Abstract
The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.
Highlights
In a recent technological globe, data are mounting rapidly, and humans are mostly relying on data
The resilient agent evaluation is carried out using a detailed setting with a machine learning classification module. e machine learning library is utilized and implemented in Python 3.8. e comparative analysis of the proposed design is provided with current proposals. e experimental results and comparison disclose the effectiveness of the proposed design. e discussion about the results is provided
We optimize the MapReduce algorithm for edge computing to utilize at every edge. us, notable efficiency is achieved in the processing time. e proposed architecture implemented using the Hadoop parallel and distributed framework along with optimized algorithms. ese datasets are preferred due to the utilization of this dataset in the literature
Summary
In a recent technological globe, data are mounting rapidly, and humans are mostly relying on data. With the rise of big data and machine learning, the notion of improving accuracy and enhancing the efficacy of AI projects is gaining importance and is largely recognized [3] Some of these factors of the evolution of data are the enhancement of technology, social media, and Internet of ings (IoT). E smart traffic environment is based on IoT devices and objects generating gigantic data (Big Data) which requires efficient aggregation, processing, and analysis to achieve optimal results for decision-making [14, 15]. Some big data analytics methods are found in the past other than traditional methods; there is no allinclusive, common, and effective resolution proposed to aggregate and process the big data produced in an IoT-based smart traffic environment [16,17,18]. To comprehend the reliability of the smart traffic, many challenges are required to be addressed where privacy is one of the most brutal between the imperative challenges
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