Detection of Source Code in Internet Texts Using Automatically Generated Machine Learning Models

Badurowicz, Marcin

doi:10.35784/acs-2022-7

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…In his paper [45] Badurowicz in 2022 has proven that extracting data using web scraping methods and then building machine learning models based on them can be a very effective approach. They built a program to analyze the content of a software forum based on automatic machine learning models and achieved a 95% success rate in detecting untagged source code.…”

Section: Data Obtaining Processmentioning

confidence: 99%

Threats and Crisis Events Detection Using Machine Learning Techniques With Social Media Data

ORGANIŚCIAK,

KURAŚ,

KOWAL

2023

Zeszyty Naukowe WST

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In the paper, the authors present the outcome of web scraping software allowing for the automated classification of threats and crisis events detection. In order to improve the safety and comfort of human life, an analysis was made to quickly detect threats using a modern information channel such as social media. For this purpose, social media services that are popular in the examined region were reviewed and the appropriate ones were selected using the criteria of accessibility and popularity. Approximately 300 unique posts from local groups of cities and other administrative centers were collected and analyzed. The decision of which entry was classified as a threat was defined using the ChatGPT tool and the human expert. Both variants were tested using machine learning (ML) methods. The paper tested whether the ChatGPT tool would be effective at detecting presumed events and compared this approach to the classic ML approach.

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Section: Data Obtaining Processmentioning

confidence: 99%

Threats and Crisis Events Detection Using Machine Learning Techniques With Social Media Data

ORGANIŚCIAK,

KURAŚ,

KOWAL

2023

Zeszyty Naukowe WST

View full text Add to dashboard Cite

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“…The values of weights can be changed, which allows the network to learn and adapt to the problem being solved. ANNs find application in solving problems related to data processing and analysis, prediction and classification especially when the analyzed issues involve poorly known phenomena and processes (Badurowicz, 2022;Kosicka, Krzyzak, Dorobek & Borowiec, 2022;Rogala, 2020;Szabelski, Karpiński & Machrowska, 2022).…”

Section: Machine Learningmentioning

confidence: 99%

Knee Joint Osteoarthritis Diagnosis Based on Selected Acoustic Signal Discriminants Using Machine Learning

Karpiński

2022

acs

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This paper presents the results of a preliminary study on simplified diagnosis of osteoarthritis of the knee joint based on generated vibroacoustic processes. The analysis was based on acoustic signals recorded in a group of 50 people, half of whom were healthy, and the other half - people with previously confirmed degenerative changes. Selected discriminants of the signals were determined and statistical analysis was performed to allow selection of optimal discriminants used at a later stage as input to the classifier. The best results of classification using artificial neural networks (ANN) of RBF (Radial Basis Function) and MLP (Multilevel Perceptron) types are presented. For the problem involving the classification of cases into one of two groups HC (Healthy Control) and OA (Osteoarthritis) an accuracy of 0.9 was obtained, with a sensitivity of 0.885 and a specificity of 0.917. It is shown that vibroacoustic diagnostics has great potential in the non-invasive assessment of damage to joint structures of the knee.

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Detection of Source Code in Internet Texts Using Automatically Generated Machine Learning Models

Cited by 2 publications

References 18 publications

Threats and Crisis Events Detection Using Machine Learning Techniques With Social Media Data

Threats and Crisis Events Detection Using Machine Learning Techniques With Social Media Data

Knee Joint Osteoarthritis Diagnosis Based on Selected Acoustic Signal Discriminants Using Machine Learning

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