Relevant bibliographies by topics / Neural Network (Artificial)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neural Network (Artificial)'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Neural Network (Artificial)"

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

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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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Nahar, Kapil. "Artificial Neural Network." COMPUSOFT: An International Journal of Advanced Computer Technology 01, no. 02 (2012): 25–27. https://doi.org/10.5281/zenodo.14591511.

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An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. Ann’s, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning processing. Learning in biological
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Al-Abaid, Shaimaa Abbas. "Artificial Neural Network Based Image Encryption Technique." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (2020): 1184–89. http://dx.doi.org/10.5373/jardcs/v12sp3/20201365.

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

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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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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 (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
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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 (2016): 1191–99. http://dx.doi.org/10.13089/jkiisc.2016.26.5.1191.

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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 a
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Begum, Afsana, Md Masiur Rahman, and Sohana Jahan. "Medical diagnosis using artificial neural networks." Mathematics in Applied Sciences and Engineering 5, no. 2 (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
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Dissertations / Theses on the topic "Neural Network (Artificial)"

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BRUCE, WILLIAM, and OTTER EDVIN VON. "Artificial Neural Network Autonomous Vehicle : Artificial Neural Network controlled vehicle." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191192.

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This thesis aims to explain how a Artificial Neural Network algorithm could be used as means of control for a Autonomous Vehicle. It describes the theory behind the neural network and Autonomous Vehicles, and how a prototype with a camera as its only input can be designed to test and evaluate the algorithms capabilites, and also drive using it. The thesis will show that the Artificial Neural Network can, with a image resolution of 100 × 100 and a training set with 900 images, makes decisions with a 0.78 confidence level.<br>Denna rapport har som mal att beskriva hur en Artificiellt Neuronnatve
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Смаль, Богдан Віталійович. "Artificial Neural Network." Thesis, Київський національний університет технологій та дизайну, 2017. https://er.knutd.edu.ua/handle/123456789/7384.

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Chambers, Mark Andrew. "Queuing network construction using artificial neural networks /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193665234291.

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Leija, Carlos Ivan. "An artificial neural network with reconfigurable interconnection network." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Alkharobi, Talal M. "Secret sharing using artificial neural network." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/1223.

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Secret sharing is a fundamental notion for secure cryptographic design. In a secret sharing scheme, a set of participants shares a secret among them such that only pre-specified subsets of these shares can get together to recover the secret. This dissertation introduces a neural network approach to solve the problem of secret sharing for any given access structure. Other approaches have been used to solve this problem. However, the yet known approaches result in exponential increase in the amount of data that every participant need to keep. This amount is measured by the secret sharing scheme
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Zhao, Lichen. "Random pulse artificial neural network architecture." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0006/MQ36758.pdf.

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Parzhin, Yu, А. Rohovyi, and V. Nevliudova. "Detector Artificial Neural Network. Neurobiological rationale." Thesis, ХНУРЕ, 2019. http://openarchive.nure.ua/handle/document/10037.

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On the basis of the formulated hypotheses the information model of a neuron-detector is suggested, the detector being one of the basic elements of a detector artificial neural network (DANN). The paper subjects the connectionist paradigm of ANN building to criticism and suggests a new presentation paradigm for ANN building and neuroelements (NE) learning. The adequacy of the suggested model is proved by the fact that is does not contradict the modern propositions of neuropsychology and neurophysiology.
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Ng, Justin. "Artificial Neural Network-Based Robotic Control." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1846.

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Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The pa
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Lukashev, A. "Basics of artificial neural networks (ANNs)." Thesis, Київський національний університет технологій та дизайну, 2018. https://er.knutd.edu.ua/handle/123456789/11353.

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Khazanova, Yekaterina. "Experiments with Neural Network Libraries." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1527607591612278.

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Books on the topic "Neural Network (Artificial)"

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Shanmuganathan, Subana, and Sandhya Samarasinghe, eds. Artificial Neural Network Modelling. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8.

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S, Mohan. Artificial neural network modelling. Indian National Committee on Hydrology, 2007.

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Zeidenberg, Matthew. Neural network models in artificial intelligence and cognition. Ellis Horwood, 1989.

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Bisi, Manjubala, and Neeraj Kumar Goyal. Artificial Neural Network for Software Reliability Prediction. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119223931.

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Roberts, S. G. The evolution of artificial neural network structures. UMIST, 1997.

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Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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North Atlantic Treaty Organization. Advisory Group for Aerospace Research and Development. Artificial neural network approaches in guidance and control. AGARD, 1991.

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Smith, Amelia. Predicting disease outcomes using an artificial neural network. Oxford Brookes University, 2003.

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Book chapters on the topic "Neural Network (Artificial)"

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Zhang, Dengsheng. "Artificial Neural Network." In Texts in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17989-2_9.

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Rathore, Heena. "Artificial Neural Network." In Mapping Biological Systems to Network Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29782-8_7.

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Shekhar, Shashi, and Hui Xiong. "Artificial Neural Network." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_72.

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Zhou, Hong. "Artificial Neural Network." In Learn Data Mining Through Excel. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5982-5_11.

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Ayyadevara, V. Kishore. "Artificial Neural Network." In Pro Machine Learning Algorithms. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3564-5_7.

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Zhang, Zhihua. "Artificial Neural Network." In Multivariate Time Series Analysis in Climate and Environmental Research. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67340-0_1.

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Attew, David. "Artificial Neural Network." In Perspectives in Neural Computing. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0151-2_18.

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Wang, Sun-Chong. "Artificial Neural Network." In Interdisciplinary Computing in Java Programming. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0377-4_5.

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Weik, Martin H. "artificial neural network." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_860.

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Majumder, Mrinmoy. "Artificial Neural Network." In Impact of Urbanization on Water Shortage in Face of Climatic Aberrations. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-4560-73-3_3.

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Conference papers on the topic "Neural Network (Artificial)"

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Singh, Jagmeet, Kondrakunta Prasanth Babu, Samala Harshith, and Suman Lata Tripathi. "CMOS Analog Artificial Neural Network." In 2025 Devices for Integrated Circuit (DevIC). IEEE, 2025. https://doi.org/10.1109/devic63749.2025.11012358.

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Gomes, David Emanuel, António Espírito Santo, and José Páscoa. "Artificial Neural Network SoftSensor for Water Distribution Networks." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905827.

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Silverman, D. C., and E. M. Rosen. "Corrosion Prediction from Polarization Scans Using an Artificial Neural Network Integrated with an Expert System." In CORROSION 1992. NACE International, 1992. https://doi.org/10.5006/c1992-92264.

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Abstract Artificial neural networks are forms of artificial intelligence which learn correlative patterns between input and output information without a specific model. They then use the learned relationships to make predictions. An artificial neural network was constructed to recognize certain relationships in potentiodynamic polarization scans to predict if crevice corrosion, pitting, and general corrosion are possible concerns. The network so constructed was shown to be able to make appropriate predictions using scans not included in the original training. The resulting network was incorpor
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Fatima, Eeman, Muhammad Fahad, Hiba Abrar, Haroon-ur-Rashid, and Haroon Waris. "FPGA Based Artificial Neural Network Accelerator." In 2024 26th International Multitopic Conference (INMIC). IEEE, 2024. https://doi.org/10.1109/inmic64792.2024.11004346.

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Zheng, Shengjie, Lang Qian, Pingsheng Li, Chenggang He, Xiaoqi Qin, and Xiaojian Li. "An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence." In 8th International Conference on Artificial Intelligence (ARIN 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121010.

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Stemming from the rapid development of artificial intelligence, which has gained expansive success in pattern recognition, robotics, and bioinformatics, neuroscience is also gaining tremendous progress. A kind of spiking neural network with biological interpretability is gradually receiving wide attention, and this kind of neural network is also regarded as one of the directions toward general artificial intelligence. This review summarizes the basic properties of artificial neural networks as well as spiking neural networks. Our focus is on the biological background and theoretical basis of s
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Yang, Zhun, Adam Ishay, and Joohyung Lee. "NeurASP: Embracing Neural Networks into Answer Set Programming." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/243.

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We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neural network in symbolic computation and how it can improve the neural network's perception result by applying symbolic reasoning in answer set programming. Also, NeurASP can make use of ASP rules to train a neural network better so that a n
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Kumari, Neha, and Vani Bhargava. "Artificial Neural Network." In 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, 2019. http://dx.doi.org/10.1109/icict46931.2019.8977685.

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Mehdizadeh, Nasser S., Payam Sinaei, and Ali L. Nichkoohi. "Modeling Jones’ Reduced Chemical Mechanism of Methane Combustion With Artificial Neural Network." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-31186.

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The present work reports a way of using Artificial Neural Networks for modeling and integrating the governing chemical kinetics differential equations of Jones’ reduced chemical mechanism for methane combustion. The chemical mechanism is applicable to both diffusion and premixed laminar flames. A feed-forward multi-layer neural network is incorporated as neural network architecture. In order to find sets of input-output data, for adapting the neural network’s synaptic weights in the training phase, a thermochemical analysis is embedded to find the chemical species mole fractions. An analysis o
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Pryor, Connor, Charles Dickens, Eriq Augustine, Alon Albalak, William Yang Wang, and Lise Getoor. "NeuPSL: Neural Probabilistic Soft Logic." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/461.

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In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To model the boundary between neural and symbolic representations, we propose a family of energy-based models, NeSy Energy-Based Models, and show that they are general enough to include NeuPSL and many other NeSy approaches. Using this framework, we show how to seamlessly integrate neural and symbolic parameter learning and inference in NeuPSL. Through an extensive empirical evaluatio
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Zhan, Tiffany. "Hyper-Parameter Tuning in Deep Neural Network Learning." In 8th International Conference on Artificial Intelligence and Applications (AI 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121809.

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Deep learning has been increasingly used in various applications such as image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. In deep learning, a convolutional neural network (CNN) is regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The full connectivity of these networks makes them prone to overfitting data. T
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Reports on the topic "Neural Network (Artificial)"

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Powell, Bruce C. Artificial Neural Network Analysis System. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada392390.

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Sgurev, Vassil. Artificial Neural Networks as a Network Flow with Capacities. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.09.12.

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Karakowski, Joseph A., and Hai H. Phu. A Fuzzy Hypercube Artificial Neural Network Classifier. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada354805.

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Markova, Oksana, Serhiy Semerikov та Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the sp
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Vitela, J. E., U. R. Hanebutte, and J. Reifman. An artificial neural network controller for intelligent transportation systems applications. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/219376.

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Vela, Daniel. Forecasting latin-american yield curves: an artificial neural network approach. Banco de la República, 2013. http://dx.doi.org/10.32468/be.761.

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Hsieh, Bernard B., and Charles L. Bartos. Riverflow/River Stage Prediction for Military Applications Using Artificial Neural Network Modeling. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada382991.

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Huang, Wenrui, and Catherine Murray. Application of an Artificial Neural Network to Predict Tidal Currents in an Inlet. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada592255.

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Fitch, J. The radon transform for data reduction, line detection, and artificial neural network preprocessing. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6874873.

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Reifman, Jaques, and Javier Vitela. Artificial Neural Network Training with Conjugate Gradients for Diagnosing Transients in Nuclear Power Plants. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10198077.

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