APPLYING MACHINE LEARNING TO IDENTIFY COUNTERFEIT FOODS

نویسندگان

چکیده

Currently, the shelves of shops and supermarkets are filled with food that people consume daily, many products coming from abroad. However, all these useful for human body, do they meet standards? In this article, we will talk about how to identify low-quality using modern machine learning. Recognition classification images text based on learning can be a key technology in fight against low[1]quality food. Automatic image recognition product information enable end customers counterfeit accurately quickly by comparing them trained templates. it is clear does not apply processing enterprises. production, non-standard used reduce cost product. Manufacturers change their replacing higher quality lower ones. They may use confusing terms label mislead you. When buying serving products, consumers suffer different ways. First, getting nutrients need, adulterated foods safe health, also an economic loss consumers. We evaluate technical feasibility components fraud detection architecture real-world scenario, including models distinguish multiple each other. It allows you control circulation at state level, thereby protecting consumer purchasing potentially dangerous goods. MobileNetV2 model multiclass evaluated received angles.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Applying machine learning to identify autistic adults using imitation: An exploratory study

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor ...

متن کامل

Applying machine learning techniques to ecological data

This thesis is about modelling carbon flux in forests based on meterological variables using modern machine learning techniques. The motivation is to better understand the carbon uptake process from trees and find the driving factors of it, using totally automated techniques. Data from two British forests were used, (Griffin and Harwood) but finally results were obtained only with Harwood becau...

متن کامل

Applying Machine Learning to Product Categorization

We present a method for classifying products into a set of known categories by using supervised learning. That is, given a product with accompanying informational details such as name and descriptions, we group the product into a particular category with similar products, e.g., ‘Electronics’ or ‘Automotive’. To do this, we analyze product catalog information from different distributors on Amazo...

متن کامل

Applying Machine Learning Techniques to ASP Solving

Having in mind the task of improving the solving methods for Answer Set Programming (ASP), there are two usual ways to reach this goal: (i) extending state-of-the-art techniques and ASP solvers, or (ii) designing a new ASP solver from scratch. An alternative to these trends is to build on top of state-of-the-art solvers, and to apply machine learning techniques for choosing automatically the “b...

متن کامل

Using Machine Learning to Identify Intonational Segments

The intonational phrase is hypothesized to represent a meaningful unit of analysis in spoken language interpretation. We present results on the identification of intonational phrase boundaries from acoustic features using classification and regression trees (CART). Our training and test data are taken from the Boston Directions Corpus (task-oriented monologue) and the HUB-IV Broadcast News data...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Scientific journal of Astana IT University

سال: 2023

ISSN: ['2707-9031', '2707-904X']

DOI: https://doi.org/10.37943/13tfmt6695