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

Fayaz, Sheikh Amir; Zaman, Majid; Butt, Muheet Ahmed

doi:10.19101/ijatee.2021.874586

Cited by 16 publications

(10 citation statements)

References 16 publications

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“…Algorithm 2: Snapshot of M5 model tree approach with prune & Error function Fayaz et al [14] provided a stepwise mathematical implementation of the logistic model tree (LMT), and the data for this study was acquired from the Indian Metrological Department (IMD) in Pune from 2012 to 2017, and it contains roughly 5580 records. Five factors make up the data, including humidity and temperature as independent variables and rainfall as the objective variable.…”

Section: Literature Reviewmentioning

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

Self Cite

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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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Section: Literature Reviewmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other Elements: The research identified a number of other factors like learning environment, family issues and instabilities, parental habits and peer relationships that were good predictors of student performance. These included a student's success in online coursework and the ratio of credits attempted to credits finished [30][31][32][33].…”

Section: Student Performance: Analysis Of the Futurementioning

confidence: 99%

Big Data in Academia: A Proposed Framework for Improving Students Performance

Banday¹,

Zaman²,

Quadri³

et al. 2022

RIA

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The way people learn has radically changed as a result of information technology. As an informal method of learning, fragmented learning has become a popular way to learn new technology and expertise. Academic organizations generate a large amount of heterogeneous data, and academic leaders want to make the most of it by analyzing the large amount of data in order to make better decisions. The volume isn't the only issue; the organization's data structure (structured, semi structured, and unstructured) adds to the complexity of academic work and decision-making on a daily basis. As big data has become more prevalent in educational settings, new data-driven techniques to enhance informed decision-making and efforts to improve educational efficacy have emerged. Traditional data sources and approaches were previously too expensive to obtain with digital traces of student behaviour, which offer more scalable and finer-grained comprehension and support of learning processes. This study provides a fragmented learning solution for students in a data environment that can suggest subjects to them based on their geographical location, gender, and district of residence, among other factors. This suggested framework is expected to play a key role in directing the development of a society that values lifelong learning.

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“…One common method for addressing data heterogeneity problems is to transform and combine them into a single data source [7][8][9][10]. Lexical, syntactic, and geometrical inconsistencies that arise during data integration may cause some information to be lost when all of the data sources are integrated into one source, increasing the storage capacity requirement to that of a data warehouse.…”

Section: Review Of Literaturementioning

confidence: 99%

A Scalable Framework to Analyze Data from Heterogeneous Sources at Different Levels of Granularity

Hasan¹,

Rizvi²,

Zaman³

et al. 2022

IDA

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There is an enormous amount of data present in many different formats, including databases (MsSql, MySQL, etc.), data repositories (.txt, html, pdf, etc.), and MongoDB (NoSQL, etc.). The processing, storing, and management of the data are complicated by the varied locations in which the data is stored. If combined, this data from several sites can yield a lot of important information. Since many researchers have suggested different methods to extract, examine, and integrate the data. To manage heterogeneous data, researchers propose data warehouse and big data as solutions. However, when it comes to handling a variety of data, each of these methods have limitations. It is necessary to comprehend and use this information, as well as to evaluate the massive quantities that are increasing day by day. We propose a solution that facilitates data extraction from a variety of sources. It involves two steps: first, it extracts the pertinent data, and second, then to identify the machine learning algorithm to analyze the data. This paper proposes a system for retrieving data from many sources, such as databases, data sources, and NoSQL. Later, the framework was put to the test on a variety of datasets to extract and integrate data from diverse sources, and it was found that the integrated dataset performed better than the individual datasets in terms of accuracy, management, storage, and other factors. Thus, our prototype scales and functions effectively as the number of heterogeneous data sources increases.

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

Cited by 16 publications

References 16 publications

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

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

Big Data in Academia: A Proposed Framework for Improving Students Performance

A Scalable Framework to Analyze Data from Heterogeneous Sources at Different Levels of Granularity

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