3 research outputs found
Enhancing Human Activity Recognition through Machine Learning Models: A Comparative Study
No full textThis study explores Human Activity Recognition (HAR), a machine learning technique utilized in health monitoring and human-computer interaction. HAR identifies human actions through sensor data from accelerometers and gyroscopes in smartphones and wearables. Key components of this technique include model selection, feature extraction, preprocessing, and data collection to classify activities such as standing, lying, sitting, and walking. Despite its potential, privacy concerns warrant further research for effective deployment. A comprehensive analysis of HAR techniques has been described in this research work
Continuous Deployment in Action: Developing a Cloud-Based Image Matching Game
No full textThis project aims to develop an interactive image-matching card game leveraging HTML, JavaScript, and CSS, with a focus on deploying and hosting the game on a cloud computing platform. The game will be designed to enhance users’ cognitive skills and entertainment experience through challenging memory exercises and engaging visuals. The development process involves creating a user-friendly interface using HTML for the structure, JavaScript for the game logic and interactivity, and CSS for styling and visual enhancements. Furthermore, the project will explore the integration of cloud computing technologies for hosting and deploying the game. This includes utilizing cloud storage solutions for storing game assets and user data, as well as deploying the game application on a cloud server for accessibility and scalability. By leveraging cloud computing infrastructure, the game will benefit from improved reliability, scalability, and accessibility, allowing users to access the game seamlessly from anywhere with an internet connection. Moreover, cloud-based deployment will facilitate easier updates and maintenance of the game, ensuring a smooth and uninterrupted gaming experience for players. Overall, the development of this image-matching card game demonstrates the potential of cloud computing in enhancing the accessibility, scalability, and performance of web-based applications, while also providing an entertaining and educational experience for users
A Comparative Analysis of Rainfall-Prediction Using Optimized Machine Learning Algorithms
No full textThe difficult challenge of predicting rainfall is brought on by the daily observations of erratic rainfall patterns and climatic fluctuations. Predicting when the rain will fall can help avoid floods and even aid in crop growth in agriculture. Timely and precise predictions can prevent loss of life and assets. The ability to forecast the amount of rainfall requires an understanding of weather-related elements such as pressure, humidity, wind speed, latitude, longitude, and precipitable water with varying x and y-axis parameters. The research in this study involves using fundamental machine learning techniques to create weather forecasting models that use the day\u27s meteorological data to predict whether or not it will rain tomorrow. By utilizing previously identified trends from historical meteorological data, machine learning helps forecast rainfall. We are using a classification model in our supervised data model, and the techniques utilized to forecast the amount of rain include random forest, KNN, decision tree, and logistic regression. Using machine learning algorithms to examine past weather data and find patterns that can be applied to forecast future rainfall patterns is the suggested technique for rainfall prediction. A more accurate weather forecast is made possible by all of the aforementioned factors. We will handle the information that aids in eliminating erroneous and incomplete data. As a component of data preparation, normalization helps to improve feature approximation by adjusting the range of independent variables. Model training is carried out following data preparation, during which data is divided into training and test sets. The test set aids in prediction-making, while the training set serves as the foundation for model training
