A Survey on Controllable Abstractive Text Summarization

Karnik, Madhuri P.; Kodavade, D. V.

doi:10.1007/978-3-031-35507-3_30

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Automotive Intrusion Detection using SVM and Genetic Algorithm [16] [17] The research presents a hybrid approach combining SVM with a genetic algorithm for intrusion detection. Accuracy and performance measures include detection rate, ML-Based IDS for Automotive Networks [18][19] The study presents a ML-IDS using SVM. Accuracy and performance are evaluated using detection accuracy, false positive rate, and computational overhead Efficient Detection of Cyber Attacks in Connected Vehicles using Support Vector Machine Ensemble [24][25] The research focuses on efficient detection of cyber-attacks using SVM ensembles.…”

Section: Table 1 Summary Of Related Workmentioning

confidence: 99%

The performance of Logistic Regression, Decision Tree, KNN, Naive Bayes and SVM for identifying Automotive Cybersecurity Attack and Prevention: An Experimental Study

Sonali Kadam

2024

jes

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The automotive industry has witnessed a significant increase in cyber threats as vehicles become more connected and reliant on software-driven systems. To safeguard against these threats, effective cybersecurity measures must be implemented. This paper explores the use of Support Vector Machine (SVM) algorithms as a means of bolstering automotive cybersecurity attack prevention. SVM algorithms have demonstrated remarkable capabilities in various domains due to their ability to handle complex and high-dimensional data. By leveraging SVM algorithms, this research aims to enhance the detection and prevention of cybersecurity attacks targeting automotive systems. The proposed approach involves training SVM models using labeled datasets that include both normal and anomalous driving scenarios. By analyzing various features and patterns extracted from the data, the SVM models can learn to differentiate between normal and malicious behaviour. These models can then be used in real-time to identify and mitigate potential cyber threats. The results of this study is compared with other machine learning algorithm like Logistic Regression, Naive Bayes, KNN, Decision Tree, highlight the effectiveness of SVM algorithms in detecting and preventing automotive cybersecurity attacks. The models exhibit high accuracy rates in distinguishing between normal and anomalous behavior, providing a robust defense mechanism against potential threats. In conclusion, this research emphasizes the significance of leveraging SVM learning algorithms for automotive cybersecurity attack prevention. By harnessing the power of machine learning, automotive systems can be fortified against cyber threats, ensuring the safety and integrity of vehicles and their passengers.

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Section: Table 1 Summary Of Related Workmentioning

confidence: 99%

The performance of Logistic Regression, Decision Tree, KNN, Naive Bayes and SVM for identifying Automotive Cybersecurity Attack and Prevention: An Experimental Study

Sonali Kadam

2024

jes

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Analyzing the Performance of Naive Bayes, Logistic Regression, SVM and Random Forest for Identifying Hate speech from Twitter Social Media

Wankhede,

S.,

Manikjade

et al. 2023

2023 7th International Conference on Computing, Communication, Control and Automation (ICCUBEA)

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Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model

Wankhede,

Shelke,

Shrivastava

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Brain tumors have become a major global health concern, characterized by the abnormal growth of brain cells that can negatively affect surrounding tissues. These cells can either be malignant (cancerous) or benign (non-cancerous), with their impact varying based on their location, size and type. OBJECTIVE: Early detection and classification of brain tumors are challenging due to their complex and variable structural makeup. Accurate early diagnosis is crucial to minimize mortality rates. METHOD: To address this challenge, researchers proposed an optimized model based on Convolutional Neural Networks (CNNs) with transfer learning, utilizing architectures like Inception-V3, AlexNet, VGG16, and VGG19. This study evaluates the performance of these adjusted CNN models for brain tumor identification and classification using MRI data. The TCGA-LGG and The TCIA, two well-known open-source datasets, were employed to assess the model's performance. The optimized CNN architecture leveraged pre-trained weights from large image datasets through transfer learning. RESULTS: The refined ResNet50-152 model demonstrated impressive performance metrics: for the non-tumor class, it achieved a precision of 0.98, recall of 0.95, F1 score of 0.93, and accuracy of 0.94; for the tumor class, it achieved a precision of 0.87, recall of 0.92, F1 score of 0.88, and accuracy of 0.96. CONCLUSION: These results indicate that the refined CNN model significantly improves accuracy in classifying brain tumors from MRI scans, showcasing its potential for enhancing early diagnosis and treatment planning.

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A Survey on Controllable Abstractive Text Summarization

Cited by 3 publications

References 27 publications

The performance of Logistic Regression, Decision Tree, KNN, Naive Bayes and SVM for identifying Automotive Cybersecurity Attack and Prevention: An Experimental Study

The performance of Logistic Regression, Decision Tree, KNN, Naive Bayes and SVM for identifying Automotive Cybersecurity Attack and Prevention: An Experimental Study

Analyzing the Performance of Naive Bayes, Logistic Regression, SVM and Random Forest for Identifying Hate speech from Twitter Social Media

Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model

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