Analysis & Numerical Simulation of Indian Food Image Classification Using Convolutional Neural Network

Recognition of Indian food can be assumed to be a fine-grained visual task owing to recognition property of various food classes. It is therefore important to provide an optimized approach to segmentation and classification for different applications based on food recognition. Food computation mainly utilizes a computer science approach which needs food data from various data outlets like real-time images, social flat-forms, food journaling, food datasets etc, for different modalities. In order to consider Indian food images for a number of applications we need a proper analysis of food images with state-of-art-techniques. The appropriate segmentation and classification methods are required to forecast the relevant and upgraded analysis. As accurate segmentation lead to proper recognition and identification, in essence we have considered segmentation of food items from images. Considering the basic convolution neural network (CNN) model, there are edge and shape constraints that influence the outcome of segmentation on the edge side. Approaches that can solve the problem of edges need to be developed; an edge-adaptive As we have solved the problem of food segmentation with CNN, we also have difficulty in classifying food, which has been an important area for various types of applications. Food analysis is the primary component of health-related applications and is needed in our day to day life. It has the proficiency to directly predict the score function from image pixels, input layer to produce the tensor outputs and convolution layer is used for self- learning kernel through back-propagation. In this method, feature extraction and Max-Pooling is considered with multiple layers, and outputs are obtained using softmax functionality. The proposed implementation tests 92.89% accuracy by considering some data from yummly dataset and by own prepared dataset. Consequently, it is seen that some more improvement is needed in food image classification. We therefore consider the segmented feature of EA-CNN and concatenated it with the feature of our custom Inception-V3 to provide an optimized classification. It enhances the capacity of important features for further classification process. In extension we have considered south Indian food classes, with our own collected food image dataset and got 96.27% accuracy. The obtained accuracy for the considered dataset is very well in comparison with our foregoing method and state-of-the-art techniques.

Visão computacional para deteção de hábitos alimentares

O excesso de peso e a obesidade são fatores comportamentais que têm vindo a causar um aumento substancial de mortes em Portugal. Estes fatores podem trazer complicações musculoesqueléticas, efeitos metabólicos como diabetes, riscos cardiovasculares, efeitos sobre a saúde mental e o aparecimento e/ou agravamento de cancro. Seguir uma dieta saudável é importante não apenas para controlar os níveis de açúcar, mas também o perfil lipídico, a tensão arterial, minimizando assim o risco cardiovascular e de complicações microvasculares. Torna-se, portanto, crucial a implementação de soluções que orientem os utilizadores a optar por opções alimentares mais benéficas à sua saúde, para que os indivíduos previnam o aparecimento de outras doenças ou exacerbações das doenças que já possam possuir. Estas soluções podem ser manuais como a contagem manual de hidratos de carbono ou digitais como as várias aplicações móveis existentes no mercado que permitem a monitorização de doenças e o controlo nutricional. Atualmente, grande parte da sociedade possui um dispositivo móvel com capacidade de tirar fotografias e cada vez mais os telemóveis são usados como assistentes pessoais, ajudando o ser humano a ser mais eficaz nas suas tarefas diárias. Estes dispositivos representam um recurso computacional versátil, com grande capacidade de deteção e inferência. As técnicas de machine learning aplicadas nas câmaras dos telemóveis permitem a estabilização de imagem, tradução de texto automática, deteção de objetos, reconhecimento de rostos, entre outros. Os próprios sensores dos telemóveis são cada vez mais complexos e podem ser usados para detetar movimentos e padrões, inferir níveis de stress e emocionais do utilizador, reconhecimento de lugares, estimativa de profundidade dos elementos numa fotografia, e assim por diante. Estes sensores possibilitam a extração de dados sem que o utilizador tenha de realizar uma tarefa específica. O objetivo desta tese foi implementar e estudar sistemas inovadores que, através de visão computacional, auxiliem na tarefa de controlo nutricional e que permitam a monitorização de doenças. Neste âmbito, desenvolveuse um sistema de reconhecimento de alimentos utilizando Detectron2 com o modelo PointRend que, com o auxílio de um modelo de Regressão Linear capaz de prever uma estimativa do peso dos alimentos presentes em uma imagem, permitiu que o controlo nutricional se tornasse em uma tarefa simples. A solução proposta nesta dissertação permitirá que o utilizador poupe tempo e esforço, e que realize decisões alimentares mais conscientes. Além disso, esta solução também estará preparada para auxiliar pacientes diabéticos, indicando, por exemplo, as unidades de insulina que deve injetar, tendo em conta a refeição que irá ingerir.Overweight and obesity are behavioral factors that have been causing a substantial increase in deaths in Portugal. These factors can bring musculoskeletal complications, metabolic effects such as diabetes, cardiovascular risks, effects on mental health, and the onset and/or worsening of cancer. Following a healthy diet is important not only to control sugar levels but also the lipid profile, and blood pressure, thus minimizing the risk of cardiovascular and microvascular complications. It is therefore crucial to implement solutions that guide users to choose food options that are more beneficial to their health so that individuals prevent the onset of other diseases or exacerbations of diseases they may already have. These solutions can be manual, such as the manual counting of carbohydrates, or digital, such as the multiple mobile applications on the market that allow disease monitoring and nutritional control. Currently, a large part of society has a mobile device capable of taking pictures and mobile phones are increasingly used as personal assistants, helping human beings to be more effective in their daily tasks. These devices represent a versatile computing resource, with great detection and inference capabilities. Machine learning techniques applied to mobile phone cameras allow image stabilization, automatic text translation, object detection, and face recognition, among others. Mobile phone sensors themselves are increasingly complex and can be used to detect movements and patterns, infer user stress and emotional levels, place recognition, estimate the depth of elements in a photograph, and so on. These sensors make it possible to extract data without the user having to perform a specific task. This thesis objective was to implement and study innovative systems that, through computer vision, help in nutritional control and allow disease monitoring. In this context, a food recognition system was developed using Detectron2 with the PointRend model which, with the aid of a Linear Regression model capable of predicting an estimate of the weight of the food present in an image, allowed nutritional control to become a simple task. The solution proposed in this dissertation will allow the user to save time and effort, and to make more conscious food decisions. In addition, this solution will also be prepared to help diabetic patients, indicating, for example, the units of insulin that must be injected, considering the meal that will be ingested

Effects of Quercetin on Uric Acid Metabolism

Background and Objective: High blood uric acid (hyperuricemia) is a common phenomenon in populations with hypertension, hyperglycemia, obesity and/or dyslipidemia. This study was to investigate the effects of quercetin supplementation on blood uric acid level and the biochemical mechanism behind it. Methods: A pilot trial confirmed the delivery of quercetin from a supplement tablet in healthy males (n=6). Randomised, double-blind, cross-over, placebo-controlled 4-week dietary intervention trial with the same supplement tablet containing 500 mg quercetin d-1 was conducted in selected healthy males (n=22, with higher blood uric acid but within normal range). Changes of uric acid and glucose were analysed in fasting blood plasma at 0, 2 and 4 weeks. Plasma metabolomics were profiled by 1H-NMR. Where quercetin and its metabolites may affect in the pathway of uric acid metabolism was investigated in vitro and ex vivo. Results: At the end of the 4-week trial, plasma uric acid levels were significantly reduced (mean change -26.5 µM, 95% CI -7.6 to -45.5, P = 0.008, n=22), as were diastolic blood pressures in normotensive subjects (-3.1 mm Hg, -0.5 to -5.8, P = 0.048, n=10). Paired plasma 1H-NMR spectrum showed lowered glutamine (P = 0.008), acetoacetate (P = 0.005) and lactate (P = 0.03) after quercetin treatment. A dose-dependent inhibition of quercetin, quercetin-3'-O-sulfate and 3,4-dihydroxyphenylacetic acid on xanthine oxidase in vitro and a mild inhibitory effect of quercetin on plasma adenosine deaminase was found. Conclusions: Quercetin supplementation can maintain blood uric acid level and blood pressure within a low-risk range. It is probably a result of regulated purine metabolism by quercetin, its microbial derivatives and their metabolites