Elimination of Glass Artifacts and Object Segmentation

Many images nowadays are captured from behind the glasses and may have certain stains discrepancy because of glass and must be processed to make differentiation between the glass and objects behind it. This research paper proposes an algorithm to remove the damaged or corrupted part of the image and make it consistent with other part of the image and to segment objects behind the glass. The damaged part is removed using total variation inpainting method and segmentation is done using kmeans clustering, anisotropic diffusion and watershed transformation. The final output is obtained by interpolation. This algorithm can be useful to applications in which some part of the images are corrupted due to data transmission or needs to segment objects from an image for further processing

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

Detection of Fake News Using Machine Learning

For some past recent years, largely since people started obtaining quick access to social media, fake news have became a serious downside and are spreading a lot of and quicker than the true news. As incontestable by the widespread effects of the big onset of fake news, humans are incapable of detecting whether the news is genuine or fake. With this, efforts have been made to research the method of fake news detection. The most popular and well-liked of such efforts is “blacklists” of sources and authors that don't seem to be trustworthy. Whereas these tools area helpful, so as to form a more complete end to end resolution, we also account for tougher cases wherever reliable sources and authors unharnessed false news. The motive of this project is to form a tool for investigation the language patterns that characterize wrong and right news through machine learning. The results of this project represent the flexibility for machine learning to be helpful during this task. We have made a model that detects several instinctive indicator of right and wrong news

Detection of Fake News Using Machine Learning

For some past recent years, largely since people started obtaining quick access to social media, fake news have became a serious downside and are spreading a lot of and quicker than the true news. As incontestable by the widespread effects of the big onset of fake news, humans are incapable of detecting whether the news is genuine or fake. With this, efforts have been made to research the method of fake news detection. The most popular and well-liked of such efforts is “blacklists” of sources and authors that don't seem to be trustworthy. Whereas these tools area helpful, so as to form a more complete end to end resolution, we also account for tougher cases wherever reliable sources and authors unharnessed false news. The motive of this project is to form a tool for investigation the language patterns that characterize wrong and right news through machine learning. The results of this project represent the flexibility for machine learning to be helpful during this task. We have made a model that detects several instinctive indicator of right and wrong news

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

Insight into epidemiology of male infertility in central India

Background: Approximately 10% to 15% of couples in developing countries are infertile. Male infertility is responsible for 20-43% of infertility cases and contributes to another 12-20% of cases. Azoospermia, oligozoospermia, asthenozoospermia, teratozoospermia, and oligoasthenoteratozoospermia are abnormal sperm parameters causing male infertility. Male infertility is often poorly responsive to primary treatment and often requires supportive secondary measures. The understanding of causes and modifiable risk factors for male infertility would enable their prevention and primary treatment. Aims and objectives of current study was to analyze the epidemiology and clinical factors of male infertility in Central India and identify its risk factors.Methods: 100 male patients attending outpatient for treatment of infertility were evaluated using a questionnaire. Semen samples were collected and spermatozoa were assessed according to WHO 2021 data for semen analysis. The results were tabulated and analyzed.Results: Amongst patients were semen abnormalities, the majority (34%) of patients had oligoasthenoteratozoospermia. All semen abnormalities were most common in the age group 35-45 years and in patients with 5-10 years duration of infertility. All semen abnormalities except azoospermia were most common in people with a monthly income of >2,000-5,000. The majority of the patients had a past history of urogenital tract infection, except oligoasthenospermic males in whom the majority had varicocele. All semen abnormalities were more common among businessmen and also more prevalent among smokers.Conclusions: Couples should be educated about infertility causes and the contribution of male infertility to it. Multifactorial analysis along with clinicopathological analysis should contribute to accurate diagnosis of the cause of male infertility and proposal of adequate measures

A SOFTWARE REQUIREMENT ENGINEERING TECHNIQUE USING OOADA-RE AND CSC FOR IOT BASED HEALTHCARE APPLICATIONS

<p>This Internet of things is one of the most trending technology with wide range of applications. Here we are going to focus on Medical and Healthcare applications of IOT. Generally such IOT applications are very complex comprising of many different modules. Thus a lot of care has to be taken during the requirement engineering of IOT applications. Requirement Engineering is a process of structuring all the requirements of the users. This is the base phase of software development which greatly affects the rest of the phases. Thus our best should be given in the engineering of requirements because if the effort goes down here, it will greatly affect the quality of the end product. In this study we have presented an approach to improve the requirements engineering phase of IOT applications development by using Object Oriented Analysis and Design Approach(OOADA) along with Constraints Story Card(CSC) templates.</p