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Cardiovascular diseases are one of the most fatal health conditions worldwide, posing a significant burden on national healthcare systems. Several risk factors, including high blood pressure, family history, stress, age, gender, cholesterol, BMI, and an unhealthy lifestyle, have been identified for these diseases. Early diagnosis is crucial for effective treatment, but the accuracy of existing approaches needs improvement due to the life-threatening nature of cardiovascular disorders. This study proposes a MaLCaDD framework for precise prediction of cardiovascular diseases. The methodology addresses missing data and imbalances, using Feature Importance to select relevant features, and an ensemble of Logistic Regression, SVM, Random Forest, Decision Tree, and KNN classifiers for accurate prediction. The study shows that MaLCaDD outperforms current state-of-the-art techniques, making it a trustworthy tool for early detection of cardiovascular diseases. The developed framework will be thoroughly tested and validated using different evaluation metrics, including sensitivity, specificity, and area under the curve, to ensure its accuracy and reliability. The ultimate goal of this project is to provide healthcare practitioners and policymakers with a reliable tool for predicting an individual's cardiovascular disease risk, which can help in early intervention and management, and ultimately reduce the burden of cardiovascular disease on society.

Object detection and Image processing are combinedly vast area of research. Various Notable works have been done in the field of transportation in order to ensure a healthy and lawful traffic environment. A precise and quick vehicle number plate recognition system is a solution to traffic control and resultant in improved regulations. This paper is based on optical character recognition which uses improved techniques and ensures optimized results. A full fledged algorithm of number plate recognition is proposed and the same acquires upgraded accuracy over existing system. The entire system can be mounted as a solution traffics issues related to law and security. Automatic Number Plate Recognition (ANPR) system consists integration of machine learning together with image processing and pattern identification. Optical character recognition (OCR) is an algorithm that plays an important role in automatic number plate recognition and it is one of the main aspect of Research in machine learning and computer vision that has evolved immensely since its inception.

Simulation is the process of designing a model of a real system and conducting experiment with this model for the purpose of either understanding the behavior of the system or evaluating various strategies for the operation of the system. Simulation in nursing education has become an important element specifically teaching the necessary skills and knowledge to develop competent nurse. Objectives: To Assess the knowledge level regarding CPR among students of GNSU Jamuhar. To Find out the post-test knowledge score regarding CPR among students of GNSU Jamuhar. To Find out the association between post-test knowledge regarding CPR among students of GNSU with the demographical variables. Methodology: A quasi experimental study was with one group pre test and post test design was adopted for the present study . Total 60 students were selected by simple random sampling technique. All students fit to inclusion criteria & consent were taken as study subject, structured questionnaire was used for data collection among students in studying at GNSU, Jamuhar .Data was collected regarding knowledge about Cardiopulmonary resuscitation. Statistics, Data entry & analysis was carried out in Microsoft excel.Statistical analysis was done by using frequency. Results: Out of 60 respondents, Majority 17.%(10) of the participants in socio demographic data are belongs to age group (17-19)years, and 73.%(44) participant belongs to age group (20-22) year of age, and 10%(6) participate belongs to the age group (23-25) year , and 0.00% (0) participate belongs to the age group 26 & above. Majority 47% (28) of the participate are male and 53% (32) are female. Majority 53%(32) students are urban area and 47%(28) students are rural area. Majority 40%(24) participate in the given study is from nuclear family and 57%(34) participate belongs to joint family , and 3% (2) participate belongs to extended family. Majority 5% (3) participate are completed 12 intermediate from stream in commerce ,and 8.33%(5) are art ,and 58.33%(35) are mathematics ,and 28.33%(17) are others. Majority 0.00%(0) participate are course in journalism , and 38%(23) of the participate in agriculture ,and 62%(37) of the participate in BCA ,and 0.00%(0) of the participate in LAW. Percentage wise distribution of students in relation to their previous knowledge about CPR that majority 20%(12) of them were well know about CPR and 80%(48) of them were unknown. The post test knowledge is increased among students after giving simulation training programme form (pre – test) 31% students have adequate knowledge Cardiopulmonary resuscitation. Recommendation: The findings of the present study can be used as a guide of future research. Iterventional study can be undertaken to improve the knowledge and practice about CPR. A study can be conducted in preventing malpractice during CPR procedure. Conclusion: Cardiopulmonary resuscitation was satisfactory among students lack of knowledge & awareness regarding cardiopulmonary. Education regarding cardiopulmonary resuscitation should be included as a part of school curriculum.All students irrespective of their education status should be taught to break their inhibitions about discussing with their regarding cardiopulmonary..

The aim of our project to automate the application to overcome from the language barrier among countries and also states within the country, the above-mentioned application will perform the various features in the application. Language technology has become pervasive in everyday life. Neural networks are a key component in this technology thanks to their ability to model large amounts of data. Text recognition in images is a research area which attempts to develop a computer system with the ability to automatically read the text from images. Speech is the first important primary need, and the most convenient means of communication between people. The communication among human computer interaction is called human computer interface. Machine translation is a domain of computational linguistics, which explores the use of software to translate text from one language to another. Machine translation simply performs substitution of words in one language for words in other, but that may not assure good translation. In the Project we combine all three domains in one system. Here we integrate Text extract from image, Speech recognition and Text translation from one language to another in one web application.