Low-Cost Smart Refrigerator

Wu, Heng; Chuang, Yung-Ting

doi:10.1109/ieee.edge.2017.41

Cited by 23 publications

(22 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also has a camera to keep monitoring the fridge content and this can be done from remote locations. Articles in [ 12 – 16 ] worked on a refrigerator that is based on IoT for remotely monitoring for different parameters in real-time mode. Articles in [ 17 – 19 ] proposed different methods related to the smart refrigerator for avoiding food wastage in a fridge.…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Predictive Machine Learning Model for Efficiently Storing Temperature-Sensitive Medical Products, Such as Vaccines: Case Study: Pharmacies in Rwanda

Habiyaremye

Zennaro

Mikeka

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Temperature control is the key element during medicine storage. Pharmacies sell some medical products which are kept in fridges. The opening and closing of the fridge while taking some medicine makes the outside hot air enter the fridge, which will increase the inner fridge temperature. When the frequency of opening and closing of the fridge is increased, the temperature may go beyond the allowed storage temperature range. In this paper, we are proposing a model with the help of machine learning that will be used in multiple chambers fridges to keep indicating the time remaining for the inner temperature to go beyond the allowed range, and if the time is short, the system will propose to the pharmacist not to open that particular room and proposes a room that has enough time slots (time to reach the upper limit temperature). By using training data got from a thermoelectric cooler-based fridge, we constructed a multiple linear regression model that can predict the required time for a given room to reach the cut-off temperature in case that room is opened. The built model was evaluated using the coefficient of determination R2 and is found to be 77%, and then it can be used to develop a multiple room smart fridge for efficiently storing highly sensitive medical products.

show abstract

Section: Related Workmentioning

confidence: 99%

A Data-Driven Predictive Machine Learning Model for Efficiently Storing Temperature-Sensitive Medical Products, Such as Vaccines: Case Study: Pharmacies in Rwanda

Habiyaremye

Zennaro

Mikeka

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…One of the most crucial tasks for any smart refrigerator is food item scanning and its correct identification [1]. It is evident from the literature that many smart refrigerators have been developed [2][3][4][5][6][7][8][9][10][11][12][13] for this core functionality using technologies like RFID scan, Quick Response (QR) code Scan, Image capture and processing, Fuzzy Logic, Artificial Intelligence (AI) and Computer Vision etc. The major objective is to avoid food wastage by early identification of food items that are near expiry via timely notification to the user via Graphical User Interface (GUI), Short Message Service (SMS) or email etc.…”

Section: Introductionmentioning

confidence: 99%

“…1 based on parameters like scanning mechanism and whether food expiry check and cloud platform is provided or not. The major issue associated with smart refrigerators available in market today is high cost [7] and the availability of only brand-specific applications for remote access of database of the items kept inside the fridge. The need of the hour is to design such algorithms for intelligent and cost effective systems which can add smartness to existing conventional refrigerators.…”

Section: Introductionmentioning

confidence: 99%

Automated Identification Algorithm Using CNN for Computer Vision in Smart Refrigerators

Jain¹,

Chawla²,

Masud³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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 a 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 the 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, this work proposes an automated identification algorithm for computer vision in smart refrigerators using InceptionV3 and MobileNet Convolutional Neural Network (CNN) architectures. The designed module and algorithm have been elaborated in detail and are considerably evaluated for its accuracy using test images on standard fruits and vegetable datasets. A total of eight test cases are considered with accuracy and training time as the performance metric. In the end, real-time testing results are also presented which validates the system's performance.

show abstract

“…In the past years, several works have been proposed to design IoT-based smart refrigerator systems [16][17][18][19]. An IoT-based low-cost smart refrigerator is presented in [20] which is designed in order to reduce the food loss and help in providing a more comfortable life. The application given by Android or Mac applications has given a real-time update even though the individual is not physically present at home.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework of an IOT-Blockchain-Based Intelligent System

Alabdali

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

With the growing need of technology into varied fields, dependency is getting directly proportional to ease of user-friendly smart systems. The advent of artificial intelligence in these smart systems has made our lives easier. Several Internet of Things- (IoT-) based smart refrigerator systems are emerging which support self-monitoring of contents, but the systems lack to achieve the optimized run time and data security. Therefore, in this research, a novel design is implemented with the hardware level of integration of equipment with a more sophisticated software design. It was attempted to design a new smart refrigerator system, which has the capability of automatic self-checking and self-purchasing, by integrating smart mobile device applications and IoT technology with minimal human intervention carried through Blynk application on a mobile phone. The proposed system automatically makes periodic checks and then waits for the owner’s decision to either allow the system to repurchase these products via Ethernet or reject the purchase option. The paper also discussed the machine level integration with artificial intelligence by considering several features and implemented state-of-the-art machine learning classifiers to give automatic decisions. The blockchain technology is cohesively combined to store and propagate data for the sake of data security and privacy concerns. In combination with IoT devices, machine learning, and blockchain technology, the proposed model of the paper can provide a more comprehensive and valuable feedback-driven system. The experiments have been performed and evaluated using several information retrieval metrics using visualization tools. Therefore, our proposed intelligent system will save effort, time, and money which helps us to have an easier, faster, and healthier lifestyle.

show abstract

Low-Cost Smart Refrigerator

Cited by 23 publications

References 5 publications

A Data-Driven Predictive Machine Learning Model for Efficiently Storing Temperature-Sensitive Medical Products, Such as Vaccines: Case Study: Pharmacies in Rwanda

A Data-Driven Predictive Machine Learning Model for Efficiently Storing Temperature-Sensitive Medical Products, Such as Vaccines: Case Study: Pharmacies in Rwanda

Automated Identification Algorithm Using CNN for Computer Vision in Smart Refrigerators

A Novel Framework of an IOT-Blockchain-Based Intelligent System

Contact Info

Product

Resources

About