Academic Enhancement System using EDM Approach

Toppireddy, Hitesh Kumar Reddy; Saini, Bhavna; Hada, Priyank Singh

doi:10.1109/icaccp.2019.8882954

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…[1], [2], [23], [32], [9], [19], [28], [17], [26], [23], [5], [33], [38 ], [11], [4], [20], [3], [25], [3 1], [32], [15], [34], [7], [37], [18], [31] M02 CRISP-DM [14], [6], [16] M03 SEMMA…”

Section: Code Methodology Articlementioning

confidence: 99%

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Predicting Academic Performance through Data Mining: A Systematic Literature

Daza¹,

Guerra²,

Cervera³

et al. 2022

TEM Journal

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The main objective of this work is to make a systematic review of the literature on the prediction of the academic performance of university students by applying data mining techniques. For this purpose, an exhaustive search was carried out and after the analysis of the documentation collected, aspects such as: methodology, attributes, selection algorithms, techniques, tools, and metrics were considered, which served as the basis for the elaboration of this document. The results of the study showed that the most used methodology is KDD(database knowledge extraction), the most important attribute to achieve prediction is CGPA(academic performance), the most commonly used variable selection algorithm is InfoGain-AttributeEval, among the most efficient techniques are Naïve Bayes, Neural Networks (MLP) and Decision Tree (J48), the most used tools for the development of the models is the Weka software and finally the metrics necessary to determine the effectiveness of the model were Precision and Recall.

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Section: Code Methodology Articlementioning

confidence: 99%

“…Tool Article H01 Weka [2], [18], [21], [19], [28], [26], [12] , [23], [5], [34], [1], [11], [4], [14], [16], [6], [10], [15], [25], [32], [36], [39] H02 Rapid Miner [9], [35], [23], [1], [31] H03 R Programming [37], [29] H04 Python [33], [11], [29], [24] H05 SQL Server 2008 Data Mining [38]…”

Section: Codementioning

confidence: 99%

Predicting Academic Performance through Data Mining: A Systematic Literature

Daza¹,

Guerra²,

Cervera³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

show abstract

“…Table 3 shows that apply this filter on the dataset leads to 2983 improve the performance of the classifiers where this filter gives them the power to recognize the classes efficiently by generating instances for minority classes that make the classes almost balanced. The Decision tree, Random tree [43,44], and PART classifiers are results of more accuracy by using this filter, where, increasing the numbers of instances give the classifies wide area to calculating the gain for building the tree. On the other side, this filter adversely affects the performance of a system that used Naïve Bayes and Bayes net classifiers because these classifiers treat the data independently.…”

Section: Resample Filtermentioning

confidence: 99%

Human gait recognition using preprocessing and classification techniques

Jameel¹,

Qadir²,

Ahmed³

2020

IJECE

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Biometric recognition systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problems of obtaining images indeed not appearing the resolution or the object completely. In this work, the object movement reliance was considered to distinguish the human through his/her gait. Some losing features probably weaken the system’s capability in recognizing the people, hence, we propose using all data recorded by the Kinect sensor with no employing the feature extraction methods based on the literature. In these studies, coordinates of 20 points are recorded for each person in various genders and ages, walking with various directions and speeds, creating 8404 constraints. Moreover, pre-processing methods are utilized to measure its influences on the system efficiency through testing on six types of classifiers. Within the proposed approach, a noteworthy recognition rate was obtained reaching 91% without examining the descriptors.

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“…These algorithms play a vital role in understanding the strategies that need to be implemented during the change of a speci c change in a political event [7][8][9]. In this context, internet-oriented news resources such as new channel archives and online newspapers that have dramatically grown in converges, volume and number possesses potential and authentic data [11][12]. This signi cant and authentic data provides indispensable information in order to actually visualize the change in the political events happening over a speci c geographical area and time period [13][14].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Analysis of Archived Web News for Precise Political Event Detection and Impact Analysis in India's Southern States

Muthamizharasan¹,

Ponnusamy²

2022

Preprint

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The national political system has the directly impacts the economy. Every political party has their own economic policies. This has the major impact on the economic stability of the nation. Sometimes, few political parties rule either the state or national Governments. Their policies directly reflect in the state or national level financial systems. In general political parties, pay more attention to the state in which they control or aspire to establish their party's rule. Political discourse has an unspoken impact on the state's financial stability. Data from unstructured political events (1200 records) produced for accurate political event prediction; data was pulled from the archives of web news accumulated over the last 5 years to better understand the impact of such impact discovered in this article. This recommended work includes the advantages of Spatiotemporal Analysis of Web News Archives for forecasting network features modeled through political events and converting them into possible information based on natural language processing qualities. The suggested approach constructs a spatial analysis based on hotspots for estimating the sequence of political events in India. Lastly, the resulting data is nurtured into several classifiers for a more reliable prediction and identification procedure. This proposed technique reached a maximum frequency of 78.21 percent and 93.58 percent, respectively, when compared to the Random forest method and the K-neighbor neighbour (KNN) algorithms.

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Academic Enhancement System using EDM Approach

Cited by 3 publications

References 5 publications

Predicting Academic Performance through Data Mining: A Systematic Literature

Predicting Academic Performance through Data Mining: A Systematic Literature

Human gait recognition using preprocessing and classification techniques

Spatio-Temporal Analysis of Archived Web News for Precise Political Event Detection and Impact Analysis in India's Southern States

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