Currently, internet is the best tool for distributed computing, which involves spreading of data geographically. But, retrieving information from huge data is critical and has no relevance unless it provides certain information. Prediction of missing associations can be viewed as fundamental problems in machine learning where the main objective is to determine decisions for the missing associations. Mathematical models such as naive Bayes structure, human composed network structure, Bayesian network modelling, etc., were developed to this end. But, it has certain limitations and failed to include uncertainties. Therefore, effort has been made to process inconsistencies in the data with the introduction of rough set theory. This paper uses two processes, pre-process and post-process, to predict the decisions for the missing associations in the attribute values. In preprocess, rough set is used to reduce the dimensionality, whereas neural network is used in postprocess to explore the decision for the missing associations. A real-life example is provided to show the viability of the proposed research. © 2015 Inderscience Enterprises Ltd.

Anitha A

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Debi Prasanna Acharjya

Department of Computational Intelligence

School of Computer Science and Engineering

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

Neural network and rough set hybrid scheme for prediction of missing associations

International Journal of Bioinformatics Research and Applications

The growth of information and communication technology makes people neglect their cultural heritage due to various factors, and it leads to a lack of cultural heritage transformation from one generation to the next generation. It greatly impacts on pilgrimage attitude towards cultural heritage. Besides, the expansion of heritage places improves the economic worth of any nation. Further, pilgrimage attraction is a major concern, which in turn improves the business opportunities. In general, cultural heritage depends on the historical, aesthetic, and architectural value of a particular place. Apart from these factors, some other parameters are also associated with cultural heritage. Therefore, it is significant to understand the behavioral pattern of the pilgrimage and their likeliness. This paper makes a phenomenological approach to uncover subliminal values associated with the cultural heritage places of Odisha, India. The prime objective is to study the attitude of pilgrimage towards visiting cultural heritage places. The attitude of pilgrimage depends on different dimensions, such as historical, aesthetic, architectural, spiritual, environment, economic, and management. Looking into uncertainty and frequent changes in human behavior, we employ variance-based structural equation modeling using partial least square and rough set for analyzing the information system. Variance-based structural equation modeling using partial least square help us to identify the factors that are essential for the study, and then the rough set is used to generate the rules. It, in turn, study the attitude of pilgrimage towards cultural heritage place of Odisha. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Journal of Ambient Intelligence and Humanized Computing

An integrated partial least square and rough set approach for studying pilgrimage attitude towards cultural heritage of Odisha

Computational intelligence innovation and the use of computers have changed the entire healthcare delivery system. Nurses are the leading crew of healthcare organization. But, these nurses are either lacking in computer usage or automated analysis generated by computers. Therefore, it motivates to study the use of computers and information technology by nurses in Indian healthcare system. Further, it is essential to identify the chief factors where these nurses are lacking while using computers and information technology. This will help the management to take necessary measure to train them and make the healthcare industry more productive in perception with usage of computer and information technology. To this end, data has collected from nurses in hospitals in the state of Tamilnadu, India. Data collection is not beneficial unless it is analyzed and meaningful information obtained from it. In this paper, we hybridize rough set and formal concept analysis to arrive at chief factors affecting the decisions. Rough set is used to analyze the data and to generate rules. These generated rules further passed into formal concept analysis to identify the chief characteristics affecting the decisions. This in turn help the organization to provide adequate training to the nurses and the healthcare system will move further to the next stage. © 2019, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature.

Health and Technology

Computational intelligence techniques for efficient delivery of healthcare

Advancing the knowledge and understanding of plants growing around us plays a very important and crucial role in medicinally, economically and in a sustainable agriculture. The identification of plant images in the field of computer vision has become an interdisciplinary focus. Taking benign conditions of quick advancement in computer vision and deep learning algorithms, convolutional neural networks (CNN) approach is taken into consideration to learn feature representation of 185 classes of leaves taken from Columbia University, the University of Maryland, and the Smithsonian Institution. For the large-scale classification of plants in the natural environment, a 50-layer deep residual learning framework consisting of 5 stages is designed. The proposed model achieves a recognition rate of 93.09 percent as the accuracy of testing on the LeafSnap dataset, illustrating that deep learning is a highly promising forestry technology. © 2019 The Authors. Published by Elsevier Ltd.

Fulltext

Procedia Computer Science

Deep Residual Networks for Plant Identification

In Indian economy, agriculture is the prime vocation that avails in the overall development of the country. Tamil Nadu occupies approximately 7% of the nation's population, with 3% of water resources and 4% of land resources at the country level. The crop suitability prediction is of prime importance to enhance the nutritional security to the developing country. Based on several crops grown in a particular place, and the availability of natural resources, one can identify the suitability of crops that can be grown in a particular place. To this end, many mathematical tools were developed, but they failed to include processing of uncertainties present in the accumulated data. Therefore, in this paper an effort has been made to process the uncertainties by hybridizing rough set on fuzzy approximation space and neural network. The rough set on fuzzy approximation space identifies the almost indiscernibility among the natural resources and helps in minimizing the computational procedure on employing data reduction techniques, whereas neural network helps in prediction process. The proposed model is analysed on agriculture data of Vellore District of Tamil Nadu, India, and achieved 93% of classification accuracy in validation. The model is compared with an earlier model and achieved 8% more accuracy while predicting unseen associations. © 2017, The Natural Computing Applications Forum.

Neural Computing and Applications

Crop suitability prediction in Vellore District using rough set on fuzzy approximation space and neural network

This article describes how agriculture is the main occupation of India, and how the economy depends on agricultural production. Most of the land in India is dedicated to agriculture and people depend on the production of agricultural products. Therefore, forecasting the accuracy of future events based on extracted patterns plays a vital role in improving agricultural productivity. By considering the availability of micronutrients and macronutrients of the soil and water in a particular place, the growth of a plant is determined. This helps people to determine the crops to be cultivated at a certain place. In this article, the forecasting is carried out using rough sets and genetic algorithms. Rough sets are used to produce the decision rules whereas genetic algorithms are used to refine the rules and improve classification accuracy. Accuracy of the classification rules is analyzed using different selection methods and crossover operators. Results show that genetic algorithms with a roulette wheel selection and single point crossover provides better performance when compared with other existing techniques.

International Journal of Fuzzy System Applications

A Rule Based Classification for Vegetable Production Using Rough Set and Genetic Algorithm

The information technology revolution has brought drastic change in the way data is collected or generated for decision mining. The accumulated data has no relevance unless it provides certain useful information pertaining to the interest of an organization. The real challenge lies in converting high dimensional data into knowledge and to use this knowledge for the development of the organization. On the other hand, hiding an organization’s sensitive information is a major concern. Much research has been carried out in this direction. This chapter discusses various privacy preservation techniques that can be employed in an information system to safeguard the sensitive information of an organization. This chapter also highlights sensitive fuzzy association rules that can be generated from an information system. The authors provide illustrations wherever necessary to give a clear idea of the concepts developed.

Advances in Secure Computing, Internet Services, and Applications Advances in Information Security, Privacy, and Ethics

Privacy Preservation in Information System

Medical diagnosis process vary in the degree to which they attempt to deal with different complicating aspects of diagnosis such as relative importance of symptoms, varied symptom pattern and the relation between diseases themselves. Rough set approach has two major advantages over the other methods. First, it can handle different types of data such as categorical, numerical etc. Secondly, it does not make any assumption like probability distribution function in stochastic modeling or membership grade function in fuzzy set theory. It involves pattern recognition through logical computational rules rather than approximating them through smooth mathematical functional forms. In this paper we use rough set theory as a data mining tool to derive useful patterns and rules for kidney cancer faulty diagnosis. In particular, the historical data of twenty five research hospitals and medical college is used for validation and the results show the practical viability of the proposed approach.

Journal	International Journal of Bioinformatics Research and Applications
Publisher	Inderscience Publishers
ISSN	1744-5485
Open Access	0