Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text Mai A. Shaaban, Yasser F. Hassan, Shawkat K. Guirguis
Type Journal article Journal Complex & Intelligent Systems (JCR impact factor: 6.700 Q1) Publication date 2022-04-26 DOI 10.1007/s40747-022-00741-6
Abstract The increase in people’s use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people’s fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%. Keywords: Ensemble methods · Deep learning · Machine learning · Spam classification · Text messages
OptBA: Optimizing Hyperparameters with the Bees Algorithm for Improved Medical Text Classification Mai A. Shaaban, Mariam Kashkash, Maryam Alghfeli, Adham Ibrahim
Type Preprint Journal arXiv preprint Publication year 2023 DOI 10.48550/arXiv.2303.08021
Abstract One of the challenges that artificial intelligence engineers face, specifically in the field of deep learning is obtaining the optimal model hyperparameters. The search for optimal hyperparameters usually hinders the progress of solutions to real-world problems such as healthcare. To overcome this hurdle, the proposed work introduces a novel mechanism called ``OptBA" to automatically fine-tune the hyperparameters of deep learning models by leveraging the Bees Algorithm, which is a recent promising swarm intelligence algorithm. In this paper, the optimization problem of OptBA is to maximize the accuracy in classifying ailments using medical text, where initial hyperparameters are iteratively adjusted by specific criteria. Experimental results demonstrate a noteworthy enhancement in accuracy with approximately 1.4%. This outcome highlights the effectiveness of the proposed mechanism in addressing the critical issue of hyperparameter optimization and its potential impact on advancing solutions for healthcare and other societal challenges.
PECon: Contrastive Pretraining to Enhance Feature Alignment Between CT and EHR Data for Improved Pulmonary Embolism Diagnosis Santosh Sanjeev, Salwa K. Al Khatib, Mai A. Shaaban, Ibrahim Almakky, Vijay Ram Papineni, Mohammad Yaqub
Type Conference paper Journal Machine Learning in Medical Imaging Publication year 2023 DOI https://doi.org/10.1007/978-3-031-45673-2_43
Abstract One of the challenges that artificial intelligence engineers face, specifically in the field of deep learning is obtaining the optimal model hyperparameters. The search for optimal hyperparameters usually hinders the progress of solutions to real-world problems such as healthcare. To overcome this hurdle, the proposed work introduces a novel mechanism called ``OptBA" to automatically fine-tune the hyperparameters of deep learning models by leveraging the Bees Algorithm, which is a recent promising swarm intelligence algorithm. In this paper, the optimization problem of OptBA is to maximize the accuracy in classifying ailments using medical text, where initial hyperparameters are iteratively adjusted by specific criteria. Experimental results demonstrate a noteworthy enhancement in accuracy with approximately 1.4%. This outcome highlights the effectiveness of the proposed mechanism in addressing the critical issue of hyperparameter optimization and its potential impact on advancing solutions for healthcare and other societal challenges.