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Journal articles on the topic 'Artificial neural network'

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Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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1

CVS, Rajesh, and Nadikoppula Pardhasaradhi. "Analysis of Artificial Neural-Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 418–28. http://dx.doi.org/10.31142/ijtsrd18482.

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2

O., Sheeba, Jithin George, Rajin P. K., Nisha Thomas, and Thomas George. "Glaucoma Detection Using Artificial Neural Network." International Journal of Engineering and Technology 6, no. 2 (2014): 158–61. http://dx.doi.org/10.7763/ijet.2014.v6.687.

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3

Al-Abaid, Shaimaa Abbas. "Artificial Neural Network Based Image Encryption Technique." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (February 28, 2020): 1184–89. http://dx.doi.org/10.5373/jardcs/v12sp3/20201365.

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4

Gupta, Sakshi. "Concrete Mix Design Using Artificial Neural Network." Journal on Today's Ideas-Tomorrow's Technologies 1, no. 1 (June 3, 2013): 29–43. http://dx.doi.org/10.15415/jotitt.2013.11003.

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5

Al-Rawi, Kamal R., and Consuelo Gonzalo. "Adaptive Pointing Theory (APT) Artificial Neural Network." International Journal of Computer and Communication Engineering 3, no. 3 (2014): 212–15. http://dx.doi.org/10.7763/ijcce.2014.v3.322.

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6

Jung, Jisoo, and Ji Won Yoon. "Author Identification Using Artificial Neural Network." Journal of the Korea Institute of Information Security and Cryptology 26, no. 5 (October 31, 2016): 1191–99. http://dx.doi.org/10.13089/jkiisc.2016.26.5.1191.

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7

Mahat, Norpah, Nor Idayunie Nording, Jasmani Bidin, Suzanawati Abu Hasan, and Teoh Yeong Kin. "Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance." Journal of Computing Research and Innovation 7, no. 1 (March 30, 2022): 29–38. http://dx.doi.org/10.24191/jcrinn.v7i1.264.

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Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the second period grade while one target output is used which is the final grade. This study also aims to identify which training function is the best among three Feed-Forward Neural Networks known as Network1, Network2 and Network3. Three types of training functions have been selected in this study, which are Levenberg-Marquardt (TRAINLM), Gradient descent with momentum (TRAINGDM) and Gradient descent with adaptive learning rate (TRAINGDA). Each training function will be compared based on Performance value, correlation coefficient, gradient and epoch. MATLAB R2020a was used for data processing. The results show that the TRAINLM function is the most suitable function in predicting mathematics students’ performance because it has a higher correlation coefficient and a lower Performance value.

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8

Yashchenko, V. O. "Artificial brain. Biological and artificial neural networks, advantages, disadvantages, and prospects for development." Mathematical machines and systems 2 (2023): 3–17. http://dx.doi.org/10.34121/1028-9763-2023-2-3-17.

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The article analyzes the problem of developing artificial neural networks within the framework of creating an artificial brain. The structure and functions of the biological brain are considered. The brain performs many functions such as controlling the organism, coordinating movements, processing information, memory, thinking, attention, and regulating emotional states, and consists of billions of neurons interconnected by a multitude of connections in a biological neural network. The structure and functions of biological neural networks are discussed, and their advantages and disadvantages are described in detail compared to artificial neural networks. Biological neural networks solve various complex tasks in real-time, which are still inaccessible to artificial networks, such as simultaneous perception of information from different sources, including vision, hearing, smell, taste, and touch, recognition and analysis of signals from the environment with simultaneous decision-making in known and uncertain situations. Overall, despite all the advantages of biological neural networks, artificial intelligence continues to rapidly progress and gradually win positions over the biological brain. It is assumed that in the future, artificial neural networks will be able to approach the capabilities of the human brain and even surpass it. The comparison of human brain neural networks with artificial neural networks is carried out. Deep neural networks, their training and use in various applications are described, and their advantages and disadvantages are discussed in detail. Possible ways for further development of this direction are analyzed. The Human Brain project aimed at creating a computer model that imitates the functions of the human brain and the advanced artificial intelligence project – ChatGPT – are briefly considered. To develop an artificial brain, a new type of neural network is proposed – neural-like growing networks, the structure and functions of which are similar to natural biological networks. A simplified scheme of the structure of an artificial brain based on a neural-like growing network is presented in the paper.

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9

JORGENSEN, THOMAS D., BARRY P. HAYNES, and CHARLOTTE C. F. NORLUND. "PRUNING ARTIFICIAL NEURAL NETWORKS USING NEURAL COMPLEXITY MEASURES." International Journal of Neural Systems 18, no. 05 (October 2008): 389–403. http://dx.doi.org/10.1142/s012906570800166x.

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This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.

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10

Begum, Afsana, Md Masiur Rahman, and Sohana Jahan. "Medical diagnosis using artificial neural networks." Mathematics in Applied Sciences and Engineering 5, no. 2 (June 4, 2024): 149–64. http://dx.doi.org/10.5206/mase/17138.

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Medical diagnosis using Artificial Neural Networks (ANN) and computer-aided diagnosis with deep learning is currently a very active research area in medical science. In recent years, for medical diagnosis, neural network models are broadly considered since they are ideal for recognizing different kinds of diseases including autism, cancer, tumor lung infection, etc. It is evident that early diagnosis of any disease is vital for successful treatment and improved survival rates. In this research, five neural networks, Multilayer neural network (MLNN), Probabilistic neural network (PNN), Learning vector quantization neural network (LVQNN), Generalized regression neural network (GRNN), and Radial basis function neural network (RBFNN) have been explored. These networks are applied to several benchmarking data collected from the University of California Irvine (UCI) Machine Learning Repository. Results from numerical experiments indicate that each network excels at recognizing specific physical issues. In the majority of cases, both the Learning Vector Quantization Neural Network and the Probabilistic Neural Network demonstrate superior performance compared to the other networks.

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11

Fahd, Syed Muhammed. "Artificial Neural Network Model for Friction Stir Processing." International Journal of Engineering Research 3, no. 6 (June 1, 2014): 396–97. http://dx.doi.org/10.17950/ijer/v3s6/606.

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12

Zainal, Azavitra. "pH Neutralization Plant Optimization Using Artificial Neural Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1466–72. http://dx.doi.org/10.5373/jardcs/v12sp4/20201625.

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13

Singh, Vikash, Samreen Bano, and Anand Kumar Yadav Dr Sabih Ahmad. "Feasibility of Artificial Neural Network in Civil Engineering." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 724–28. http://dx.doi.org/10.31142/ijtsrd22985.

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14

Yoon, B. L. "Artificial neural network technology." ACM SIGSMALL/PC Notes 15, no. 3 (August 1989): 3–16. http://dx.doi.org/10.1145/74657.74658.

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15

Nedjah, Nadia, Ajith Abraham, and Luiza M. Mourelle. "Hybrid artificial neural network." Neural Computing and Applications 16, no. 3 (February 28, 2007): 207–8. http://dx.doi.org/10.1007/s00521-007-0083-0.

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16

OOE, Ryosuke, Ikuo SUZUKI, Masahito YAMAMOTO, and Masashi FURUKAWA. "Composite Artificial Neural Network." Journal of the Japan Society for Precision Engineering 79, no. 6 (2013): 552–58. http://dx.doi.org/10.2493/jjspe.79.552.

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17

Borankulova, Gauhar Sarsenbaevna, and Aigul Turyszhanovna Tungatarova. "ARTIFICIAL NEURAL NETWORK FEATURES." Theoretical & Applied Science 72, no. 04 (April 30, 2019): 71–74. http://dx.doi.org/10.15863/tas.2019.04.72.12.

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18

Walczak, Steven. "Artificial Neural Network Research in Online Social Networks." International Journal of Virtual Communities and Social Networking 10, no. 4 (October 2018): 1–15. http://dx.doi.org/10.4018/ijvcsn.2018100101.

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Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the maintenance of online social network “friends” is developed to demonstrate the applicability of artificial neural networks for virtual community research.

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19

Kumar, Vikash, Shivam Kumar Gupta, Harsh Sharma, Uchit Bhadauriya, and Chandra Prakash Varma. "Voice Isolation Using Artificial Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1249–53. http://dx.doi.org/10.22214/ijraset.2022.42237.

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Abstract: The paper reflects the use of Artificial Neural Networks with the help of various machine learning algorithms for voice isolation. In particular, we consider the case of a voice sample recognition by analyzing the speech signals with the help of machine learning algorithms such as artificial neural networks, independent component analysis, activation function. The strategies by which our central nervous network decodes the network stimuli same as artificial neural network will analyze the given speech sample. After first step, a set of machine learning algorithms will be used like independent component analysis algorithm and gradient function algorithm for processing. After the processing, a decision statement will be applied to generate the desired output. Keywords: Artificial Neural Network, Voice Isolation, Fast Independent Component Analysis, Gradient Descent

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20

Schaub, Nicholas J., and Nathan Hotaling. "Assessing Efficiency in Artificial Neural Networks." Applied Sciences 13, no. 18 (September 14, 2023): 10286. http://dx.doi.org/10.3390/app131810286.

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The purpose of this work was to develop an assessment technique and subsequent metrics that help in developing an understanding of the balance between network size and task performance in simple model networks. Here, exhaustive tests on simple model neural networks and datasets are used to validate both the assessment approach and the metrics derived from it. The concept of neural layer state space is introduced as a simple mechanism for understanding layer utilization, where a state is the on/off activation state of all neurons in a layer for an input. Neural efficiency is computed from state space to measure neural layer utilization, and a second metric called the artificial intelligence quotient (aIQ) was created to balance neural network performance and neural efficiency. To study aIQ and neural efficiency, two simple neural networks were trained on MNIST: a fully connected network (LeNet-300-100) and a convolutional neural network (LeNet-5). The LeNet-5 network with the highest aIQ was 2.32% less accurate but contained 30,912 times fewer parameters than the network with the highest accuracy. Both batch normalization and dropout layers were found to increase neural efficiency. Finally, networks with a high aIQ are shown to be resistant to memorization and overtraining as well as capable of learning proper digit classification with an accuracy of 92.51%, even when 75% of the class labels are randomized. These results demonstrate the utility of aIQ and neural efficiency as metrics for determining the performance and size of a small network using exemplar data.

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21

Tun, Myat Thida. "Myanmar Alphabet Recognition System Based on Artificial Neural Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 1343–48. http://dx.doi.org/10.31142/ijtsrd17054.

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22

Sultana, Zakia, Md Ashikur Rahman Khan, and Nusrat Jahan. "Early Breast Cancer Detection Utilizing Artificial Neural Network." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (March 18, 2021): 32–42. http://dx.doi.org/10.37394/23208.2021.18.4.

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Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs of the body. For that early detection of benign and malignant tumor is important for confining the death of breast cancer. In these research study different neural networks such as, Multilayer Perceptron (MLP) Neural Network, Jordan/Elman Neural Network, Modular Neural Network (MNN), Generalized Feed-Forward Neural Network (GFFNN), Self-Organizing Feature Map (SOFM) Neural Network, Support Vector Machine (SVM) Neural Network, Probabilistic Neural Network (PNN) and Recurrent Neural Network (RNN) are used for classifying breast cancer tumor. And compare the results of these networks to find the best neural network for detecting breast cancer. The networks are tested on Wisconsin breast cancer (WBC) database. Finally, the comparing result showed that Probabilistic Neural Network shows the best detection result than other networks.

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23

Hamdan, Baida Abdulredha. "Neural Network Principles and its Application." Webology 19, no. 1 (January 20, 2022): 3955–70. http://dx.doi.org/10.14704/web/v19i1/web19261.

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Neural networks which also known as artificial neural networks is generally a computing dependent technique that formed and designed to create a simulation to the real brain of a human to be used as a problem solving method. Artificial neural networks gain their abilities by the method of training or learning, each method have a certain input and output which called results too, this method of learning works to create forming probability-weighted associations among both of input and the result which stored and saved across the net specifically among its data structure, any training process is depending on identifying the net difference between processed output which is usually a prediction and the real targeted output which occurs as an error, then a series of adjustments achieved to gain a proper learning result, this process called supervised learning. Artificial neural networks have found and proved itself in many applications in a variety of fields due to their capacity to recreate and simulate nonlinear phenomena. System identification and control (process control, vehicle control, quantum chemistry, trajectory prediction, and natural resource management. Etc.) In addition to face recognition which proved to be very effective. Neural network was proved to be a very promising technique in many fields due to its accuracy and problem solving properties.

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24

Mitra, Manu. "Neural processor in artificial intelligence advancement." Journal of Autonomous Intelligence 1, no. 1 (October 14, 2018): 2. http://dx.doi.org/10.32629/jai.v1i1.13.

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A neuron network is a computational model based on structure and functions of biological neural networks. Information that flows through the network affects the structure of the neuron network because neural network changes-or learns, in a sense-based on that input and output. Although neural network being highly complex (for example change of weights for every new data within the time frame) an experimental model of high level architecture of neural processor is proposed. Neural Processor performs all the functions that an ordinary neural network does like adaptive learning, self-organization, real time operations and fault tolerance. In this paper, analysis of neural processing is discussed and presented with experiments, graphical representation including data analysis.

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25

Næs, Tormod, Knut Kvaal, Tomas Isaksson, and Charles Miller. "Artificial Neural Networks in Multivariate Calibration." Journal of Near Infrared Spectroscopy 1, no. 1 (January 1993): 1–11. http://dx.doi.org/10.1255/jnirs.1.

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This paper is about the use of artificial neural networks for multivariate calibration. We discuss network architecture and estimation as well as the relationship between neural networks and related linear and non-linear techniques. A feed-forward network is tested on two applications of near infrared spectroscopy, both of which have been treated previously and which have indicated non-linear features. In both cases, the network gives more precise prediction results than the linear calibration method of PCR.

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26

Jeong, Yeongsang, and Sungshin Kim. "A Study of Arrow Performance using Artificial Neural Network." Journal of Korean Institute of Intelligent Systems 24, no. 5 (October 25, 2014): 548–53. http://dx.doi.org/10.5391/jkiis.2014.24.5.548.

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27

Deng, Limei, and Ying Chang. "Risk Management of Investment Projects Based on Artificial Neural Network." Wireless Communications and Mobile Computing 2022 (May 9, 2022): 1–13. http://dx.doi.org/10.1155/2022/5606316.

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The benefit evaluation of investment projects is the key to the whole investment activities. This paper mainly describes the risk management of investment projects using an artificial neural network. It generally adopts the index system of project risk through modern scientific measurement methods, to evaluate whether the investment project of artificial neural network is feasible or not. It establishes a benefit evaluation model based on an artificial neural network, from the analysis and consideration of 4 groups of experiments, comparing four sets of data: BP network convergence rate, artificial neural network identification efficiency, enterprise risk, artificial neural network output, and error; it is concluded that the relative risk is reduced by about 20% after using the artificial neural network. This also verifies the feasibility of artificial neural networks in the application of raw materials.

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28

Chen, Heng, Fengmei Lu, and Bifang He. "Topographic property of backpropagation artificial neural network: From human functional connectivity network to artificial neural network." Neurocomputing 418 (December 2020): 200–210. http://dx.doi.org/10.1016/j.neucom.2020.07.103.

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29

Parks, Allen D. "Characterizing Computation in Artificial Neural Networks by their Diclique Covers and Forman-Ricci Curvatures." European Journal of Engineering Research and Science 5, no. 2 (February 13, 2020): 171–77. http://dx.doi.org/10.24018/ejers.2020.5.2.1689.

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The relationships between the structural topology of artificial neural networks, their computational flow, and their performance is not well understood. Consequently, a unifying mathematical framework that describes computational performance in terms of their underlying structure does not exist. This paper makes a modest contribution to understanding the structure-computational flow relationship in artificial neural networks from the perspective of the dicliques that cover the structure of an artificial neural network and the Forman-Ricci curvature of an artificial neural network’s connections. Special diclique cover digraph representations of artificial neural networks useful for network analysis are introduced and it is shown that such covers generate semigroups that provide algebraic representations of neural network connectivity.

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30

Parks, Allen D. "Characterizing Computation in Artificial Neural Networks by their Diclique Covers and Forman-Ricci Curvatures." European Journal of Engineering and Technology Research 5, no. 2 (February 13, 2020): 171–77. http://dx.doi.org/10.24018/ejeng.2020.5.2.1689.

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The relationships between the structural topology of artificial neural networks, their computational flow, and their performance is not well understood. Consequently, a unifying mathematical framework that describes computational performance in terms of their underlying structure does not exist. This paper makes a modest contribution to understanding the structure-computational flow relationship in artificial neural networks from the perspective of the dicliques that cover the structure of an artificial neural network and the Forman-Ricci curvature of an artificial neural network’s connections. Special diclique cover digraph representations of artificial neural networks useful for network analysis are introduced and it is shown that such covers generate semigroups that provide algebraic representations of neural network connectivity.

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31

Akdeniz, Esra, Erol Egrioglu, Eren Bas, and Ufuk Yolcu. "An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting." Journal of Artificial Intelligence and Soft Computing Research 8, no. 2 (April 1, 2018): 121–32. http://dx.doi.org/10.1515/jaiscr-2018-0009.

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Abstract Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.

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32

Kim, Sang-Ho, Jae-Hwan Ryu, Byeong-Hyeon Lee, and Deok-Hwan Kim. "Human Identification using EMG Signal based Artificial Neural Network." Journal of the Institute of Electronics and Information Engineers 53, no. 4 (April 25, 2016): 142–48. http://dx.doi.org/10.5573/ieie.2016.53.4.142.

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33

KONOVALOV, S. "FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS." Digital Technologies 26 (2019): 36–46. http://dx.doi.org/10.33243/2313-7010-26-36-46.

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In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid expert systems. The approaches to the analysis of natural language sentences, which are used for the work of hybrid expert systems with artificial neural networks, are considered. A bulletin board is shown, its structure and principle of operation are described. The structure of the bulletin board is divided into levels and sublevels. At sublevels, a confidence factor is applied. The dependence of the values of the confidence factor on the fulfillment of a particular condition is shown. The links between the levels and sublevels of the bulletin board are also described. As an artificial neural network architecture, the «key-threshold» model is used, the rule of neuron operation is shown. In addition, an artificial neural network has the property of training, based on the application of the penalty property, which is able to calculate depending on the accident situation. The behavior of a complex technical system, as well as its faulty states, are modeled using a model that describes the structure and behavior of a given system. To optimize the data of a complex technical system, an evolutionary algorithm is used to minimize the objective function. Solutions to the optimization problem consist of Pareto solution vectors. Optimization and training tasks are solved by using the Hopfield network. In general, a hybrid expert system is described using semantic networks, which consist of vertices and edges. The reference model of a complex technical system is stored in the knowledge base and updated during the acquisition of new knowledge. In an emergency, or about its premise, with the help of neural networks, a search is made for the cause and the control action necessary to eliminate the accident. The considered approaches, interacting with each other, can improve the operation of diagnostic artificial neural networks in the case of emergency management, showing more accurate data in a short time. In addition, the use of such a network for analyzing the state of health, as well as forecasting based on diagnostic data using the example of a complex technical system, is presented.

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34

Kaur, Jaswinder, and Neha Gupta. "ARTIFICIAL NEURAL NETWORK: A REVIEW." International Journal of Technical Research & Science Special, Issue3 (August 15, 2020): 1–4. http://dx.doi.org/10.30780/specialissue-icaccg2020/007.

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35

Mohamed Hafez, Sherif. "Utilization of Artificial Neural Network." Academic Research Community Publication 2, no. 1 (May 7, 2018): 1–8. http://dx.doi.org/10.21625/archive.v2i1.232.

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36

Mohamed Hafez, Sherif. "Utilization of Artificial Neural Network." Academic Research Community Publication 2, no. 1 (May 7, 2018): 1–8. http://dx.doi.org/10.21625/archive.v2i1.232.g120.

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37

Didmanidze, I. Sh, G. A. Kakhiani, and D. Z. Didmanidze. "TRAINING OF ARTIFICIAL NEURAL NETWORK." Journal of Numerical and Applied Mathematics, no. 1 (135) (2021): 110–14. http://dx.doi.org/10.17721/2706-9699.2021.1.14.

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The methodology of neural networks is even more often applied in tasks of management and decision-making, including in the sphere of trade and finance. The basis of neural networks is made by nonlinear adaptive systems which proved the efficiency at the solution of problems of forecasting.

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38

Singh, Satish. "Cryptography using Artificial Neural Network." International Journal for Research in Applied Science and Engineering Technology 7, no. 2 (February 28, 2019): 379–82. http://dx.doi.org/10.22214/ijraset.2019.2046.

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39

Xin Yao and M. M. Islam. "Evolving artificial neural network ensembles." IEEE Computational Intelligence Magazine 3, no. 1 (February 2008): 31–42. http://dx.doi.org/10.1109/mci.2007.913386.

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40

nu, So, and Ravi Parkash Bhokal. "Study of Artificial Neural Network." International Journal of Mathematics Trends and Technology 47, no. 4 (July 25, 2017): 253–59. http://dx.doi.org/10.14445/22315373/ijmtt-v47p535.

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41

Staub, Selva, Emin Karaman, Seyit Kaya, Hatem Karapınar, and Elçin Güven. "Artificial Neural Network and Agility." Procedia - Social and Behavioral Sciences 195 (July 2015): 1477–85. http://dx.doi.org/10.1016/j.sbspro.2015.06.448.

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42

Er, Sadettin, and Mesut Tez. "Problems of artificial neural network." Journal of Surgical Research 222 (February 2018): 225. http://dx.doi.org/10.1016/j.jss.2017.07.017.

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43

Yang, Xubing, Songcan Chen, and Bin Chen. "Plane-Gaussian artificial neural network." Neural Computing and Applications 21, no. 2 (February 22, 2011): 305–17. http://dx.doi.org/10.1007/s00521-011-0546-1.

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Husien, Sabah, and Haitham Badi. "Artificial neural network for steganography." Neural Computing and Applications 26, no. 1 (August 28, 2014): 111–16. http://dx.doi.org/10.1007/s00521-014-1702-1.

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45

Crapanzano, G., V. M�ller, and D. Nelles. "Application of artificial neural networks in network calculations." Electrical Engineering (Archiv fur Elektrotechnik) 83, no. 5-6 (November 1, 2001): 313–25. http://dx.doi.org/10.1007/s002020100097.

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46

Mahdi, Qasim Abbood, Andrii Shyshatskyi, Oleksandr Symonenko, Nadiia Protas, Oleksandr Trotsko, Volodymyr Kyvliuk, Artem Shulhin, Petro Steshenko, Eduard Ostapchuk, and Tetiana Holenkovska. "Development of a method for training artificial neural networks for intelligent decision support systems." Eastern-European Journal of Enterprise Technologies 1, no. 9(115) (February 28, 2022): 35–44. http://dx.doi.org/10.15587/1729-4061.2022.251637.

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We developed a method of training artificial neural networks for intelligent decision support systems. A distinctive feature of the proposed method consists in training not only the synaptic weights of an artificial neural network, but also the type and parameters of the membership function. In case of impossibility to ensure a given quality of functioning of artificial neural networks by training the parameters of an artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membership function is based on the computing resources of the device and taking into account the type and amount of information coming to the input of the artificial neural network. Another distinctive feature of the developed method is that no preliminary calculation data are required to calculate the input data. The development of the proposed method is due to the need for training artificial neural networks for intelligent decision support systems, in order to process more information, while making unambiguous decisions. According to the results of the study, this training method provides on average 10–18 % higher efficiency of training artificial neural networks and does not accumulate training errors. This method will allow training artificial neural networks by training the parameters and architecture, determining effective measures to improve the efficiency of artificial neural networks. This method will allow reducing the use of computing resources of decision support systems, developing measures to improve the efficiency of training artificial neural networks, increasing the efficiency of information processing in artificial neural networks.

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Buscema, Paolo Massimo, and William J. Tastle. "Artificial Neural Network What-If Theory." International Journal of Information Systems and Social Change 6, no. 4 (October 2015): 52–81. http://dx.doi.org/10.4018/ijissc.2015100104.

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Data sets collected independently using the same variables can be compared using a new artificial neural network called Artificial neural network What If Theory, AWIT. Given a data set that is deemed the standard reference for some object, i.e. a flower, industry, disease, or galaxy, other data sets can be compared against it to identify its proximity to the standard. Thus, data that might not lend itself well to traditional methods of analysis could identify new perspectives or views of the data and thus, potentially new perceptions of novel and innovative solutions. This method comes out of the field of artificial intelligence, particularly artificial neural networks, and utilizes both machine learning and pattern recognition to display an innovative analysis.

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Wongsathan, Rati, and Pasit Pothong. "Heart Disease Classification Using Artificial Neural Networks." Applied Mechanics and Materials 781 (August 2015): 624–27. http://dx.doi.org/10.4028/www.scientific.net/amm.781.624.

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Neural Networks (NNs) has emerged as an importance tool for classification in the field of decision making. The main objective of this work is to design the structure and select the optimized parameter in the neural networks to implement the heart disease classifier. Three types of neural networks, i.e. Multi-layered Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), and Generalized Regression Neural Network (GR-NN) have been used to test the performance of heart disease classification. The classification accuracy obtained by RBFNN gave a very high performance than MLP-NN and GR-NN respectively. The performance of accuracy is very promising compared with the previously reported another type of neural networks.

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Havryliuk, Volodymyr. "Artificial neural network based detection of neutral relay defects." MATEC Web of Conferences 294 (2019): 03001. http://dx.doi.org/10.1051/matecconf/201929403001.

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The problem considered in the work is concerned to the automatic detecting and identifying defects in a neutral relay. The special design of electromechanical neutral relays is responsible for the strong asymmetry of its output signal for all possible safety-critical influences, and therefore neutral relays have negligible values of dangerous failures rate. To ensure the safe operation of relay-based train control systems, electromechanical relays should be periodically subjected to routine maintenance, during which their main operating parameters are measured, and the relays are set up in accordance with technical regulations. These measurements are mainly done manually, so they take a lot of time (up to four hours per relay), are expensive, and the results are subjective. In recent years, fault diagnosis methods based on artificial neural networks (ANN) have received considerable attention. The ANN-based classification of relay defects using the time dependence of the transient current in the relay coil during its switching is very promising for practical utilization, but for efficient use of ANN a lot of data is required to train the artificial neural network. To reduce the ANN training time, a pre-processing of the time dependence of relay transient current was proposed using wavelet transform and wavelet energy entropy, which makes it possible to reveal the features of the main defects of the relay armature, contact springs, and magnetic system. The effectiveness of the proposed approach for automatic detecting and identifying of the neutral relays defects was confirmed during testing of the relays with various artificially created defects.

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Yang, Judy X., Lily D. Li, and Mohammad G. Rasul. "A Conceptual Artificial Neural Network Model in Warehouse Receiving Management." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 130–36. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1025.

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The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

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