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Journal articles on the topic 'Neural artificial system'

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Published: 5 June 2025

Last updated: 16 July 2025

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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 (2018): 1343–48. http://dx.doi.org/10.31142/ijtsrd17054.

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WU, CATHY H., HSI-LIEN CHEN, and SHENG-CHIH CHEN. "GENE CLASSIFICATION ARTIFICIAL NEURAL SYSTEM." International Journal on Artificial Intelligence Tools 04, no. 04 (1995): 501–10. http://dx.doi.org/10.1142/s0218213095000255.

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A gene classification artificial neural system has been developed for rapid annotation of the molecular sequencing data being generated by the Human Genome Project. Three neural networks have been implemented, one full-scale system to classify protein sequences according to PIR (Protein Identification Resources) superfamilies, one system to classify ribosomal RNA sequences into RDP (Ribosomal Database Project) phylogenetic classes, and one pilot system to classify proteins according to Blocks motifs. The sequence encoding schema involved an n-gram hashing method to convert molecular sequences into neural input vectors, a SVD (singular value decomposition) method to compress vectors, and a term weighting method to extract motif information. The neural networks used were three-layered, feed-forward networks that employed back-propagation or counter-propagation learning paradigms. The system runs faster by one to two orders of magnitude than existing method and has a sensitivity of 85 to 100%. The gene classification artificial neural system is available on the Internet, and may be extended into a gene identification system for classifying indiscriminately sequenced DNA fragments.

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Wu, Cathy, George Whitson, Jerry Mclarty, Adisorn Ermongkonchai, and Tzu-Chung Chang. "Protein classification artificial neural system." Protein Science 1, no. 5 (1992): 667–77. http://dx.doi.org/10.1002/pro.5560010512.

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Chun, Sungwoo, Jong-Seok Kim, Yongsang Yoo, et al. "An artificial neural tactile sensing system." Nature Electronics 4, no. 6 (2021): 429–38. http://dx.doi.org/10.1038/s41928-021-00585-x.

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Buhari, M. I., M. H. Habaebi, and B. M. Ali. "Artificial Neural System for Packet Filtering." Journal of Computer Science 1, no. 2 (2005): 259–69. http://dx.doi.org/10.3844/jcssp.2005.259.269.

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TISDALE, E. ROBERT, and WALTER J. KARPLUS. "SYSTEM IDENTIFICATION WITH ARTIFICIAL NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 01 (1992): 93–111. http://dx.doi.org/10.1142/s0218001492000059.

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System identification is the term scientists and engineers use to refer to the process of building mathematical models of dynamical systems based on observed data. This paper approaches system identification as a pattern recognition problem. We use computers to simulate the system response for a variety of different mathematical models. For each distinct system model, simulated system responses tend to remain segregated in one or more amorphous regions of system response space despite (1) large variations in system parameters, (2) experimental errors, and (3) noise. The actual system response is classified with the model corresponding to the region of system response space where it is found. The classifier is an Artificial Neural Network (ANN) which implements a Generalized Vector Quantizer (GVQ). A small number of simple but powerful discriminant functions facilitate the correct classification of most of the responses in any given region. The required distribution of discriminants among the regions evolves automatically as they learn their respective functions.

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Rana, Arti, Arvind Singh Rawat, Himanshu Bahuguna, and Anchit Bijalwan. "Artificial Neural Network based Diagnosis System." International Journal of Computer Trends and Technology 48, no. 4 (2017): 189–91. http://dx.doi.org/10.14445/22312803/ijctt-v48p134.

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Devi, Kharibam Jilenkumari, and Khelchandra Thongam. "A Survey of Automatic Speaker Recognition System Using Artificial Neural Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 453–56. http://dx.doi.org/10.5373/jardcs/v11sp10/20192832.

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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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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 systems involves adjustment to the synaptic connections that exists between the neurons. This is true of Ann’s as well. The first artificial neuron was produced in1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits.

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Loresco, Pocholo James M., and Elmer Dadios. "Vision-Based Lettuce Growth Stage Decision Support System Using Artificial Neural Networks." International Journal of Machine Learning and Computing 10, no. 4 (2020): 534–41. http://dx.doi.org/10.18178/ijmlc.2020.10.4.969.

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Subramanian, Kayalvizhi. "Advanced Decision Support System for Postoperative Care Unit Using Artificial Neural Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1116–22. http://dx.doi.org/10.5373/jardcs/v11sp10/20192913.

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Mehmood. "Indoor Positioning System Using Artificial Neural Network." Journal of Computer Science 6, no. 10 (2010): 1219–25. http://dx.doi.org/10.3844/jcssp.2010.1219.1225.

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Bretas, A. S., and A. G. Phadke. "Artificial neural networks in power system restoration." IEEE Transactions on Power Delivery 18, no. 4 (2003): 1181–86. http://dx.doi.org/10.1109/tpwrd.2003.817500.

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Bretas, A. S., and A. G. Phadke. "Artificial Neural Networks in Power System Restoration." IEEE Power Engineering Review 22, no. 10 (2002): 61. http://dx.doi.org/10.1109/mper.2002.4311755.

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SAITO, TOSHIMICHI, KENYA JIN'NO, and HIROYUKI TORIKAI. "Chaotic artificial neural system and its control†." International Journal of Electronics 79, no. 6 (1995): 797–806. http://dx.doi.org/10.1080/00207219508926313.

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Rashid, Md Mamunur, and A. K. M. Akatar Hossai . "Fingerprint Verification System Using Artificial Neural Network." Information Technology Journal 5, no. 6 (2006): 1063–67. http://dx.doi.org/10.3923/itj.2006.1063.1067.

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Gumus, Fatma, and Derya Yiltas-Kaplan. "Congestion Prediction System With Artificial Neural Networks." International Journal of Interdisciplinary Telecommunications and Networking 12, no. 3 (2020): 28–43. http://dx.doi.org/10.4018/ijitn.2020070103.

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Software Defined Network (SDN) is a programmable network architecture that provides innovative solutions to the problems of the traditional networks. Congestion control is still an uncharted territory for this technology. In this work, a congestion prediction scheme has been developed by using neural networks. Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm was performed on the data collected from the OMNET++ simulation. The novelty of this study also covers the implementation of mRMR in an SDN congestion prediction problem. After evaluating the relevance scores, two highest ranking features were used. On the learning stage Nonlinear Autoregressive Exogenous Neural Network (NARX), Nonlinear Autoregressive Neural Network, and Nonlinear Feedforward Neural Network algorithms were executed. These algorithms had not been used before in SDNs according to the best of the authors knowledge. The experiments represented that NARX was the best prediction algorithm. This machine learning approach can be easily integrated to different topologies and application areas.

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Ragini, Dr K., Spoorthi G. Kunch, B. Sathvika, K. Swathi, and G. Prashanthi. "Artificial Neural Network Based Integrated Ambulance System." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3673–78. http://dx.doi.org/10.22214/ijraset.2023.54184.

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Abstract: In emergency situations, almost all hospital beds were fully occupied, and the entire health department had to collapse. and the issues faced by the ambulance drivers who take the patients to the hospital had to wait outside the hospital for a prolonged time due to the unavailability of beds, ventilators, medical ICU, and a lack of oxygen supply, which led to the deaths of many patients. The major issue faced is that there is no proper system connecting all hospitals nearby for analyzing the condition of the patient inside the ambulance at the same time, and hospitals with the required equipment should be chosen at the right time. To prevent these situations, a system has been proposed in which decision-making will be done by analyzing the patient's condition using artificial neural networks and by retrieving information about the hospital and medical system's availability from a centralized server page connecting to all nearby hospitals.

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Mohammed Waleed. "Braille Identification System Using Artificial Neural Networks." Tikrit Journal of Pure Science 22, no. 2 (2023): 140–45. http://dx.doi.org/10.25130/tjps.v22i2.640.

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The Braille system is a widely used method by the blind to read and write. Information technology revolution is changing the way Braille reading and writing, making it easier to use. All kinds of materials can be put into Braille representation, such as bank statements, bus ticket, maps, and music note. In this paper, an artificial neural networks are designed to identify the number's image from (0-9) in Braille representation system. Networks will be trained and tested to be used for identify the scanned English number in Braille representation system. Some of the numbers are noised with some type of noise to simulate somehow the real world environment. According to the experiment the result of the identification of number that written in Braille representation using Artificial Neural Networks the training accuracy was 97.1% and testing accuracy was 85%.

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Morgos, Jan, Jaroslav Vorcak, and Karol Hrudkay. "Parking information system with artificial neural network." Transportation Research Procedia 74 (2023): 624–31. http://dx.doi.org/10.1016/j.trpro.2023.11.190.

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Shrivastava, Dr Anurag. "Career Recommendation System using Artificial Neural Network." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 616–20. http://dx.doi.org/10.22214/ijraset.2024.58020.

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Abstract: Artificial Neural Network is an important tool used in machine learning. The name 'neural' implies that it is a system that is derived from the human nervous system (brain) and is aimed at replicating the way humans comprehend. Neural Network is basically comprised of input layer, output layer and a hidden layer which consists of units that process the input data and transform them into the information that the output layer can utilize. There is a thing among the youth in choosing their career paths that they generally opt on either the recommendation of their colleagues or the job roles that are the highest paying in terms of salary. They lack the awareness of their strengths and skills that results in them choosing their career arbitrarily, which leads to frustration and demoralization. Besides, when the recruiters recruit the candidates, they need to evaluate them in many different facets. Therefore, there is a need for a system that can help such students in deciding a job role that is best suited for him/her which is in accordance with their skill set and other evaluation metrics which can now be achieved by the advancement in the field of deep learning. We propose an automated system using Artificial Neural Network which examines the personality traits of the individual along with academics and personal interests to predict which job role in computer science would be the best suited for them.

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Kuptsov, P. V., A. V. Kuptsova, and N. V. Stankevich. "Artificial Neural Network as a Universal Model of Nonlinear Dynamical Systems." Nelineinaya Dinamika 17, no. 1 (2021): 5–21. http://dx.doi.org/10.20537/nd210102.

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We suggest a universal map capable of recovering the behavior of a wide range of dynamical systems given by ODEs. The map is built as an artificial neural network whose weights encode a modeled system. We assume that ODEs are known and prepare training datasets using the equations directly without computing numerical time series. Parameter variations are taken into account in the course of training so that the network model captures bifurcation scenarios of the modeled system. The theoretical benefit from this approach is that the universal model admits applying common mathematical methods without needing to develop a unique theory for each particular dynamical equations. From the practical point of view the developed method can be considered as an alternative numerical method for solving dynamical ODEs suitable for running on contemporary neural network specific hardware. We consider the Lorenz system, the Rössler system and also the Hindmarch – Rose model. For these three examples the network model is created and its dynamics is compared with ordinary numerical solutions. A high similarity is observed for visual images of attractors, power spectra, bifurcation diagrams and Lyapunov exponents.

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AL-Fayyadh, Hayder Rahm Dakheel, Salam Abdulabbas Ganim Ali, and Dr Basim Abood. "Modelling an Adaptive Learning System Using Artificial Intelligence." Webology 19, no. 1 (2021): 01–18. http://dx.doi.org/10.14704/web/v19i1/web19001.

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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.

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Drevetskiy, Volodymyr, and Marko Klepach. "THE INTELLIGENT SYSTEM FOR AUTOMOTIVE FUELS QUALITY DEFINITION." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 3, no. 3 (2013): 11–13. http://dx.doi.org/10.35784/iapgos.1455.

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An intelligent system, based on hydrodynamic method and artificial neural networks usage for automotive fuels quality definition have been developed. Artificial neural networks optimal structures for the octane number of gasoline, cetane number, cetane index of diesel fuel definition have been substantiated and their accuracy has been analyzed. The implementation of artificial neural networks by means of microcontroller-based systems has been considered.

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Еськов, В. М., М. А. Филатов, Г. В. Газя, and Н. Ф. Стратан. "Artificial Intellect with Artificial Neural Networks." Успехи кибернетики / Russian Journal of Cybernetics, no. 3 (October 11, 2021): 44–52. http://dx.doi.org/10.51790/2712-9942-2021-2-3-6.

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В настоящее время не существует единого определения искусственного интеллекта. Требуется такая классификация задач, которые должны решать системы искусственного интеллекта. В сообщении дана классификация задач при использовании искусственных нейросетей (в виде получения субъективно и объективно новой информации). Показаны преимущества таких нейросетей (неалгоритмизируемые задачи) и показан класс систем (третьего типа — биосистем), которые принципиально не могут изучаться в рамках статистики (и всей науки). Для изучения таких биосистем (с уникальными выборками) предлагается использовать искусственные нейросети, которые решают задачи системного синтеза (отыскание параметров порядка). Сейчас такие задачи решает человек в режиме эвристики, что не моделируется современными системами искусственного интеллекта. Currently, there is no single definition of artificial intelligence. We need a Such categorization of tasks to be solved by artificial intelligence. The paper proposes a task categorization for artificial neural networks (in terms of obtaining subjectively and objectively new information). The advantages of such neural networks (non-algorithmizable problems) are shown, and a class of systems (third type biosystems) which cannot be studied by statistical methods (and all science) is presented. To study such biosystems (with unique samples) it is suggested to use artificial neural networks able to perform system synthesis (search for order parameters). Nowadays such problems are solved by humans through heuristics, and this process cannot be modeled by the existing artificial intelligence systems.

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Myint, Thuzar, and Hnin Moh Moh Aung Cho. "Artificial Neural Network for Solar Photovoltaic System Modeling and Simulation." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2110–14. https://doi.org/10.5281/zenodo.3591117.

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This paper presented neural network based maximum power point tracking on the design of photovoltaic power input to a DC DC boot converter to the load. Simulink model of photovoltaic array tested the neural network with different temperature and irradiance for maximum power point of a photovoltaic system. DC DC boot converter is used in load when an average output voltage is stable required which can be lower than the input voltage. At the end, the different temperature and irradiance of the data collected from the photovoltaic array system is used to train the neutral network and output efficiency of the designed DC DC boot converter with MPPT control strategy is accepted the maximum power amount to show the result voltage, current and power output for each different have been presented. And also demonstrated that the neural network based MPPT tracking require less time and more accurate results than the other algorithm based MPPT. Myint Thuzar | Cho Hnin Moh Moh Aung "Artificial Neural Network for Solar Photovoltaic System Modeling and Simulation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27867.pdf

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Han, Feng Shan, and Xin Li Wu. "Study on Rock Bolt Support of Roadway of Coal Mine Using Neural Network." Applied Mechanics and Materials 448-453 (October 2013): 3799–802. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3799.

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The artificial neural network has been widely used in various field of science and engineering. The artificial neural network has marvelous ability to gain knowledge. In this paper, according to principle of artificial neural network , Model of artificial neural network of rock bolt support of roadway of coal mine has been constructed,Learning system of BP artificial neural network has been trained,it is shown by engineering application that artificial neural network can handle imperfect or incomplete data and it can capture nonlinear and complex relationships among variables of a system. the artificial neural network is emerging as a powerful tool for modeling with the complex system. Method and parameters of rock bolt support of roadway of coal mine can be predicated accurately using artificial neural network, that is of significance and valuable to those subjects of investigation and design of mining engineering

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Farber, Boris, Vladimir Proseanic, Boris Zlotin, and Artur Martynov. "Progressive artificial neural network for medical applications." Annals of Mechnikov Institute, no. 4 (December 8, 2021): 91–100. https://doi.org/10.5281/zenodo.5767065.

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This article is presented by professionals, working in diverse fields and combining their knowledge in artificial neural networks with decades of experience in application of TRIZ, the Theory of Inventive Problem Solving. The article describes problems associated with the impossibility of effectively using existing neural networks for the analysis of medical information. Their solution proposes utilization of the recently invented, fundamentally new system - Progressive Artificial Neural Network (PANN). A description of PANN and its advantages is presented. The example of PANN implementation is shown with some possible applications of PANN, together with TRIZ, for conducting important research in the field of medicine, invention, and the development of new medical equipment and diagnostic and treatment methods. In particular, the authors propose: • Intelligent Database Management System in Medicine (IDBMSM), • System "Medical Advisor" • System "Personal Physician" • System "Physician Assistant - Researcher" • Medical Control System “Identify an error (mistake)” • System "Development of medical technology" • System of mass medical monitoring • Medical training system The authors believe that the use of the PANN system will be a very important step in the development of both the theory and practical aspects of medicine.

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Gan, Shu Chuan, Ling Tang, Li Cao, and Ying Gao Yue. "Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN." Applied Mechanics and Materials 483 (December 2013): 630–34. http://dx.doi.org/10.4028/www.scientific.net/amm.483.630.

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An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

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Xu, Qinyi. "How neural networks can improve the performance of electrical power systems?" Highlights in Science, Engineering and Technology 29 (January 31, 2023): 214–18. http://dx.doi.org/10.54097/hset.v29i.4571.

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As a new technology, artificial neural network is applied in more and more fields. It is not only popular in the computer field, but also in the traditional energy system. Artificial neural network can solve the problem that which traditional methods used in power system are having difficulty about speed, accuracy and efficiency. This paper will introduce the types of artificial neural networks and its application in power system to analyze how artificial neural networks improve the efficiency of power system. Artificial neural networks have been studied since the 1980s with the rise of artificial intelligence and are dedicated to using nonlinear adaptive information processing capabilities to handle information that cannot be processed by traditional methods. Additionally, applicability of artificial neural network to the collection of clean electricity such as wind energy, solar energy, and tidal energy is discussed. And how ANN can help people choose the right location to build power stations under the interference of complex natural environmental factors. Finally, the defects in the current power system and the possible future development direction of artificial neural networks is explained.

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Jerome, Jovitha, and P. Vinoth. "ARTIFICIAL INTELLIGENT SYSTEM FOR MEASUREMENT OF HARMONIC POWERS." ASEAN Journal on Science and Technology for Development 25, no. 1 (2017): 47–59. http://dx.doi.org/10.29037/ajstd.230.

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The importance of the electric power quality (PQ) demands new methodologies and measurement tools in the power industry for the analysis and measurement of the basic electric magnitudes necessary. This paper presents a new measurement procedure based on neural networks for the estimation of harmonic amplitudes of current/voltage and respective harmonic powers. The measurement scheme is built with two neural network modules. The first module is an adaptive linear neuron (ADALINE) that is the kernel part of estimation of complex harmonic coefficients of the current/voltage. The second module is feedforward neural network that obtains the harmonic active/reactive powers. In order to perform digital simulation the Feedforward and Adaline neural network tools were developed in LabVIEW. This measurement algorithm was tested for the practical cases and found to be robust, computationally fast and efficient.

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Zakaria, Mamang, Luther Pagiling, and Wa Ode Siti Nur Alam. "Sistem Penyiraman Otomatis Tanaman Semusim Berbasis Jaringan Saraf Tiruan Multilayer Perceptron." Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) 7, no. 1 (2022): 35. http://dx.doi.org/10.33772/jfe.v7i1.24050.

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In general, farmers water plants when the conditions are met, such as dry soil, no rain, and cold temperatures. One of the efficient ways to control it is to use an artificial neural network-based automatic plant watering system. The purpose of this study was to determine the success of artificial neural networks as decision-makers to water plants automatically. The stages of designing an automatic watering system based on an artificial neural network were to build software including artificial neural network modeling and Arduino microcontroller programming, automatically watering tools, evaluating tool performance, and testing tools in real-time. The test results show that the artificial neural network-based automatic plant watering system can water plants according to the given input pattern. The artificial neural network structure obtained is three neurons in the input layer, eight neurons in the hidden layer, and one neuron in the output layer. The artificial neural network-based automatic plant watering system succeeded in automatically watering two areas of land that the success rate is a 100%.Keyword— Automatic Watering, Microcontroller, ANN, Annual Crops.

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Simonovic, Milos, Vlastimir Nikolic, Emina Petrovic, and Ivan Ciric. "Heat load prediction of small district heating system using artificial neural networks." Thermal Science 20, suppl. 5 (2016): 1355–65. http://dx.doi.org/10.2298/tsci16s5355s.

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Accurate models for heat load prediction are essential to the operation and planning of a utility company. Load prediction helps a heat utility to make important and advanced decisions in district heating systems. As a popular data driven method, artificial neural networks are often used for prediction. The main idea is to achieve quality prediction for a short period in order to reduce the consumption of heat energy production and increased coefficient of exploitation of equipment. To improve the short term prediction accuracy, this paper presents a kind of improved artificial neural network model for 1 to 7 days ahead prediction of heat consumption of energy produced in small district heating system. Historical data set of one small district heating system from city of Nis, Serbia, was used. Particle swarm optimization is applied to adjust artificial neural network weights and threshold values. In this paper, application of feed forward artificial neural network for short-term prediction for period of 1, 3, and 7 days, of small district heating system, is presented. Two test data sets were considered with different interruption non-stationary performances. Comparison of prediction accuracy between regular and improved artificial neural network model was done. The comparison results reveal that improved artificial neural network model have better accuracy than that of artificial neural network ones.

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Ma, Lianfeng. "Macroeconomic Forecast Model System Based on Digital Information and Blockchain Technology." Mobile Information Systems 2022 (June 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/7208805.

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Since the reform and opening up, China’s economy has developed rapidly, becoming the second largest economy in the world. The state of macroeconomic development has a great influence on the government’s policy introduction and the investment decisions of individual institutions. Therefore, forecasting the macroeconomy is of great significance to the country. This paper aims to study the macroeconomic forecasting model system based on digital information and blockchain technology. This paper proposes an artificial neural network prediction algorithm based on digital information and blockchain technology, and the artificial neural network and particle swarm algorithm are combined to become the hybrid artificial neural network algorithm, and the conception of the establishment of the macroeconomic forecast model is proposed. The experimental results of this paper show that according to the prediction results of artificial neural network, the prediction and actual error in 2010 is 0.10, while the new method proposed in this paper predicts the error of 0.056. In 2016, the prediction error based on artificial neural network was 0.14, while the prediction result of the new method proposed in this paper showed an error of 0.008. In each year’s economic forecast, the neural network model of the new method proposed in this paper has higher prediction accuracy and smaller error. It can be seen that the neural network model based on artificial neural network and PSO algorithm proposed in this paper is beneficial to macroeconomic forecasting.

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Huang, Jing, Hai Bin Chen, Jiang Zhang, and Han Bo Zhang. "Hacker Intrusion Detection System Based on Artificial Neural Network." Applied Mechanics and Materials 263-266 (December 2012): 2924–28. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2924.

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In this paper, some scholars’ idea of applying neural network technology in the design of hacker intrusion detection system model and making a hacker intrusion detection system model based on artificial neural network is adopted. This study selects KDDCup’99 for network intrusion detection data set to learn the characteristics of the intrusion accurately; completes the normalization of all characteristics to achieve rapid convergence of the artificial neural network; analyses the advantages and disadvantages of different neural network training functions; achieves a high accuracy rate for intrusion detection successfully.

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Bukhari, Syeda Sana, Waqar Ahmad, Khurram Khan Jadoon, and Shahab U. Ansari. "Artificial Neural Network-Based Color Contrast Recommendation System." MATEC Web of Conferences 398 (2024): 01029. http://dx.doi.org/10.1051/matecconf/202439801029.

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Color contrast pertains to graphics and the field of design. Visual objects can be described nicely with the best contrast combinations used in their representation. Color contrast suggestion is usually done with color theory, which defines two colors exactly opposite or adjacent in color hue are good contrast with each other. Herein, this paper presents a Color Contrast Recommendation System (CCRS) as an innovative solution based on Artificial Neural Networks (ANN). The main aim of the paper is to facilitate different users to find suitable contrast for any base color. We used a simple neural network model with two hidden layers for a regression task. The proposed model suggests three contrast layers for the base color given by the user. We prepare a data set of 420 color combinations for training our Neural Network model that looks appealing together and enhances the visuals. The proposed color contrast recommendation application based on Neural Networks represents a significant advancement in leveraging AI technology to streamline the design process, improve accessibility, and enhance user experiences across digital platforms.

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Hertz, J. A., T. W. Kjær, E. N. Eskandar, and B. J. Richmond. "MEASURING NATURAL NEURAL PROCESSING WITH ARTIFICIAL NEURAL NETWORKS." International Journal of Neural Systems 03, supp01 (1992): 91–103. http://dx.doi.org/10.1142/s0129065792000425.

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We show how to use artificial neural networks as a quantitative tool in studying real neuronal processing in the monkey visual system. Training a network to classify neuronal signals according to the stimulus that elicited them permits us to calculate the information transmitted by these signals. We illustrate this for neurons in the primary visual cortex with measurements of the information transmitted about visual stimuli and for cells in inferior temporal cortex with measurements of information about behavioral context. For the latter neurons we also illustrate how artificial neural networks can be used to model the computation they do.

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Hakem Alsaeedi, Ali, Ali Hussein Aljanabi, Mehdi Ebady Manna, and Adil L. Albukhnefis. "A proactive metaheuristic model for optimizing weights of artificial neural network." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 2 (2020): 976. http://dx.doi.org/10.11591/ijeecs.v20.i2.pp976-984.

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<span>This paper proposes the Particle Swarm Optimization model for enhancing the performance of an Artificial Neural Network. The learning process of Artificial Neural Network requires a long time to satisfy requirements because of processing complexity of the backpropagation algorithm that has been used in training Artificial Neural Network. It is a nonlinear complex model that can be used to configure and train an artificial neuron system. Both Artificial Neural Network and Particle Swarm Optimization model have been managed to solve and optimize several nonlinear models. Heuristic Optimization Weight of Artificial Neural Network (HNN) is a proactive metaheuristic model proposed to optimize the performance of Artificial Neural Network. The proposed system applies Particle Swarm Optimization to find the optimum weights of the Artificial Neural Network instead of using the Backpropagation algorithm. Experimentally, the proposed system has required less processing time (average of 76.91 Sec.) than Backpropagation (average of 93.32 Sec). Furthermore, It has provided better classification accuracy (start from 80% to 97.20%) comparing with Backpropagation (start from 75.32% to 94.32%).</span>

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Türkyılmaz, İbrahim, and Kirami Kaçan. "License Plate Recognition System Using Artificial Neural Networks." ETRI Journal 39, no. 2 (2017): 163–72. http://dx.doi.org/10.4218/etrij.17.0115.0766.

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Rezzwan Radzman, Muhammad Iqmmal, Abd Kadir Mahamad, Siti Zarina Mohd Muji, et al. "Pipe leakage detection system with artificial neural network." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 977. http://dx.doi.org/10.11591/ijai.v11.i3.pp977-985.

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<span lang="EN-US">This project aims to develop a system that can monitor to detect leaks in water distribution networks. It has been projected that leakage from pipelines may lead to significant economic losses and environmental damage. The loss of water from leaks in pipeline systems accounts for a large portion of the water supply. Pipelines are maintained throughout their lives span; however, it is difficult to avoid a leak occurring at some point. A tremendous amount of water could be saved globally if automated leakage detection systems were introduced. An embedded system that monitors water leaks can efficiently aid in water conservation. This project focuses on developing a real-time water leakage detection system using a few types of sensors: water flow rate sensor, vibration sensor, and water pressure sensor. The data from the sensors is uploaded and stored by the microcontroller (NodeMCU V3) to the database cloud (Google Sheets). The data that is stored in the database is analyzed by artificial neural network (ANN) by using Matlab software. An application is developed based on results from ANN training to detect the leakage event. Implementing the proposed system can increase operations efficiency, reduce delay times, and reduce maintenance costs after leaks are detected.</span>

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Zeng, P. "Artificial Neural Network Assessment System for Fatigue Life." Key Engineering Materials 145-149 (October 1997): 393–98. http://dx.doi.org/10.4028/www.scientific.net/kem.145-149.393.

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Mon, Khaing Myat, Zo Nun Khuma, and Dr Cho Cho Myint. "Artificial Neural Network for Facial Feature Extraction System." International Journal of Science and Engineering Applications 8, no. 7 (2019): 243–47. http://dx.doi.org/10.7753/ijsea0807.1013.

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Malgwi, Yusuf Musa, Ibrahim Goni, and Bamanga Mahmud Ahmad. "Artificial Neural Network Model for Intrusion Detection System." Mediterranean Journal of Basic and Applied Sciences 06, no. 01 (2022): 20–26. http://dx.doi.org/10.46382/mjbas.2022.6103.

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Artificial Intelligence (AI) breakthroughs in the last few years have accelerated dramatically as a result of the industry's vast technological use. Neural Networks (NN) is one of the most vital areas of AI, as they allow for commercial use of features that were previously not accessible via the use of computers. The Intrusion Detection System (IDS) is one of the areas in which Neural Networks are being extensively investigated to provide comprehensive computer network security and data confidentiality. During the realization of this work Artificial Neural Network (ANN) were used to shape the proposed model using a realistic CICIDS2017 dataset retrieved from the Canadian Institute for Cyber-Security (CIC) website. Following implementation and testing, it was discovered that the new model performs exceptionally well, with an average. In addition, the receiver operator characteristic curve (ROC) has a 9.999 % area under the Receiver Operator Characteristic Curve (AUC). Finally, it was discovered that the new model is exceptional and has a high level of accuracy. The new model will aid in an improved knowledge of various orders in which IDS research has been conducted. It will be useful for those working on AI-based solutions in IDS and similar domains. It is possible to enhance the new model's detection capabilities to incorporate all other lingering forms of incidents in this actual datasets, which contains all real-time and existing incidents.

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Sureban, Manjula S., and Shekhappa G. Ankaliki. "Artificial Neural Networks Based Power System State Estimation." i-manager's Journal on Software Engineering 9, no. 2 (2014): 9–16. http://dx.doi.org/10.26634/jse.9.2.3323.

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Phegade, Kirti S. "Heart Attack Prediction System using Artificial Neural Network." International Journal for Research in Applied Science and Engineering Technology 7, no. 2 (2019): 971–74. http://dx.doi.org/10.22214/ijraset.2019.2151.

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Popova, Yu V. "Artificial Neural Network in the CATS Training System." Digital Transformation, no. 2 (August 6, 2019): 53–59. http://dx.doi.org/10.38086/2522-9613-2019-2-53-59.

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This paper presents a variant of using an artificial neural network (ANN) for adaptive learning. The main idea of using ANN is to apply it for a specific educational material, so that after completing the course or its separate topic, the student can determine, not only his level of knowledge, without the teacher’s participation, but also get some recommendations on what material needs to be studied further due to gaps in the studied issues. This approach allows you to build an individual learning trajectory, significantly reduce the time to study academic disciplines and improve the quality of the educational process. The training of an artificial neural network takes place according to the method of back propagation of an error. The developed ANN can be applied to study any academic discipline with a different number of topics and control questions. The research results are implemented and tested in the CATS adaptive training system. This system is the author's development.

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Olaosebikan, Samuel O. "Highway Compliance Monitoring System using Artificial Neural Network." International Journal for Research in Applied Science and Engineering Technology 7, no. 9 (2019): 1193–97. http://dx.doi.org/10.22214/ijraset.2019.9172.

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M. Vinod Kumar, S. C. Srivastava, D. "Power System State Forecasting Using Artificial Neural Networks." Electric Machines & Power Systems 27, no. 6 (1999): 653–64. http://dx.doi.org/10.1080/073135699269091.

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Palancar, María C., José M. Aragón, and José S. Torrecilla. "pH-Control System Based on Artificial Neural Networks." Industrial & Engineering Chemistry Research 37, no. 7 (1998): 2729–40. http://dx.doi.org/10.1021/ie970718w.

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