Arun Kumar Sangaiah

Bio: Arun Kumar Sangaiah is an academic researcher from VIT University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 57, co-authored 398 publications receiving 10691 citations. Previous affiliations of Arun Kumar Sangaiah include National Yunlin University of Science and Technology & Dalian University of Technology.

The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems

Abstract: Cloud Computing (CC) and the Internet of Things (IoT) have emerged as new platforms in the ICT revolution of the twenty-first century. The adoption of the CloudIoT paradigm in the healthcare field can bring several opportunities to medical IT, and experts believe that it can significantly improve healthcare services and contribute to its continuous and systematic innovation. This paper presents a comprehensive review of the current literature on integration of CC and IoT to solving various problems in healthcare applications such as smart hospitals, medicine control, and remote medical services. Also, a brief introduction to cloud computing and internet of things with an application to health care is given. This paper presents a new concept of the integration of CC and IoT for healthcare applications, which is what we; call the CloudIoT-Health paradigm. The term CloudIoT-Health and some key integration issues are presented in this paper to offer a practical vision to integrate current components of CC and the IoT in healthcare applications. Also, this paper aims to present the state of the art and gap analysis of different levels of integration components, analyzing different existing proposals in CloudIoT-Health systems. Finally, related researches of CC and IoT integration for healthcare systems have been reviewed. Challenges to be addressed and future directions of research are identified, and an extensive bibliography is presented.

An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

Abstract: This study proposed a hybrid decision support method (ANN and Fuzzy_AHP) for heart failure prediction.The performance of the proposed method was examined using three performance metrics.From the evaluations results, the proposed method performed better than the conventional ANN approachThe proposed method would provide improved and realistic result for efficient therapy administration. Heart failure (HF) has been considered as one of the deadliest human diseases worldwide and the accurate prediction of HF risks would be vital for HF prevention and treatment. To predict HF risks, decision support systems based on artificial neural networks (ANN) have been widely proposed in previous studies. Generally, these existing ANN-based systems usually assumed that HF attributes have equal risk contribution to the HF diagnosis. However, several previous investigations have shown that the risk contributions of the attributes would be different. Thus the equal risk assumption concept associated with existing ANN methods would not properly reflect the diagnosis status of HF patients. In this study, the commonly used 13 HF attributes were considered and their contributions were determined by an experienced cardiac clinician. And Fuzzy analytic hierarchy process (Fuzzy_AHP) technique was used to compute the global weights for the attributes based on their individual contribution. Then the global weights that represent the contributions of the attributes were applied to train an ANN classifier for the prediction of HF risks in patients. The performance of the newly proposed decision support system based on the integration of ANN and Fuzzy_AHP methods was evaluated by using online clinical dataset of 297 HF patients and compared with that of the conventional ANN method. Our result shows that the proposed method could achieve an average prediction accuracy of 91.10%, which is 4.40% higher in comparison to that of the conventional ANN method. In addition, the newly proposed method also had better performance than seven previous methods that reported prediction accuracies in the range of 57.85-89.01%. The improvement of the HF risk prediction in the current study might be due to both the various contributions of the HF attributes and the proposed hybrid method. These findings suggest that the proposed method could be used to accurately predict HF risks in the clinic.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.