Intelligent Management of Remote Facilities through a Ubiquitous Cloud Middleware

2009 ◽  
Author(s):  
Chang Ho Yun ◽  
Hyuck Han ◽  
Hae Sun Jung ◽  
Heon Young Yeom ◽  
Yong Woo Lee
Keyword(s):  
Intelligent Management

Intelligent management systems in operations: a review

1998 ◽  
Vol 49 (7) ◽  
pp. 682-699 ◽  
Author(s):  
N C Proudlove ◽  
S Vaderá ◽  
K A H Kobbacy
Keyword(s):  
Management Systems ◽  
Intelligent Management

Construction of full-process intelligent management and service platform of agricultural products logistics

2014 ◽  
Author(s):  
Xin Chen ◽  
Run Xiang ◽  
Wanlin Gao ◽  
Guosheng HuangPu ◽  
Hui Hu ◽  
...  
Keyword(s):  
Agricultural Products ◽  
Service Platform ◽  
Intelligent Management

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

Cloud Computing With Intelligent Management To Improve Stability And Security

2018 ◽  
pp. 327
Author(s):  
مازن هيثم رزوقى الشيخلي
Keyword(s):  
Cloud Computing ◽  
Intelligent Management ◽  
Improve Stability

Hybrid Combination of GIS,GPS,WSN and GPRS Technology in Modern Digital Agriculture Application

2010 ◽  
Vol 108-111 ◽  
pp. 1158-1163 ◽  
Author(s):  
Peng Cheng Nie ◽  
Di Wu ◽  
Weiong Zhang ◽  
Yan Yang ◽  
Yong He
Keyword(s):  
Large Scale ◽  
Field Application ◽  
Wireless Sensor ◽  
Information Collection ◽  
Scale Field ◽  
Large Scale Field ◽  
Field Information ◽  
Intelligent Management ◽  
Digital Agriculture ◽  
And Control

In order to improve the information management of the modern digital agriculture, combined several modern digital agriculture technologies, namely wireless sensor network (WSN), global positioning system (GPS), geographic information system (GIS) and general packet radio service (GPRS), and applied them to the information collection and intelligent control process of the modern digital agriculture. Combining the advantage of the local multi-channel information collection and the low-power wireless transmission of WSN, the stable and low cost long-distance communication and data transmission ability of GPRS, the high-precision positioning technology of the DGPS positioning and the large-scale field information layer-management technology of GIS, such a hybrid technology combination is applied to the large-scale field information and intelligent management. In this study, wireless sensor network routing nodes are disposed in the sub-area of field. These nodes have GPS receiver modules and the electric control mechanism, and are relative positioned by GPS. They can real-time monitor the field information and control the equipment for the field application. When the GPS position information and other collected field information are measured, the information can be remotely transmitted to PC by GPRS. Then PC can upload the information to the GIS management software. All the field information can be classified into different layers in GIS and shown on the GIS map based on their GPS position. Moreover, we have developed remote control software based on GIS. It can send the control commands through GPRS to the nodes which have control modules; and then we can real-time manage and control the field application. In conclusion, the unattended automatic wireless intelligent technology for the field information collection and control can effectively utilize hardware resources, improve the field information intelligent management and reduce the information and intelligent cost.

scholarly journals An intelligent management system for aquaponics

2021 ◽  
Vol 69 (4) ◽  
pp. 345-350
Author(s):  
Divas Karimanzira ◽  
Thomas Rauschenbach
Keyword(s):  
Food Production ◽  
Large Scale ◽  
Optimum Conditions ◽  
Sustainable Food ◽  
Water Recirculation ◽  
Increase Productivity ◽  
Intelligent Management ◽  
Set Up ◽  
Aquaculture Production ◽  
The Eu

Abstract Population rise, climate change, soil degradation, water scarcity, and food security require efficient and sustainable food production. Aquaponics is a highly efficient way of farming and is becoming increasingly popular. However, large scale aquaponics still lack stability, standardization and proof of economical profitability. The EU-INAPRO project helps to overcome these limitations by introducing digitization, enhanced technology, and developing standardized modular scalable solutions and demonstrating the viability of large aquaponics. INAPRO is based on an innovation a double water recirculation system (DRAPS), one for fish, and the other one for crops. In DRAPS, optimum conditions can be set up individually for fish and crops to increase productivity of both. Moreover, the integration of digital technologies and data management in the aquaculture production and processing systems will enable full traceability and transparency in the processes, increasing consumers’ trust in aquaculture products. In this paper, the innovations and the digitization approach will be introduced and explained and the key benefits of the system will be emphasized.

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