Ocular diseases can significantly affect vision and overall quality of life, with diagnosis often being time-consuming and dependent on expert interpretation. While previous computer-aided diagnostic systems have focused primarily on medical imaging, this paper proposes VisionTrack, a multi-modal AI system for predicting multiple retinal diseases, including Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), drusen, Central Serous Retinopathy (CSR), and Macular Hole (MH), as well as normal cases. The proposed framework integrates a Convolutional Neural Network (CNN) for image-based feature extraction, a Graph Neural Network (GNN) to model complex relationships among clinical risk factors, and a Large Language Model (LLM) to process patient medical reports. By leveraging diverse data sources, VisionTrack improves prediction accuracy and offers a more comprehensive assessment of retinal health. Experimental results demonstrate the effectiveness of this hybrid system, highlighting its potential for early detection, risk assessment, and personalized ophthalmic care. Experiments were conducted using two publicly available datasets, RetinalOCT and RFMID, which provide diverse retinal imaging modalities: OCT images and fundus images, respectively. The proposed multi-modal AI system demonstrated strong performance in multi-label disease prediction. On the RetinalOCT dataset, the model achieved an accuracy of 0.980, F1-score of 0.979, recall of 0.978, and precision of 0.979. Similarly, on the RFMID dataset, it reached an accuracy of 0.989, F1-score of 0.881, recall of 0.866, and precision of 0.897. These results confirm the robustness, reliability, and generalization capability of the proposed approach across different imaging modalities.

Diabetic Retinopathy (DR) is one of the leading causes of blindness worldwide, where early detection is critical to preventing irreversible vision loss. Traditional diagnostic methods primarily rely on single-modality data, such as retinal images, or the analysis of image features, which may limit diagnostic accuracy. To overcome these limitations, we propose Diabetic Retinopathy Diagnosis, namely "DRdiag", a novel model proposed for the early diagnosis of DR. DRdiag integrates multiple modalities: the Fundus images and the duration of disease evolution which is an important factor in the diagnosis of DR as the duration of the disease directly influences the onset and progression of retinal lesions, leveraging two distinct models: a Convolutional Neural Network (CNN) based on DenseNet121 for image feature extraction and a Graph Neural Network (GNN) for capturing complex relationships between patient features. By combining the strengths of these advanced models, DRDiag aims to provide a more comprehensive and accurate diagnostic system, ultimately supporting timely intervention and improved patient outcomes. The experiments are conducted using two datasets, APTOS2019 and Messidor-2, which provide diverse retinal images for robust evaluation. The proposed model achieves high performance on diabetic retinopathy classification. On Messidor-2, it attains 0.976 accuracy, 0.957 kappa, 0.961 F1-score, 0.958 recall, 0.965 precision, and 0.990 specificity. On APTOS2019, it achieves 0.980 accuracy, 0.960 kappa, 0.963 F1-score, 0.959 recall, 0.968 precision, and 0.995 specificity. These results confirm its robustness and reliability.

Recommendation systems have been developed to address the immense volume of information accessible on the Internet. These systems employ filtering methodologies and customize their recommendations by drawing insights from user profiles, ultimately enhancing the relevance and utility of their suggestions. In this paper, we present our system called Smart Food and Restaurant Advisor based on nutritional needs and user profiling (SFRA); a multi-criteria food and restaurant recommendation system designed to prioritize personalized and health-conscious selections. This system uses user profiling to offer customized recommendations that cater to individual preferences, requirements, and geographic locations. Our primary objective is to enhance users’ decision-making processes by promoting healthier and more refined lifestyle choices. The results of the proposed system demonstrate that it is well-suited for promoting a healthier lifestyle while offering comprehensive coverage of users’ practices and preferences.

Recommendation systems represent complex algorithms that direct the user to interesting resources within the vast data space available on the Internet, taking into account his personal information, preferences, etc. Machine learning and multi-criteria methods have brought about significant development in recommendation systems, providing personalized and accurate solutions for recommending products or services, etc. However, the problem that machine learning models face is that they tend to lack robustness and accuracy if they lack features that help personalize recommendations. In this paper, we address this problem by proposing a new system known as ML-TOPSIS (Machine Learning and Preference Ranking Technique by Similarity to Ideal Solution) for personalized restaurant recommendations based on health, location, and ratings. The primary goal of this system is to develop an application that attempts to provide food from restaurants that matches a person’s health status using the multi-criteria TOPSIS method, as well as their geographic location and similar ratings using machine learning algorithms based on collaborative filtering. By considering the nutritional requirements of individuals, especially for individuals suffering from obesity and diabetes. The results of the proposed system show that it helps users find restaurants according to their needs and aspirations to provide better suggestions.