IIM Indore seeks to be a contextually-relevant business school with world-class academic standards that develops socially-conscious managers, leaders and entrepreneurs. IIM Indore is committed to: Excellence in management education, research, and training, Use of contemporary participant-centric pedagogies and teaching methods, A presence in emerging segments of management education.

IIM Indore

Path-finding is a fundamental problem, which involves for solving a planning problem seeking optimal paths from start state to a goal state. This review paper focused on usage of Radio Frequency Identification (RFID) technology in path-finding and navigation for an autonomous mobile robot. The RFID tags (IC tags) are used for the purpose of tracing the current co-ordinate/location of the Robot. Various searching algorithms are analyzed for the purpose towards navigation of mobile robot. Different soft-computing techniques like Genetic Algorithm, Neural Network, and Fuzzy Logic are considered. This research also focused on some hybrid algorithm for path-finding and navigation of mobile robot. Different methods like cellular automata, Network Simplex Method, Complete Coverage Navigation etc. are also studied.&nbsp;

Information Systems

World University root node

Analysis on Intelligent Based Navigation and Path Finding of Autonomous Mobile Robot

Bharti Airtel is an early adaptor of business intelligence solution in Indian telecom industry. Over a period of time, the company undertook many IT initiatives. In order to align IT with organization’s business strategy, the company has selected and adopted appropriate IT infrastructure and enterprise information systems such as Enterprise Resource Planning (ERP), and Customer Relationship Management (CRM). Subsequently the company implemented Data Warehousing (DW) and Business Intelligence (BI) systems to leverage the IT systems implemented. The company achieved many benefits out of these systems. This Case describes how the company adopted several IT initiatives, leveraged the systems and derived business value by using BI systems.

Annals of Information Systems

Business Intelligence at Bharti Airtel Ltd

This study seeks to deepen the understanding of research in business value of information technology by examining the role of contextual factors such as firm age and export activity in information technology impact following contingency theory perspective. The study is set in India and provides generalization in a new setting of emerging economy. An empirical study was conducted using data from 320 firms between 2007 and 2010. The results indicate that information technology investments enable firms to reduce their operational costs without contingencies related to firm age and export activity. However, the impact of information technology investment for improving profit is contingent on export levels such that the impact is higher for high exports firms. This work contributes to the literature by examining firm age and export activity as contextual factors and reveals the impact of information technology might be either contingent or non-contingent, subject to specific organizational outcome.

Journal of Global Information Technology Management

Information technology impact and role of firm age and export activity: An emerging economy context

Information Technology (IT) investment by firms in India and across globe is forecasted to increase at a good pace as per the recent reports published by various market research firms. However, very few research studies have been conducted to explain the factors that determine the level of IT investment by firms. In this research, we study the determinants of IT in-vestment at the firm level in Indian context. Data from 239 Indian firms over 2007-2010 period were studied using multiple linear regression analysis. Results of the study reveal IT capital stock and slack resources as the determining factors of the level of future IT investment. Sales growth, financial leverage, and industry competition etc. does not influence IT investment of firms in India. The results can provide useful guidance to IT producers while planning their operations.

Journal of Information Technology Management

Investigating determinants of information technology investments by indian firms

Unsolicited e-mail (Spam) has become a major issue for each e-mail user. In recent days it is very difficult to filter spam emails as these emails are written or generated in a very special way so that anti-spam filters cannot detect such emails. This Paper compares and discusses performance measures of certain categories of supervised machine learning techniques such as Bayes algorithms, lazy algorithms, tree algorithms, neural network, and support vector machines for classifying a spam e-mail corpus maintained by UCI Machine Learning Repository. The objective of this study is to consider the content of the emails, learn a finite dataset available and to develop a classification model that will able to predict whether an e-mail is spam or not.

Proceedings  4th International Conference on Computational Intelligence and Communication Networks, CICN 2012

A Comparative Study of Supervised Machine Learning Techniques for Spam E-mail Filtering

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.

International Journal of Computer Applications

A review of financial accounting fraud detection based on data mining techniques

Marketing literature states that it is more costly to engage a new customer than to retain an existing loyal customer. Churn prediction models are developed by academics and practitioners to effectively manage and control customer churn in order to retain existing customers. As churn management is an important activity for companies to retain loyal customers, the ability to correctly predict customer churn is necessary. As the cellular network services market becoming more competitive, customer churn management has become a crucial task for mobile communication operators. This paper proposes a neural network (NN) based approach to predict customer churn in subscription of cellular wireless services. The results of experiments indicate that neural network based approach can predict customer churn with accuracy more than 92%. Further, it was observed that medium sized NNs perform best for the customer churn prediction when different neural network’s topologies were experimented.

Fulltext

Neural and Evolutionary Computing

A neural network based approach for predicting customer churn in cellular network services

In today's knowledge based society, financial fraud has become a common phenomenon. Moreover, the growth in knowledge discovery in databases and fraud audit has made the detection of internal financial fraud a major area of research. On the other hand, auditors find it difficult to apply a majority of techniques in the fraud auditing process and to integrate their domain knowledge in this process. In this Paper a framework called "Knowledge-driven Internal Fraud Detection (KDIFD)" is proposed for detecting internal financial frauds. The framework suggests a process-based approach that considers both forensic auditor's tacit knowledge base and computer-based data analysis and mining techniques. The proposed framework can help auditor in discovering internal financial fraud more efficiently.