Dhanya Pramod

A Fuzzy Petri-Net Based Conceptual Model for Risk Prediction in Enterprise Resource Planning (ERP) Acquisition Decisions for Small and Medium Enterprises

This study identifies and evaluates certain critical pitfalls in the acquisition phase of ERP adoption in Small and Medium Enterprises (SMEs) and develops a model for evaluating the risk of failure. On the acquisition stage a set of risks has been identified based on past research work and each of these risks have been explained through a set of well-defined risk factors or pitfalls. An evaluation model based on the concept of Fuzzy Petri Nets (FPN) is proposed in this study. FPN is used because it is simple and efficient and can quantitatively evaluate the risks inherent in the acquisition stage of ERP adoption by SMEs.

A Strategic Approach Using Governance, Risk and Compliance Model to Deal with Online Counterfeit Market

One major aspect that attracts Indian online retail customer is buying products at ease & convenience at competitive prices. The India e-commerce growth story has so far been remarkable. The starts ups are being immensely funded by angel investor and venture capitalists and the valuation game is keeping them lucrative. Although the growth of many e-commerce in the past five to six years have been impressive, sale of counterfeit products and the online counterfeit market is a chronic problem for many industries, brands and also to the government. In this paper the authors have analyzed the factors that influence online counterfeit market to flourish, the present policies, framework and strategies available to deal with this threat. This paper also attempts to understand using a survey, if youngsters in the age group of 20 to 30 years, who shop online are aware of the online counterfeit market and the existing solution to deal with it. An attempt has been made to propose strategies to deal with the online counterfeit market. The authors have given a terminology for online counterfeit market and also have created a Governance Risk Compliance model at al level to deal with online counterfeiting.

Document clustering: TF-IDF approach

Recent advances in computer and technology resulted into ever increasing set of documents. The need is to classify the set of documents according to the type. Laying related documents together is expedient for decision making. Researchers who perform interdisciplinary research acquire repositories on different topics. Classifying the repositories according to the topic is a real need to analyze the research papers. Experiments are tried on different real and artificial datasets such as NEWS 20, Reuters, emails, research papers on different topics. Term Frequency-Inverse Document Frequency algorithm is used along with fuzzy K-means and hierarchical algorithm. Initially experiment is being carried out on small dataset and performed cluster analysis. The best algorithm is applied on the extended dataset. Along with different clusters of the related documents the resulted silhouette coefficient, entropy and F-measure trend are presented to show algorithm behavior for each data set.

Multi-Step Iterative Algorithm for Feature Selection on Dynamic Documents

The authors propose clustering based multistep iterative algorithm. The important step is where terms are grouped by synonyms. It takes advantage of semantic relativity measure between the terms. Term frequency is computed of the group of synonyms by considering the relativity measure of the terms appearing in the document from the parent term in the group. This increases the importance of terms which though individually appear less frequently but together show their strong presence. The authors tried experiments on different real and artificial datasets such as NEWS 20, Reuters, emails, research papers on different topics. Resulted entropy shows that their algorithm gives improved result on certain set of documents which are well-articulated, such as research papers. The results are marginal on documents where the message is emphasized by repetitions of terms specifically the documents that are rapidly generated such as emails. The authors also observed that newly arrived documents get appropriately mapped based on proximity to the semantic group.

Semantic Clustering Driven Approaches to Recommender Systems

Recommender Systems (RS) have increasingly evolved from novelties used by few E-commerce sites to an essential component of business tools handling the world of E-commerce. Recommender Systems have been widely used for product recommendations such as books and movies as well as, it is also gaining ground in service recommendations such as hotels, restaurants and travel attractions. Collaborative filtering based on reviews and ratings is usually applied that uses Clustering technique. The primary step of converting textual reviews into a Feature Matrix (FM) can be greatly refined by using semantic similarity between terms. In this paper Wordnet based Synset grouping approach is presented that not only reduces dimensions in FM but also generates Feature vectors (FV) for each cluster with significantly improved cluster quality. The paper presents a three step approach of validating the reviews, grouping of reviews and review based recommendations using Feature vector. Real datasets extracted from travel sites are used for experiments.