Evaluation of Supervised Machine Learning Classification Algorithms for Fingerprint Recognition

Rojas, Andres; Dolecek, Gordana Jovanovic

doi:10.1109/gc-eleceng52322.2021.9788164

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Measure the accuracy of the model using a confusion matrix. A confusion matrix is a tool for analyzing how a classification model identifies a different set of data [21].…”

Section: Discussionmentioning

confidence: 99%

A Comparison Support Vector Machine, Logistic Regression And Naïve Bayes For Classification Sentimen Analisys user Mobile App

Baihaqi,

Setyawan,

Manongga

et al. 2023

Int. J. Artif. Intell. Res

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Data is the most important thing, the use of data can be useful to get an evaluation from the user of a system or application that is built based on mobile. Not only, the assessment or acceptance results of mobile applications during the trial stage are considered important, assessments and comments from direct users are also important things that can be input for mobile application developers. Data mining, or known in English as data mining, is the answer to the process of retrieving data on any media. In this research, data mining is carried out on the media mobile application download service provider Google Playstore, which provides data in the form of comments and ratings. After scraping the data and obtaining the latest data parameters determined by the latest 2000 comments, the data is pre-processed by removing the emot icon character and eliminating unneeded variables so that the data obtained can be processed to the next stage, namely classification based on ratings and sentiment comments. The algorithms used or compared in this research are Support Vector machine, logistic regression and naïve bayes which are known to be reliable in data mining processing. In this research, the accuracy results are 88% for SVM, 90.5% for Logistic Regression and 91% for naïve bayes.

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“…Measure the accuracy of the model using a confusion matrix. A confusion matrix is a tool for analyzing how a classification model identifies a different set of data [21].…”

Section: Discussionmentioning

confidence: 99%

A Comparison Support Vector Machine, Logistic Regression And Naïve Bayes For Classification Sentimen Analisys user Mobile App

Baihaqi,

Setyawan,

Manongga

et al. 2023

Int. J. Artif. Intell. Res

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show abstract

“…The study analyzed numerous articles related to EEG signal processing, identified limitations, and analyzed future development trends. Besides biomedical and BdM fields, the role of signal processing in ML has been addressed in various fields such as audio analysis and recognition [14]- [20], seismic signal analysis [20]- [22], and telecommunications [23]- [30].…”

Section: Introductionmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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Evaluation of Supervised Machine Learning Classification Algorithms for Fingerprint Recognition

Cited by 2 publications

References 7 publications

A Comparison Support Vector Machine, Logistic Regression And Naïve Bayes For Classification Sentimen Analisys user Mobile App

A Comparison Support Vector Machine, Logistic Regression And Naïve Bayes For Classification Sentimen Analisys user Mobile App

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

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