BIG data based on healthcare analysis using IOT devices
01 Nov 2017-Vol. 263, Iss: 4, pp 042059
TL;DR: In this paper, quiet individual data and wellbeing condition will be dissect by the specialist and they can see the patient condition and furthermore give the correct answer for the patient.
Abstract: IOT (INTERNET OF THINGS) makes the brilliant protest in social insurance. The heterogeneous registering, remotely imparting arrangement of gadget that interfaces with human body and wellbeing gave to analyse, screen, track and store virtual factual and restorative data. In this paper, quiet individual data and wellbeing condition will be dissect by the specialist and they can see the patient condition and furthermore give the correct answer for the patient. The more number of information (huge information) will store and move into the specific association record framework. It makes conceivable to social occasion of rich data demonstrative of our physical and psychological wellness.
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TL;DR: A new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model is introduced to securely transmit the medical images and diagnose the disease with maximum detection rate.
Abstract: At these times, internet of things (IoT) technologies have become ubiquitous in the healthcare sector. Because of the increasing needs of IoT, massive quantity of patient data is being gathered and is utilized for diagnostic purposes. The recent developments of artificial intelligence (AI) and deep learning (DL) models are commonly employed to accurately identify the diseases in real-time scenarios. Despite the benefits, security, energy constraining, insufficient training data are the major issues which need to be resolved in the IoT enabled medical field. To accomplish the security, blockchain technology is recently developed which is a decentralized architecture that is widely utilized. With this motivation, this paper introduces a new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model. The goal of the BDL-SMDTD model is to securely transmit the medical images and diagnose the disease with maximum detection rate. The BDL-SMDTD model incorporates different stages of operations such as image acquisition, encryption, blockchain, and diagnostic process. Primarily, moth flame optimization (MFO) with elliptic curve cryptography (ECC), called MFO-ECC technique is used for the image encryption process where the optimal keys of ECC are generated using MFO algorithm. Besides, blockchain technology is utilized to store the encrypted images. Then, the diagnostic process involves histogram-based segmentation, Inception with ResNet-v2-based feature extraction, and support vector machine (SVM)-based classification. The experimental performance of the presented BDL-SMDTD technique has been validated using benchmark medical images and the resultant values highlighted the improved performance of the BDL-SMDTD technique. The proposed BDL-SMDTD model accomplished maximum classification performance with sensitivity of 96.94%, specificity of 98.36%, and accuracy of 95.29%, whereas the feature extraction is performed based on ResNet-v2
37 citations
01 Feb 2016
TL;DR: An overview of storing and retrieval methods, Big Data tools and techniques used in healthcare clouds, role of Big Data Analytics in healthcare and discusses the benefits, outlooks in nascent fields of predictive analytics, faces challenges and provides solutions are given.
Abstract: In today's world the massive set of data is generated from different organizations throughout the world. This huge and heterogeneous data is called Big Data. Big Data Analytics offers tremendous insights to different organizations especially in healthcare. The traditional database architectures are not up to the mark to face the challenge with huge data, which is pouring into organizations today, and it creates a big havoc. Big Data plays an important role in achieving predictive analysis in the healthcare domain. Big Data can handle huge explosion of data, which is found in many medical organizations. Big Data Analytics plays a major role in solving issues and challenges arises in healthcare domain. This paper gives an overview of storing and retrieval methods, Big Data tools and techniques used in healthcare clouds, role of Big Data Analytics in healthcare and discusses the benefits, outlooks in nascent fields of predictive analytics, faces challenges and provides solutions. The results also shows the astronomical role of Big Data Analytics in healthcare.
36 citations
TL;DR: The EOBL concept makes it easier to populate the FFO algorithm’s population initialization, which results in an increase in the exploration rate and the results described the sovereignty of the EOFFO-NLWN method associated to existing techniques.
Abstract: Wireless networks include a set of nodes which are connected to one another via wireless links for communication purposes. Wireless sensor networks (WSN) are a type of wireless network, which utilizes sensor nodes to collect and communicate data. Node localization is a challenging problem in WSN which intends to determine the geographical coordinates of the sensors in WSN. It can be considered an optimization problem and can be addressed via metaheuristic algorithms. This study introduces an elite oppositional farmland fertility optimization-based node localization method for radio communication networks, called EOFFO-NLWN technique. It is the goal of the proposed EOFFO-NLWN technique to locate unknown nodes in the network by using anchor nodes as a starting point. As a result of merging the principles of elite oppositional-based learning (EOBL) and the agricultural fertility optimization algorithm (FFO), we have developed the EOFFO-NLWN approach, which is described in detail below. The EOBL concept makes it easier to populate the FFO algorithm’s population initialization, which results in an increase in the exploration rate. Various BNs and CRs were tested, and the findings revealed that the EOFFO-NLWN technique outperformed all other known techniques in all cases. A comprehensive experimental result analysis of the EOFFO-NLWN technique is performed under several measures, and the results described the sovereignty of the EOFFO-NLWN method associated to existing techniques.
24 citations
TL;DR: This research analyzes how to accurately classify new HSI from limited samples with labels using a compressed synergic deep convolution neural network with Aquila optimization model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO).
Abstract: The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary for improved classification accuracy. In the classification of hyperspectral images, deep learning has gained significant traction. This research analyzes how to accurately classify new HSI from limited samples with labels. A novel deep-learning-based categorization based on feature extraction and classification is designed for this purpose. Initial extraction of spectral and spatial information is followed by spectral and spatial information integration to generate fused features. The classification challenge is completed using a compressed synergic deep convolution neural network with Aquila optimization (CSDCNN-AO) model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO). The HSI, the Kennedy Space Center (KSC), the Indian Pines (IP) dataset, the Houston U (HU) dataset, and the Salinas Scene (SS) dataset are used for experiment assessment. The sequence testing on these four HSI-classified datasets demonstrate that our innovative framework outperforms the conventional technique on common evaluation measures such as average accuracy (AA), overall accuracy (OA), and Kappa coefficient (k). In addition, it significantly reduces training time and computational cost, resulting in enhanced training stability, maximum performance, and remarkable training accuracy.
5 citations
TL;DR: In this article , a prototype of an all-around emitting antenna based on basis of a segmental dielectric resonator is proposed, and its characteristics in the far zone are studied.
Abstract: This work is devoted to the study of the emitting properties of azimuthally inhomogeneous segmental dielectric resonators excited by whispering gallery modes. Due to the diffraction of the azimuthal waves on local nonhomogeneities located equidistantly along the azimuthal coordinate, intense electromagnetic radiation is achieved in the azimuthal sector of the angles 0°-360°. A prototype of an all-around emitting antenna based on basis of a segmental dielectric resonator is proposed, and its characteristics in the far zone are studied. It is shown that such an antenna forms a multilobe radiation pattern (72 lobes) in the circle sector of angles. The antenna gain in the lobes at the resonant frequency reaches 12 dB. Antenna optimization is achieved in the proposed method because of the large gain produced by the antenna. It is also analyzed that these types of radiation patterns observed by antennas are well used for the Internet of Things- (IoT-) based applications.
3 citations
References
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01 Oct 2016
TL;DR: An analytics toolkit based on open-source modules that facilitate the exploration of healthcare-related datasets is created and a controversial issue in the medical field regarding the relationship between seniority of medical professionals and clinical outcomes is examined.
Abstract: We create an analytics toolkit based on open-source modules that facilitate the exploration of healthcare-related datasets. We illustrate our framework by providing a detailed analysis of physician and hospital ratings data. Our technique should prove valuable to software developers, big-data architects, hospital administrators, policy makers and patients. As an illustration of the capabilities of our toolkit, we examine a controversial issue in the medical field regarding the relationship between seniority of medical professionals and clinical outcomes. We use a publicly available dataset of national hospital ratings in the USA to suggest that there is no significant association between experience of medical professionals and hospital ratings as defined by the US government.
26 citations