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• W4285267271 abstract "In this modern era, the transformation of conventional objects into smart ones via internet vitality, data management, together with many more are the main aim of the Internet of Things (IoT) centered Big Data (BD) analysis. In the past few years, significant augmentation in the IoT-centered Healthcare (HC) monitoring can be seen. Nevertheless, the merging of health-specific parameters along with IoT-centric Health Monitoring (HM) systems with BD handling ability is turned out to be a complicated research scope. With the aid of Map-Reduce and LSQN3 techniques, this paper proposed IoT devices in Wireless Sensors Networks (WSN) centered BD Mining (BDM) approach. Initially, the heart disease prediction dataset is acquired from publicly available sources in the proposed approach. Following that, the dataset is mitigated by reducing redundant data using Map-Reduce and making it useful for the upcoming examination. During the mapping step, the Linear Log induced K-Means Algorithm (LL-KMA) clustering algorithm is used. The LF-CSO technique is used in the reduction phase to select the optimal Cluster Centroids (CC). The features are extracted from the reduced data. After that, utilizing the Pearson Correlation Coefficient based Generalized Discriminant Analysis (PCC-GDA), the extracted features’ dimensionality is mitigated. Subsequently, the features being reduced are neumaralised for classification purposes. Lastly, to classify the disease, the Log Sigmoid activation based Quasi-Newton Neural network (LSQN3) classifier is employed. The proposed method is contrasted with the existing methodologies to assess the performance. The experiential outcomes displayed that the proposed work is highly efficient than the other methodologies." @default.
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• W4285267271 date "2022-01-01" @default.
• W4285267271 modified "2023-10-01" @default.
• W4285267271 title "IoT Based Disease Prediction Using Mapreduce and LSQN3 Techniques" @default.
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• W4285267271 doi "https://doi.org/10.32604/iasc.2022.025792" @default.
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