Design of Commodity Settlement System Based on Deep Learning

Abstract In order to solve the problem that the salesmen need to scan the bar code of commodity price one by one in supermarket settlement system, a commodity settlement system is proposed. The system hardware includes smart phone, PC and LCD. The image information is obtained after the smart phone scans goods, then the acquired image is uploaded to the upper computer, which identifies commodity and obtains the unit price of goods through the image, finally, the payment QR code is displayed on the LCD screen. Without scanning the cods again at the checkout counter, the payment could be accomplished in a manner that saved time and manpower cost.

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Aplikasi Kuartu Berbasis Android Sebagai Media Pertukaran Informasi Kartu Nama

Jurnal ULTIMATICS ◽

10.31937/ti.v9i1.560 ◽

2017 ◽

Vol 9 (1) ◽

pp. 25-32

Author(s):

Nandi Syukri ◽

Eko Budi Setiawan

Keyword(s):

Mobile Devices ◽

Information Exchange ◽

Smart Phone ◽

Black Box ◽

Qr Code ◽

Business Card ◽

Large Numbers ◽

The World ◽

The Face ◽

Index Terms

Business Card is the most efficient, effective and appropriate tool for every business men no matter they are owners, employees, more over marketers to provide information about their businesses. Unfortunately, it is very difficult to bring and manage business card in large numbers also to remember the face of the business card owner. A Business Card application need to be built to solve all those issues mentioned above. The Application or software must be run in media which can be accessed anywhere and anytime such as smart phone. Kuartu is as business card application run in mobile devices. Kuartu is developed using object base modeling for mobile sub system. The platform of the mobile sub system is android, as it is the most widely used platform in the world. The Kuartu application utilizing NFC and QR Code technology to support the business card information exchange and the Chatting feature for communication. Based on the experiment and test using black box methodology, it can be concluded that Kuartu application makes business card owner to communicate each other easily, business card always carried, easy to manage the cards and information of the business card owner can be easily obtained. Index Terms— Business Card, Android, Kuartu, NFC, QrCode, Chatting.

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Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture

Agronomy ◽

10.3390/agronomy11081551 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1551

Author(s):

Tamoor Khan ◽

Jiangtao Qiu ◽

Hafiz Husnain Raza Sherazi ◽

Mubashir Ali ◽

Sukumar Letchmunan ◽

...

Keyword(s):

Deep Learning ◽

Crop Production ◽

Agricultural Development ◽

Production Efficiency ◽

Fruit Production ◽

National Bureau ◽

System A ◽

Future Production ◽

Learning Techniques ◽

Agricultural Yields

Agricultural advancements have significantly impacted people’s lives and their surroundings in recent years. The insufficient knowledge of the whole agricultural production system and conventional ways of irrigation have limited agricultural yields in the past. The remote sensing innovations recently implemented in agriculture have dramatically revolutionized production efficiency by offering unparalleled opportunities for convenient, versatile, and quick collection of land images to collect critical details on the crop’s conditions. These innovations have enabled automated data collection, simulation, and interpretation based on crop analytics facilitated by deep learning techniques. This paper aims to reveal the transformative patterns of old Chinese agrarian development and fruit production by focusing on the major crop production (from 1980 to 2050) taking into account various forms of data from fruit production (e.g., apples, bananas, citrus fruits, pears, and grapes). In this study, we used production data for different fruits grown in China to predict the future production of these fruits. The study employs deep neural networks to project future fruit production based on the statistics issued by China’s National Bureau of Statistics on the total fruit growth output for this period. The proposed method exhibits encouraging results with an accuracy of 95.56% calculating by accuracy formula based on fruit production variation. Authors further provide recommendations on the AGR-DL (agricultural deep learning) method being helpful for developing countries. The results suggest that the agricultural development in China is acceptable but demands more improvement and government needs to prioritize expanding the fruit production by establishing new strategies for cultivators to boost their performance.

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Deep Learning based Face Mask Recognition System -A Review

2021 6th International Conference on Inventive Computation Technologies (ICICT) ◽

10.1109/icict50816.2021.9358515 ◽

2021 ◽

Author(s):

Priya. K Hari ◽

S Malathi

Keyword(s):

Deep Learning ◽

Face Mask ◽

Recognition System ◽

System A

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Brain Tumor Diagnostic System—A Deep Learning Application

Machine Vision Inspection Systems, Volume 2 ◽

10.1002/9781119786122.ch4 ◽

2021 ◽

pp. 69-90

Author(s):

T. Kalaiselvi ◽

S.T. Padmapriya

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Diagnostic System ◽

System A

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The Effectiveness of the WTO Dispute Settlement System: A Statistical Analysis

Perspectives in Law, Business and Innovation - Transnational Commercial and Consumer Law ◽

10.1007/978-981-13-1080-5_1 ◽

2018 ◽

pp. 1-43 ◽

Cited By ~ 1

Author(s):

Arie Reich

Keyword(s):

Statistical Analysis ◽

Dispute Settlement ◽

Settlement System ◽

System A ◽

Wto Dispute Settlement ◽

Dispute Settlement System

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Research on the Application of Artificial Intelligence Machine Learning Technology in Improving the Accuracy of Engineering Image Processing

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/4/042007 ◽

2021 ◽

Vol 2083 (4) ◽

pp. 042007

Author(s):

Xiaowen Liu ◽

Juncheng Lei

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Deep Learning ◽

Image Recognition ◽

Recognition Performance ◽

Color Space ◽

Gaussian Model ◽

Image Feature ◽

Layer By Layer ◽

Image Information

Abstract Image recognition technology mainly includes image feature extraction and classification recognition. Feature extraction is the key link, which determines whether the recognition performance is good or bad. Deep learning builds a model by building a hierarchical model structure like the human brain, extracting features layer by layer from the data. Applying deep learning to image recognition can further improve the accuracy of image recognition. Based on the idea of clustering, this article establishes a multi-mix Gaussian model for engineering image information in RGB color space through offline learning and expectation-maximization algorithms, to obtain a multi-mix cluster representation of engineering image information. Then use the sparse Gaussian machine learning model on the YCrCb color space to quickly learn the distribution of engineering images online, and design an engineering image recognizer based on multi-color space information.

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Stencil control in the automatic insertion of a PIM Company

International Journal for Innovation Education and Research ◽

10.31686/ijier.vol8.iss10.2697 ◽

2020 ◽

Vol 8 (10) ◽

pp. 417-440

Author(s):

Cláudio César Vasconcelos Barros ◽

Jonas Gomes da Silva

Keyword(s):

Qr Code ◽

Process Data ◽

Bar Code ◽

Ishikawa Diagram ◽

Manufactured Products ◽

Quality Tools ◽

A Company ◽

Computerized System

The article evaluated the control of Stencil in the subprocess of Printing of the SMD line of a company located in the Industrial Pole of Manaus (PIM), to provide subsidies to develop a computerized system. With computerization, the focus of employees will be directed to the activities of production and quality of manufactured products, also, the collection of process data, done in real-time, will allow managers to better monitor and take actions in the process. To this end, a case study, bibliographic research of articles, dissertations, and theses involving the theme, and documentary research (forms, records, etc.) with the sectors involved were used. The descriptive statistics method was applied, quality tools were used, aimed at identifying and solving problems such as PDCA, Pareto, Ishikawa Diagram, flow chart, and 5W2H. A study of the activities related to the control of the Stencil was carried out, of the documentation used in the process, as well as of the factors and causes related to the effective Stencil control. Among the results, 24 causes affect the performance of the Stencil control, concluding that the main failures were human, due to the prioritization of production goals by the employees, leaving the other activities in the background, which is why the 24 guidelines proposed for the computerization of this process become relevant, some of which are: defining means to identify each Stencil using a bar code or QR code; do not allow the use of the Stencil if one of the activities unfinished in the process; stop production when an activity is not performed; digitize the documents used in this process; create an automatic notification to those responsible, when an action is necessary, etc.

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