Background: Cardiovascular disease (CVD) is a major global health issue, contributing significantly to morbidity, mortality, and economic burden. Despite medical advancements, developing effective cardiovascular drugs remains challenging due to the complexity of these disorders and the high failure rates in clinical trials. Traditional drug discovery methods, reliant on empirical testing, are often slow and inefficient. Objective: This book examines the transformative potential of artificial intelligence (AI) in addressing the challenges of cardiovascular drug discovery. It aims to elucidate how AI can improve drug development processes and enhance personalized medicine. Methods: The book reviews AI's applications throughout the drug discovery process, from target identification to clinical trials. It integrates case studies, expert opinions, and real-world examples to illustrate AI's impact on improving drug design, predictive accuracy, and therapeutic outcomes. Results: AI has demonstrated significant promise in revolutionizing cardiovascular drug discovery. In analyzing extensive biomedical data, AI enables the identification of novel patterns and insights that traditional methods may overlook. This facilitates more effective target identification, drug design, and prediction of drug efficacy and safety. Conclusion: The integration of AI into cardiovascular drug discovery offers substantial potential to overcome existing limitations, streamline the development process, and improve patient outcomes. The book underscores the role of AI in reshaping cardiovascular healthcare and addressing the global burden of CVD.

Objective: The usage of power and automated control systems can save energy, but creating an intelligent control system via the Internet of Things (IoT) is an advanced technique because it provides remote access and control through the Internet, especially for those who are facing issues with monitoring their home’s smart gears remotely. In this sense, NodeMcu is an internet-enabled device that operates under different conditions to monitor smart home gear securely. Methods: Therefore, this research has been conducted to provide a solid platform for remote home control and monitoring, together with a complete solution. In this research, we have built a small model by integrating hardware components and connecting online to Google Firebase, allowing the end-user to monitor and control their online home environment efficiently. Results: The online interface helps end-users care for their home equipment, lock doors, and install all intelligent gear inside smart homes. This interface ensures efficient and seamless interaction with all integrated smart home devices, enhancing the user experience and providing a higher level of convenience and security. Through this platform, end-users can perform a wide range of tasks such as adjusting lighting, regulating temperature, monitoring security cameras, and managing energy consumption. The system’s user-friendly design and reliable connectivity ensure that users can maintain control over their home environment from any location with internet access. Conclusion: The proposed model is Cost effective, making it accessible to a boarder demographic. This advancement in home automation enhances security and improves the overall management of smart home system.