Business viability of potential software projects using artificial neural network

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
O. Okolo ◽  
B.Y Baha
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Project Selection ◽  
Business Value ◽  
Synthetic Approach ◽  
Software Project ◽  
Unseen Data ◽  
Artificial Neural

Selection of a software project is a critical decision. This selection involves prediction to ascertain a project that provides the best business value to the organization. The process of selection is carefully undertaken to optimize scarce resources available, which makes it impossible to simultaneously invest in all business ideas and systems. The current traditional method of software selection does not consider risk factors among the many variables necessary to predict a project that could provide the best business value. More so, the current method such as an artificial intelligence approach, where project managers use more robust models to make predictions have not received the needed attention in developing models for software project selection. This research applied a branch of Artificial Intelligence called Artificial Neural Network to classify projects into three levels. The research designed an artificial neural network of four inputs, one hidden layer with twenty-seven (27) neurons, and three outputs. Keras, a python deep learning library that runs on a theano background was used to implement the model. This research used a secondary dataset, which was enhanced by the synthetic approach, to make the required data features needed in machine learning applications. Backpropagation Algorithm enabled the model to train and learn from the data, and K-fold cross-validation was used to measure the accuracy of the model on unseen data. The results of the simulation showed that the model performed up to 98.67% accuracy with a standard deviation of 2.6% performance on unseen data. The research concludes that using the artificial neural network for software project selection unleashes a new vista of opportunities in artificial i ntelligence where intelligent systems are developed based on robust models from data forproject selection.Keywords: Artificial Neural Network, Project selection, Machine LearningVol. 26, No. 1, June 2019

Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Tamer Moussa ◽  
Salaheldin Elkatatny ◽  
Mohamed Mahmoud ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Well Log ◽  
Well Test ◽  
Log Data ◽  
Hydrocarbon Reservoir ◽  
Reservoir Permeability ◽  
Artificial Neural ◽  
Core Permeability

Permeability is a key parameter related to any hydrocarbon reservoir characterization. Moreover, many petroleum engineering problems cannot be precisely answered without having accurate permeability value. Core analysis and well test techniques are the conventional methods to determine permeability. These methods are time-consuming and very expensive. Therefore, many researches have been introduced to identify the relationship between core permeability and well log data using artificial neural network (ANN). The objective of this research is to develop a new empirical correlation that can be used to determine the reservoir permeability of oil wells from well log data, namely, deep resistivity (RT), bulk density (RHOB), microspherical focused resistivity (RSFL), neutron porosity (NPHI), and gamma ray (GR). A self-adaptive differential evolution integrated with artificial neural network (SaDE-ANN) approach and evolutionary algorithm-based symbolic regression (EASR) techniques were used to develop the correlations based on 743 actual core permeability measurements and well log data. The obtained results showed that the developed correlations using SaDE-ANN models can be used to predict the reservoir permeability from well log data with a high accuracy (the mean square error (MSE) was 0.0638 and the correlation coefficient (CC) was 0.98). SaDE-ANN approach is more accurate than the EASR. The introduced technique and empirical correlations will assist the petroleum engineers to calculate the reservoir permeability as a function of the well log data. This is the first time to implement and apply SaDE-ANN approaches to estimate reservoir permeability from well log data (RSFL, RT, NPHI, RHOB, and GR). Therefore, it is a step forward to eliminate the required lab measurements for core permeability and discover the capabilities of optimization and artificial intelligence models as well as their application in permeability determination. Outcomes of this study could help petroleum engineers to have better understanding of reservoir performance when lab data are not available.

scholarly journals Analyses of the method development of decisions in an expert system with the use of information from an artificial neural network

2018 ◽  
Vol 178 ◽  
pp. 07002 ◽  
Author(s):  
Stanisław Duer ◽  
Konrad Zajkowski ◽  
Serghei Scaticailov ◽  
Paweł Wrzesień
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Intelligent Systems ◽  
Method Development ◽  
Diagnostic System ◽  
Intelligence System ◽  
Intelligent Maintenance ◽  
The Neural Network ◽  
Artificial Neural

The present article covers the use of an artificial intelligence system in the organization of the prevention of technical objects. For this purpose, the composition of this system including an intelligent diagnostic system and an intelligent maintenance system was characterized and described. An artificial neural network and an expert system, which work among others on the basis of the information developed by the neural network, perform a special function in these systems. It was mentioned in the article that the mathematical model of the regeneration process of the functional properties (prevention) of an object forms the basis of the organization of the prevention activities of technical devices and objects with the use of intelligent systems. This model demonstrated the possibilities and directions for the use of maintenance intelligent systems.

scholarly journals Security-Aware Routing Protocol Based on Artificial Neural Network Algorithm and 6LoWPAN in the Internet of Things

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Jiangdong Lu ◽  
Dongfang Li ◽  
Penglong Wang ◽  
Fen Zheng ◽  
Meng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Routing Protocols ◽  
Intelligent Systems ◽  
Smart Devices ◽  
The Internet ◽  
Routing Mechanism ◽  
Artificial Neural ◽  
The Internet Of Things

Today, with increasing information technology such as the Internet of Things (IoT) in human life, interconnection and routing protocols need to find optimal solution for safe data transformation with various smart devices. Therefore, it is necessary to provide an enhanced solution to address routing issues with respect to new interconnection methodologies such as the 6LoWPAN protocol. The artificial neural network (ANN) is based on the structure of intelligent systems as a branch of machine interference, has shown magnificent results in previous studies to optimize security-aware routing protocols. In addition, IoT devices generate large amounts of data with variety and accuracy. Therefore, higher performance and better data handling can be achieved when this technology incorporates data for sending and receiving nodes in the environment. Therefore, this study presents a security-aware routing mechanism for IoT technologies. In addition, a comparative analysis of the relationship between previous approaches discusses with quality of service (QoS) factors such as throughput and accuracy for improving routing mechanism. Experimental results show that the use of time-division multiple access (TDMA) method to schedule the sending and receiving of data and the use of the 6LoWPAN protocol when routing the sending and receiving of data can carry out attacks with high accuracy.

scholarly journals Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

2021 ◽  
Vol 54 (6) ◽  
pp. 891-895
Author(s):  
Fawaz S. Abdullah ◽  
Ali N. Hamoodi ◽  
Rasha A. Mohammed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Power Plants ◽  
Thermal Power ◽  
Machine Learning Algorithms ◽  
Circuit Board ◽  
Electric Circuits ◽  
The Neural Network ◽  
Artificial Neural

Artificial intelligence has proven its effectiveness in many industrial fields to enhance the existing functionality. Artificial intelligence and machine learning algorithms integrated with turbines can be useful in controlling important variables such as pressure, temperature, speed, and humidity. In this research, the Simulink library from MATLAB is used to build an artificial neural network. The NARMA L2 neural controller is used to generate data and for training networks. To obtain the result and compare it with the real-time power plant, data is collected. The input variables provided to the neural network have a large effect on the hidden layer and the output of the neural network. The circuit board used in this research has a DC bridge, a transformer and voltage regulators. The result comparison shows that the integration of artificial neural networks and electric circuits shows enhanced performance with high accuracy of prediction. It was observed that the ANN integration system and electric circuit design have a result deviation of less than 1%. This shows that the integration of ANN improves the performance of turbines.

scholarly journals PENERAPAN MODEL BERBASIS ARTIFICIAL NEURAL NETWORK UNTUK MEMPREDIKSI KUALITAS AIR DI SUNGAI SUBAYANG KABUPATEN KAMPAR

2020 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Endang Agus Damanhuri ◽  
Yusni Ikhwan Siregar ◽  
Elfizar Elfizar
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Water Flow ◽  
Mean Squared Error ◽  
Human Life ◽  
Water Quality Management ◽  
River Quality ◽  
Artificial Neural

Water quality management is very important to do, because water is an inseparable part of everyday human life. Monitoring water quality is a way to maintain the quality of waters, especially rivers. River quality monitoring that is usually done requires a lot of equipment, effort and expertise so that its application becomes expensive and complicated. Technology that is growing rapidly nowadays puts forward artificial intelligence as the backbone of the Industrial Revolution 4.0 which promises many conveniences for industry and government. One of artificial intelligence technology is machine learning with Artificial Neural Network algorithm which is commonly used to predict or forecast a future value. This artificial neural network can be used to help monitor river water quality. The objective of this research to develop Artificial Neural Networks (ANN) model to predict the paramater of river quality (DO, pH, turbidity, temperature, water flow, conductivity) in the Subayang River, Kampar Regency, using software Rapidminer. The performance of the ANN models was evaluated using root mean squared error (RMSE) and correlation squared (R2) as a second comparison, then the results of the testing implementation are compared with direct measurements in the field. With the RMSE values obtained in the test results of each parameter DO = 1.613, pH = 0.098, turbidity = 4.730, temperature = 0.493, water flow = 0.121 and conductivity = 0.909. The lower the RMSE level, the closer it is to Artificial Neural Network accuracy for value prediction.  

scholarly journals An Automatic Logistics System using Artificial Intelligence

2019 ◽  
Vol 8 (2S4) ◽  
pp. 924-926
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Optimal Route ◽  
Logistics System ◽  
The Neural Network ◽  
Artificial Intelligence Technology ◽  
Artificial Neural ◽  
Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

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