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Prasanna Venkatesan, V.
- Applying Services Orchestration on Loan E-Market
Abstract Views :147 |
PDF Views:3
Authors
Affiliations
1 Department of Banking Technology, School of Management, Pondicherry University, IN
1 Department of Banking Technology, School of Management, Pondicherry University, IN
Source
Software Engineering, Vol 3, No 8 (2011), Pagination: 356-360Abstract
Due to the rapid advancements in the field of Internet and web technologies a paradigm shift in the field of software architecture to design complex software systems that address the constant changing business requirements is needed. Existing models and systems have constraints and limitations in achieving this. With the help of web services and other related technologies Service-Oriented Architecture (SOA) seems to provide a viable solution to implement dynamic E-Business solution. The focus of this paper is to discuss about SOA and Services Orchestration and how it fits to implement a E-business solution to the Loan e-market using SOA’s Services Orchestration.Keywords
Integration, Portal, SOA, Services Composition, Services Orchestration.
- Building Reusable Services–A framework Based Design Methodology
Abstract Views :153 |
PDF Views:3
Authors
Affiliations
1 Department of Banking Technology, Pondicherry University, Puducherry, IN
1 Department of Banking Technology, Pondicherry University, Puducherry, IN
Source
Software Engineering, Vol 3, No 8 (2011), Pagination: 361-365Abstract
Service Oriented Architecture (SOA) is becoming an increasingly popular architectural style for many organizations due to promised agility and flexibility benefits. SOA provide more anticipates for reusable components. However the mechanism for achieving reusability of services is weakly understood at present and there is a fact that design of services for reuse is not a prime objective when implementing SOA. There are still many challenges for services to become a new paradigm to support reusability and also the quality of services. The objective of this paper is to point out the design level challenges in building reusable services and propose a Service Workshop (SERWO) Tool to overcome those challenging issues. The proposed tool is used to transform or change the existing service which does not possess reusability into reusable service. This SERWO Tool would use pattern(s) for developing reusable service. To get a better understanding of SERWO tool, we perform a case study on Flight Reservation System.Keywords
SOA, Reusable Services, Reusability Tool, Service Quality, and Service Design.- The Adaptive Framework of Internet Banking Services Based on Customer Classification
Abstract Views :179 |
PDF Views:3
Authors
Affiliations
1 Pondicherry University, Pondicherry, IN
2 Department of MBA, Banking Technology, Pondicherry University, Pondicherry, IN
1 Pondicherry University, Pondicherry, IN
2 Department of MBA, Banking Technology, Pondicherry University, Pondicherry, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 10 (2011), Pagination: 650-655Abstract
The following paper deals with the Customer Pattern of internet banking of different banks proposition of an Architecture model for the various Measures suggested for the internet banking users of the banking services provided for the customers in India, for effective utilization of the internet banking services & better capitalization of the untapped market potential of the internet banking in India. To use the services provided under Internet banking has following characteristics of Speedy Transactions, Faster processing, No need to stand in Queue, Lower transaction cost, Better utilization of resources, One click Access to Wide-Products. But due to the problem of service disruption of internet banking due to ineffectiveness of the existing system as to the service dis-contentment among the services and service monitoring of the internet banking. We propose Architecture Pattern styles and suggestive framework for effective utilization of Internet banking from the Customer Pattern.Keywords
Architecture Pattern, Customer Pattern, Internet Banking, Banking Services, Service Monitoring.- An Analysis on Qualitative Bankruptcy Models to Frame Bankruptcy Prediction Rules Using Ant Colony Algorithm
Abstract Views :209 |
PDF Views:2
Authors
Affiliations
1 Department of Banking Technology, Pondicherry University, Puducherry, IN
2 Sri Manakula Vinayakar Engineering College, Puducherry, IN
1 Department of Banking Technology, Pondicherry University, Puducherry, IN
2 Sri Manakula Vinayakar Engineering College, Puducherry, IN
Source
Automation and Autonomous Systems, Vol 3, No 10 (2011), Pagination: 492-496Abstract
Many Qualitative Bankruptcy Prediction models are available. These models use non-financial information as Qualitative factors and from which Bankruptcy is predicted. In the prior researches Genetic Algorithm was applied to generate Qualitative Bankruptcy Prediction Rules. However this Model uses only very less number of Qualitative factors and the generated rules has Repetitions and overlapping. To improve the Prediction accuracy we have proposed a model which applies more number of Qualitative factors and Prediction Rules are generated using Ant Colony Optimization Algorithm (ACO). The concept pheromone depositing and updating in Ant Colony Algorithm reduce the false negative rules in the bankruptcy prediction. The heuristic and probabilistic features of Ant Colony Algorithm increase the prediction accuracy of Bankruptcy.Keywords
Ant Colony Algorithm, Genetic Algorithm, Qualitative Bankruptcy Prediction, Repetitions and Overlapping, Qualitative Factors, Pheromone Deposit and Update, Heuristic and Probabilistic Features, Prediction Accuracy.- To Find Best Bankruptcy Model Using Genetic Algorithm
Abstract Views :186 |
PDF Views:4
Authors
Affiliations
1 Department of Banking Technology, Pondicherry University, Puducherry, IN
2 Department of Computer Science, St. Joseph's College, Cuddalore, IN
1 Department of Banking Technology, Pondicherry University, Puducherry, IN
2 Department of Computer Science, St. Joseph's College, Cuddalore, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 3, No 9 (2011), Pagination: 601-607Abstract
In the globalized stiff business environment for the survival of any organization an effective technology is required to take right decisions at right time by right people. One such a prime technology is business intelligence. Bankruptcy prediction is one of the business intelligence techniques. Among so many challenges bankruptcy is very important for a financial institution or any business. Prediction of bankruptcy is crucial for the smooth running of business. Many bankruptcy models are available. Each bankruptcy model is described by quantity equation, which is based on the non linear relationship between various financial ratios used in that model. The Genetic process is applied to find the non linear relationship between financial ratios which are having more impact on bankruptcy model. In this research three bankruptcy models Altman, Edmister and Deakin model were chosen. Genetic algorithm is applied in these three bankruptcy models to find most impacted ratios. Altman model is has more impact on its financial ratios compare to other bankruptcy models. The impacted threshold value is 98% matches with the original threshold value of Altman.Keywords
Genetic Algorithm, Bankruptcy Models, Deakin Model, Altman Model, Edmister Model, Financial Ratios, Business Intelligence.- Ensemble Model - Based Bankruptcy Prediction
Abstract Views :25 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Pondicherry University, IN
2 Department of Banking Technology, Pondicherry University, IN
1 Department of Computer Science, Pondicherry University, IN
2 Department of Banking Technology, Pondicherry University, IN
Source
ICTACT Journal on Soft Computing, Vol 14, No 1 (2023), Pagination: 3147-3153Abstract
Bankruptcy prediction is a crucial task in the determination of an organization’s economic condition, that is, whether it can meet its financial obligations or not. It is extensively researched because it includes a crucial impact on staff, customers, management, stockholders, bank disposition assessments, and profitableness. In recent years, Artificial Intelligence and Machine Learning techniques have been widely studied for bankruptcy prediction and Decision-making problems. When it comes to Machine Learning, Artificial Neural Networks perform really well and are extensively used for bankruptcy prediction since they have proven to be a good predictor in financial applications. various machine learning models are integrated into one called the ensemble technique. It lessens the bias and variance of the ml model. This improves prediction power. The proposed model operated on quantitative and qualitative datasets. This ensemble model finds key ratios and factors of Bankruptcy prediction. LR, decision tree, and Naive Bayes models were compared with the proposed model’s results. Model performance was evaluated on the validation set. Accuracy was taken as a metric for the model’s performance evaluation purpose. Logistic Regression has given 100% accuracy on the Qualitative Bankruptcy Data Set dataset, resulting in the Ensemble model also performing well.Keywords
Machine Learning, Ensemble Model, Bankruptcy Prediction, Qualitative Bankruptcy Data, Ensemble Blending.References
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