scholarly journals Back Propagation and Transformation Methods in Artificial Intelligence Systems

2006 ◽  
Vol 1 ◽  
pp. 367
Author(s):  
G. Moskvin
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Back Propagation ◽  
Automatic Measuring ◽  
A Priority ◽  
Transformation Methods ◽  
First Time ◽  
And Training ◽  
Artificial Intelligence Systems

Detailed description of methods of back propagation and back transformation also distributions for training of neural networks is given. A comparative estimation of a priority of methods of back transformation and back propagation for the decision of tasks of synthesis and training of neural networks, also for intelligent automatic measuring and AI systems for the first time is carried out.

scholarly journals Emotion Detection using Facial Expressions with Convolution Neural Networks

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3612-3615
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Facial Expressions ◽  
Health Monitoring ◽  
Human Condition ◽  
Emotion Detection ◽  
Human Environment ◽  
Interaction Field ◽  
Research Theme ◽  
Artificial Intelligence Systems

Artificial intelligence systems to perceive human feeling have pulled in much research premium, and potential uses of such frameworks flourish, spreading over areas, for example, client mindful showcasing, health monitoring wellbeing observing, and genuinely shrewd robotic interfaces. Human are enthusiastic creatures and it assumes a significant job behind their thoughts and activity. In this way, it is important that emotion handling capacities are assimilated for planning of human condition. The investigation, recognition and synthesis of feelings can plan the human environment. In this procedure the data uses, for example, sound, visual, composed and mental data. An epic research theme to be developed in the Human Computer Interaction field is Emotion Recognition utilizing Facial Expressions.

Combining BP with PSO algorithms in weights optimisation and ANNs training for mass appraisal of properties

2018 ◽  
Vol 11 (2) ◽  
pp. 290-314 ◽  
Author(s):  
Joseph Awoamim Yacim ◽  
Douw Gert Brand Boshoff
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Accuracy ◽  
Back Propagation ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Content Type ◽  
Attribute Weights ◽  
Artificial Neural ◽  
And Training

Purpose The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models. Design/methodology/approach The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics. Findings The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes. Research limitations/implications A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values. Originality/value This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.

scholarly journals Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

2020 ◽  
Vol 10 (11) ◽  
pp. 3967 ◽  
Author(s):  
Jittiphong Klomjit ◽  
Atthapol Ngaopitakkul
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Transmission Line ◽  
Fault Location ◽  
Back Propagation ◽  
Fault Classification ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
Hybrid Transmission

This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems.

scholarly journals Technical note: AI-Track-tive: automated fission track recognition using computer vision (Artificial Intelligence)

2020 ◽  
Author(s):  
Simon Nachtergaele ◽  
Johan De Grave
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Neural Networks ◽  
Fission Track ◽  
Detection Algorithm ◽  
Technical Note ◽  
Success Rates ◽  
Automatic Counting ◽  
First Time

Abstract. Artificial intelligence techniques such as deep neural networks and computer vision are developed for fission track recognition and included in a computer program for the first time. These deep neural networks use the Yolov3 object detection algorithm, which is currently one of the most powerful and fastest object recognition algorithms. These deep neural networks can be used in new software called AI-Track-tive. The developed program successfully finds most of the fission tracks in the microscope images, however, the user still needs to supervise the automatic counting. The success rates of the automatic recognition range from 70 % to 100 % depending on the areal track densities in apatite and (muscovite) external detector. The success rate generally decreases for images with high areal track densities, because overlapping tracks are less easily recognizable for computer vision techniques.

scholarly journals Using CycleGAN to Achieve the Sketch Recognition Process of Sketch-Based Modeling

2021 ◽  
pp. 26-34
Author(s):  
Yuqian Li ◽  
Weiguo Xu
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Learning Technology ◽  
Modeling Technology ◽  
Image Translation ◽  
Mapping Process ◽  
And Training ◽  
Difficult Part ◽  
2D Sketches

AbstractArchitects usually design ideation and conception by hand-sketching. Sketching is a direct expression of the architect’s creativity. But 2D sketches are often vague, intentional and even ambiguous. In the research of sketch-based modeling, it is the most difficult part to make the computer to recognize the sketches. Because of the development of artificial intelligence, especially deep learning technology, Convolutional Neural Networks (CNNs) have shown obvious advantages in the field of extracting features and matching, and Generative Adversarial Neural Networks (GANs) have made great breakthroughs in the field of architectural generation which make the image-to-image translation become more and more popular. As the building images are gradually developed from the original sketches, in this research, we try to develop a system from the sketches to the images of buildings using CycleGAN algorithm. The experiment demonstrates that this method could achieve the mapping process from the sketches to images, and the results show that the sketches’ features could be recognised in the process. By the learning and training process of the sketches’ reconstruction, the features of the images are also mapped to the sketches, which strengthen the architectural relationship in the sketch, so that the original sketch can gradually approach the building images, and then it is possible to achieve the sketch-based modeling technology.

scholarly journals Instruments of Artificial Intelligence in Management of High Technology Production

2018 ◽  
Vol 7 (3.14) ◽  
pp. 347
Author(s):  
Mikhail Leizerovich Krichevsky ◽  
Svetlana Vladimirovna Dmitrieva ◽  
Julia Anatolevna Martynova
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Production Management ◽  
Practical Importance ◽  
High Technology ◽  
Educational Process ◽  
Knowledge And Skills ◽  
Hr Management ◽  
Level Of Training ◽  
And Training

The instruments of artificial intelligence (AI) that can be used in management of high technology production and training students are considered. Specific differences and characteristics of high technology production (HTP) that set certain requirements for such production management are specified. Brief information from the AI methods that include artificial neural networks, fuzzy logic, genetic algorithms and their combinations are given. It is indicated that there is a relation between the level of training masters and the requirements of modern productions. The necessity to use techniques and methods of AI when training students to form their competencies, knowledge and skills that comply with the HTP is explained. The techniques of using AI instruments in the educational process focused on the practical importance of the tasks being solved in such disciplines as HR management, risk management, strategic management, etc. are shown.  

scholarly journals Modelling an Adaptive Learning System Using Artificial Intelligence

Webology ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 01-18
Author(s):  
Hayder Rahm Dakheel AL-Fayyadh ◽  
Salam Abdulabbas Ganim Ali ◽  
Dr. Basim Abood
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Adaptive Learning ◽  
Back Propagation ◽  
Learning System ◽  
Spiking Neural Networks ◽  
Domain Specific Language ◽  
Domain Specific ◽  
Adaptive Learning System ◽  
Artificial Neural

The goal of this paper is to use artificial intelligence to build and evaluate an adaptive learning system where we adopt the basic approaches of spiking neural networks as well as artificial neural networks. Spiking neural networks receive increasing attention due to their advantages over traditional artificial neural networks. They have proven to be energy efficient, biological plausible, and up to 105 times faster if they are simulated on analogue traditional learning systems. Artificial neural network libraries use computational graphs as a pervasive representation, however, spiking models remain heterogeneous and difficult to train. Using the artificial intelligence deductive method, the paper posits two hypotheses that examines whether 1) there exists a common representation for both neural networks paradigms for tutorial mentoring, and whether 2) spiking and non-spiking models can learn a simple recognition task for learning activities for adaptive learning. The first hypothesis is confirmed by specifying and implementing a domain-specific language that generates semantically similar spiking and non-spiking neural networks for tutorial mentoring. Through three classification experiments, the second hypothesis is shown to hold for non-spiking models, but cannot be proven for the spiking models. The paper contributes three findings: 1) a domain-specific language for modelling neural network topologies in adaptive tutorial mentoring for students, 2) a preliminary model for generalizable learning through back-propagation in spiking neural networks for learning activities for students also represented in results section, and 3) a method for transferring optimised non-spiking parameters to spiking neural networks has also been developed for adaptive learning system. The latter contribution is promising because the vast machine learning literature can spill-over to the emerging field of spiking neural networks and adaptive learning computing. Future work includes improving the back-propagation model, exploring time-dependent models for learning, and adding support for adaptive learning systems.

ANN Development with EC Tools

2011 ◽  
pp. 125-130
Author(s):  
Daniel Rivero ◽  
Juan Rabuñal
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Process ◽  
Network Architecture ◽  
Great Part ◽  
Whole Process ◽  
The One ◽  
Architecture Development ◽  
And Training

Among all of the Artificial Intelligence techniques, Artificial Neural Networks (ANNs) have shown to be a very powerful tool (McCulloch & Pitts, 1943) (Haykin, 1999). This technique is very versatile and therefore has been succesfully applied to many different disciplines (classification, clustering, regression, modellization, etc.) (Rabuñal & Dorado, 2005). However, one of the greatest problems when using ANNs is the great manual effort that has to be done in their development. A big myth of ANNs is that they are easy to work with and their development is almost automatically done. This development process can be divided into two parts: architecture development and training and validation. As the network architecture is problem-dependant, the design process of this architecture used to be manually performed, meaning that the expert had to test different architectures and train them until finding the one that achieved best results after the training process. The manual nature of the described process determines its slow performance although the training part is completely automated due to the existence of several algorithms that perform this part. With the creation of Evolutionary Computation (EC) tools, researchers have worked on the application of these techniques to the development of algorithms for automatically creating and training ANNs so the whole process (or, at least, a great part of it) can be automatically performed by computers and therefore few human efforts has to be done in this process

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