ML based approach for covid-19 future forecasting

ML based forecast systems have demonstrated their significance in expecting the preoperative result in to further develop independent direction in regards to the future course of action.ML models have for some time been utilized in numerous application regions requiring the ID and prioritization of troublesome variables for a danger. Understanding and characterizing chest x-beam (CXR) and figured tomography (CT) pictures are critical for the finding of COVID19. To resolve these issues, we utilized the CNN Vggnet19 engineering to analyse Coronavirus in light of CXR lung pictures. Such a device can save time in deciphering chest x-beams and increment exactness and consequently work on our clinical capacity to identify and analyse COVID19. Research is that arrangement of clinical x-beam lung pictures (which incorporate typical pictures, contaminated with microorganisms, and tainted infections including COVID19) were utilized to frame a profound CNN that could make the differentiation among clamour and helpful data then utilize this preparation to decipher new pictures by perceiving designs that show specific sicknesses, for example, Covid disease in individual pictures

Probability of Semantic Similarity and N-grams Pattern Learning for Data Classification

Semantic learning is an important mechanism for the document classification, but most classification approaches are only considered the content and words distribution. Traditional classification algorithms cannot accurately represent the meaning of a document because it does not take into account semantic relations between words. In this paper, we present an approach for classification of documents by incorporating two similarity computing score method. First, a semantic similarity method which computes the probable similarity based on the Bayes' method and second, n-grams pairs based on the frequent terms probability similarity score. Since, both semantic and N-grams pairs can play important roles in a separated views for the classification of the document, we design a semantic similarity learning (SSL) algorithm to improves the performance of document classification for a huge quantity of unclassified documents. The experiment evaluation shows an improvisation in accuracy and effectiveness of the proposal for the unclassified documents

Detection of Covid-19 from X-ray Images using Deep Learning Techniques

Machine Learning (ML) based forecast systems have demonstrated their significance results in detecting several diseases. ML models have for some time been utilized in numerous application regions requiring the ID and prioritization of troublesome variables for a danger. Understanding and characterizing chest x-beam (CXR) and figured tomography (CT) pictures are critical for the finding of COVID19. To resolve these issues, the CNN Vggnet19 has been utilized to analyze Corona virus in light of CXR lung pictures. Such a device can save time in deciphering chest x-beams and increment exactness and consequently work on our clinical capacity to identify and analyze COVID19. In this work, arrangement of clinical x-beam lung pictures (which incorporate typical pictures, contaminated with microorganisms, and tainted infections including COVID19) were utilized to frame a profound CNN that could make the differentiation among clamour and helpful data, then utilize this preparation to decipher new pictures by perceiving designs that show specific sicknesses, for example, Covid disease in individual pictures

First report of Lividin and Spinulosain peptides from the skin secretion of an Indian frog

Here, we report two novel peptides identified from the skin secretion, having homologies to Lividin and Spinulosain, of an endemic frog, Hydrophylax bahuvistara, of Western Ghats. This is the first report of these peptides from Indian frogs and first identification of Lividin from the Hydrophylax genus. Both peptides exhibited weak antimicrobial activity but very low haemolytic activity. The problems of naming amphibian host defense peptides (HDPs) are also discussed

Role of gravity wave like seed perturbations on the triggering of ESF - a case study from unique dayglow observation

First observational evidence, from the Indian longitudes, for the presence of gravity wavelike perturbations with periods of 20-30 min, acting as probable seeds for Equatorial Spread F (ESF) irregularities is described. The study is based on the daytime optical measurements of the mesopause temperature and the intensity of the thermospheric O(1D) 630.0 nm dayglow emissions using the unique MultiWavelength Dayglow PhotoMeter from Trivandrum (8.5° N; 77° E; dip lat ˜0.5° N), a dip equatorial station. Measurements during the equinoctial months of a solar maximum (2001) and a solar minimum year (2006) have been used in this study. It is shown that under identical background ionospheric conditions within a solar epoch, the power of the gravity waves have a deterministic role in the generation of ESF. The mesopause temperature simultaneously observed, indicate that possible source regions for these perturbations lie in the lower atmosphere

Rare association of multiple etiologies in a severe oligoasthenospermic male

We report a rare case of a 30 year old man diagnosed with severe oligoasthenospermia, where the infertile condition is traced back to a multiple etiologies. Routine semen analysis and sperm function tests followed by hormone analysis are carried out to diagnose the condition as well as the severity. The initial findings prompt us to perform Ultrasound scanning of testis and Trans Rectal Ultrasound Scanning (TRUS) to check the anatomical and functional status of the accessory reproductive organs. Semen analysis and sperm function tests provide an insight into the severity of the condition. The hormonal analysis, Ultrasound scanning of testis and TRUS of accessory reproductive glands confirms the association of hormonal imbalance, testis and accessory gland defects which results in the observed infertile condition with severe sperm defects. A thorough investigation of infertile subjects is essential for appropriate diagnosis and effective personalized treatment owing to the probability of multiple etiologies. Incomplete diagnosis can have adverse effects in treatment and Assisted Reproductive Techniques (ART)

Nano-biosupercapacitors enable autarkic sensor operation in blood

Today’s smallest energy storage devices for in-vivo applications are larger than 3 mm3 and lack the ability to continuously drive the complex functions of smart dust electronic and microrobotic systems. Here, we create a tubular biosupercapacitor occupying a mere volume of 1/1000 mm3 (=1 nanoliter), yet delivering up to 1.6 V in blood. The tubular geometry of this nano-biosupercapacitor provides efficient self-protection against external forces from pulsating blood or muscle contraction. Redox enzymes and living cells, naturally present in blood boost the performance of the device by 40% and help to solve the self-discharging problem persistently encountered by miniaturized supercapacitors. At full capacity, the nano-biosupercapacitors drive a complex integrated sensor system to measure the pH-value in blood. This demonstration opens up opportunities for next generation intravascular implants and microrobotic systems operating in hard-to-reach small spaces deep inside the human body