IoT Information Status Using Data Fusion and Feature Extraction Method

Abstract

The Internet of Things (IoT) role is instrumental in the technological advancement of the healthcare industry. Both the hardware and the core level of software platforms are the progress resulted from the accompaniment of Medicine 4.0. Healthcare IoT systems are the emergence of this foresight. The communication systems between the sensing nodes and the processors; and the processing algorithms to produce output obtained from the data collected by the sensors are the major empowering technologies. At present, many new technologies supplement these empowering technologies. So, in this research work, a practical feature extraction and classification technique is suggested for handling data acquisition besides data fusion to enhance treatment-related data. In the initial stage, IoT devices are gathered and pre-processed for fusion processing. Dynamic Bayesian Network is considered an improved balance for tractability, a tool for CDF operations. Improved Principal Component Analysis is deployed for feature extraction along with dimension reduction. Lastly, this data learning is attained through Hybrid Learning Classifier Model for data fusion performance examination. In this research, Deep Belief Neural Network and Support Vector Machine are hybridized for healthcare data prediction. Thus, the suggested system is probably a beneficial decision support tool for multiple data sources prediction and predictive ability enhancement.

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

“…Their paper The irregularity sources of the announcement standard are differentiated using the method of machine learning. The performance, accuracy, and effectiveness are measured with the help of the composing method Rahman et al [27] have proposed an IDS approach namely the Scalable Machine Learning for IoT-Enabled Smart Cities. Their paper addressed the restriction of centralized IDS by proposing semi-distributed and distributed methods.…”

“…The Proposed Model attack detection results is evaluated in terms of four categories includes True_Positive (TP), True_Negative (TN), False_Positive (FP) and False_Negative (FN) which is denoted as a confusion matrix [27] as shown in Table 3.…”

“…The performance of the proposed model DTP-CRFE-ODNN is compared with the various feature [31], Improved Principal Component Analysis [27] and Modified kNN [21]. The analyzed results are shown in Table 7.…”