WEB VULNERABILITIES DETECTION USING A HYBRID MODEL OF CNN, GRU AND ATTENTION MECHANISM by Sarbast H. Ali, Arman I. Mohammed, Sarwar MA. Mustafa, Sardar Omar Salih

“…Therefore, this paper proposes a hybrid model built on the base of Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU) and an attention mechanism to detect vulnerabilities in application code. …”
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Ulnar variance detection from radiographic images using deep learning by Sahar Nooh, Abdelrahim Koura, Mohammed Kayed

“…In this paper, a deep learning-based methodology is used to automatically detect ulnar variance from radiographic images. Advanced Convolutional Neural Networks are exploited instead of traditional manual methods. …”
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A Hybrid Approach for Color Face Recognition Based on Image Quality Using Multiple Color Spaces by Mahdi Hosseinzadeh, Mohammad Mehdi Pazouki, Önsen Toygar

“…Based on the categorized face images, three feature extraction and classification methods as Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Convolutional Neural Networks (CNN) are applied to face images using RGB, YCbCr, and HSV color spaces to extract the features and then classify the images for face recognition. …”
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GLClick: Interactive Segmentation Combining Global and Local Features by Jiaying Tang, Hongyuan Wang, Zongyuan Ding, Zihao Xin

“…Convolutional neural networks (CNNs) are the backbone of most modern interactive segmentation algorithms. …”
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Classification Performance Comparison of BERT and IndoBERT on SelfReport of COVID-19 Status on Social Media by Irwan Budiman, Mohammad Reza Faisal, Astina Faridhah, Andi Farmadi, Muhammad Itqan Mazdadi, Triando Hamonangan Saragih, Friska Abadi

“…Many deep learning-based algorithms, such as Convolutional Neural Networks (CNN) or Long Short-Term Memory (LSTM), have been used for text classification. …”
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A comparative study of machine learning algorithms for fall detection in technology-based healthcare system: Analyzing SVM, KNN, decision tree, random forest, LSTM, and CNN by Afuan Lasmedi, Isnanto R. Rizal

“…This study aims to compare the performance of six classification algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) in detecting fall incidents using wearable sensor data such as accelerometers and gyroscopes. …”
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Probabilistic Prediction of Dst Storms One‐Day‐Ahead Using Full‐Disk SoHO Images by A. Hu, C. Shneider, A. Tiwari, E. Camporeale

“…The model is developed using an ensemble of Convolutional Neural Networks that are trained using Solar and Heliospheric Observatory (SoHO) images (Michelson Doppler Imager, Extreme ultraviolet Imaging Telescope, and Large Angle and Spectrometric Coronagraph). …”
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Classification of Severity of Lung Parenchyma Using Saliency and Discrete Cosine Transform Energy in Computed Tomography of Patients With COVID-19 by Santiago Tello-Mijares, Francisco Flores, Fomuy Woo

“…A final fused (FF) image combining Q and DCT of PP and GGO-PI images was then obtained. Five convolutional neural networks (CNNs) and five classic classification techniques, trained using FF and grayscale PP images, were tested. …”
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Comparison of deep transfer learning models for classification of cervical cancer from pap smear images by Harmanpreet Kaur, Reecha Sharma, Jagroop Kaur

“…Traditional classification algorithms often require segmentation and feature extraction techniques to detect cervical cancer. In contrast, convolutional neural networks (CNN) models require large datasets to reduce overfitting and poor generalization. …”
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Research on Detecting AI-Generated Forged Handwritten Signatures via Data-Efficient Image Transformers by Yanli Hao, Zuyang Zheng

“…Hence, in our experimental states, the DeiT model shows better performance in forged signature identification than other models, including the Vision Transformer (ViT), VGG16, ResNet, Convolutional Neural Networks (CNN), and XGBoost. The DeiT model demonstrated superior performance with AUC values of 100% for the Huawei and Baidu datasets, 99.99% for the combined Huawei and Baidu datasets, 100% for the AI & Handwriting dataset, and 99.72% for the Shouji dataset. …”
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Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier by Ranjana Agrawal, Sucheta Kulkarni, Madan Deshpande, Anita Gaikwad, Rahee Walambe, Ketan V. Kotecha

“…. • This study aims to improve accuracy of Stages 1–3 classification and identify Pre-plus/ Plus disease using MultiCNN_LSTM networks. This is accomplished by using multiple CNNs (Convolutional Neural Networks) to extract features and LSTM (Long Short-Term Memory) classifier to classify images. • Cropped STAGE dataset and HVDROPDB-PLUS dataset are constructed with RetCam and Neo images. • The proposed networks outperform individual CNNs and CNN_LSTM networks in terms of accuracy and F1 score.…”
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Facial masks and soft‐biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images by Fernando Alonso‐Fernandez, Kevin Hernandez‐Diaz, Silvia Ramis, Francisco J. Perales, Josef Bigun

“…However, state‐of‐the‐art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. …”
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Deep Learning for Traffic Scene Understanding: A Review by Parya Dolatyabi, Jacob Regan, Mahdi Khodayar

“…The paper synthesizes insights from a broad range of studies, tracing the evolution from traditional image processing methods to sophisticated DL techniques, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). …”
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A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis by Xiaofeng Han, Diego Guffanti, Alberto Brunete

“…Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. …”
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Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data by S. R. Mani Sekhar, D. M. Mushtaq Ahmed, G. M. Siddesh

“…Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to analyze this data and predict real-time contextual information, including weather conditions, traffic patterns, and social events. …”
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A Deep-Learning Method for Remaining Useful Life Prediction of Power Machinery via Dual-Attention Mechanism by Fan Wang, Aihua Liu, Chunyang Qu, Ruolan Xiong, Lu Chen

“…To tackle this limitation, we propose a multi-feature fusion model leveraging a dual-attention mechanism. Initially, convolutional neural networks (CNNs) and channel attention mechanisms are employed to preliminarily extract spatial features. …”
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Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries by Azeez Okikiola Lawal, Tolutope Joseph Ogunniyi, Oriire Idunnuoluwa Oludele, Oluwaloseyi Ayomipo Olorunfemi, Olalekan John Okesanya, Jerico Bautista Ogaya, Emery Manirambona, Mohamed Mustaf Ahmed, Don Eliseo Lucero-Prisno

“…The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. …”
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Chinese Mathematical Knowledge Entity Recognition Based on Linguistically Motivated Bidirectional Encoder Representation from Transformers by Wei Song, He Zheng, Shuaiqi Ma, Mingze Zhang, Wei Guo, Keqing Ning

“…In order to improve the accuracy of mathematical knowledge entity recognition and provide effective support for subsequent functionalities, this paper adopts the latest pre-trained language model, LERT, combined with a Bidirectional Gated Recurrent Unit (BiGRU), Iterated Dilated Convolutional Neural Networks (IDCNNs), and Conditional Random Fields (CRFs), to construct the LERT-BiGRU-IDCNN-CRF model. …”
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A computational fluid dynamics analysis of the aerodynamic influence of angles of attack on the Skylon spaceplane by Vivekamanickam Koothan Venkateswaran, Unai Fernandez Gamiz, Ana Boyano, Jesus Maria Blanco

“…The total time consumed by the simulation and the possibility of using its data for other less time-consuming methods, such as convolutional neural networks, are considered. This research establishes a foundation for understanding the aerodynamic effects of specific angles of attack by comparing theoretical and simulation values. …”
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Color fundus photograph-based diabetic retinopathy grading via label relaxed collaborative learning on deep features and radiomics features by Chao Zhang, Guanglei Sheng, Jie Su, Lian Duan

“…First, we utilize convolutional neural networks to extract deep features from color fundus photographs and employ radiomic methodologies to extract radiomic features. …”
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