Pub Date : 2020-10-07DOI: 10.1109/AICT50176.2020.9368770
Aktham Sawan, Rashid Jayousi
Globalization and liberalization of the economy dramatically shifted the nature of business competition. The emergence of new technology in business operations has intensified rivalry and generated new opportunities for service providers. In order to deal with increasing situations, businesses are turning their focus to maintaining current clients rather than acquiring new ones. This is more cost-effective and therefore needs fewer energy. In this article, future mobile internet customers are investigated on the basis of machine learning and deep learning strategies applied to consumer activity and usage knowledge, which can assist new mobile internet customers. This paper utilized consumer usage and similar knowledge from a telephone service provider to examine mobile internet customers in the telecommunications industry. XGBoost and Random Forest the decision tree ensembles are used as basic statistical machine learning models for the development of a binary mobile internet classifier. The implementation component was developed using Python, a state-of-the-art structured data processing platform for machine learning and data mining. Many ML and deep learning approaches such as (K-Nearest Neighbors KNN, logistic regression, Support Vector Machine SVM, and Deep Neural Network DNN) have been tested to achieve greater and more successful outcomes and results.
{"title":"Machine Learning Approaches to Predict New Mobile Internet Customers","authors":"Aktham Sawan, Rashid Jayousi","doi":"10.1109/AICT50176.2020.9368770","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368770","url":null,"abstract":"Globalization and liberalization of the economy dramatically shifted the nature of business competition. The emergence of new technology in business operations has intensified rivalry and generated new opportunities for service providers. In order to deal with increasing situations, businesses are turning their focus to maintaining current clients rather than acquiring new ones. This is more cost-effective and therefore needs fewer energy. In this article, future mobile internet customers are investigated on the basis of machine learning and deep learning strategies applied to consumer activity and usage knowledge, which can assist new mobile internet customers. This paper utilized consumer usage and similar knowledge from a telephone service provider to examine mobile internet customers in the telecommunications industry. XGBoost and Random Forest the decision tree ensembles are used as basic statistical machine learning models for the development of a binary mobile internet classifier. The implementation component was developed using Python, a state-of-the-art structured data processing platform for machine learning and data mining. Many ML and deep learning approaches such as (K-Nearest Neighbors KNN, logistic regression, Support Vector Machine SVM, and Deep Neural Network DNN) have been tested to achieve greater and more successful outcomes and results.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128222614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/AICT50176.2020.9368790
Zhukabayeva T. K, Mardenov E. M, Abdildaeva A.A
There are many vulnerabilities to attack in wireless sensor networks. Among them, the sybil attack is especially malicious to generate many false nodes and enter false information on the network. They are detrimental to many functions of the FSU, such as data pooling, fair distribution of resources, etc. Therefore, it is crucial to protect and detect Sybil attacks. The Sybil attack has a significant impact on network performance, and once it was detected, network performance will be obviously improving.In this article, we consider a new method of detecting Sybil attacks using random keys. In the proposed method, signs are used that there is a weak connection between the group of normal nodes and the group of false nodes. Experiment results show that the proposed method detects a false node with a probability of more than 90% with a small energy consumption.
{"title":"Sybil Attack Detection In Wireless Sensor Networks","authors":"Zhukabayeva T. K, Mardenov E. M, Abdildaeva A.A","doi":"10.1109/AICT50176.2020.9368790","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368790","url":null,"abstract":"There are many vulnerabilities to attack in wireless sensor networks. Among them, the sybil attack is especially malicious to generate many false nodes and enter false information on the network. They are detrimental to many functions of the FSU, such as data pooling, fair distribution of resources, etc. Therefore, it is crucial to protect and detect Sybil attacks. The Sybil attack has a significant impact on network performance, and once it was detected, network performance will be obviously improving.In this article, we consider a new method of detecting Sybil attacks using random keys. In the proposed method, signs are used that there is a weak connection between the group of normal nodes and the group of false nodes. Experiment results show that the proposed method detects a false node with a probability of more than 90% with a small energy consumption.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132516621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/AICT50176.2020.9368835
K. Nisar, Ibrahim A. Lawal, Usman Ismail Abdulmalik, A. Mu'azu, B. Chowdhry, Sohrab Khan, Shuaib K. Memon
Mobile Ad Hoc Networks (MANETs) are types of wireless networks that communicate with mobile devices without centralized infrastructures. MANET networks are established through interconnected devices that communicate wirelessly within a relatively small, shared area. In MANET every single mobile node is presumed to travel in all directions at different speeds with challenges and open issues. Hence there is no guaranteed long-term path from one node to the next. This work proposes testing the three most common ad hoc routing protocols Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR) and Optimized Link State Routing Protocol (OLSR) using Optimized Network Engineering Tool (OPNET) Modeler simulations using the performance metrics of Throughput, Delay, and Network loading to find an effective routing protocol for routing. The traffic network is used by the File Transfer Protocol (FTP), Digital Education, Battlefield, Surveillance and Security Agencies may benefit from the research work. MANETs reduced the costs of installation, maintenance and operation of such facilities as base stations and also reduced the risk to a minimum such as pollution. The outcome of the simulation shows that: according to the AODV and DSR, the lowest delay in 50 nodes was around 31.25 seconds respectively. And OLSR also had a high throughput performance of around 80 per cent compared with AODV and DSR. And it can be concluded that OLSR is the most suitable routing protocol for MANET, based on the routing protocols suggested.
{"title":"QoS Analysis of the MANET routing protocols with Respect to Delay, Throughput, & Network load: Challenges and Open Issues","authors":"K. Nisar, Ibrahim A. Lawal, Usman Ismail Abdulmalik, A. Mu'azu, B. Chowdhry, Sohrab Khan, Shuaib K. Memon","doi":"10.1109/AICT50176.2020.9368835","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368835","url":null,"abstract":"Mobile Ad Hoc Networks (MANETs) are types of wireless networks that communicate with mobile devices without centralized infrastructures. MANET networks are established through interconnected devices that communicate wirelessly within a relatively small, shared area. In MANET every single mobile node is presumed to travel in all directions at different speeds with challenges and open issues. Hence there is no guaranteed long-term path from one node to the next. This work proposes testing the three most common ad hoc routing protocols Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR) and Optimized Link State Routing Protocol (OLSR) using Optimized Network Engineering Tool (OPNET) Modeler simulations using the performance metrics of Throughput, Delay, and Network loading to find an effective routing protocol for routing. The traffic network is used by the File Transfer Protocol (FTP), Digital Education, Battlefield, Surveillance and Security Agencies may benefit from the research work. MANETs reduced the costs of installation, maintenance and operation of such facilities as base stations and also reduced the risk to a minimum such as pollution. The outcome of the simulation shows that: according to the AODV and DSR, the lowest delay in 50 nodes was around 31.25 seconds respectively. And OLSR also had a high throughput performance of around 80 per cent compared with AODV and DSR. And it can be concluded that OLSR is the most suitable routing protocol for MANET, based on the routing protocols suggested.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128505672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/AICT50176.2020.9368653
S. V. Begicheva
This study was carried out in order to develop a simulation model for a complex assessment of the quality of emergency medical services in a large metropolitan area. The study methods are based on the Donabedian model, a conceptual model that provides a framework for evaluating the quality of health care and emergency medical services. According to the Donabedian model, information about the quality of care can be drawn from three categories: the quality of structure, the quality of the process, and the quality of outcomes. The fourth category — environmental quality — is proposed to be added to the model in order to consider the specifics of emergency medical services in a large metropolitan area. The proposed conceptual model for the quality of emergency medical services defines the approach to emergency medical services modeling, i.e. creating a set of submodels that would allow assessing all the quality categories for emergency medical services. The paper includes a case study for such a model that combines methods from system dynamics and agent-based modeling. The study results can be used for health care institution modeling in order to lay down recommendations regarding how the health care institution management strategy can be improved.
{"title":"Donabedian Approach for Simulation Modeling to Evaluate the Quality of Emergency Medical Services in a Large Metropolitan Area: A Case Study","authors":"S. V. Begicheva","doi":"10.1109/AICT50176.2020.9368653","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368653","url":null,"abstract":"This study was carried out in order to develop a simulation model for a complex assessment of the quality of emergency medical services in a large metropolitan area. The study methods are based on the Donabedian model, a conceptual model that provides a framework for evaluating the quality of health care and emergency medical services. According to the Donabedian model, information about the quality of care can be drawn from three categories: the quality of structure, the quality of the process, and the quality of outcomes. The fourth category — environmental quality — is proposed to be added to the model in order to consider the specifics of emergency medical services in a large metropolitan area. The proposed conceptual model for the quality of emergency medical services defines the approach to emergency medical services modeling, i.e. creating a set of submodels that would allow assessing all the quality categories for emergency medical services. The paper includes a case study for such a model that combines methods from system dynamics and agent-based modeling. The study results can be used for health care institution modeling in order to lay down recommendations regarding how the health care institution management strategy can be improved.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123485490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/AICT50176.2020.9368678
Obadah Hammoud, I. Tarkhanov
this research is dedicated to the development of a prototype of open infrastructure for users’ internet traffic filtering on a browser level. We described the advantages of a distributed approach in comparison with current centralized solutions. Besides, we suggested a solution to define the optimum size for a URL storage block in Ethereum network. This solution may be used for the development of infrastructure of DApps applications on Ethereum network in future. The efficiency of the suggested approach is supported by several experiments.
{"title":"Blockchain-based open infrastructure for URL filtering in an Internet browser","authors":"Obadah Hammoud, I. Tarkhanov","doi":"10.1109/AICT50176.2020.9368678","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368678","url":null,"abstract":"this research is dedicated to the development of a prototype of open infrastructure for users’ internet traffic filtering on a browser level. We described the advantages of a distributed approach in comparison with current centralized solutions. Besides, we suggested a solution to define the optimum size for a URL storage block in Ethereum network. This solution may be used for the development of infrastructure of DApps applications on Ethereum network in future. The efficiency of the suggested approach is supported by several experiments.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114663966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/AICT50176.2020.9368692
Veysel Sercan Ağzıyağlı, H. Oğul
Recent interest in e-learning and distance education services has significantly increased the amount of lecture video data in public and institutional repositories. In their current forms, users can browse in these collections using meta-data-based search queries such as course name, description, instructor and syllabus. However, lecture video entries have rich contents, including image, text and speech, which can not be easily represented by meta-data annotations. Therefore, there is an emerging need to develop tools that will automatically annotate lecture videos to facilitate more targeted search. A simple way to realize this is to classify lectures into known categories. With this objective, this paper presents a method for classifying videos based on extracted text content in several semantic levels. The method is based on Bidirectional Long-Short Term Memory (Bi-LSTM) applied on word embedding vectors of text content extracted by Optical Character Recognition (OCR). This approach can outperform conventional machine learning models and provide a useful solution for automatic lecture video annotation to support online education.
{"title":"Multi-level lecture video classification using text content","authors":"Veysel Sercan Ağzıyağlı, H. Oğul","doi":"10.1109/AICT50176.2020.9368692","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368692","url":null,"abstract":"Recent interest in e-learning and distance education services has significantly increased the amount of lecture video data in public and institutional repositories. In their current forms, users can browse in these collections using meta-data-based search queries such as course name, description, instructor and syllabus. However, lecture video entries have rich contents, including image, text and speech, which can not be easily represented by meta-data annotations. Therefore, there is an emerging need to develop tools that will automatically annotate lecture videos to facilitate more targeted search. A simple way to realize this is to classify lectures into known categories. With this objective, this paper presents a method for classifying videos based on extracted text content in several semantic levels. The method is based on Bidirectional Long-Short Term Memory (Bi-LSTM) applied on word embedding vectors of text content extracted by Optical Character Recognition (OCR). This approach can outperform conventional machine learning models and provide a useful solution for automatic lecture video annotation to support online education.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116802963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/AICT50176.2020.9368665
A. Mashkova, E. Novikova, O. Savina
In this paper we present the agent-based model of the Russian Federation spatial development as a tool for simulation of business projects in key industries and their impact on the regional economy. The model interface is used to define the region and industries for each project, as well as structure of its co-financing by organizations, federal and regional budget. Within the algorithm of the project realization the plan of investment supplies is determined, supplementary processes of the organization are simulated and reflected in the accounting. Within the computational experiment results of business projects realization in various regions were studied. The experimental program includes four regions from different federal districts. In each region, three key industries were selected according to perspective economic specializations defined in the Strategy of the Russian Federation spatial development. The results show significant increase in the output, property and equipment of the organizations, and also in employment and income of population in the selected regions.
{"title":"Simulation of Business Projects in the Agent Model of the Russian Federation Spatial Development","authors":"A. Mashkova, E. Novikova, O. Savina","doi":"10.1109/AICT50176.2020.9368665","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368665","url":null,"abstract":"In this paper we present the agent-based model of the Russian Federation spatial development as a tool for simulation of business projects in key industries and their impact on the regional economy. The model interface is used to define the region and industries for each project, as well as structure of its co-financing by organizations, federal and regional budget. Within the algorithm of the project realization the plan of investment supplies is determined, supplementary processes of the organization are simulated and reflected in the accounting. Within the computational experiment results of business projects realization in various regions were studied. The experimental program includes four regions from different federal districts. In each region, three key industries were selected according to perspective economic specializations defined in the Strategy of the Russian Federation spatial development. The results show significant increase in the output, property and equipment of the organizations, and also in employment and income of population in the selected regions.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121258363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/AICT50176.2020.9368866
Z. Yuldashev, A. Nemirko, D. Ripka
Detection algorithm of the ventricular late potentials using surface ECG signal for the diagnostics of abnormal electrical excitation of ventricular myocardium is developed, ventricular late potentials indicators reflecting the area size and degree of the ventricular excitation abnormality, verification method of abnormality detection using intracardial electrograms are suggested.
{"title":"Algorithm for the Abnormal Ventricular Electrical Excitation Detection","authors":"Z. Yuldashev, A. Nemirko, D. Ripka","doi":"10.1109/AICT50176.2020.9368866","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368866","url":null,"abstract":"Detection algorithm of the ventricular late potentials using surface ECG signal for the diagnostics of abnormal electrical excitation of ventricular myocardium is developed, ventricular late potentials indicators reflecting the area size and degree of the ventricular excitation abnormality, verification method of abnormality detection using intracardial electrograms are suggested.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115165765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/AICT50176.2020.9368862
I. Karimov, S. Jafarova, M. Zeynalli, S. Rustamov, A. Adamov, Aslan Babakhanov
This research paper examines the full lifecycle of the case of turning the gas and oil industry to a data-driven operation model. The study presents the approach of re-engineering production data, building a predictive model for temperature forecast, statistical analysis, and visualization of Distributed Temperature Sensing (DTS) data provided by the oil-gas industry. For better analysis, the raw data have been pre-processed and organized according to the proper model. Furthermore, after the data organization, we proceed with observing relationships among three features (Date, Depth and Temperature), analyze vague upheavals and similarities utilizing plot histograms, scatterplots, box plots, heatmaps, violin plots for better visualization. Since the drastic temperature change indicates the anomaly, several alternative Outlier Detection Techniques are offered to predict early equipment failure and prevent production outage. Our results indicated a high correlation between depth and temperature, presence of trend in temperature distribution, and temperature drops in specific ranges. Proper analysis of the data allows the specialist to understand reservoir performance and prolong the production file of the wells.
{"title":"Transformation, Analysis and Visualization of Distributed Temperature Sensing Data generated by Oil Wells","authors":"I. Karimov, S. Jafarova, M. Zeynalli, S. Rustamov, A. Adamov, Aslan Babakhanov","doi":"10.1109/AICT50176.2020.9368862","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368862","url":null,"abstract":"This research paper examines the full lifecycle of the case of turning the gas and oil industry to a data-driven operation model. The study presents the approach of re-engineering production data, building a predictive model for temperature forecast, statistical analysis, and visualization of Distributed Temperature Sensing (DTS) data provided by the oil-gas industry. For better analysis, the raw data have been pre-processed and organized according to the proper model. Furthermore, after the data organization, we proceed with observing relationships among three features (Date, Depth and Temperature), analyze vague upheavals and similarities utilizing plot histograms, scatterplots, box plots, heatmaps, violin plots for better visualization. Since the drastic temperature change indicates the anomaly, several alternative Outlier Detection Techniques are offered to predict early equipment failure and prevent production outage. Our results indicated a high correlation between depth and temperature, presence of trend in temperature distribution, and temperature drops in specific ranges. Proper analysis of the data allows the specialist to understand reservoir performance and prolong the production file of the wells.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124049368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-07DOI: 10.1109/AICT50176.2020.9368798
S. Rakhimov, A. Seytov, N. Rakhimova, Bahrom Xonimqulov
Mathematical models of optimum water distribution in channels of irrigation systems under the conditions of discrete water supply to consumers have been developed. These include the models of direct wave, kinematical wave, convectional and diffusive model, as well as complete model of unsteady water flow in channel sections, and take into account all main hydraulic characteristics of the channel section.
{"title":"Mathematical models of optimal distribution of water in main channels","authors":"S. Rakhimov, A. Seytov, N. Rakhimova, Bahrom Xonimqulov","doi":"10.1109/AICT50176.2020.9368798","DOIUrl":"https://doi.org/10.1109/AICT50176.2020.9368798","url":null,"abstract":"Mathematical models of optimum water distribution in channels of irrigation systems under the conditions of discrete water supply to consumers have been developed. These include the models of direct wave, kinematical wave, convectional and diffusive model, as well as complete model of unsteady water flow in channel sections, and take into account all main hydraulic characteristics of the channel section.","PeriodicalId":136491,"journal":{"name":"2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125303465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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