Martin Aruldoss

Bio: Martin Aruldoss is an academic researcher from Pondicherry University. The author has contributed to research in topics: Business intelligence & Business rule. The author has an hindex of 5, co-authored 5 publications receiving 332 citations.

A Survey on Multi Criteria Decision Making Methods and Its Applications

Abstract: Multi Criteria Decision Making (MCDM) provides strong decision making in domains where selection of best alternative is highly complex. This survey paper reviews the main streams of consideration in multi criteria decision making theory and practice in detail. The main purpose is to identify various applications and the approaches, and to suggest approaches which are most robustly and effectively useable to identify best alternative. This survey work also addresses the problem in fuzzy multi criteria decision making techniques. Multi criteria decision making have been applied in many domains. MCDM method helps to choose the best alternatives where many criteria have come into existence, the best one can be obtained by analyzing the different scope for the criteria, weights for the criteria and the choose the optimum ones using any multi criteria decision making techniques. This survey provides the comprehensive developments of various methods of FMCDM and its applications.

A survey on recent research in business intelligence

Abstract: Purpose – Business intelligence (BI) has been applied in various domains to take better decisions and it provides different level of information to its stakeholders according to the information needs. The purpose of this paper is to present a literature review on recent works in BI. The two principal aims in this survey are to identify areas lacking in recent research, thereby offering potential opportunities for investigation. Design/methodology/approach – To simplify the study on BI literature, it is segregated into seven categories according to the usage. Each category of work is analyzed using parameters such as purpose, domain, problem identified, solution applied, benefit and outcome. Findings – The BI contribution in various domains, ongoing research in BI, the convergence of BI domains, problems and solutions, results of congregated domains, core problems and key solutions. It also outlines BI and its components composition, widely applied BI solutions such as algorithm-based, architecture-based a...

A Framework for Predicting Phishing Websites Using Neural Networks

Abstract: In India many people are now dependent on online banking. This raises security concerns as the banking websites are forged and fraud can be committed by identity theft. These forged websites are called as Phishing websites and created by malicious people to mimic web pages of real websites and it attempts to defraud people of their personal information. Detecting and identifying phishing websites is a really complex and dynamic problem involving many factors and criteria. This paper discusses about the prediction of phishing websites using neural networks. A neural network is a multilayer system which reduces the error and increases the performance. This paper describes a framework to better classify and predict the phishing sites using neural networks.

A Framework for Business Intelligence Application using Ontological Classification

Abstract: Every business needs knowledge about their competitors to survive better. One of the information repositories is web. Retrieving Specific information from the web is challenging. An Ontological model is developed to capture specific information by using web semantics. From the Ontology model, the relations between the data are mined using decision tree. From all these a new framework is developed for Business Intelligence.

A reference model for business intelligence to predict bankruptcy

Abstract: Purpose – Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues. Design/methodology/approach – To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the...

State of art surveys of overviews on MCDM/MADM methods

Abstract: Decision-making is primarily a process that involves different actors: people, groups of people, institutions and the state. As a discipline, multi-criteria decision-making has a relatively short history. Since 1950s and 1960s, when foundations of modern multi-criteria decision-making methods have been laid, many researches devoted their time to development of new multi-criteria decision-making models and techniques. In the past decades, researches and development in the field have accelerated and seem to continue growing exponentially. Despite the intensive development worldwide, few attempts have been made to systematically present the theoretical bases and developments of multi-criteria decision-making methods. However, the methodological choices and framework for assessment of decisions are still under discussion. The article describes the situation with reviews of MCDM/MADM methods. Furthermore, there is a need for research to study the strengths and weaknesses of different decision-making me...

A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization

Abstract: While Pareto-based multiobjective optimization algorithms continue to show effectiveness for a wide range of practical problems that involve mostly two or three objectives, their limited application for many-objective problems, due to the increasing proportion of nondominated solutions and the lack of sufficient selection pressure, has also been gradually recognized. In this paper, we revive an early developed and computationally expensive strength Pareto-based evolutionary algorithm by introducing an efficient reference direction-based density estimator, a new fitness assignment scheme, and a new environmental selection strategy, for handling both multiobjective and many-objective problems. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental studies demonstrate that the proposed method shows very competitive performance on both multiobjective and many-objective problems considered in this paper. Besides, our extensive investigations and discussions reveal an interesting finding, that is, diversity-first-and-convergence-second selection strategies may have great potential to deal with many-objective optimization.