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    Fuzzy logic based decision support system for loan risk assessment
    (IJCSt Vol. 2, ISSue 2, 2011) Sangeet Kumar; Nitin Bhatia; Namarta kapoor
    This paper is concerned with risks associated with the loan Risk analysis and related adaptation strategies have grown in importance and complexity during last years. The large amount of data and information that needs to be handled and integrated requires specific methodologies and tools. So selecting a new loan applicant is a important issue of a financial organizations or Banks. There are so many different factors which effect on the selection of applicant who apply for loan. So, it’s a problem for Banks and other private financers to choose right applicant so that minimum risk involved during loan process using fuzzy logic. We control this problem in quicker and cheapest way using knowledge and experience of experts which are involved in this process and having knowledge about this field. This paper provides risk analysis based on fuzzy inference system. And provide GUI based interface tool regarding different parameters of an applicant which effect loan risk assessment. The analysis of experimental results of different applicants checks the correctness and consistency of decision Support system for correct decision making.
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    Wireless Sensor Networks: A Profound Technology
    (IJCSt Vol. 2, ISSue 2, 2011) Namarta Kapoor; Nitin Bhatia; Sangeet Kumar; Simranjeet Kaur
    Networking plays an important role in today’s worldwide communication. Internet is reflecting the profound network technology that has changed the way how people communicate. Wireless Sensor network is one of the most interesting research areas. Wireless Sensor networks have features like low cost, flexibility, fault tolerance, high sensing fidelity, creating many new and exciting application areas for remote sensing. So, wireless sensor network has emerged as a promising tool for monitoring the physical world with wireless sensor that can sense, process and communicate. There are many issues of wireless sensor network which need to be addressed. As researchers are working in the area of wireless sensor network, more and more data is collected, the refined the models and techniques will become in the future. In this paper, we are most concerned about the study of wireless sensor networks as it is an evolving field which offers scope for research.
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    Fuzzy Cognitive Map based Approach for Software Quality Risk Analysis
    (ACM SIGSOFT Software Engineering Notes, 2011) Nitin Bhatia; Namarta Kapoor
    This paper presents a software risk prediction tool for risk analysis during the development of a software product. The term “Risk” refers to a problem that can threaten the success of the software project but has not happened yet. Risks are uncertain. The main objective of each organization is to provide very high quality software to their customers. The term “Quality” is a value to the person. But there is a long list of software risks that can have adverse impact on the quality of the software. It is necessary to address all the risks; otherwise, they may lead to undesireable results. Fuzzy Cognitive Maps (FCMs) describe different concepts with different aspects of the behaviour of complex systems. A software tool based upon FCM has been developed for assessing software risks. This paper describes the reasoning behind the focus on risk management during the software development process and its importance in delivering high quality software by assessing software risks during the development process using fuzzy cognitive maps.
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    FUZZY LOGIC BASED TOOL FOR LOAN RISK PREDICTION
    (Apeejay Journal of Computer Science Applications Vol. 2, 2014) Sangeet Kumar; Nitin Bhatia; Namarta kapoor
    Loan risk is a major decision for any financial organization like banks or for decision maker for loan applicant. The uncertain domain of risk assessment has long been in need of a reliable and consistent system to help simplify the decision making process. Fuzzy Logic provides a completely different, unorthodox way to approach a control problem. This method focuses on what the system should do rather than trying to understand how it works. One can concentrate on solving the problem rather than trying to model the system mathematically, if that is even possible. This almost invariably leads to quicker, cheaper solutions. This research work will help in analysis risk for a loan applicant according to different factors like its job, salary, loan amount and loan period etc. the decision factor helps in making decision about how to select correct loan applicant. Using fuzzy logic decision variable has been defined, new rules defined, according to different rules added and takes average decision based on questionnaires filled by different categories of applicant’s. The analysis of case studies shows the consistency and effectiveness of the approach in making correct decision.
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    Comparative Study of Forward and Backward Chaining in Artificial Intelligence
    (International Journal Of Engineering And Computer Science Volume 5 Issue 4 April 2016, Page No. 16239-16242, 2016) Namarta Kapoor; Nischay Bahl
    An artificial intelligence system is capable of elucidating and representing knowledge along with storing and manipulating data. Knowledge could be a collection of facts and principles build up by human. It is the refined form of information. Knowledge representation is to represent knowledge in a manner that facilitates the power to draw conclusions from knowledge. Knowledge representation is a good approach as conventional procedural code is not the best way to use for solving complex problems. Frames, Semantic Nets, Systems Architecture, Rules, and Ontology are its techniques to represent knowledge. Forward and backward chaining are the two main methods of reasoning used in an inference engine. It is a very common approach for “expert systems”, business and systems. This paper focus on the concept of knowledge representation in artificial intelligence and the elaborating the comparison of forward and backward chaining.
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    Comparative Study of Forward and Backward Chaining in Artificial Intelligence
    (International Journal Of Engineering And Computer Scienc Volume 5 Issue 4 April 2016, Page No. 16239-16242, 2016) Namarta Kapoor; Dr. Nischay Bahl
    An artificial intelligence system is capable of elucidating and representing knowledge along with storing and manipulating data. Knowledge could be a collection of facts and principles build up by human. It is the refined form of information. Knowledge representation is to represent knowledge in a manner that facilitates the power to draw conclusions from knowledge. Knowledge representation is a good approach as conventional procedural code is not the best way to use for solving complex problems. Frames, Semantic Nets, Systems Architecture, Rules, and Ontology are its techniques to represent knowledge. Forward and backward chaining are the two main methods of reasoning used in an inference engine. It is a very common approach for “expert systems”, business and systems. This paper focus on the concept of knowledge representation in artificial intelligence and the elaborating the comparison of forward and backward chaining.
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    Choosing the Right Approach: Comparative Study of Software Process Models
    (International Journal of Advance Research in Computer Science and Management Studies, 2016) Monika, Chopra; Namarta Kapoor
    This paper depicts the study of myriad Software Process Model. In the Software Process Model we focus on the activities related to production of the software such as design, coding, testing etc. Organisations find software development to be more convenient to work with. There are mainly four development models that deal with the area of software development, popularly known as Software Project Life Cycle (Waterfall Model, Iteration Model, V-Shaped Model, and Spiral Model). These models have their advantages and disadvantages. This paper shows number of model of software development, comparison between them on the basis of their requirements.
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    Requisition of Artificial Intelligence
    (Int. Journal of Engineering Research and Application Vol. 7, Issue 11, (Part -3) pp.69-72, 2017) Namarta Kapoor; Dr. Nischay Bahl
    Artificial Intelligence is the backbone of modern era. It is the branch of science and engineering of making intelligent machines. It is concerned with getting computers doing things intelligently like human beings. It is the concept of reading that how a human brain thinks, learn, take decisions, and act while trying to find solution of the provided situation, and then in the same way we use such results of the reading and understandings for developing intelligent and smart machines. While analyzing the power and capacity of the computer systems, a developer always wonders that is it possible for a machine to think. So, the simulation of Artificial Intelligence started with the motivation of creating intelligent machines like human beings. In the coming years, the whole world will be handled by artificial intelligence. A major seek of Artificial Intelligence is in the simulation of computer programs, algorithms and functions related with human intelligence, such as thinking, learning, reasoning and solving a particular problem. This paper discovers the requisition of artificial intelligence. The history of Artificial Intelligence had many cycles of progress and success. Further research and development programs are simultaneously carried out for betterment in technology. Recent progress in research and simulation of the artificial intelligence concepts are going hand to hand of researchers with enhancements in the abilities of real systems.
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    Expert systems in artificial intelligence
    (INTERNATIONAL JOURNAL FOR INNOVATIVE RESEARCH IN MULTIDISCIPLINARY FIELD Volume - 5, Issue - 3, Mar – 2019, 2019) Namarta Kapoor; Kanika Sharma
    Artificial Intelligence is the branch of science and engineering of making intelligent machines. Computers are doing things intelligently like human beings. It is accomplished by studying how human brain thinks, learn, decide, and act while trying to solve a particular problem, and then in the same way we use such outcomes of the study as a basis of developing intelligent machines. A developer always wonders that human being is so intelligent who has made it possible for a machine to think. So, the development of Artificial Intelligence started with the intention of creating intelligent machines like human beings. In future, even human being will be handled by artificial intelligence. Expert systems are computer programs that derive from Artificial Intelligence and a latest product of Artificial Intelligence. They started to come into existence as university research projects from 1960s to 1970s. They have now become one of the more important innovations of AI as commercial products as well as interesting research tools. This paper focus on Expert systems including its detailed knowledge as it is one of the esteemed concept of upcoming digital technology.