Study of Different Data Mining Tools-An Overview

Sharma, Anubha; Patidar, Ritu; Dave, Rupali

doi:10.23956/ijarcsse/v7i6/0122

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…This versatile software accommodates both structured and unstructured data and boasts robust data visualization capabilities. Furthermore, Knime is renowned for its user-friendly nature, offering an intuitive interface for designing analytical workflows [8]. The dataset employed in this study was sourced from javapoint.com, comprising 400 instances and 3 attributes, all free from missing values.…”

Section: Methodsmentioning

confidence: 99%

Enhancing Prediction Accuracy through Normalization Techniques: Practical Insights from MLP Neural Network

2023

Proceeding Book of 2nd International Conference on Recent Academic Studies ICRAS 2023

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Data normalization plays a pivotal role in enhancing the effectiveness of machine learning models across a myriad of applications. This article delves into the influence of various data normalization techniques on the performance of a Multilayer Perceptron (MLP) classifier, a prominent architecture in Artificial Neural Networks (ANN). The study explores the effects of three distinct normalization methods: Min-Max, Z-Score, and Decimal Scaling, supported by the Knime environment. The impact of these normalization techniques on model performance is investigated using a dataset sourced from social networking sites, containing user information relevant to a new SUV model's potential purchase. The research employs a visual representation of confusion matrices and ROC Curve charts to assess the outcomes of each normalization technique. Findings reveal that without normalization, the model's performance is suboptimal, with low accuracy and high error rates. In contrast, the application of normalization techniques significantly improves model performance. Notably, Z-Score normalization emerges as the most effective, achieving an impressive 92.5% accuracy with a reduced error rate. This study underscores the critical role of data normalization in machine learning and provides valuable insights into the choice of appropriate techniques for different datasets. Such insights can lead to more robust and reliable results, ultimately enhancing the quality and reliability of machine learning applications.

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Section: Methodsmentioning

confidence: 99%

Enhancing Prediction Accuracy through Normalization Techniques: Practical Insights from MLP Neural Network

2023

Proceeding Book of 2nd International Conference on Recent Academic Studies ICRAS 2023

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“…Knime is wellequipped to handle both structured and unstructured data and offers robust data visualization capabilities. As detailed in reference [7], this software played a pivotal role in our data analysis pipeline.…”

Section: Methodsmentioning

confidence: 99%

Boosting Predictive Power: Random Forest and Gradient Boosted Trees in Ensemble Learning

2023

Proceeding Book of 2nd International Conference on Contemporary Academic Research ICCAR 2023

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Ensemble learning is a powerful concept in the realm of supervised machine learning, emphasizing the combination of multiple base learners or "inducers" to enhance predictive performance. This study explores the effectiveness of two ensemble algorithms, Random Forest and Gradient Boosted Trees, and their influence on predictive outcomes. The significance of ensemble methods lies in their ability to mitigate common challenges in machine learning. First, they address the issue of overfitting, which occurs when a model fits training data perfectly but fails on unseen data. Ensemble methods achieve this by averaging diverse hypotheses, reducing the risk of selecting an incorrect one and improving overall predictive performance. Second, ensemble methods provide computational advantages by avoiding local optima. Third, ensemble methods enhance representation by expanding the search space to find the best hypothesis. This extended representation facilitates more accurate modeling of complex relationships within the data. This study leverages two distinct datasets: one is hte Mushrooms and another from is the CO2 Emission by Vehicles dataset. The latter dataset, containing information on CO2 emissions from vehicles, is used for regression tasks, applying the same algorithms. The results of this study demonstrate outstanding performance from both Random Forest and Gradient Boosted Trees. In the classification task, both algorithms achieved perfect accuracy, while in the regression task, they showed remarkable explanatory power, with R-squared values of 1 for Random Forest algorithm and 0.995 for Gradient Boosted Trees. These findings emphasize the potential of ensemble learning in improving predictive accuracy and model performance.

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“…In this study, the researchers analyzed the various social engineering attacks that can be used to exploit vulnerabilities in Android devices, and proposed a set of countermeasures to prevent such attacks. there is another study entitled "A Study on Remote Administration Tools and Their Impact on Android Devices" conducted by Ravikumar, N., & Gokulnath, C [18]. In this study, the researchers analyzed the impact of remote administration tools on Android devices, and identified various security risks associated with these tools.…”

Section: Issn 2085-4552mentioning

confidence: 99%

“…AhMyth is a popular Android hacking tool and relatively easy to use on Kali Linux. Here are the steps to run AhMyth on Kali Linux [18]:…”

Section: A Operating System Installation and Remote Administration To...mentioning

confidence: 99%

Methods of Stealing Personal Data on Android using a Remote Administration Tool with Social Engineering Techniques

Satrio Hadikusuma,

Lukas,

Rizaludin

2023

Ultimatics

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IT security is a significant concern of the internet because almost all communication occurs today. The purpose of testing personal data theft with the social engineering method is to ensure that the system and network on the user's Android have security holes to be hacked if the user is not aware of social engineering that allows data theft through the remote administration tool (RAT) which is accidentally downloaded on the Android User. Installing a RAT by applying social engineering is the possible and proper way to steal Android user privacy data. This study outlines some basic concepts of data theft, from recent call data and personal data to controlling Android users' cameras and microphones remotely.

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Study of Different Data Mining Tools-An Overview

Cited by 4 publications

References 2 publications

Enhancing Prediction Accuracy through Normalization Techniques: Practical Insights from MLP Neural Network

Enhancing Prediction Accuracy through Normalization Techniques: Practical Insights from MLP Neural Network

Boosting Predictive Power: Random Forest and Gradient Boosted Trees in Ensemble Learning

Methods of Stealing Personal Data on Android using a Remote Administration Tool with Social Engineering Techniques

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