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| Innovation & Research Centre: |
| Objective - To facilitate in the areas of computer science and application |
| Board of Advisers : |
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Prof. Dr. P.K. Das |
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Prof. Dr. A.K. Rath |
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Prof. Dr. Dehuri |
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| Current Engagements: |
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| Name of the Faculty |
Major Areas |
| Sujata Chakravorty |
Machine Intelligence |
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Optimization Tech. |
| Nitu Dash |
Wireless sensor Network Security |
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VOIP security |
| Manmay Hota |
Network Security |
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VOIP security |
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Wireless sensor Network security |
| Dr. S.K.Sahu |
E-Governance |
| S.K.Pani |
Data ware housing |
| P.P.Ghose |
E-Commerce |
| M.R.Nayak |
Sensor Network |
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| (A) Network Security : |
| Brief description of problem: |
| The existing communication protocol leaves a lot of holes which can be exploited by which can be exploited by malicious lasers/Hackers. One of the results of exploitation in to incident of Denial of services Attack.This attack, are also incident by using more sophisticated technique to make a Distribution Denial of service.(DDOS). |
| We are trying to propose the following, ( to approach the above problem area): |
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We will try to detect the attack. |
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We will try to develop a rule-based system to generate the Alert signal /block the attack. |
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We will try to optimize the proposed model. |
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We will try try to implement the propose model in a real system. |
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| Name of the personals associated with the project: |
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Manmay Hota |
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Dr. S.K. Sahu |
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| (B) Wireless Sensor Network Security: |
| Brief description of problem Area: |
| Wireless Sensors Network (WSN) is an emerging technology and has a great potential to be employed in many real life and mission critical situations. However WSN suffers very critical security issues. |
| We have proposed the following security model: |
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We propose a cryptographic key key distribution scheme among sensors. |
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We propose a secured key storage model which is tampered proved. |
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We will implement the proposed scheme in a real life sensors network environment. |
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| Name of the person associated with the project: |
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Nitu Dash |
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Manmay Hota |
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| (C) Data Mining Approaches For Financial Time Series Prediction: |
| Forecasting stock market, currency exchange rate, bank bankruptcies, understanding and managing financial risk, trading futures, credit rating, loan management, bank customer profiling and money laundering analyses are core financial tasks for data mining. Some of these tasks such as bank customer profiling have many similarities with data mining for customer profiling in other fields. Stock market forecasting includes uncovering market trends, planning investment strategies, identifying the best time to purchase the stocks and what stocks to purchase. Financial time series prediction like bank prime loan, discount rate, federal funds, portfolios, prices of commodity markets like energy, etc. involve uncertainties in the nature of the data. So different data mining tools are proposed to predict and analyze the real financial datasets. Different optimization techniques are used to optimize the model parameters and compared. Some proposed Data Mining tools and Optimization techniques are given below. |
| Machine Intelligence: |
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Artificial Neural Network |
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Fuzzy Logic Systems |
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Support Vector Machine etc. |
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| Optimization techniques: |
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Genetic Algorithm |
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Particle Swarm Optimization |
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Ant Colony etc. |
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| Name of the person associated with the project: |
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Sujata Chakravorty |
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| (D) Implementation issues of ERP in SMEs: |
| Nowadays most of large companies have been using an ERP system, thus ERP vendors are moving their attention toward small and medium enterprises (SMEs), by offering simplified and cheaper solutions. Such an approach is mostly due to SMEs’ lack of internal competence and resources, while most of large companies have been investing time, money and human resources to modify their strategies and exploit the opportunities related to the business use of information technologies. |
| Experiences on the field show that SMEs often fail in recognizing the economic and organizational impacts related to the use of ERP systems; as a consequence, the evaluation of acquiring an ERP system, instead of choosing a system supporting specific business functions, is a strategic decision that, mostly within SMEs, should be supported by evaluation models. |
| The evaluation of ERP system adoption through the qualitative evaluation of the business complexity as well as the strategic and organizational objectives. The aim of this work is to verify the validity of the parameters such as; |
| Business complexity : |
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Company size (classified as micro, small, medium)
The market area (local, regional, national, international) |
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The membership of a group (either as the holding or as a con-trolled firm)
The presence of branch offices (where, how many) |
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The level of diversification (in terms of products, markets, technologies)
The degree of functional extension (number of activities carried out internally) |
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| Business complexity : |
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Local automation of existing procedures; |
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Internal integration of existing business processes; |
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Business process reengineering; |
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Business network redesign; |
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Redefinition of company boundaries through the creation of inter-organizational relationships; |
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| Objectives: |
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To collect data on the extent of ERP applications in the firms; |
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To gather information on the relative importance of major parameters used in the development of business; |
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To determine the degree of ERP standardization, vendor support, as well as implementation time and budget; |
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To ascertain impact of ERP on service quality at the organizational level by extending the research framework; |
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To determine the impact of ERP on HR Management, Finance Management, Inventory Control etc. in the SMEs. |
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