An efficient artificial intelligence approach for early detection of cross-site scripting attacks

Decision Analytics Journal, Год журнала: 2024, Номер 11, С. 100466 - 100466

Опубликована: Апрель 21, 2024

Cross-Site Scripting (XSS) attacks continue to pose a significant threat web applications, compromising the security and integrity of user data. XSS is application vulnerability where malicious scripts are injected into websites, allowing attackers execute arbitrary code in victim's browser. The consequences can be severe, ranging from financial losses sensitive information. enable deface distribute malware, or launch phishing campaigns, trust reputation affected organizations. This study proposes an efficient artificial intelligence approach for early detection attacks, utilizing machine learning deep approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as Term Frequency-Inverse Document Frequency (TFIDF), applied compared evaluate results. We introduce novel named LSTM-TFIDF (LSTF) extraction, which combines temporal TFIDF features cross-site scripting dataset, resulting new set. Extensive research experiments demonstrate that random forest method achieved high performance 0.99, outperforming state-of-the-art approaches using proposed features. A k-fold cross-validation mechanism utilized validate methods, hyperparameter tuning further enhances attack detection. have Explainable Artificial Intelligence (XAI) understand interpretability transparency model detecting attacks. makes valuable contribution growing body knowledge on provides developers practitioners enhance applications.

Язык: Английский

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Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients

Algorithms, Год журнала: 2025, Номер 18(2), С. 94 - 94

Опубликована: Фев. 7, 2025

Remote monitoring of a patient’s vital activities has become increasingly important in dealing with various medical applications. In particular, machine learning (ML) techniques have been extensively utilized to analyze electrocardiogram (ECG) signals cardiac patients classify heart health status. This trend is largely driven by the growing interest computer-aided diagnosis based on ML algorithms. However, there inadequate investigation into impact risk factors health, which hinders ability identify heart-related issues and predict conditions patients. this context, developing GUI-based classification approach can significantly facilitate online provide real-time warnings predicting potential complications. paper, general framework structure for systems proposed modeling signs order The analyzes AI-driven interventions more accurate system. To further demonstrate validity presented approach, we employ it LabVIEW-based remote tracking system three healthcare statuses (stable, unstable non-critical, critical). developed receives information about patients’ signs, then leverages novel encoding-based algorithm pre-process, analyze, patient ANN classifier model are compared other conventional ML-based models, such as Naive Bayes, SVM, KNN accuracy evaluation. obtained outcomes efficacy approaches achieving an 98.4% 98.8% technique, respectively, whereas Bayes quadratic SVM algorithms realize 94.8% 96%, respectively. short, study aims explore how enhance diagnostic accuracy, improve monitoring, optimize treatment outcomes. Meanwhile, outperforms most existing offering high status user-friendly interactive interface. Therefore, potentially be performance

Язык: Английский

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A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

Decision Analytics Journal, Год журнала: 2023, Номер 9, С. 100352 - 100352

Опубликована: Ноя. 4, 2023

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through bites of Anopheles mosquitoes that carry infection. has significant and devastating consequences on health systems fragile countries, particularly in sub-Saharan Africa. affects red blood cells invading replicating within them, destroying releasing toxic byproducts into bloodstream. The parasite's ability stick modify surface can cause them become sticky, obstructing flow vital organs such as brain spleen. Therefore, efficient approaches for early detection malaria are critical saving patients' lives. main aim this study develop an model diagnosis. We used images based parasitized uninfected experiments. applied neural network-based Neural Search Architecture Network (NASNet) compared its performance with machine learning techniques. Moreover, we proposed novel NNR (NASNet Random forest) method feature engineering. approach first extracts spatial features from input images, then class prediction probability extracted these features. set obtained data extraction trains models. Our comprehensive experiments show support vector outperformed state-of-the-art models, achieving high-performance score 99% having inference time near 0.025 s. validated using k-fold cross-validation optimized hyperparameters tuning. research improved diagnosis assist medical specialists reducing mortality rate.

Язык: Английский

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CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans

PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e2008 - e2008

Опубликована: Май 17, 2024

Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These disrupt normal brain functions in various ways, giving rise to broad spectrum of physical, cognitive, emotional challenges. The daily increase mortality rates attributed underscores the urgency this issue. In recent years, advanced imaging techniques, particularly magnetic resonance (MRI), have emerged as indispensable tools diagnosing tumors. MRI scans provide high-resolution, non-invasive visualization structures, facilitating precise detection abnormalities such This study aims propose an neural network approach Our experiments utilized multi-class image dataset comprising 21,672 images related glioma tumors, meningioma pituitary We introduced novel network-based feature engineering approach, combining 2D convolutional (2DCNN) VGG16. resulting 2DCNN-VGG16 (CVG-Net) extracted spatial features from using 2DCNN VGG16 without human intervention. newly created hybrid set is then input into machine learning models diagnose balanced data Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research demonstrate that utilizing proposed CVG-Net, k-neighbors classifier outperformed state-of-the-art studies with k-fold accuracy performance score 0.96. also applied hyperparameter tuning enhance tumor diagnosis. has potential revolutionize early diagnosis, providing professionals cost-effective diagnostic mechanism.

Язык: Английский

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Heart Failure Detection Through SMOTE for Augmentation and Machine Learning Approach for Classification

Опубликована: Янв. 24, 2025

Язык: Английский

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IoT-based heart disease risk prediction and categorization for healthcare using an optimized progressive residual attention-based holographic convolutional neural network

Signal Image and Video Processing, Год журнала: 2025, Номер 19(3)

Опубликована: Янв. 28, 2025

Язык: Английский

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Dynamic feature selection and quantum representation for precise heart disease prediction: Quantum-HeartDiseaseNet approach

Computer Methods in Biomechanics & Biomedical Engineering, Год журнала: 2025, Номер unknown, С. 1 - 22

Опубликована: Фев. 5, 2025

Cardiovascular disease is a leading cause of mortality, necessitating early and precise prediction for improved patient outcomes. This study proposes Quantum-HeartDiseaseNet, novel heart risk framework that integrates Dynamic Opposite Pufferfish Optimization Algorithm feature selection Quantum Attention-based Bidirectional Gated Recurrent Unit (QABiGRU) accurate diagnosis. The method enhances diagnosis accuracy while reducing dimensionality, Synthetic Minority Oversampling Technique (SMOTE) addresses data imbalance. Evaluated on three datasets, the proposed model achieved 98.87% accuracy, 98.74% precision, 98.56% recall, outperforming conventional methods. Experimental results validate its effectiveness in prediction.

Язык: Английский

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Heart Disease Detection and Prognosis Using IoT-Based ECG Sensor Data with Hybrid Deep Learning Architecture and Optimal Resource Allocation

Cybernetics & Systems, Год журнала: 2025, Номер unknown, С. 1 - 51

Опубликована: Фев. 17, 2025

Heart disease remains a major global cause of mortality, underscoring the need for advancements in early detection and prognosis to enhance patient recovery. This study proposes an innovative framework integrating deep learning (DL) models optimal resource allocation strategies improve heart prognosis. The begins with rigorous preprocessing Internet Things (IoT) captured Electrocardiogram (ECG) data, employing min–max normalization, advanced median filtering techniques noise reduction baseline wander correction. Statistical features are extracted from preprocessed while such Improved Empirical Mode Decomposition (EMD), RR interval, R peak, PR interval derived ECG signals. These then augmented using technique dataset diversity model robustness. Furthermore, introduces hybrid combining Deep Residual Network (DRN) Bidirectional Gated Recurrent Unit severity classification detection, leveraging features. Optimal is facilitated by Walrus Optimization Algorithm (WaOA), optimizing ventilator, Intensive Care (ICU) bed, medical staff, medication based on predicted severity. Evaluation real-world datasets demonstrates superior diagnostic accuracy utilization efficiency, highlighting transformative potential IoT AI-driven approaches cardiovascular healthcare.

Язык: Английский

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An Explainable Analytical Approach to Heart Attack Detection Using Biomarkers and Nature-Inspired Algorithms

Опубликована: Янв. 1, 2025

Язык: Английский

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A Survey on Machine Learning Techniques for Heart Disease Prediction

SN Computer Science, Год журнала: 2025, Номер 6(4)

Опубликована: Апрель 1, 2025

Язык: Английский

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