Papers by Chetana Srinivas
International journal of health sciences
In traditional Internet of Things (IoT) ecosystems, fog devices transmit data from sensors to a c... more In traditional Internet of Things (IoT) ecosystems, fog devices transmit data from sensors to a centralized cloud server. Issues with security and upkeep while updating firmware for millions of smart devices, single points of failure, as well as third-party Cloud server administration, the difficulty of frequently updating the firmware on millions of smart devices presents security and maintenance issues as well as a bottleneck in information flows, all raise privacy concerns. Blockchain technology eliminates the need for trusted third parties while also preventing single points of failure and other issues. As a result, academics are looking into the usage of blockchain in the IoT space. The most recent state-of-the-art developments in blockchain for IoT, blockchain for Cloud, blockchain for eHealth, smart cities, transportation, and other programmes are examined in this article.
The digitization of processes involved in the healthcare industry (HI) produces the massive amoun... more The digitization of processes involved in the healthcare industry (HI) produces the massive amount of data, which is characterized by all the attributes of big data definitions. The analytics of these data may provide multifold benefits in both clinical practices as well as into management perspective in HI. There are requirements of supporting tools and methodologies to acquire, store, process, warehousing and analytics processes. This paper aims to collect initially generic tools for handling these issues of big data and later inferences the existing research attributes and practices, which are closely related to big data analytics. The methodology adopted is data collection mechanism from different sources such as specific
IOSR Journal of Engineering, 2012
Breast cancer is considered to be one of the leading causes of deaths among females in United Sta... more Breast cancer is considered to be one of the leading causes of deaths among females in United States. All over the global level approximately 10000 women are diagnosed with this disease per year and approximately 3500 of these women are die from this types of cancer. In this paper, we propose a Complex Event Processing (CEP) Engine based on Support Vector Machine. Currently the Effective method for early detection and screening of Breast Cancer is Mammography Techniques. The detection of Tumor method follows the scheme of a) Mammogram image preprocessing b) The Segmentation Tumor area c) The Extraction of features and the use of support Vector Machine classification method.The monogram enhancement and segmentation techniques play an important role to improve the detection and diagnosis of breast cancer. The results reveal that the application of Event processing techniques improves the classification of images in medical domain and produces accurate results in order to help radiologist assessment.
Journal of Healthcare Engineering
Brain tumor classification is a very important and the most prominent step for assessing life-thr... more Brain tumor classification is a very important and the most prominent step for assessing life-threatening abnormal tissues and providing an efficient treatment in patient recovery. To identify pathological conditions in the brain, there exist various medical imaging technologies. Magnetic Resonance Imaging (MRI) is extensively used in medical imaging due to its excellent image quality and independence from ionizing radiations. The significance of deep learning, a subset of artificial intelligence in the area of medical diagnosis applications, has macadamized the path in rapid developments for brain tumor detection from MRI to higher prediction rate. For brain tumor analysis and classification, the convolution neural network (CNN) is the most extensive and widely used deep learning algorithm. In this work, we present a comparative performance analysis of transfer learning-based CNN-pretrained VGG-16, ResNet-50, and Inception-v3 models for automatic prediction of tumor cells in the br...
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Papers by Chetana Srinivas