In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.

Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture. Objectives: This research explores the capabilities of the CNNs and transformers to build a robust classification model that can handle both short- and long-range dependencies with global features to accurately classify food images and enhance food image recognition for better nutritional analysis. Methods: Our approach, which combines CNNs and Vision Transformers (ViTs), begins with the RestNet50 backbone model. This model is responsible for local feature extraction from the input image. The resulting feature map is then passed to the ViT encoder block, which handles further global feature extraction and classification using multi-head attention and fully connected layers with pre-trained weights. Results: Our experiments on five diverse datasets have confirmed a superior performance compared to the current state-of-the-art methods, and our combined dataset leveraging complementary features showed enhanced generalizability and robust performance in addressing global food diversity. We used explainable techniques like grad-CAM and LIME to understand how the models made their decisions, thereby enhancing the user’s trust in the proposed system. This model has been integrated into a mobile application for food recognition and nutrition analysis, offering features like an intelligent diet-tracking system. Conclusion: This research paves the way for practical applications in personalized nutrition and healthcare, showcasing the extensive potential of AI in nutritional sciences across various dietary platforms.

In the face of the escalating use of image editing tools and the rapid progress of AI technology, the demand for robust image forgery detection techniques has become more pressing. This study introduces a comprehensive approach to image forgery detection, which integrates image metadata analysis and Error Level Analysis (ELA) with deep learning, specifically Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). Using transfer learning, we evaluated seven pre-trained models alongside a custom innovative CNN model using the CASIAV2 dataset for binary classification of authentic and forged images. Metadata was extracted from all the images, and unique feature images were generated and used as inputs for all models by exploiting the differences between compressed and original images through ELA. Among the pre-trained models, VGG16, Xception, ResNet101, and MobileNetV2 demonstrated superior detection accuracy compared to the state-of-the-art models. However, our custom model surpassed six others, achieving an accuracy score of 99.08% with efficient training parameters. This study's results indicate significant advancements in image forgery detection, which has practical applications in forensic tools, automated detection systems, and educational purposes. An enhanced accuracy can benefit industries reliant on image authenticity, improve digital security protocols, protect intellectual property, and influence legal standards. Additionally, the methodology could extend to other domains like document and video authentication.