The optimization of recommendation systems (RS) is crucial for delivering personalized product suggestions. Despite their successes, RS approaches often face challenges, such as data sparsity in the user–item matrix, which can undermine their performance. To address these challenges, integrating additional information sources , such as item/user profiles and textual reviews, is essential. These sources offer valuable insights into user preferences and item characteristics, helping in understanding the contextual details of both. This study focuses on developing an advanced RS architecture that combines Singular Value Decomposition (SVD) with BERT-CB methods and a Hybrid Model-based Sentiment Analysis . By integrating BERT with Multilayer Perceptron (MLP) methods, the system gains a deeper understanding of item profiles, improving the comprehension of user preferences and item characteristics. Additionally, a novel hybrid approach for sentiment analysis is proposed, using GloVe embeddings and CNN-BiGRU, improving the accuracy and robustness of sentiment detection in user reviews. This comprehensive understanding, combined with collaborative filtering models like SVD, enables the system to provide highly accurate recommendations. The proposed approach consists of four main phases: first, embedding review text using GloVe embeddings and developing a hybrid sentiment analysis approach with CNN and BiGRU architectures; second, creating a BERT language model for generating embeddings from item profile texts, followed by dimensionality reduction using Auto-Encoder; third, using these vectors to build a novel MLP model; fourth, developing a Collaborative Filtering method using SVD, and finally, combining these methods into a hybrid approach and conducting a comprehensive evaluation. Empirical results clearly show the effectiveness of our approach, particularly the combination of GloVe-CNN-BiGRU and BERT-CB with SVD methodology, demonstrating significant improvements across various performance metrics. This confirms the practical value of using contextualized data from BERT-CB and the sentiment analysis approach, enhancing the recommendation system’s effectiveness.

The efficacy of e-commerce conversion rates relies on precise and personalized product recommendations within recommendation systems (RS). While collaborative filtering-based RS has demonstrated success, challenges such as sparsity and cold-start issues in the user-item matrix can impede optimal functionality. To address these challenges, there is a need to integrate additional information sources, encompassing item/user profiles and textual reviews. This study introduces an innovative RS architecture that seamlessly combines self-supervised learning (SSL) and collaborative filtering techniques with BERT-DNN to surmount these obstacles. The distinctiveness of our approach lies in integrating self-supervised learning with collaborative filtering and contextualized data obtained from BERT-DNN, providing a profound understanding of item profiles to enhance comprehension of user preferences and item characteristics. This refined understanding, operational in conjunction with collaborative filtering models like ItemKNN and UserKNN, empowers the system to generate highly personalized recommendations. The proposed method entails several pivotal steps: developing the BERT language model for textual embeddings in item profiles, conducting dimensionality reduction, constructing a Deep Neural Network, implementing self-supervised learning with both UserKNN and ItemKNN CF methods, and employing an ensemble learning technique. Empirical results substantiate the efficacy of our approach, with a specific focus on the innovative fusion of BERT-DNN with self-supervised learning and KNN CF methodologies, showcasing substantial improvements across diverse performance metrics. This underscores the practical importance of leveraging contextualized BERT-DNN data, strengthening the recommendation mechanism, and ultimately enhancing the overall performance of the RS.

In this article, we propose Deep Transfer Learning (DTL) Model for recognizing covid-19 from chest x-ray images. The latter is less expensive, easily accessible to populations in rural and remote areas. In addition, the device for acquiring these images is easy to disinfect, clean and maintain. The main challenge is the lack of labeled training data needed to train convolutional neural networks. To overcome this issue, we propose to leverage Deep Transfer Learning architecture pre-trained on ImageNet dataset and trained Fine-Tuning on a dataset prepared by collecting normal, COVID-19, and other chest pneumonia X-ray images from different available databases. We take the weights of the layers of each network already pre-trained to our model and we only train the last layers of the network on our collected COVID-19 image dataset. In this way, we will ensure a fast and precise convergence of our model despite the small number of COVID-19 images collected. In addition, for improving the accuracy of our global model will only predict at the output the prediction having obtained a maximum score among the predictions of the seven pre-trained CNNs. The proposed model will address a three-class classification problem: COVID-19 class, pneumonia class, and normal class. To show the location of the important regions of the image which strongly participated in the prediction of the considered class, we will use the Gradient Weighted Class Activation Mapping (Grad-CAM) approach. A comparative study was carried out to show the robustness of the prediction of our model compared to the visual prediction of radiologists. The proposed model is more efficient with a test accuracy of 98%, an f1 score of 98.33%, an accuracy of 98.66% and a sensitivity of 98.33% at the time when the prediction by renowned radiologists could not exceed an accuracy of 63.34% with a sensitivity of 70% and an f1 score of 66.67%.

Three-Dimensional Hahn moments are performant tool in the domain of image processing applications and pattern classification. In this work, we propose a new method for computing the Three-Dimensional Hahn moments. This method is based on matrix multiplication and symmetry property to decrease the complexity and computational time for volumetric image reconstruction. Experimental results showed that the proposed method is very efficient in terms of computation time, but also in terms of volumetric image reconstruction capability.