AADYA -National Journal of Management and Technology

Deepa Jose ¹

Affiliations
1 MCA Department, Y.M.T College of Management, Kharghar, IN

Abstract

Data mining and predictive analytics-collectively referred to as "big data"-are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining's outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.

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Deepa Jose ¹

Affiliations
1 YMT College of Management, IN

Abstract

Data mining and predictive analytics-collectively referred to as "big data"-are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge con-siders the extent to which data mining's outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.

Full Text

   

Deepa Jose ¹

Affiliations
1 MCA Department, Y.M.T College of Management, Kharghar, Navi Mumbai, Maharashtra, IN

Abstract

Congestion occurs in the network when arrival rate to a router is greater than its departure rate. In this paper, using fuzzy logic approach, we have proposed a modified TCP delay-based congestion avoidance mechanism which is based on traditional TCP-Africa algorithm. Here we present our fuzzy controller which is expected to act as a congestion controller in the routers. The proposed fuzzy controller, uses queue delay and link capacity as input linguistic variables. The output of fuzzy controller is the window size to which the sending window must be adjusted. Both the current standard and most of the experimental congestion control methods are fundamentally loss-based, that is they rely on packet loss to detect that the network is above full capacity.

Keywords

Fuzzy, TCP/IP, Bandwidth, Congestion, Queue Delay, Window Size.

Full Text

   

Deepa Jose ¹

Affiliations
1 MCA Department, Y.M.T College of Management, Kharghar, Navi Mumbai, Maharashtra, IN

Abstract

Universities either public or private and its colleges enroll thousands of students into various courses or programs every year. They collect information from students at the time of admissions and store the same in computers. Understanding the benefits of data is essential from business point of view. Data can be used for classifying and predicting the students behaviour, performance, dropouts as well as teachers' performance. Therefore, this paper examines the role of data mining in an education sector. In addition, lays emphasis on application of data mining that contribute to offer competitive courses and improve their business. Data mining and predictive analytics-collectively referred to as "big data"-are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining's outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.

Full Text