Classification of Fire and Smoke Images using Decision Tree Algorithm in Comparison with Logistic Regression to Measure Accuracy, Precision, Recall, F-score

Reddy, B. Haranadh; Karthikeyan, P.

doi:10.1109/macs56771.2022.10022449

Cited by 14 publications

(4 citation statements)

References 42 publications

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“…The "False Positive Rate (FPR)" quantifies the ratio of negative instances that are erroneously classified as positive by the model. Put simply, it measures the frequency at which the model commits errors through incorrectly predicting a positive outcome when the true label is negative [47,48].…”

Section: Experimental Analysis and Presentation Of The Resultsmentioning

confidence: 99%

Hybrid Detection Technique for IP Packet Header Modifications Associated with Store-and-Forward Operations

Munshi

2023

Applied Sciences

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The detection technique for IP packet header modifications associated with store-and-forward operation pertains to a methodology or mechanism utilized for the identification and detection of alterations made to packet headers within a network setting that utilizes a store-and-forward operation. The problem that led to employing this technique lies with the fact that previous research studies expected intrusion detection systems (IDSs) to perform everything associated with inspecting the entire network transmission session for detecting any modification. However, in the store-and-forward process, upon arrival at a network node such as a router or switch, a packet is temporarily stored prior to being transmitted to its intended destination. Throughout the duration of storage, IDS operation tasks would not be able to store that packet; however, it is possible that certain adjustments or modifications could be implemented to the packet headers that IDS does not recognize. For this reason, this current research uses a combination of a convolutional neural network and long short-term memory to predict the detection of any modifications associated with the store-and-forward process. The combination of CNN and LSTM suggests a significant improvement in the model’s performance with an increase in the number of packets within each flow: on average, 99% detection performance was achieved. This implies that when comprehending the ideal pattern, the model exhibits accurate predictions for modifications in cases where the transmission abruptly increases. This study has made a significant contribution to the identification of IP packet header modifications that are linked to the store-and-forward operation.

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Section: Experimental Analysis and Presentation Of The Resultsmentioning

confidence: 99%

Hybrid Detection Technique for IP Packet Header Modifications Associated with Store-and-Forward Operations

Munshi

2023

Applied Sciences

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“…Model Implementation: Logistic regression [14], [15] was chosen for its efficacy in binary classification tasks. The logistic function, or sigmoid function, used to estimate probabilities, is defined as [16], [17]:…”

Section: Data Analysis Methodsmentioning

confidence: 99%

Assessing the Performance of Logistic Regression in Heart Disease Detection through 5-Fold Cross-Validation

Azis

2024

ijaimi

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This study explores the effectiveness of Logistic Regression in predicting heart disease using a dataset derived from multiple international databases. Employing a 5-fold cross-validation method, the research aimed to evaluate the model's accuracy, precision, recall, and F1-score. Results indicated that Logistic Regression performs robustly, with accuracy ranging from 80% to 88.29%, and high recall rates, highlighting its potential as a valuable tool in medical diagnostics. Despite some variability in precision, which may lead to higher false positive rates, the model's high recall is crucial in clinical settings where missing a diagnosis can have dire consequences. The research confirmed the applicability of Logistic Regression to binary classification problems in healthcare, aligning with existing literature that supports its use in similar contexts. The study contributes to the field by demonstrating the model's consistency and reliability across diverse data subsets, reinforcing the potential for machine learning applications in healthcare diagnostics. Future research should focus on integrating Logistic Regression with other models to improve accuracy and testing the model on more current, varied datasets to enhance its generalizability and effectiveness in real-world settings.

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“…The F1 score calculates the harmonic mean of precision and recall, furnishing a balanced assessment of classification performance that considers both precision and recall. Accuracy determines the proportion of correctly classified samples across all classes, presenting an overall gauge of the accuracy of classification [43]. In addition, receiver operating characteristic (ROC) plots offer a comprehensive view of a classifier's performance across various levels of specificity.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal

Moshrefi,

Tawfik,

Elsayed

et al. 2024

Sensors

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Ultrasonic diagnostics is the earliest way to predict industrial faults. Usually, a contact microphone is employed for detection, but the recording will be contaminated with noise. In this paper, a dataset that contains 10 main faults of pipelines and motors is analyzed from which 30 different features in the time and frequency domains are extracted. Afterward, for dimensionality reduction, principal component analysis (PCA), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are performed. In the subsequent phase, recursive feature elimination (RFE) is employed as a strategic method to analyze and select the most relevant features for the classifiers. Next, predictive models consisting of k-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM) are employed. Then, in order to solve the classification problem, a stacking classifier based on a meta-classifier which combines multiple classification models is introduced. Furthermore, the k-fold cross-validation technique is employed to assess the effectiveness of the model in handling new data for the evaluation of experimental results in ultrasonic fault detection. With the proposed method, the accuracy is around 5% higher over five cross folds with the least amount of variation. The timing evaluation of the meta model on the 64 MHz Cortex M4 microcontroller unit (MCU) revealed an execution time of 11 ms, indicating it could be a promising solution for real-time monitoring.

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Classification of Fire and Smoke Images using Decision Tree Algorithm in Comparison with Logistic Regression to Measure Accuracy, Precision, Recall, F-score

Cited by 14 publications

References 42 publications

Hybrid Detection Technique for IP Packet Header Modifications Associated with Store-and-Forward Operations

Hybrid Detection Technique for IP Packet Header Modifications Associated with Store-and-Forward Operations

Assessing the Performance of Logistic Regression in Heart Disease Detection through 5-Fold Cross-Validation

Industrial Fault Detection Employing Meta Ensemble Model Based on Contact Sensor Ultrasonic Signal

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