Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/423309
Title: Design and Implementation of an Intelligent System based on ML and IOT for Ordinary to Smart Refrigerator Conversion
Researcher: Jain, Pulkit
Guide(s): Chawla, Paras
Keywords: Engineering
Engineering and Technology
Engineering Electrical and Electronic
University: Chandigarh University
Completed Date: 2021
Abstract: Machine Learning has evolved with a variety of algorithms to enable state of the art computer vision applications. In particular the need for automating the process of real time food item identification, there is a huge surge of research so as to make smarter refrigerators. According to a survey by the Food and Agriculture Organization of the United Nations (FAO), it has been found that 1.3 billion tons of food is wasted by consumers around the world due to either food spoilage or expiry and large amount of food is wasted from homes and restaurants itself. Smart refrigerators have been very successful in playing a pivotal role in mitigating this problem of food wastage. But a major issue is high cost of available smart refrigerators and the lack of accurate design algorithms which can help achieve computer vision in any ordinary refrigerator. To address these issues, the current work proposes an automated identification algorithm for computer vision in smart refrigerators using VGG-16, InceptionV3 and Mobile Net CNN architectures. newline
Pagination: 
URI: http://hdl.handle.net/10603/423309
Appears in Departments:Dept of Electronics & Communication Engineering

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01_title.pdfAttached File24.33 kBAdobe PDFView/Open
02_prelim page.pdf321.44 kBAdobe PDFView/Open
03_content.pdf27.57 kBAdobe PDFView/Open
04_abstract.pdf7.32 kBAdobe PDFView/Open
05_chapter 1.pdf554.78 kBAdobe PDFView/Open
06_chapter 2.pdf172.3 kBAdobe PDFView/Open
07_chapter 3.pdf93.81 kBAdobe PDFView/Open
08_chapter 4.pdf724.69 kBAdobe PDFView/Open
09_chapter 5.pdf2.16 MBAdobe PDFView/Open
10_chapter 6.pdf7.64 MBAdobe PDFView/Open
11_chapter 7.pdf89.11 kBAdobe PDFView/Open
12_annexure.pdf119.9 kBAdobe PDFView/Open
80_recommendation.pdf109.86 kBAdobe PDFView/Open
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