Debnath Bhattacharyya – författare

Machine Intelligence with Generative AI is one of the most trending topics with applications in almost all fields of life. In healthcare, it is not only accelerating the development of new products, but also automating the generation of new and synthetic content making it easier to train and improve machine learning models.

Some of the biggest achievements of Generative AI in healthcare have been drug discovery, personalized care, differentially private synthetic data generation, operational efficiency, and many more. Generative AI models like Generative Adversarial Networks, and Variational Autoencoders are employed to generate synthetic medical images, aiding in data augmentation, facilitating disease diagnosis, and enabling advanced medical imaging research. Additionally, Generative AI techniques are being utilized for creating realistic electronic health records (EHRs) and simulated patient data, supporting privacy-preserving data sharing, and empowering innovative studies for personalized medicine and drug development. NLP models like ClinicalBERT use transformer-based deep learning architecture to understand and represent contextual information in large clinical text datasets, such as electronic health records (EHRs) and medical literature, and can better grasp medical terminologies, domain-specific language, and contextual nuances that are unique to the healthcare field.

This volume delves into the realm of Machine Intelligence with Generative AI and explores its impact on the healthcare industry.

Machine Intelligence with Generative AI is one of the most trending topics with applications in almost all fields of life. In healthcare, it is not only accelerating the development of new products, but also automating the generation of new and synthetic content making it easier to train and improve machine learning models.

Some of the biggest achievements of Generative AI in healthcare have been drug discovery, personalized care, differentially private synthetic data generation, operational efficiency, and many more. Generative AI models like Generative Adversarial Networks, and Variational Autoencoders are employed to generate synthetic medical images, aiding in data augmentation, facilitating disease diagnosis, and enabling advanced medical imaging research. Additionally, Generative AI techniques are being utilized for creating realistic electronic health records (EHRs) and simulated patient data, supporting privacy-preserving data sharing, and empowering innovative studies for personalized medicine and drug development. NLP models like ClinicalBERT use transformer-based deep learning architecture to understand and represent contextual information in large clinical text datasets, such as electronic health records (EHRs) and medical literature, and can better grasp medical terminologies, domain-specific language, and contextual nuances that are unique to the healthcare field.

This volume delves into the realm of Machine Intelligence with Generative AI and explores its impact on the healthcare industry.

The book presents the proceedings of the EAI International Conference on Computational Intelligence and Generative AI (ICCIGAI 2024), which took place March 8-9, 2024, in Guntur, India. The book explores the exciting and rapidly evolving field of artificial intelligence, with a specific focus on computational intelligence and generative models. The conference introduces cutting-edge techniques and methodologies that enable machines to simulate human-like intelligence and creativity. Readers can find a comprehensive overview of state-of-the-art AI methods, including neural networks, evolutionary algorithms, deep learning, and generative adversarial networks (GANs). The conference explores real-world applications in a range of areas, including computer vision, natural language processing, and the creation of original content.

This book features high-quality, peer-reviewed research papers presented at the International Conference on Smart Technologies in Data Science and Communication (Smart-DSC 2019), held at Vignan’s Institute of Information Technology (Autonomous), Visakhapatnam, Andhra Pradesh, India on 13–14 December 2019. It includes innovative and novel contributions in the areas of data analytics, communication and soft computing.

This book gathers selected papers presented at the International Conference on Machine Intelligence and Soft Computing (ICMISC 2021), organized by Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India during 22 – 24 September 2021. The topics covered in the book include the artificial neural networks and fuzzy logic, cloud computing, evolutionary algorithms and computation, machine learning, metaheuristics and swarm intelligence, neuro-fuzzy system, soft computing and decision support systems, soft computing applications in actuarial science, soft computing for database deadlock resolution, soft computing methods in engineering, and support vector machine.