Artificial Neural Networks Based Decision Support System for the Detection of Diabetic Retinopathy

Şentürk, Zehra Karapınar

doi:10.16984/saufenbilder.630482

Cited by 9 publications

(1 citation statement)

References 19 publications

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“…It allows us to see how well the algorithm can predict the correct class. The rows of the matrix represent the predicted class and the columns represent the true class [43]. For a binary classification problem where the classes are "positive" and "negative", the general structure of the complexity matrix looks like Figure 2.…”

Section: Model Performance Measurementmentioning

confidence: 99%

Machine Learning Based Classification for Spam Detection

Keskin,

Sevli

2024

Sakarya University Journal of Science

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Electronic Electronic messages, i.e. e-mails, are a communication tool frequently used by individuals or organizations. While e-mail is extremely practical to use, it is necessary to consider its vulnerabilities. Spam e-mails are unsolicited messages created to promote a product or service, often sent frequently. It is very important to classify incoming e-mails in order to protect against malware that can be transmitted via e-mail and to reduce possible unwanted consequences. Spam email classification is the process of identifying and distinguishing spam emails from legitimate emails. This classification can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the user's inbox. In this study, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms are used to classify spam emails and the results are compared. Algorithms with different approaches were used to determine the best solution for the problem. 5558 spam and non-spam e-mails were analyzed and the performance of the algorithms was reported in terms of accuracy, precision, sensitivity and F1-Score metrics. The most successful result was obtained with the RF algorithm with an accuracy of 98.83%. In this study, high success was achieved by classifying spam emails with machine learning algorithms. In addition, it has been proved by experimental studies that better results are obtained than similar studies in the literature.

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Section: Model Performance Measurementmentioning

confidence: 99%