike many other high-latitude locations, meteorology plays a significant role in the long-term trends in O3 at this tropical site with water vapour abundance and strong solar irradiance which favour photochemistry. A comparison with the corresponding changes in the satellite-retrieved tropospheric column O3 (TCO) also showed an increase of 0.03 DU year-1 during 1996-2005 which enhanced to 0.12 DU year-1 after 2005. Both surface O3 and satellite-retrieved TCO were positively correlated with daily maximum temperature, increasing at the rate of 1.54 ppb °C-1 and 1.9 DU °C-1, respectively, on yearly basis. Surface O3 is found to be negatively correlated with water vapour content (ρv) at this tropical site, but at higher levels of ρv, O3 shows a positive trend.

A study on stroke victims in Rajiv Gandhi Government General Hospital, Chennai.

OBJECTIVES: Stroke is a devastating and disabling cerebrovascular disease with some amount of residual deficit leading onto economic loss. Recent Indian studies have shown a stroke prevalence rate of 471.58 / 1,00,000 population. This study was undertaken to analyse the clinical profile and to arrive at important facts contributing to stroke in both the sexes. METHODS: This is a descriptive, retrospective cross-sectional study carried out on acute stroke patients, admitted to the I.I.M., RGGGH, Chennai. 150 patients were studied over a period of 3 months in the year 2011. RESULTS: 58% males and 42% females constituted our study population. Among males 18.4% and among females 22.2% were young stroke patients. Only 33.3% of patients were brought to the hospital within 6 hours. 90% patients had mild GCS score(> or =13/15) and presented with hemiplegia. 76% and 18% had infarct and intracerebral haemorrhage (ICH) respectively. RISK FACTORS: Type A personality (70.7%), Tobacco (60.7%) and Alcohol (44.7%) abuse, Systemic Hypertension (60.7%), Diabetes Mellitus (33.3%), Cardiac disorders (14%). CONCLUSION: Significant proportion of Cerebrovascular accidents(CVA) was seen in the young females. Type A personality was seen in large number of study subjects. Personal habits in males and chronic comorbid illness in females had a strong association with occurrence of stroke. A holistic approach encompassing public awareness, behavioural modification and comorbid medical illness management is the need of the hour.