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C. B. Sivaparthipan

Bio: C. B. Sivaparthipan is an academic researcher from SNS College of Technology. The author has contributed to research in topics: Computer science & Big data. The author has an hindex of 10, co-authored 18 publications receiving 271 citations.

Papers
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Journal ArticleDOI
TL;DR: Wearable sensor which is connected to Internet of things (IoT) based big data i.e. data mining analysis in healthcare is proposed and Regularization _ Genome wide association study (GWAS) is used to predict the diseases.
Abstract: Humans with good health condition is some more difficult in today’s life, because of changing food habit and environment. So we need awareness about the health condition to the survival. The health-support systems faces significant challenges like lack of adequate medical information, preventable errors, data threat, misdiagnosis, and delayed transmission. To overcome this problem, here we proposed wearable sensor which is connected to Internet of things (IoT) based big data i.e. data mining analysis in healthcare. Moreover, here we design Generalize approximate Reasoning base Intelligence Control (GARIC) with regression rules to gather the information about the patient from the IoT. Finally, Train the data to the Artificial intelligence (AI) with the use of deep learning mechanism Boltzmann belief network. Subsequently Regularization _ Genome wide association study (GWAS) is used to predict the diseases. Thus, if the people has affected by some diseases they will get warning by SMS, emails. Etc., after that they got some treatments and advisory from the doctors.

138 citations

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TL;DR: The ideas and methods for the detection of flood disaster based on IoT, BD, and convolutional deep neural network (CDNN) to overcome such difficulties are proposed and is very accurate result than other methods.

119 citations

Journal ArticleDOI
TL;DR: A Deep Belief Network (DBN) with Recurrent LSTM Neural Network (R-LSTM-NN) is proposed for prediction of big data that are collected from smart cities based on IoT, mainly concentrates in predicting the fire hazard values that gathered fromsmart cities using IoT devices.

65 citations

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TL;DR: An enhanced graph-based semi-supervised learning algorithm (EGSLA) to detect fake users from a large volume of Twitter data is proposed and achieves 90.3% accuracy in spotting fake users.
Abstract: Over the the past decade, social networking services (SNS) have proliferated on the web. The nature of such sites makes identity deception easy, providing a fast means for creating and managing identities, and then connecting with and deceiving others. Fake users are those accounts specifically created for purposes such as stalking or abuse of another user, for slander, or for marketing. The current system for detecting deception depends on behavioral, non-behavioral and user-generated content (UGC) information gathered from users. Although these methods have high detection accuracy, they cannot be implemented in databases with massive volumes of data. To address this issue, this paper proposes an enhanced graph-based semi-supervised learning algorithm (EGSLA) to detect fake users from a large volume of Twitter data. The proposed method encompasses four modules: data collection, feature extraction, classification and decision making. Data collected from Twitter using Scrapy is utilized for the evaluation. The performance of the proposed algorithm is tested with existing game theory, k-nearest neighbor (KNN), support vector machine (SVM) and decision tree techniques. The results show that the proposed EGSLA algorithm achieves 90.3% accuracy in spotting fake users.

60 citations

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TL;DR: The proposed Internet of Things driven Physical Activity Recognition System (IoT-DPARS) has been proposed for higher education to support and encourage physical education methods and teaching methods in practice and create a healthy environment.

41 citations


Cited by
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Journal ArticleDOI
TL;DR: The main outcomes of the review introductory article contributed to the better understanding of current technological progress in IoT application areas as well as the environmental implications linked with the increased application of IoT products.

297 citations

Journal ArticleDOI
TL;DR: This paper aims to identify, compare systematically, and classify existing investigations taxonomically in the Healthcare IoT (HIoT) systems by reviewing 146 articles between 2015 and 2020, and presents a comprehensive taxonomy in the HIoT.

161 citations

Journal ArticleDOI
TL;DR: An optimal approach to anonymization using small data is proposed in this study and it is shown that the suggested method will always finish ahead of the existing method by using the least amount of time while ensuring the greatest level of security.
Abstract: An optimal approach to anonymization using small data is proposed in this study. Map Reduce is a big data processing framework used across distributed applications. Prior to the development of a map reduce framework, data are distributed and clustered using a hybrid clustering algorithm. The algorithm used for grouping together similar techniques utilises the k-means clustering algorithm, along with the MFCM clustering algorithm. Clustered data is then fed into the map reduce frame work after it has been clustered. In order to guarantee privacy, the optimal k anonymization method is recommended. When using generalisation and randomization, there are two techniques that can be employed: K-anonymity, which is unique to each, depends on the type of the quasi identifier attribute. Our method replaces the standard k anonymization process by employing an optimization algorithm that dynamically determines the optimal k value. This algorithm uses the Modified Grey Wolf Optimization (MGWO) algorithm for optimization. The memory, execution time, accuracy, and error value are used to assess the recommended method’s practise. This experiment has shown that the suggested method will always finish ahead of the existing method by using the least amount of time while ensuring the greatest level of security. The current technique gets the lowest accuracy and the privacy proposed achieves the maximum accuracy while compared to the current technique. The solution is implemented in Java with Hadoop Map-Reduce, and it is tested and deployed in the cloud on Google Cloud Platform.

110 citations

Journal ArticleDOI
TL;DR: This paper investigates the role of cloud, fog, and edge computing in the IoT environment, and covers in detail, different IoT use cases with edge and fog computing, the task scheduling in edge Computing, the merger of software-defined networks (SDN) and network function virtualization (NFV) with edge computing, security and privacy efforts.

89 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a bidirectional Gated Recurrent Unit (BiGRU) multi-step flood prediction model with attention mechanism, which can automatically adjust the matching degree between the input features and output.

71 citations