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.

&nbsp;

&nbsp;

VIT University

Vast volumes of raw data are generated from the digital world each day. Acquiring useful information and chief features from this data is challenging, and it has become a prime area of current research. Another crucial area is knowledge inferencing. Much research has been carried out in both directions. Swarm intelligence is used for feature selection whereas for knowledge inferencing either fuzzy or rough computing is widely used. Hybridization of intelligent and swarm intelligence techniques are booming recently. In this research work, the authors hybridize both artificial bee colony and rough set. At the initial phase, they employ an artificial bee colony to find the chief features. Further, these main features are analyzed using rough set generating rules. The proposed model indeed helps to diagnose a disease carefully. An empirical analysis is carried out on hepatitis dataset. In addition, a comparative study is also presented. The analysis shows the viability of the proposed model. © 2021 IGI Global. All rights reserved.

Department of Database Systems

School of Computer Science and Engineering

Vellore Campus

Department of Computational Intelligence

Fulltext

International Journal of Healthcare Information Systems and Informatics

Knowledge inferencing using artificial bee colony and rough set for diagnosis of hepatitis disease

Large volumes of raw data are created from the digital world every day. Acquiring useful information from these data is challenging, and it turned into a prime zone of momentum explore. More research is done in this direction. Further, in disease diagnosis, many uncertainties are involved in the information system. To handle such uncertainties, intelligent techniques are employed. In this paper, we present an integrated scheme for heart disease diagnosis. The proposed model integrates cuckoo search and rough set for inferencing decision rules. At the underlying phase, we employ a cuckoo search to discover the main features. Further, these main features are analyzed using rough set generating rules. An empirical analysis is carried out on heart disease. Besides, a comparative study is also presented. The comparative study demonstrates the feasibility of the proposed model.

VIT Unitversity

Journal of Medical Systems

A Hybrid Scheme for Heart Disease Diagnosis Using Rough Set and Cuckoo Search Technique

Due to advancement in technology, a huge volume of data is generated. Extracting knowledgeable data from this voluminous information is a difficult task. Therefore, machine learning techniques like classification, clustering, information retrieval, feature selection and data analysis has become core of recent research. These techniques can also be solved using Nature Inspired Algorithms. Nature Inspired Algorithms is inspired by processes, observed from nature. Feature Selection is helpful in finding subset of prominent components to enhance prescient precision and to expel the excess features. This chapter surveys seven nature inspired algorithms, namely Particle Swarm Optimization, Ant Colony Optimization Algorithms, Artificial Bees Colony Algorithms, Firefly Algorithms, Bat Algorithms, Cuckoo Search and Genetic Algorithms and its application in feature selections. The significance of this chapter is to present comprehensive review of nature inspired algorithms to be applied in feature selections.

Department of Structural and Geotechical Engineering

School of Civil Engineering

Advances in Computational Intelligence and Robotics Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms

A Comprehensive Review of Nature-Inspired Algorithms for Feature Selection

Since the beginning of Artificial Intelligence, Computer Vision and the trend of automated systems in various industrial departments, there have been drastic improvements and more applications of such systems than one could ever imagine. These applications have in recent ties moved to the more domestic environments too. From automating coffee machines, air conditioners, etc. Home based automation systems have come far. Still a centralized automation system is missing for such environments, where multiple devices are integrated and automated, with dependency on multiple parameters. The suggested system intends to automate all this with a centralized automation system covering multiple domains including security, entertainment, etc. in home environments, all this at cost effective rates and with good robust systems. Automation of home and other domestic systems is a technological requirement of the society now. Cost effectiveness has been a major problem with such systems, which makes it difficult to installing households, etc. Our module consists of a cost effective yet performance efficient domestic automation system which includes modules like smart lock based on facial detection and recognition, gesture automated appliance and software assistance and also automated payment gateway using optical character recognition, thus fulfilling the aim of moving domestic households, etc. to a more digital and technology friendly environment. © 2018 IAEME Publication.

World University root node

International Journal of Civil Engineering and Technology

Multi domain automation systems in domestic environments

Adaptation of information technology has leads to creation of several applications in health care informatics. Health care informatics is generating large amount of data. These data can be processed using data mining techniques to predict the diseases. Data mining is the process of analyzing, extracting data and furnishes the data as knowledge which forms the relationship within the available data. Some of the data mining techniques include association, clustering, classification and prediction. Various data mining tools are compared to analyze the performance of health care data and disease prediction. © 2017, Pharmainfo Publications. All rights reserved.

Chennai Campus

Computer Science

Journal of Pharmaceutical Sciences and Research

Analysis of data mining tools for disease prediction

Currently, a huge amount of data is available across various domains including biological data. Classification of these data, clustering, and data analysis is tedious and has become popular in recent research. In particular, bio-inspired computing is the field that mends together mathematics, computer science, and biology to develop tools to store, scrutinize, and interpret the biological data. It is also used to solve real life problems like sequencing biological data, data clustering, and optimization. Swarm intelligence is an emerging field of biologically inspired artificial intelligence technique that is based on the behavioral models of social insects. This chapter provides an overview of swarm intelligence algorithms in solving bio-inspired computing problems. It is an attempt to explore the working nature, applications, and generative power of various bio-inspired computing algorithms. The main intent is to furnish a comprehensive study of swarm intelligence algorithms in the literature so as to inspire further research in the area of biologically inspired computing.

Nature-Inspired Computing

Swarm Intelligence in Solving Bio-Inspired Computing Problems

Since the rapid growth of social media content has promulgated in digital space, the amount of data that has been generated via social media got multiplied every day. Moreover, the social media contents are very complicated and loosely coupled for integration. Besides it paves the tough path for capturing the data and analyzing the data for future decision making process. In order to facilitate this difficult operation, we have proposed here the clustering technique to effectively cluster the data in lesser amount of time. This paper implements the PSO algorithm with the modification required to match the Twitter data streams. The outcomes clearly depict the performance of the PSO algorithms and shows that there is an increase in number of particles in the larger selection of data streams. The proposed work has taken two different approaches to cluster the Twitter data streams using the modified PSO algorithm. First, it has used to select the centroids based on the number of clusters given by the user. Second, refine the cluster for giving the high convergence to the selected data sets.

Department of Computer Applications

School of Information Technology and Engineering

Research Journal of Pharmaceutical, Biological and Chemical Sciences

Pervasive utilization of PSO techniques over twitter data streams

Digital technology has made tremendous revolution in health care analytics. It includes electronic health record, medical imaging, remote patient monitoring and bioinformatics. This paper focuses on medical imaging analysis using soft computing techniques. Magnetic resonance imaging (MRI) is an advanced and quality medical imaging technique compared to X-ray furnishes rich information on the human soft tissue anatomy. Soft computing techniques like K-means and Fuzzy C means (FCM) is applied to analyze the geometric characteristics of brain tumor images, and has been applied widely to many applications such as image detection, image segmentation, image reduction and so on. Image Segmentation is done to extract meaningful features and performing analysis based on segmentation of images using K means clustering. Image reduction is done for fast processing of images using FCM technique. The proposed system can be widely used for treatment of brain tumor using medical image processing.

Brain tumor detection in medical imaging using soft computing techniques

Advances in Computational Intelligence and Robotics Handbook of Research on Swarm Intelligence in Engineering

At the heart of personality psychology lies one single fundamental motive and that is to be able to anticipate how an individual will think, behave and feel at any future instant. Quite unfortunately this field has not been very successful in achieving this. Though this field has given us great insights about the working of the mind, cognitive processes and emotions, it has failed to accomplish its central objective i.e., to predict human behaviour. We propose in this paper a novel technique of predicting human behaviour without the need of any abstraction about the mind or its internal workings. We propose the use of simple and straightforward statistics for this purpose. Applying simple association rule mining on behaviours of thousands of people, association rules having high confidence values can be identified. And based on these rules, strong conclusions can be made in anticipating the behaviour of an individual. An analytical study was conducted on answers provided by 1414 candidates to a 163-question personality survey. The survey was based on the famous questionnaire prepared by Raymond Cattell. This survey was chosen to first try and prove the ambiguity in the current psychological concepts. Following that simple association rule mining was applied on the data to obtain associations between variables. The strongest association obtained with 97.2% confidence was an inter-class association rather than an intra- class association as would be expected from traditional psychology point-of-view.