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What is Data Pre-Processing

Applied Approach to Privacy and Security for the Internet of Things
Data Pre-processing is a strategy that is utilized to change over the raw information into a clean data collection. At whatever point the information is assembled from various sources it is gathered in a crude configuration which feasible for the analysis. Hence, data pre-processing is necessary.
Published in Chapter:
Anomaly Detection in IoT Frameworks Using Machine Learning
Phidahunlang Chyne (North-Eastern Hill University, India), Parag Chatterjee (National Technological University, Argentina & University of the Republic, Uruguay), Sugata Sanyal (Tata Institute of Fundamental Research, India), and Debdatta Kandar (North-Eastern Hill University, India)
DOI: 10.4018/978-1-7998-2444-2.ch004
Rapid advancements in hardware programming and communication innovations have encouraged the development of internet-associated sensory devices that give perceptions and information measurements from the physical world. According to the internet of things (IoT) analytics, more than 100 IoT devices across the world connect to the internet every second, which in the coming years will sharply increase the number of IoT devices by billions. This number of IoT devices incorporates new dynamic associations and does not totally replace the devices that were purchased before yet are not utilized any longer. As an increasing number of IoT devices advance into the world, conveyed in uncontrolled, complex, and frequently hostile conditions, securing IoT frameworks displays various challenges. As per the Eclipse IoT Working Group's 2017 IoT engineer overview, security is the top worry for IoT designers. To approach the challenges in securing IoT devices, the authors propose using unsupervised machine learning model at the network/transport level for anomaly detection.
Full Text Chapter Download: US $37.50 Add to Cart
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Restaurant Sales Prediction Using Machine Learning
Refers to the technique of data mining utilized for turning raw data into a more appropriate format. This includes removing missing entries, null values, normalization, enrichment, and cleaning of data.
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Big Data Analytics Lifecycle
Data pre-processing involves the necessary steps to prepare raw data for analysis. The process encompasses many techniques such as data cleansing, data integration, data reduction, data transformation, and data discretization, which are employed to ensure that the data aligns with the analysis prerequisites.
Full Text Chapter Download: US $37.50 Add to Cart
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