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

INFLUENCE OF ACHIEVEMENT MOTIVATION AND PSYCHOLOGICAL ADJUSTMENT ON ACADEMIC ACHIEVEMENT: A CROSS-SECTIONAL STUDY OF SCHOOL STUDENTS

Purpose: The aim of the current research was to understand the role of achievement motivation and psychological adjustment on the academic performance of the school students. Methodology: A cross-sectional research design was employed for the study. A sample of 283 urban adolescent school students participated in the study. The students were administered measures of achievement motivation and psychological adjustment. The total percentage of marks secured in the tenth standard was used as the measure of academic performance. Pearson’s correlation coefficient and multiple hierarchical regression analysis were performed to analyze the obtained data. SPSS version 21 was used for data analysis. Main Findings: The results revealed a significant association of achievement motivation and educational adjustment with the academic performance of the students. However, there was no significant association between emotional and social adjustment with academic performance. Applications: The observations shed light on how cultivating enhanced student engagement and nurturing aspirations both within and outside classrooms may enhance the academic achievement of school students. Thus, the findings can provide greater insight to teachers, psychologists, and educational institutions to better plan the academic environment around the students. Novelty/Originality: The study gives a contemporary model to enhance the academic performance of students. Contrary to the popular perception, the results of the current study indicate no significant association of emotional and social adjustment with academic performance. However, educational adjustment and achievement motivation are associated with academic performance

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

Reflections on Sustaining Morale and Combat Motivation in Soldiers

Military morale and motivation formulate the signature strength of a fighting force. However, sustenance of these faculties is a bigger challenge than generating them. The exponential development in the neo-cortex and emerging social structure has made human beings hardcore individualistic. The sense of ‘self’ has become much stronger than the sense of the whole. This results in the recurrent violation of collective identity, as evidenced by the rise in numbers of misconduct behaviors, mutinies, estranged leader-led relations, desertion, fragging, and suicides. Utilizing the lessons from various ecological systems and derived scientific principles, the present paper takes note of significant researches in the area to arrive at a reflective model of Morale and Combat Motivation in soldiers. Firstly, it attempts to understand ‘why and why not the soldiers shall fight’ and subsequently give suggestive guidelines to ‘how they will continue to fight’ with particular reference to the Indian military setup. The model can be utilised by military leaders and policymakers alike who are entrusted with the herculean task of upkeeping battle-mind state of soldiers in military organisations

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)