Decision tree approach to build a model for water quality

This paper presents a Classification data model using decision tree for the purpose of analyzing water quality data of MAA Narmada River at Harda district. The data model was implemented in WEKA software.  Classification using decision tree was applied to classify /predict the pollutant class of water. It is observed in the analysis that the Nitrogen (NH3_N ,NO3_N), pH ,Temp _C, BOD, COD, other parameter relevant to water processes play an important role to assess the quality of river water. In this experiment we have used five attribute of water quality data which can affect accuracy of water

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts

Fuzzy Association Rule Mining based Model to Predict Students’ Performance

The major intention of higher education institutions is to supply quality education to its students. One approach to get maximum level of quality in higher education system is by discovering knowledge for prediction regarding the internal assessment and end semester examination. The projected work intends to approach this objective by taking the advantage of fuzzy inference technique to classify student scores data according to the level of their performance. In this paper, student’s performance is evaluated using fuzzy association rule mining that describes Prediction of performance of the students at the end of the semester, on the basis of previous database like Attendance, Midsem Marks, Previous semester marks and Previous Academic Records were collected from the student’s previous database, to identify those students which needed individual attention to decrease fail ration and taking suitable action for the next semester examination

Self Organizing Map (SOM) based Modelling Technique for Student Academic Performance Prediction

Over the years, student academic performance mapping is considered an important issue for academic institutions and designing such system is very complicated. However, the student performances rely on various factors such as attendance, marks, family background, curriculum activities, social behavior etc. and mapping of all these attributes is very complicated. In the past, various data mining software and techniques have been proposed to classify student data set. These software�s and techniques have been failed to classify student dataset correctly. Now advances of Artificial Intelligence (AI) and data mining techniques made it possible to classify student data set and draw useful patterns efficiently. In this study, real data set of Government Girls College (GGC) vidisha of 250 students is considered. The main concern of this study is to apply SOM clustering approach to classify student dataset. Finally, experimental results demonstrated that 4 clusters have been formed based on category like very good, good, average, and poor

Certain Recurrence Relations of Two Parametric Mittag-Leffler Function and Their Application in Fractional Calculus

The purpose of this paper is to develop some new recurrence relations for the two parametric Mittag-Leffler function. Then, we consider some applications of those recurrence relations. Firstly, we express many of the two parametric Mittag-Leffler functions in terms of elementary functions by combining suitable pairings of certain specific instances of those recurrence relations. Secondly, by applying Riemann–Liouville fractional integral and differential operators to one of those recurrence relations, we establish four new relations among the Fox–Wright functions, certain particular cases of which exhibit four relations among the generalized hypergeometric functions. Finally, we raise several relevant issues for further research

Certain Recurrence Relations of Two Parametric Mittag-Leffler Function and Their Application in Fractional Calculus

The purpose of this paper is to develop some new recurrence relations for the two parametric Mittag-Leffler function. Then, we consider some applications of those recurrence relations. Firstly, we express many of the two parametric Mittag-Leffler functions in terms of elementary functions by combining suitable pairings of certain specific instances of those recurrence relations. Secondly, by applying Riemann–Liouville fractional integral and differential operators to one of those recurrence relations, we establish four new relations among the Fox–Wright functions, certain particular cases of which exhibit four relations among the generalized hypergeometric functions. Finally, we raise several relevant issues for further research