Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics. Agents introduced in various architectures represent its functionality as functional elements of the architecture and shows the interaction between other components present in the architecture. The Internet of things (IoT) reveals as a frequent technology that allows accessing the physical objects present in the world. IoT systems utilize wireless sensor network to transmit and receive data by establishing communication. Wireless Sensor Networks transmits digital signals to the cyber-world for analyzing and processing the information into useful data by either formulating or communicating with the intelligent and innovative system. While talking about IoT and WSN, agents introduced in such environments assist in making decisions quickly by perceiving the input from the environment. The number of agents needed for an application depends upon the complexity of the problem. Multi-Agent architectures discussed in the article describe their association, roles, functionality and interaction. This paper gives a detailed survey of various agent/multi-agent learning architectures introduced over IoT and WSN. Moreover, this survey with the performance and the SWOT analysis on the Agent-based learning architecture helps the reader and paves a way to pursue research on Agent-based architectural deployment over IoT and WSN paradigms. © 2020 World Scientific Publishing Company.

Department of Analytics

School of Computer Science and Engineering

Vellore Campus

International Journal of Wavelets, Multiresolution and Information Processing

A survey on agent learning architecture that adopts internet of things and wireless sensor networks

Though there are enormous amount of information available in the web, only very small portion of the available information is visible to the users. Due to the non-visibility of huge information, the traditional search engines cannot index or access all information present in the web. The main challenge in the mining of the relevant information from a huge hidden web database is to identify the entry points to access the hidden web databases. The existing web crawlers cannot retrieve all information from the hidden web databases. To retrieve all the relevant information from the hidden web, this paper proposes an architecture that uses genetic algorithm and intelligent agents for accessing hidden web databases. The proposed architecture is termed as genetic algorithm-based intelligent multi-agent system (GABIAS). The experimental results show that the proposed architecture provides better precision and recall than the existing web crawlers. Copyright © 2020 Inderscience Enterprises Ltd.

World University root node

International Journal of Business Intelligence and Data Mining

Genetic algorithm-based intelligent multiagent architecture for extracting information from hidden web databases

sRecent researches in IOT bring out smartness with the basis of machine learning techniques. IOT architecture describes the gateways or fog, an analysis engine and an insight layer. These layers are embedded between the cloud and the edge devices. The insight layer employs various learning modules onto the data in the cloud. The fog layer is the most significant layer that improves the efficiency of IOT architecture. Cloud computing and Fog computing is mutually operated. Fog based application should address the issue of the data to be kept in the fog device and to identify the data present in the nearest fog device with the relevant search query. Learning helps to identify the data that are referred frequently and predict the data requirements of the near future. Agents are introduced in the IOT architecture which reacts intelligently using its learning capability and its characteristics improve the system performance. © Springer Nature Switzerland AG 2020.

Intelligent Systems Reference Library

Paradigms for intelligent IOT architecture

The Internet of Things (IoT) paves the way to interact with the smart objects namely sensors, hardware, circuits and software. Research in IoT ensures that collecting, processing and distributing the data needs to be improved to carryout data aggregation, processing and dissemination tasks of IoT data management. Data Processing focuses on the characteristics Velocity, Volume, Variety, Variability, and Veracity. IoT Data Management may further be categorized as Communication, Storage and Processing. Data communication involves data processing among objects, sensor data and hardware. To store the data, Cloud or distributed storage is used and processing involves filtering and analytics. Data dissemination distributes the processed data to end users. Message-delay in multi-hop massive IoT network is significantly optimized. This chapter enumerates the IoT data management frameworks, challenges and issues. Also, deployment of IoT Data management for smart home and smart city is described. © Springer Nature Switzerland AG 2020.

IOT data management, data aggregation and dissemination

Medical image processing and analysis is the tool to assist radiologists in the diagnosis process to obtain a more accurate and faster diagnosis. In this work, we have developed a neural network to classify the computer tomography (CT) brain tumor image for automatic diagnosis. This system is divided into four steps namely enhancement, segmentation, feature extraction and classifcation. In the frst phase, an edge-based selective median flter is used to improve the visibility of the loss of the gray-white matter interface in CT brain tumor images. Second phase uses a modifed version of shift genetic algorithm for the segmentation. Next phase extracts the textural features using statistical texture analysis method. These features are fed into classifers like BPN, Fuzzy k-NN, and radial basis function network. The performances of these classifers are analyzed in the fnal phase with receiver operating characteristic and precision-recall curve. The result shows that the CAD system is only to develop the tool for brain tumor and proposed method is very accurate and computationally more effcient and less time consuming. © 2012, DESIDOC.