In modern era of computing, there is a need of development in data analysis and decision making. Most of our tools are crisp, deterministic and precise in character. But general real life situations contains uncertainties. To handle such uncertainties many theories are developed such as fuzzy set, rough set, rough set on fuzzy approximation spaces etc. But all these theories have their own limitations. To overcome the limitations, the concept of soft set is introduced. But, soft set also fails if the attributes in the information system are almost identical rather exactly identical. In this paper, we propose a decision making model that consists of two processes such as preprocess and postprocess to mine decisions. In preprocess we use rough set on fuzzy approximation spaces to get the almost equivalence classes whereas in postprocess we use soft set techniques to obtain decisions. The proposed model is tested over an institutional dataset and the results show practical viability of the proposed research. Copyright © 2014 Inderscience Enterprises Ltd.

Tapan Kumar Das

Department of Digital Communications

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

A decision making model using soft set and rough set on fuzzy approximation spaces

International Journal of Intelligent Systems Technologies and Applications

Hospital ranking is a cumbersome task, as it involves dealing with a large volume of underlying data. Rankings are usually accomplished by comparing different dimensions of quality and services. Even the quality care measurement of a hospital is multi-dimensional: It includes the experience of both clinical care and patient care. In this research, however, the authors focus on ratings based only on customer perception. A framework which consists of two stages—Stage I and Stage II—is designed. In the first stage, the model uses a rough set in a fuzzy approximation space (RSFAS) technique to classify the data; whereas in the second stage, a fuzzy soft set (FSS) technique is employed to generate the rating score. The model is employed for comparing USA hospitals by region using annual HCAHPS survey data. This article shows how ranking of the healthcare institutions can be carried out using the RSFAS (rough set in a fuzzy approximation space) and fuzzy soft set techniques.

International Journal of Healthcare Information Systems and Informatics

A Framework for Ranking Hospitals Based on Customer Perception Using Rough Set and Soft Set Techniques

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.

Fulltext

Journal of Medical Systems

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

Behavioural finance has gained research interest among researchers because of investor behavior and market anomalies. Investor behaviour varies with demographics and geographic characteristics. Further, investor behavior towards a gold exchange trade fund is gaining research interest due to various factors. Not much research has been carried out in this direction, with the exception of some comparisons. Therefore, the performance of a gold exchange traded fund needs to be assessed from the investor behavior perspective. Additionally, the investors behavior contains uncertainties. Thus, there is a need for intelligent techniques for identifying the investors behavior despite the presence of uncertain behavioral characteristics. Therefore, to study uncertain behavior characteristic in gold exchange traded fund, in this article the authors employ a fuzzy rough set. They employ fuzzy rough quick reduct algorithm to find the superfluous attributes. Further decision rules are generated to identify the chief feature of investors' behavior towards gold exchange traded fund.

International Journal of Business Analytics

A Fuzzy Rough Feature Selection Framework for Investors Behavior Towards Gold Exchange-Traded Fund

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.

Procedia Computer Science

Deep Residual Networks for Plant Identification

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 amount of data collected across various sources in real life situations is never in its complete form. That is, it is never precise or it never gives definite knowledge. It always contains uncertainty and vagueness. Therefore, most of our traditional tools for formal modelling, reasoning and computing can not handle efficiently. Therefore, it is very challenging to organize this data in formal system which provides information in more relevant, useful, and structured manner. There are many techniques available for knowledge extraction form this high dimensional data. This chapter discusses various rough computing based knowledge extraction techniques to obtain meaningful knowledge from large amount of data. A real life example is provided to show the viability of the proposed research.

Improving Knowledge Discovery through the Integration of Data Mining Techniques Advances in Data Mining and Database Management

Knowledge Extraction from Information System Using Rough Computing

International Journal of Modern Education and Computer Science

A Bootstrap Approach of Benchmarking Organizational Maturity Model of Software Product With Educational Maturity Model

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 Intelligent Systems Technologies and Applications
Publisher	Inderscience Publishers
ISSN	1740-8865
Open Access	No