Hindi Podcast Genre Prediction using Support Vector Classifier

Mahrishi, Mehul; Jain, Mudeet; Sharma, Girish

doi:10.22541/au.167569023.32793802/v1

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Through a voting mechanism, the method combines these evaluations to make a final determination, enhancing accuracy and robustness in identifying potential SQL injection threats. LR Logistic Regression (LR) [28], [29] operates by modeling the relationship between a set of input features and the probability of a binary outcome, typically classifying queries as either benign or indicative of an SQL injection attack. It jain2023heartestimates coefficients for each feature, enabling the creation of a decision boundary, and when new queries are inputted, LR calculates the probability of belonging to each class and assigns the query to the one with the higher probability, thus facilitating SQL injection attack detection.…”

Section: Methodsmentioning

confidence: 99%

AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

Thalji,

Raza,

Islam

et al. 2023

IEEE Access

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Structured Query Language (SQL) injection attacks represent a critical threat to databasedriven applications and systems, exploiting vulnerabilities in input fields to inject malicious SQL code into database queries. This unauthorized access enables attackers to manipulate, retrieve, or even delete sensitive data. The unauthorized access through SQL injection attacks underscores the critical importance of robust Artificial Intelligence (AI) based security measures to safeguard against SQL injection attacks. This study's primary objective is the automated and timely detection of SQL injection attacks through AI without human intervention. Utilizing a preprocessed database of 46,392 SQL queries, we introduce a novel optimized approach, the Autoencoder network (AE-Net), for automatic feature engineering. The proposed AE-Net extracts new high-level deep features from SQL textual data, subsequently input into machine learning models for performance evaluations. Extensive experimental evaluation reveals that the extreme gradient boosting classifier outperforms existing studies with an impressive k-fold accuracy score of 0.99 for SQL injection detection. Each applied learning approach's performance is further enhanced through hyperparameter tuning and validated via k-fold cross-validation. Additionally, statistical t-test analysis is applied to assess performance variations. Our innovative research has the potential to revolutionize the timely detection of SQL injection attacks, benefiting security specialists and organizations.

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Section: Methodsmentioning

confidence: 99%

AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

Thalji,

Raza,

Islam

et al. 2023

IEEE Access

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“…XGBoost has been used in data mining and has extensive applications for various problems [48]. Notably, XGBoost demonstrates advantageous traits such as fast computation, robustness, and accurate prediction [49], [50]. However, few studies have explored the potential of XGBoost in the treatment of chronic diseases.…”

Section: B Extreme Gradient Boosting Algorithmmentioning

confidence: 99%

Synergistic Feature Engineering and Ensemble Learning for Early Chronic Disease Prediction

Al-Jamimi

2024

IEEE Access

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Chronic diseases, a global public health challenge, necessitate the deployment of cutting-edge predictive models for early diagnosis and personalized interventions. This study presents an advanced methodology for early prediction of chronic diseases, including heart attack, diabetes, breast cancer, and kidney disease, leveraging a synergistic combination of cutting-edge techniques. Recognizing the challenge posed by extensive medical datasets with numerous features, we introduce a novel approach that begins with Feature Engineering using Recursive Feature Elimination (RFE) in conjunction with a Support Vector Machine (SVM). The presented methodology identifies and removes irrelevant features to simplify data complexity. The refined dataset is then input into the robust eXtreme Gradient Boosting (XGBoost) classifier, known for its efficiency and adeptness in predicting complex relationships within the data. The chosen ensemble learning algorithm demonstrates significant prowess in inducing intricate patterns crucial for chronic disease prediction. To enhance model performance, an essential phase of optimization is introduced through hyperparameter tuning using Bayesian optimization. This strategically navigates the hyperparameter space, maximizing the efficiency of the search process and fine-tuning the model for optimal predictive accuracy. The proposed approach showcases a substantial improvement in the early prediction of chronic diseases, demonstrating the effectiveness of the proposed approach.

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Hindi Podcast Genre Prediction using Support Vector Classifier

Cited by 2 publications

References 26 publications

AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

AE-Net: Novel Autoencoder-Based Deep Features for SQL Injection Attack Detection

Synergistic Feature Engineering and Ensemble Learning for Early Chronic Disease Prediction

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