Data Mining - Concepts and Techniques.

The Blockchain Random Neural Network for cybersecure IoT and 5G infrastructure in Smart Cities

Internet of Things (IoT) connects different objects in the physical world to the Internet, and various Internet protocols are used to provide communication services to a large number of these embedded devices termed as smart devices. But, these devices are resource‐constrained, low configured, and have very low power storage capacity, which depends on the services offered by the protocols. For the exchange of information to the end‐users, smart devices communicate through an open channel, such as the Internet, which is not secure enough. Moreover, efficient delivery ratio, secure data forwarding are not achieved because of the enormous amount of data produced by these smart devices and the possibility of security threats. So there is a need to devise a secure and reliable data dissemination scheme for the IoT environment. Motivated from the these facts, this paper presents a systematic review and propose a solution taxonomy for secure data dissemination techniques for various smart IoT‐based applications. This paper also includes a comparison of the state‐of‐the‐art data dissemination techniques used for the Internet of Vehicles (IoVs), Internet of Drones (IoDs), and Internet of Battlefield Things (IoBTs) applications along with their merits and demerits. Finally, the research challenges and possible countermeasures are also discussed in detail, which gives insights to the beginners who want to start work in this emerging area.

Secure data dissemination techniques for IoT applications: Research challenges and opportunities

An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images.

Credit card is the commonly used payment mode in the recent years. As the technology is developing, the number of fraud cases are also increasing and finally poses the need to develop a fraud detection algorithm to accurately find and eradicate the fraudulent activities. This research work proposes different machine learning based classification algorithms such as logistic regression, random forest, and Naive Bayes for handling the heavily imbalanced dataset. Finally, this research work will calculate the accuracy, precision, recall, f1 score, confusion matrix, and Roc-auc score.

Credit Card Fraud Detection Using Machine Learning

Recent 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.

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.

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 moreaccurate 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 classification. In the first phase, an edge-based selective median filter is usedto improve the visibility of the loss of the gray-white matter interface in CT brain tumor images. Second phaseuses a modified version of shift genetic algorithm for the segmentation. Next phase extracts the textural featuresusing statistical texture analysis method. These features are fed into classifiers like BPN, Fuzzy k-NN, and radialbasis function network. The performances of these classifiers are analyzed in the final phase with receiver operating characteristic and precision-recall curve. The result shows that the CAD system is only to develop the tool for braintumor and proposed method is very accurate and computationally more efficient and less time consuming. Defence Science Journal, 2012, 62(4), pp.212-218 , DOI:http://dx.doi.org/10.14429/dsj.62.1830

http://publications.drdo.gov.in/ojs/index.php/dsj/article/download/1830/1291

Analysis of CT Brain Images using Radial Basis Function Neural Network

Agents are software programs that perform tasks on behalf of others and they are used to clean the text data with their characteristics. Agents are task oriented with the ability to learn by themselves and they react to the situation. Learning characteristics of an agent is done by verifying its previous experience from its knowledgebase. An agent concept is a complementary approach to the Object Oriented paradigm with respect to the design and implementation of the autonomous entities driven by beliefs, goals and plans. Preference based text data cleaning is based on the selection issue. Preferences are given by the user in the form of alphabets, numbers and special characters. Preference based Text data cleaning process transforms the given text data into structured database and extracts the required information using the given keyword. Agents incorporated in the architectural design of a Text data cleaning process combines the features of Multi-Agent System (MAS) Framework, MAS with Learning (MAS-L) Framework. MAS framework reduces the development time and the complexity of implementing the software agents. MAS-L framework incorporates the intelligence and learning properties of agents present in the system. MAS-L Framework makes use of the Decision Tree learning and an evaluation function to decide the next best decision that applies to the machine learning technique. This paper proposes the design for Multi-Agent based Data Cleaning Architecture that incorporates the structural design of agents into object model. The design of an architectural model for an Intelligent Multi-Agent based Data Cleaning inherits the features of the Multi-Agent System (MAS) and uses the MAS-L framework to design the intelligence and learning characteristics.

/pdf/object-oriented-intelligent-multi-agent-system-data-cleaning-10yiabcfym.pdf

Object Oriented Intelligent Multi-Agent System Data Cleaning Architecture to clean Preference based Text Data

Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics. Agents introduced in various architectures represent its...

T. Joshva Devadas

Papers

Paradigms for Intelligent IOT Architecture

IOT data management, data aggregation and dissemination

Analysis of CT Brain Images using Radial Basis Function Neural Network

Object Oriented Intelligent Multi-Agent System Data Cleaning Architecture to clean Preference based Text Data

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