Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of tra...Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.展开更多
A robotic wheelchair is assumed to be capable of doing tasks like navigation, obstacle detection, etc. using sensors and intelligence. The initial part of the work was development of a cap controlled wheelchair to tes...A robotic wheelchair is assumed to be capable of doing tasks like navigation, obstacle detection, etc. using sensors and intelligence. The initial part of the work was development of a cap controlled wheelchair to test and verify the gesture operation. Following that, a real time operating wheelchair was developed consisting of mode changing option between joystick control mode and head gesture control mode as per as the user’s requirement. The wheelchair consists of MPU6050 sensor, joystick module, RF module, battery, dc motor, toggle switch and Arduino. The movement of the head is detected by MPU6050 and the signal is transmitted to the microcontroller. Then the signal is processed by controller and motion of wheelchair is enabled for navigation. The wheelchair was capable of moving left, right, forward and backward direction. The speed of the wheelchair was 4.8 km/h when tested. Design objective of the wheelchair included cost effectiveness without compromising safety, flexibility and mobility for the users.展开更多
文摘Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.
文摘A robotic wheelchair is assumed to be capable of doing tasks like navigation, obstacle detection, etc. using sensors and intelligence. The initial part of the work was development of a cap controlled wheelchair to test and verify the gesture operation. Following that, a real time operating wheelchair was developed consisting of mode changing option between joystick control mode and head gesture control mode as per as the user’s requirement. The wheelchair consists of MPU6050 sensor, joystick module, RF module, battery, dc motor, toggle switch and Arduino. The movement of the head is detected by MPU6050 and the signal is transmitted to the microcontroller. Then the signal is processed by controller and motion of wheelchair is enabled for navigation. The wheelchair was capable of moving left, right, forward and backward direction. The speed of the wheelchair was 4.8 km/h when tested. Design objective of the wheelchair included cost effectiveness without compromising safety, flexibility and mobility for the users.
