Rajesh Kanna B

Computer Science

School of Computer Science and Engineering

Chennai Campus

Dinesh Jackson Samuel R

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Multimedia Tools and Applications

The principle restorative step in the treatment of ischemic stroke depends on how fast the lesion is delineated from the Magnetic Resonance Imaging (MRI) images. This will serve as a vital aid to estimate the extent of damage caused to the brain cells. However, manual delineation of the lesion is time-consuming and it is subjected to intra-observer and inter-observer variability. Most of the existing methods for ischemic lesion segmentation rely on extracting handcrafted features followed by application of a machine learning algorithm. Identifying such features demand multi-domain expertise in Neuro-radiology as well as Image processing. This can be accomplished by learning the features automatically using Convolutional Neural Network (CNN). To perform segmentation, the spatial arrangement of pixel needs to be preserved in addition to learning local features of an image. Hence, a deep supervised Fully Convolutional Network (FCN) is presented in this work to segment the ischemic lesion. The highlight of this research is the application of Leaky Rectified Linear Unit activation in the last two layers of the network for a precise reconstruction of the ischemic lesion. By doing so, the network was able to learn additional features which are not considered in the existing U-Net architecture. Also, an extensive analysis was conducted in this research to select optimal hyper-parameters for training the FCN. A mean segmentation accuracy of 0.70 has been achieved from the experiments conducted on ISLES 2015 dataset. Experimental observations show that our proposed FCN method is 10% better than the existing works in terms of Dice Coefficient. © 2019 Elsevier B.V.

Applied Soft Computing

A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network

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. © 2017, Springer-Verlag GmbH Germany, part of Springer Nature.

Journal of Ambient Intelligence and Humanized Computing

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

Over the last decade, there has been an increasing interest in big data research, especially for health services applications. The adoption of the cloud computing and the Internet of Things (IoT) 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 in a big data environment such as Industry 4.0 applications. However, the required resources to manage such data in a cloud-IoT environment are still a big challenge. Accordingly, this paper proposes a new model to optimize virtual machines selection (VMs) in cloud-IoT health services applications to efficiently manage a big amount of data in integrated industry 4.0. Industry 4.0 applications require to process and analyze big data, which come from different sources such as sensor data, without human intervention. The proposed model aims to enhance the performance of the healthcare systems by reducing the stakeholders’ request execution time, optimizing the required storage of patients’ big data and providing a real-time data retrieval mechanism for those applications. The architecture of the proposed hybrid cloud-IoT consists of four main components: stakeholders’ devices, stakeholders’ requests (tasks), cloud broker and network administrator. To optimize the VMs selection, three different well-known optimizers (Genetic Algorithm (GA), Particle swarm optimizer (PSO) and Parallel Particle swarm optimization (PPSO) are used to build the proposed model. To calculate the execution time of stakeholders’ requests, the proposed fitness function is a composition of three important criteria which are CPU utilization, turn-around time and waiting time. A set of experiments were conducted to provide a comparative study between those three optimizers regarding the execution time, the data processing speed, and the system efficiency. The proposed model is tested against the state-of-the-art method to evaluate its effectiveness. The results show that the proposed model outperforms on the state-of-the-art models in total execution time the rate of 50%. Also, the system efficiency regarding real-time data retrieve is significantly improved by 5.2%. © 2018 Elsevier B.V.

Future Generation Computer Systems

A hybrid model of Internet of Things and cloud computing to manage big data in health services applications

Due to the advances in computer-based communication and health services over the past decade, the need for image security becomes urgent to address the requirements of both safety and non-safety in medical applications. This paper proposes a new fragile watermarking-based scheme for image authentication and self-recovery for medical applications. The proposed scheme locates image tampering as well as recovers the original image. A host image is broken into 4\times 4 blocks and singular value decomposition (SVD) is applied by inserting the traces of block wise SVD into the least significant bit of the image pixels to figure out the transformation in the original image. Two authentication bits namely block authentication and self-recovery bits are used to survive the vector quantization attack. The insertion of self-recovery bits is determined with Arnold transformation, which recovers the original image even after a high tampering rate. SVD-based watermarking information improves the image authentication and provides a way to detect different attacked area of the watermarked image. The proposed scheme is tested against different types of attacks such as text removal attack, text insertion attack, and copy and paste attack. Compared with the state-of-the art methods, the proposed scheme greatly improves both tamper localization accuracy and the peak signal to noise ratio of self-recovered image. © 2013 IEEE.

Fulltext

IEEE Access

Secure and Robust Fragile Watermarking Scheme for Medical Images

GEDRAG & ORGANISATIE REVIEW

Deep Root Memory Optimized Indexing Methodology in Image Search Engines

Measurement

A machine learning model for improving healthcare services on cloud computing environment

Cybernetic microbial detection system using transfer learning

Journal	Data powered by TypesetMultimedia Tools and Applications
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	1380-7501
Open Access	0