scholarly journals Recycled Clothing Classification System Using Intelligent IoT and Deep Learning with AlexNet

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sun-Kuk Noh
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Object Recognition ◽  
Environmental Pollution ◽  
Classification System ◽  
Raw Material ◽  
Raspberry Pi ◽  
Utilization Rate ◽  
Automation System ◽  
Labor Costs

Recently, Internet of Things (IoT) and artificial intelligence (AI), led by machine learning and deep learning, have emerged as key technologies of the Fourth Industrial Revolution (4IR). In particular, object recognition technology using deep learning is currently being used in various fields, and thanks to the strong performance and potential of deep learning, many research groups and Information Technology (IT) companies are currently investing heavily in deep learning. The textile industry involves a lot of human resources in all processes, such as raw material collection, dyeing, processing, and sewing, and the wastage of resources and energy and increase in environmental pollution are caused by the short-term waste of clothing produced during these processes. Environmental pollution can be reduced to a great extent through the use of recycled clothing. In Korea, the utilization rate of recycled clothing is increasing, the amount of used clothing is high with the annual consumption being at $56.2 billion, but it is not properly utilized because of the manual recycling clothing collection system. It has several problems such as a closed workplace environment, workers’ health, rising labor costs, and low processing speed that make it difficult to apply the existing clothing recognition technology, classified by deformation and overlapping of clothing shapes, when transporting recycled clothing to the conveyor belt. In this study, I propose a recycled clothing classification system with IoT and AI using object recognition technology to the problems. The IoT device consists of Raspberry pi and a camera, and AI uses the transfer-learned AlexNet to classify different types of clothing. As a result of this study, it was confirmed that the types of recycled clothing using artificial intelligence could be predicted and accurate classification work could be performed instead of the experience and know-how of working workers in the clothing classification worksite, which is a closed space. This will lead to the innovative direction of the recycling clothing classification work that was performed by people in the existing working worker. In other words, it is expected that standardization of necessary processes, utilization of artificial intelligence, application of automation system, various cost reduction, and work efficiency improvement will be achieved.

scholarly journals IoT programming to Develop Self Driving Robotics Car using OpenCV and Other Emerging Technologies

2020 ◽  
Author(s):  
Mohammed Maaz ◽  
Sabah Mohammed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
The Self ◽  
Reverse Direction ◽  
Raspberry Pi ◽  
Specific Direction ◽  
Different Types ◽  
Practical Model ◽  
Individual Effort

<p>The advancement of Artificial Intelligence & Deep Learning has catalyzed the field of technology. The progression in these fields is exponentially increasing, and the discoveries which were once just an imagination are now changed into reality. The evolution of cars each year has made a lot of difference in people travelling from one place to another. One such reform involving Artificial Intelligence & Deep Learning is the birth of a self-driving car. The future is here where one can reach their destination hassle-free safely without the fear of accidents. This paper introduces a practical model of the self-driving robotics car, which can travel from one position to another on different types of tracks. A Pi-camera module is attached with the help of Raspberry Pi, which sends series of image frames to the Convolutional neural network, which then foretells the car to move in a specific direction, i.e. right, left, forward and reverse direction. The outcome is the robotics car, which travels in the desired direction without any individual effort.<br></p>

scholarly journals REFLECTA - Artificial Intelligence Based Smart Mirror

2019 ◽  
Vol 8 (6S4) ◽  
pp. 468-472
Keyword(s):  
Artificial Intelligence ◽  
Service Oriented Architecture ◽  
Raspberry Pi ◽  
Automation System ◽  
Home Setting ◽  
The Face ◽  
User Data ◽  
Service Oriented ◽  
Basic Services ◽  
Home Automation System

This paper interprets the development of an artificial intelligence-based mirror that shows an inconspicuous network interface for an ideal home setting. The mirror concedes an interaction through which the users can have an access and control to the smart appliances present at homes and also an ingress to personalized services. Priority is given to ensure comfort in accessing the basic services with a minimum hinder to user’s mediation. Taking an example, the face recognition feature is used to identify the user that faces the mirror and provide a module-based interface to access user data such as calendar, email, phone, social media, and other services. Reflecta basically works on Raspberry Pi 3 Model B board. It acquires a service-oriented architecture and is developed to use various utilities. The mirror, the appliances, data and news feeds use web services and cloud platform. Reflecta’s functionalities have been exhibited by developing an easily accessible home automation system based on ESP node service that simplifies the integration of home appliances and various customized web and internet services

scholarly journals IoT programming to Develop Self Driving Robotics Car using OpenCV and Other Emerging Technologies

2020 ◽  
Author(s):  
Mohammed Maaz ◽  
Sabah Mohammed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
The Self ◽  
Reverse Direction ◽  
Raspberry Pi ◽  
Specific Direction ◽  
Different Types ◽  
Practical Model ◽  
Individual Effort

<p>The advancement of Artificial Intelligence & Deep Learning has catalyzed the field of technology. The progression in these fields is exponentially increasing, and the discoveries which were once just an imagination are now changed into reality. The evolution of cars each year has made a lot of difference in people travelling from one place to another. One such reform involving Artificial Intelligence & Deep Learning is the birth of a self-driving car. The future is here where one can reach their destination hassle-free safely without the fear of accidents. This paper introduces a practical model of the self-driving robotics car, which can travel from one position to another on different types of tracks. A Pi-camera module is attached with the help of Raspberry Pi, which sends series of image frames to the Convolutional neural network, which then foretells the car to move in a specific direction, i.e. right, left, forward and reverse direction. The outcome is the robotics car, which travels in the desired direction without any individual effort.<br></p>

scholarly journals Real Time Face Detection using DNN with Raspberry Pi 4

2019 ◽  
Vol 8 (4) ◽  
pp. 9771-9778
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Human Brain ◽  
Raspberry Pi ◽  
Application Range ◽  
Emerging Trends ◽  
The Face ◽  
Image Pixels

The concept of face recognition is in the emerging trends nowadays ,because of its wide application range .Usually ,the face recognition is used in the surveillance ,security and Here, Face recognition is used to allocate attendance for a candidate.Deep neural networks is a group of artificial intelligence entirely based on neural networks, because the algorithm will imitate the human brain, so deep learning can be a kind of imitation of the human brain.Local Binary Pattern (LBP) is a basic but also very advanced creaminess operator that names image pixels through thresholding every pixel's district and considers the outcome as just a binary number.If the recognised face is not authenticated or if unauthorised person is identified by the system ,it immediately alerts the server and the classroom door remains closed. In this project we have created our own database with faculty and students of our section using Logitech C270 HD camera with resolution of 720p/30fps

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

SELISIH BIAYA PRODUKSI SEBAGAI ALAT PENGENDALIAN MANAJEMEN PT INDO PUSAKA BERAU

2019 ◽  
Vol 3 (02) ◽  
pp. 397
Author(s):  
SAFITRI NURHIDAYATI ◽  
RIZKI AMELYA SYAM
Keyword(s):  
Raw Materials ◽  
Field Research ◽  
Analytical Tool ◽  
Production Costs ◽  
Raw Material ◽  
Labor Costs ◽  
Material Costs ◽  
Library Research ◽  
The Difference ◽  
The Cost

This study aims to analyze whether the difference that occurs in the cost of raw materials, direct labor, and factory overhead costs between the standard costs and the actual costs in PLTU LATI is a difference that is favorable or unfavorable. Data collection techniques with field research and library research. The analytical tool used is the analysis of the difference in raw material costs, the difference in direct labor costs and the difference in factory overhead costs. The hypothesis in this study is that the difference allegedly occurs in the cost of raw materials, direct labor costs, and factory overhead costs at PT Indo Pusaka Berau Tanjung Redeb is a favorable difference. The results showed that the difference in the cost of producing MWh electricity at PT Indo Pusaka Berau Tanjung Redeb in 2018, namely the difference in the price of raw material costs Rp. 548,029.80, - is favorable, the difference in quantity of raw materials is Rp. 957,216,602, - is (favorable) , the difference in direct labor costs Rp 2,602,642,084, - is (unfavorable), and the difference in factory overhead costs Rp 8,807,051,422, - is (favorable) This shows that the difference in the overall production cost budget is favorable or profitable. This beneficial difference shows that the company is really able to reduce production costs optimally in 2018.  

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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