Performance Analysis and Different Subject Combinations: An Empirical and Analytical Discourse of Educational Data Mining

Ashraf, Mudasir; Zaman, Majid; Ahmed, Muheet

doi:10.1109/confluence.2018.8442633

Cited by 18 publications

(6 citation statements)

References 8 publications

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“…Below table (Table 1), shows the ratio of the activation functions with different bird generations. In the classical Flappy Bird game, we have seen that there is no particular end to the game, even anyone may get old playing that [17][18][19] Now, we can see the proof of this being a reinforcement learning method, the results get better with each new generation, but it is crucial to note that the Bird Y:2 has the most challenging environment, which show the gameplay remains balanced at the start and end, with a flux in-the middle, the part where most of the players get eliminated. The flux, too, in pole heights, which creates a very short space for the bird to fly, evident from the last column of the Table 1 above [20].…”

Section: Figure 3 Flappy Bird Representationmentioning

confidence: 99%

Analogical Study of Activation Concept in Neural Networks with Neat- Python Module

Kaul,

Zaman

et al. 2023

RIA

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Determining real-time machine simulation and functionalities of complex AI Engines is difficult to comprehend and is rarely discussed. We present a technique to analyze the workflow of one such engine, the NEAT engine, one of the fundamental and robust training engines in the current machine learning scenario. Computer Vision also presents a great approach towards working in real-time speedy functioning virtual simulators and visual platforms, whereas NEAT is not specialized in the same, but can perform to the best of the extent in the present day Machine Learning aspect. Technologies like Python, PyGame, and CsV were used to facilitate the research. So far, we have tested both the frameworks on real time cases, and it is safe to say that the NEAT module has presented an accurate trajectory, besides greater time complexity. Thus, we not only evaluate the accuracy but the other key factors as well. This study demonstrates that NEAT has the ability to address other difficult issues in the future and can produce excellent outcomes with a relatively small population. Robotics, artificial intelligence for video games, natural language processing, and healthcare are some of the potential future applications for NEAT.

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Section: Figure 3 Flappy Bird Representationmentioning

confidence: 99%

Analogical Study of Activation Concept in Neural Networks with Neat- Python Module

Kaul,

Zaman

et al. 2023

RIA

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“…The experiment was conducted on a 70-30 ratio, with 70% serving as the training set and 30% serving as the testing set. The dataset contains four independent continuous parameters: minimum and maximum temperatures, humidity at two different intervals, and a continuous dependent variable, rainfall [24,25].…”

Section: Experimental Evaluationmentioning

confidence: 99%

How M5 Model Trees (M5-MT) on Continuous Data Are Used in Rainfall Prediction: An Experimental Evaluation

Fayaz¹,

Zaman²,

Kaul³

et al. 2022

RIA

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When using machine learning to predict a class with a continuous numeric value, there are several issues. Only a few machine-learning approaches are capable of doing so, but it remains one of the most difficult jobs to do. In this paper, we show how to use the M5 Model Tree, an approach that can handle continuous numeric data. This method is a stepwise procedure that employs linear functions at the leaf nodes of any created decision tree inducer (such as CART). These M5 model trees provide basic practical formulas such as standard deviation (SD), standard deviation reduction (SDR), cost-complexity pruning (CCP), and so on, which may be simply applied to different benchmark data by another user. This study examines the M5 Model Tree algorithm's capabilities for analysing rainfall data in the Kashmir portion of India's Union Territory of Jammu & Kashmir. One of the best suited models was the M5 model tree, which was built using (70–30) percent training and test ratios, respectively, and predicted an RMSE of 2.593, an MAE of 1.68, and a correlation coefficient (R2) of 0.478. Furthermore, M5 model trees produce models with a minimal number of trails, requiring less computing effort and making them more practical to use.

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“…Since, a lot of data is present in every field like academic data [29][30][31][32][33], agricultural data [34], weather data [35][36][37][38], cloud data [39] and other type of data [40][41][42][43][44][45][46][47] etc. Here, in this study the dataset used in this operation was collected from 2012-2017 of Kashmir province.…”

Section: 21the Modelmentioning

confidence: 99%

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

Fayaz¹,

Zaman²,

Butt³

2021

IJATEE

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Weather forecasting is considered as one of the approaches which are used to check the state of the atmosphere in the future at a specified location. A type of weather forecasting called as rainfall prediction has the highest influence in the farming & agricultural sector and other various sectors like natural disaster management etc. Accurate and timely rainfall prediction is one of the important factors in today's environment. Since the rainfall parameters keep on changing around the world in different places have different factors which are responsible for the rainfall. These parameters include the wind, vapour pressure, humidity at different intervals, temperature, season, evaporation etc. and are considered as the important meteorological parameters for predicting the rainfall in future time [1].*Author for correspondence Decision tree algorithms have been a choice of tool in the machine learning algorithm over the past decade. It was followed up by its modified version, including the more successful one i.e., random forest. However, there has been more precision and accuracy in modified versions of the various decision tree Re search Article

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Performance Analysis and Different Subject Combinations: An Empirical and Analytical Discourse of Educational Data Mining

Cited by 18 publications

References 8 publications

Analogical Study of Activation Concept in Neural Networks with Neat- Python Module

Analogical Study of Activation Concept in Neural Networks with Neat- Python Module

How M5 Model Trees (M5-MT) on Continuous Data Are Used in Rainfall Prediction: An Experimental Evaluation

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

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