scholarly journals Computer Vision Approach for Detecting Adulteration of Ghee with Foreign Fats – A Survey

2021 ◽  
Author(s):  
Aditya Upadhyay ◽  
Neha Chaudhary
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Milk Fat ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Sesame Oil ◽  
Recent Time ◽  
Machine Learning Algorithms ◽  
Nutrition Value ◽  
Instrumental Methods

Ghee is pure clarified fat derived from milk, yogurt and fresh cream. It is most commonly used milk fat product in India. The consumption and production of ghee is consistently increasing by 10% in our country in every year. In comparison to other milk fat product, ghee is expensive and short in demand because of its pleasant taste or high nutrition value. Due to its high cost and demand in market, there are high possibilities to adulterate it with cheap fats like vegetable oil/animal body fats. The adulteration detection of ghee is becoming a serious issue to chemists. Several analytical and instrumental methods are available for the detecting adulteration in ghee based on chemical principles. On the basis of study, it was observed that analytical methods are not suitable to detect the adulteration level of <15%. In recent time, digital image analysis is introduced in the field of adulteration detection in food products. A very few studies found in the area of milk fat adulteration detection with foreign fats using image analysis. Various studies found related to detection of adulteration in Oils (like Extra virgin olive oil, sesame oil etc.) with cheap oil using the various color models (like CIELAB, RGB, HSV, CMYK) and machine learning algorithms.

scholarly journals A Survey of Crowdsourcing in Medical Image Analysis

2020 ◽  
Vol 7 ◽  
pp. 1-26 ◽  
Author(s):  
Silas Nyboe Ørting ◽  
Andrew Doyle ◽  
Arno Van Hilten ◽  
Matthias Hirth ◽  
Oana Inel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Imaging Analysis ◽  
High Quality ◽  
Comprehensive Literature Review

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

scholarly journals Research on dairy products detection based on machine learning algorithm

2022 ◽  
Vol 355 ◽  
pp. 03008
Author(s):  
Yang Zhang ◽  
Lei Zhang ◽  
Yabin Ma ◽  
Jinsen Guan ◽  
Zhaoxia Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Electronic Nose ◽  
Milk Fat ◽  
Dairy Products ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

In this study, an electronic nose model composed of seven kinds of metal oxide semiconductor sensors was developed to distinguish the milk source (the dairy farm to which milk belongs), estimate the content of milk fat and protein in milk, to identify the authenticity and evaluate the quality of milk. The developed electronic nose is a low-cost and non-destructive testing equipment. (1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis , LDA) for dimensionality reduction analysis, and finally use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source (cow farm) Identify the model and evaluate and compare the classification effects. The experimental results prove that the classification effect of the SVM-LDA model based on the electronic nose odor characteristics is better than other single feature models, and the accuracy of the test set reaches 91.5%. The RF-LDA and SVM-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate. The method of estimating the model, the results show that the RF model has the best estimation performance( R2 =0.9399 for milk fat; R2=0.9301for milk protein). And it prove that the method proposed in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of dairy products.

scholarly journals The Development of a Skin Image Analysis Tool by Using Machine Learning Algorithms

Cosmetics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 67
Author(s):  
Perry Xiao ◽  
Xu Zhang ◽  
Wei Pan ◽  
Xiang Ou ◽  
Christos Bontozoglou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Imaging Techniques ◽  
Learning Algorithms ◽  
Research Work ◽  
Software Tool ◽  
Machine Learning Algorithms ◽  
Analysis Tool ◽  
Skin Imaging ◽  
User Friendly

We present our latest research work on the development of a skin image analysis tool by using machine-learning algorithms. Skin imaging is very import in skin research. Over the years, we have used and developed different types of skin imaging techniques. As the number of skin images and the type of skin images increase, there is a need of a dedicated skin image analysis tool. In this paper, we report the development of such software tool by using the latest MATLAB App Designer. It is simple, user friendly and yet powerful. We intend to make it available on GitHub, so that others can benefit from the software. This is an ongoing project; we are reporting here what we have achieved so far, and more functions will be added to the software in the future.

Relevance of Machine Learning to Cardiovascular Imaging

2021 ◽  
pp. 78-99
Author(s):  
Sumesh Sasidharan ◽  
M. Yousuf Salmasi ◽  
Selene Pirola ◽  
Omar A. Jarral
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Cognitive Learning ◽  
Cardiovascular Imaging ◽  
Machine Learning Algorithms ◽  
Patient Specific ◽  
Healthcare Data ◽  
Statistical Approaches ◽  
Specific Management

Artificial intelligence (AI) broadly concerns analytical algorithms that iteratively learn from big datasets, allowing computers to find concealed insights. These encompass a range of operations comprising several terms, including machine learning(ML), cognitive learning, deep learning, and reinforcement learning-based methods that can be used to incorporate and comprehend complex biomedical and healthcare data in scenarios where traditional statistical approaches cannot be implemented. For cardiovascular imaging in particular, machine learning guarantees to be a transformative tool that can address many unmet needs for patient-specific management, accurate prediction of disease progression, and the tracking of identifiable biomarkers of disease processes. In this chapter, the authors discuss fundamentals of machine learning algorithms for image analysis in the cardiovascular system by evaluating the need for ML in this field and examining the potential obstacles and challenges of implementation in the context of three common imaging modalities used in cardiovascular medicine.

Carotid artery ultrasound image analysis: A review of the literature

2020 ◽  
Vol 234 (5) ◽  
pp. 417-443 ◽  
Author(s):  
S Latha ◽  
Dhanalakshmi Samiappan ◽  
R Kumar
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Cardiovascular Diseases ◽  
Carotid Artery ◽  
Imaging Modality ◽  
Ultrasound Image ◽  
Machine Learning Algorithms ◽  
Patient Specific ◽  
Support Vector ◽  
Fully Automatic

Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima–media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima–media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning–based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

scholarly journals Healthy Alternatives of the Mediterranean Diet in Latvia

2013 ◽  
Vol 67 (4-5) ◽  
pp. 315-319
Author(s):  
Andrejs Ērglis ◽  
Iveta Mintāle ◽  
Anete Dinne
Keyword(s):  
Olive Oil ◽  
Mediterranean Diet ◽  
Milk Fat ◽  
Dairy Products ◽  
Cardiovascular Health ◽  
Unsaturated Fatty Acids ◽  
Healthy Lifestyle ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
The Mediterranean

Abstract The milestone of illness prophylaxis is a healthy lifestyle, which is composed of regular physical activity and a healthy diet. Following the Mediterranean diet for two years has been shown to have significant decrease in cardiovascular death by 9%, cancer by 6%, Parkinson's and Alzheimer's by 13%. This diet helps to control the perfect weight, improves lipid profile and diminishes the risk of diabetes. The Mediterranean diet consists of extra virgin olive oil, vegetables and fruit, wholegrain products, legumes, nuts and seeds, dairy products (with no other sources of fat other than milk fat), fish (at least twice a week), poultry, veal, pork in limited amount, and eggs - 0-4 per week. It is possible to adapt this kind of alimentation in the Nordic countries, but it is important to find products grown there with similar nutritional characteristics. Nowadays, fresh fruits and vegetables can be bought all year round, but it is essential to use seasonal products. In Latvia, at this point, attention should be brought to more efficient storage and conservation. We have a vast variety of legumes and cereals. The selection of dairy products should be bigger and of higher quality, because you rarely see local cheeses made in an artisanal manner at the marketplaces. There is good availability of saltwater fish in the cities, but in the countryside the only fish one can buy is salted and smoked, having exaggeratedly high amounts of salt. Consumption of meat and its products should be lowered to a maximum of three times per week. A special attention should be brought to game (such as deer), because it contains low levels of cholesterol and higher amounts of unsaturated fatty acids due to the alimentation of wild herbs. Unfortunately, there is a lack of good quality oil in Latvia, because no other product can be compared to the nutritious components of extra virgin olive oil and its effects on cardiovascular health. Consumption of high amounts of olive oil decreases the incidence of stroke by 41%. Education should be conducted widely to promote tradition and gastronomic heritage as a cultural aspect. Healthy lifestyle has to be visible to everyone at any time as a constant reminder of its importance.

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