Characterization and Stability of specific IgE to White Egg’s, Gliadin’s and Peanut’s Proteins among Children

The aim of the present study was to characterize allergen-specific immunoglobulin E (IgE) among children in Fez region. Eighty one children were recruited from the Hospital University Center of Fez. All of them had completed a questionnaire before taking sera. The sera were used to measure total IgE and specific data to proteins of egg's white (EWP), peanuts (PP) and gliadins (G). In last part, we studied the reactivity of specific IgE to native and to heat- and acid-treated allergens. Allergen-specific IgE measurement indicated more positive values for gliadins (46.9% up to 2IU/ml) than egg white's (29.6%) and peanut's proteins (22.2%). According to predictive values published by Sampson (2001), 14.3% of children are sensitive to egg white's proteins, 4.1% to gliadins and 2.7% to peanut's proteins. The allergenic potential of EWP and gliadins among children were partially diminished by heat and acid treatment. Allergen-specific IgE measurement indicates that children from Fez region are more sensitive to EWP than peanut's proteins and gliadins. Treatments of these food proteins indicated that recognition by children IgE can be reduced by thermal or acid treatment of these allergens

Fig 10 -

a. The Impact of the selected features by Shap Value on different categories of attacks and normal behavior for NSL-KDD. b. The Impact of the selected features by Shap Value on different categories of attacks and normal behavior for UNSW-NB15.</p

Fig 8 -

a. Significance of features within the NSL KDD dataset using SHAP. b. Significance of features within the UNSW15dataset using SHAP.</p

Overview of various well-established models in intrusion detection systems (IDS).

Overview of various well-established models in intrusion detection systems (IDS).</p

A framework for learning paragraph vectors [35].

The escalating prevalence of cybersecurity risks calls for a focused strategy in order to attain efficient resolutions. This study introduces a detection model that employs a tailored methodology integrating feature selection using SHAP values, a shallow learning algorithm called PV-DM, and machine learning classifiers like XGBOOST. The efficacy of our suggested methodology is highlighted by employing the NSL-KDD and UNSW-NB15 datasets. Our approach in the NSL-KDD dataset exhibits exceptional performance, with an accuracy of 98.92%, precision of 98.92%, recall of 95.44%, and an F1-score of 96.77%. Notably, this performance is achieved by utilizing only four characteristics, indicating the efficiency of our approach. The proposed methodology achieves an accuracy of 82.86%, precision of 84.07%, recall of 77.70%, and an F1-score of 80.20% in the UNSW-NB15 dataset, using only six features. Our research findings provide substantial evidence of the enhanced performance of the proposed model compared to a traditional deep-learning model across all performance metrics.</div

Table of parameters of the PVDM.

The escalating prevalence of cybersecurity risks calls for a focused strategy in order to attain efficient resolutions. This study introduces a detection model that employs a tailored methodology integrating feature selection using SHAP values, a shallow learning algorithm called PV-DM, and machine learning classifiers like XGBOOST. The efficacy of our suggested methodology is highlighted by employing the NSL-KDD and UNSW-NB15 datasets. Our approach in the NSL-KDD dataset exhibits exceptional performance, with an accuracy of 98.92%, precision of 98.92%, recall of 95.44%, and an F1-score of 96.77%. Notably, this performance is achieved by utilizing only four characteristics, indicating the efficiency of our approach. The proposed methodology achieves an accuracy of 82.86%, precision of 84.07%, recall of 77.70%, and an F1-score of 80.20% in the UNSW-NB15 dataset, using only six features. Our research findings provide substantial evidence of the enhanced performance of the proposed model compared to a traditional deep-learning model across all performance metrics.</div

Classification performance of our method with 6 and 4 attributes on NSL-KDD.

Classification performance of our method with 6 and 4 attributes on NSL-KDD.</p

Performances of classification on NSL-KDD sets.

The escalating prevalence of cybersecurity risks calls for a focused strategy in order to attain efficient resolutions. This study introduces a detection model that employs a tailored methodology integrating feature selection using SHAP values, a shallow learning algorithm called PV-DM, and machine learning classifiers like XGBOOST. The efficacy of our suggested methodology is highlighted by employing the NSL-KDD and UNSW-NB15 datasets. Our approach in the NSL-KDD dataset exhibits exceptional performance, with an accuracy of 98.92%, precision of 98.92%, recall of 95.44%, and an F1-score of 96.77%. Notably, this performance is achieved by utilizing only four characteristics, indicating the efficiency of our approach. The proposed methodology achieves an accuracy of 82.86%, precision of 84.07%, recall of 77.70%, and an F1-score of 80.20% in the UNSW-NB15 dataset, using only six features. Our research findings provide substantial evidence of the enhanced performance of the proposed model compared to a traditional deep-learning model across all performance metrics.</div

Performances of classification on UNSW15 sets.

The escalating prevalence of cybersecurity risks calls for a focused strategy in order to attain efficient resolutions. This study introduces a detection model that employs a tailored methodology integrating feature selection using SHAP values, a shallow learning algorithm called PV-DM, and machine learning classifiers like XGBOOST. The efficacy of our suggested methodology is highlighted by employing the NSL-KDD and UNSW-NB15 datasets. Our approach in the NSL-KDD dataset exhibits exceptional performance, with an accuracy of 98.92%, precision of 98.92%, recall of 95.44%, and an F1-score of 96.77%. Notably, this performance is achieved by utilizing only four characteristics, indicating the efficiency of our approach. The proposed methodology achieves an accuracy of 82.86%, precision of 84.07%, recall of 77.70%, and an F1-score of 80.20% in the UNSW-NB15 dataset, using only six features. Our research findings provide substantial evidence of the enhanced performance of the proposed model compared to a traditional deep-learning model across all performance metrics.</div