Predicting the Quality of MIS Characteristics and End-Users’ Perceptions Using Artificial Intelligence Tools: Expert Systems and Neural Network

2019 ◽  
pp. 18-30
Author(s):  
Kamal Mohammed Alhendawi ◽  
Ala Aldeen Al-Janabi ◽  
Jehad Badwan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Expert Systems ◽  
End Users

Construction of first-class university course based on artificial intelligence and neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Yingli Duan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Vocational Training ◽  
Network Algorithms ◽  
Teaching Efficiency ◽  
Neural Network Algorithm ◽  
Solid Foundation ◽  
University Level ◽  
University Course

Curriculum is the basis of vocational training, its development level and teaching efficiency determine the realization of vocational training objectives, as well as the quality and level of major vocational academic training. Therefore, the development of curriculum is an important issue. And affect the school’s teaching capacity building. The analysis of the latest developments in the main courses shows that there are some deviations or irrationalities in the curriculum in some colleges and universities, and the general problems of understanding the latest courses, such as lack of solid foundation in curriculum setting, unclear direction of objectives, unclear reform ideas, inadequate and systematic construction measures, lack of attention to the quality of education. This paper explains the rules for the establishment of first-level courses, clarifies the ideas and priorities of architecture, and explores strategies for building university-level courses using knowledge of artificial intelligence and neural network algorithms in order to gain experience from them.

scholarly journals Demand Modelling in Telecommunications

10.14311/1121 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
M. Chvalina
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Term Forecast ◽  
Artificial Intelligence Methods ◽  
Demand Modelling

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models. 

scholarly journals Astrochemistry Library with Artificial Intelligence for Quality Control

1992 ◽  
Vol 150 ◽  
pp. 19-20
Author(s):  
Sheo S. Prasad ◽  
Pradip Gangopadhaya
Keyword(s):  
Artificial Intelligence ◽  
Quality Control ◽  
Expert Systems ◽  
Large Scale

Libraries of reactions used in astrochemistry modeling have seen an explosive increase in size in recent years. Their quality control by manual effort is almost impossible. Expert systems with artificial intelligence are now needed to ensure the quality of large scale astrochemistry libraries.

Artificial Intelligence in Aviation Industries

2020 ◽  
pp. 1-35
Author(s):  
Tetiana Shmelova ◽  
Arnold Sterenharz ◽  
Serge Dolgikh
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Unmanned Aircraft ◽  
Innovative Technology ◽  
Sociotechnical System ◽  
Air Traffic Controllers ◽  
High Degree ◽  
Difficult Situations

This chapter presents opportunities to use Artificial Intelligence (AI) in aviation and aerospace industries. The AI used an innovative technology for improving the effectiveness of building aviation systems in each stage of the lifecycle for enhancing the security of aviation systems and the characteristic ability to learn, improve, and predict difficult situations. The AI is presented in Air Navigation Sociotechnical system (ANSTS) because the activity of ANSTS, is accompanied by a high degree of risk of causing catastrophic outcomes. The operator's models of decision making in AI systems are presented such as Expert Systems, Decision Support Systems for pilots of manned and unmanned aircraft, air traffic controllers, engineers, etc. The quality of operator's decisions depends on the development and use of innovative technology of AI and related fields (Big Data, Data Mining, Multicriteria Decision Analysis, Collaboration Decision Making, Blockchain, Artificial Neural Network, etc.).

scholarly journals System of providing sustainability of tower cranes from overturn in extreme wind loads

2018 ◽  
Vol 224 ◽  
pp. 02086
Author(s):  
Pavel Sorokin ◽  
Alexey Mishin ◽  
Vitaliy Antsev ◽  
Alexey Red’kin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Models ◽  
Wind Loads ◽  
Neural Network Models ◽  
Quality Of Work ◽  
Development Stages ◽  
The Neural Network ◽  
Extreme Wind

The article is devoted to the issues of ensuring stability of tower cranes from overturn. The development stages of devices for ensuring tower cranes safety are examined and their shortcomings are revealed. The system consisting of subsystems and drives is proposed and their interaction is presented. The article deals with a subsystem based on artificial intelligence methods. The neural network models of forecasting wind parameters are developed. The quality of work of neural network models is estimated. The ways of further topic development are suggested.

Implementation of Neural Network in Classifying the Product Quality for the ABS Metallization

2020 ◽  
Vol 305 ◽  
pp. 139-146
Author(s):  
Yuh Wen Chen ◽  
Sheng Chieh Wang ◽  
Pin Chuan Yao ◽  
Wen Tsung Lin ◽  
Aisyah Larasati ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Quality Control ◽  
Surface Treatment ◽  
Network Models ◽  
Acrylonitrile Butadiene Styrene ◽  
Operational Parameters ◽  
Neural Network Models ◽  
Quality Control Tool

The surface treatment conditions of a plastic surface are related to the quality of finished products. Usually, more than 20 causes dominate the success of electroplating for acrylonitrile butadiene styrene (ABS). Thus, the quality control is very complicated and challenging. Even nowadays, most of the production quality still relies on the operator's experience and intuition. This research takes a company of water hardware in Taiwan as the research object. We propose a revolutionary concept of quality management, combining artificial intelligence and surface treatment process altogether. We set up a parameter monitoring system during production to predict the quality of ABS metallization using neural network models such as artificial intelligence forms the basis of the intelligent manufacturing system. It can be used as a quality control tool to improve quality yield and industrial competitiveness. Totally 13 operational parameters (causes) and one quality parameter (consequence) of the electroplating tanks were collected from time to time to build the NN models. Interestingly, we finally find the fuzzy NN model performs better than the precise NN model. We conclude this is resulting from the limitation and vagueness of data.

scholarly journals USE OF ELEMENTS OF ARTIFICIAL INTELLIGENCE IN THE ANALYSIS OF INFOCOMMUNICATION TRAFFIC

T-Comm ◽  
2020 ◽  
Vol 14 (12) ◽  
pp. 66-71
Author(s):  
Shakhmaran Zh. Seilov ◽  
◽  
Vadim Yu. Goikhman ◽  
Yerden Zhursinbek ◽  
Mereilim N. Kassenova ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Expert Systems ◽  
Communication Networks ◽  
Network Architecture ◽  
Information Technologies ◽  
Signal Propagation ◽  
Traffic Analysis ◽  
Video Data

Modern communication networks are based on multi-service networks, which are a single telecommunications structure that can transmit large volumes of multi-format information (voice, video, data) and provide users with a variety of information and communication services. Traffic transmitted in multiservice networks differs significantly from traditional traffic of telephone or other homogeneous networks. Knowledge of the nature of modern traffic is necessary for the successful construction, operation and development of multi-service communication networks, providing users with high-quality services, and efficient use of funds allocated for network development. To learn the properties of infocommunication traffic, new methodological techniques are currently used, as well as promising information technologies such as Big Data and data mining. The article is devoted to the use of such elements of artificial intelligence as expert systems and neural network technologies in relation to the analysis of infocommunication traffic. The article examines the structure of expert systems, analyzes the applied search strategies and decision-making methods. The article also provides an overview of the architecture of neural networks in relation to traffic analysis tasks. The traffic analysis task is a classification task. The feasibility of using multi-layer neural networks with direct signal propagation for traffic analysis is shown. The following neural network architecture was chosen: the input layer, in accordance with the dimension of the input signal, contained 51 neurons, two hidden layers with 20 and 10 neurons, respectively, and the output layer with five neurons, according to the number of specified types of distributions. The results obtained showed a satisfactory quality of the neural network developed and trained in the framework of the research.

scholarly journals MoView Engine : An Open Source Movie Recommender

2020 ◽  
Vol 32 ◽  
pp. 03008
Author(s):  
Vallari Manavi ◽  
Anjali Diwate ◽  
Priyanka Korade ◽  
Anita Senathi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Human Brain ◽  
Network Model ◽  
Neural Network Model ◽  
Recommendation System ◽  
Activation Function ◽  
End Users ◽  
History Of

Recommendation is an ideology that works as choice-based system for the end users. Users are recommended with their favorite movies based on history of other watched movies or based on the category of the movies. These types of recommendations are becoming popular because of their ability to think and react as human brain. For this purpose, deep learning or artificial intelligence comes into picture. It is the ability to think as a human brain as give the output best suited to the end users liking. This paper focuses on implementing the recommendation system of movies using deep learning with neural network model using the activation function of SoftMax to give an experience to users as friendly recommendation. Moreover, this paper focuses on different scenarios of recommendation like the recommendation based on history, genre of the movie etc.

scholarly journals Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer

2019 ◽  
Vol 1 (1) ◽  
pp. 466-482 ◽  
Author(s):  
Vinícius Silva Araújo ◽  
Augusto Guimarães ◽  
Paulo de Campos Souza ◽  
Thiago Silva Rezende ◽  
Vanessa Souza Araújo
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Expert Systems ◽  
Hybrid Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Rules ◽  
Medical Specialists ◽  
Fuzzy Neural

Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such as breast cancer. In this work, we used a hybrid artificial intelligence model based on concepts of neural networks and fuzzy systems to assist in the identification of people with breast cancer through fuzzy rules. The hybrid model can manipulate the data collected in medical examinations and identify patterns between healthy people and people with breast cancer with an acceptable level of accuracy. These intelligent techniques allow the creation of expert systems based on logical rules of the IF/THEN type. To demonstrate the feasibility of applying fuzzy neural networks, binary pattern classification tests were performed where the dimensions of the problem are used for a model, and the answers identify whether or not the patient has cancer. In the tests, experiments were replicated with several characteristics collected in the examinations done by medical specialists. The results of the tests, compared to other models commonly used for this purpose in the literature, confirm that the hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer. In addition to coherent predictions, the fuzzy neural network enables the construction of systems in high level programming languages to build support systems for physicians’ actions during the initial stages of treatment of the disease with the fuzzy rules found, allowing the construction of systems that replicate the knowledge of medical specialists, disseminating it to other professionals..

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