A Survey on Nutri-Guide: An Intelligent System for Personalized Dietary Management and Nutritional Analysis

Mythri M, Sandiya R, Tejaswini N, Usha H P, Harshitha R — Wed, 23 Sep 2026 00:00:00 +0000

Maintaining a balanced diet is central to preventing lifestyle-related and chronic conditions such as obesity, diabetes, cardiovascular disease, and hypertension, yet conventional dietary assessment methods — food diaries, food-frequency questionnaires, and 24-hour dietary recall — are time-consuming, burdensome, and prone to memory-based bias. Recent advances in artificial intelligence, computer vision, and deep learning have introduced automated, image-based alternatives, in which a photograph of a meal, rather than a verbal or written description, becomes the primary data source for dietary assessment. This paper reviews and synthesizes recent research on image-based food recognition, food segmentation, portion and volume estimation, calorie and nutrient calculation, and personalized dietary recommendation, drawing on a methodological review of classification and volume-estimation algorithms, a deep-learning pipeline study covering dataset construction and nutrient-database matching, and an applied case system (IntelligentDine) that fine-tunes a 150-layer ResNet on the Food-101 dataset to jointly perform food identification and calorie regression, reporting 97.30% training accuracy and 98.02% validation accuracy. The reviewed studies show that convolutional neural networks, transfer-learning architectures such as ResNet and EfficientNet, and benchmark datasets such as Food-101 have substantially improved food-classification performance, while portion and volume estimation, dataset diversity (particularly for Indian and other regional cuisines), real-world robustness, privacy, and mobile deployability remain comparatively unresolved. Building on these findings, this paper proposes a unified conceptual framework that integrates image acquisition, preprocessing, food detection and classification, portion/volume estimation, nutrient-database lookup, and personalized health-based recommendation into a single intelligent dietary-assessment system, and outlines directions for future research toward accurate, real-time, and personalized dietary guidance.

Coordinated Neural Dynamics of Sleep-Dependent Memory Stabilization: Integrating Replay, Oscillatory Coupling, and Synaptic Regulation

Rimmy, Aastha Verma, Aditi Singh — Thu, 24 Sep 2026 00:00:00 +0000

Memory consolidation during sleep depends on complex interactions of neural oscillations, brain networks, and molecular machinery. Here, a structured narrative review of the neural mechanisms of sleep-dependent memory consolidation is presented and analyzed, concentrating on neural replay, hippocampal sharp-wave ripples (SWRs) and thalamocortical sleep spindles and cortical slow oscillations (SOs), and on hippocampo-neocortical communication, Synaptic Homeostasis, and neurochemical regulation. Research articles from January 2003 to September 2026 were retrieved through Pub Med/MEDLINE, Scopus, Web of Science, ScienceDirect, and IEEE Xplore and analyzed. Evidence from experimental studies involving humans and animals, including relevant methods, was thematically synthesized and comparatively evaluated with respect to mechanisms, study protocols, and the validity and limitations of the existing evidence and investigations. The literature consistently reveals that sleep-dependent replay and distribution of hippocampal-dependent memories during NREM sleep are critically coupled to interactions between hippocampal SWRs and sleep spindles and SOs, fitting the primary propositions of Active Systems Consolidation (ASC) theory. Specifically, neural replay and temporal coupling of oscillations may enhance hippocampo-neocortical communication, contributing to the persistence of encoded traces. Meanwhile, the hypothesis on Synaptic Homeostasis posits, with a functional approach for long-lasting potentiation, that synaptic upscaling, under sleep, might preserve only the salient memory engrams. Moreover, REM sleep could contribute significantly to consolidation of emotional memory as well as integration of the newly stored memory, in a manner not clearly determined for others. All these aspects are modulated through several neurochemical systems, mainly consisting of acetylcholine, glutamate, GABA, dopamine, and cortisol. Methodological advances such as intracranial EEG recording, neuroimaging techniques, targeted reactivation of memories, and non-invasive brain stimulation may confirm the hypotheses about the neural mechanisms. However, methodological questions remain, including differences between human and animal studies, a broad range of methodologies, many correlative studies in humans, and inconclusive evidence of causality, regarding neuronal replay and the coordination of oscillations with synaptic homeostasis and memory maintenance. Overall, the studies confirm that sleep is a very active state in which coordination across neural elements is maintained and that this process enhances not only memory maintenance but selectively modifies previously stored representations, integrating new information with existing information at the systemic level.

International Journal of Data Science, Bioinformatics and Cyber Security

A Survey on Nutri-Guide: An Intelligent System for Personalized Dietary Management and Nutritional Analysis

Coordinated Neural Dynamics of Sleep-Dependent Memory Stabilization: Integrating Replay, Oscillatory Coupling, and Synaptic Regulation