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Journal articles on the topic 'Network of expert systems'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Gupta, Ashok, and Uma Gupta. "Expert systems in network configuration." Computers & Industrial Engineering 15, no. 1-4 (1988): 236–39. http://dx.doi.org/10.1016/0360-8352(88)90092-7.

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2

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 e

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3

Ershov, A. V., F. P. Kapsargin, A. G. Berezhnoy, and M. P. Miltigashev. "Expert systems in uroflowmetry data evaluation." Herald Urology 6, no. 3 (2018): 12–16. http://dx.doi.org/10.21886/2308-6424-2018-6-3-12-16.

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Introduction. In the practice of an urologist, it is customary to assess the type of urination by two parameters: most often it is the effective volume of the bladder (V) and the maximum volume rate of urination (Qmax). Since the expert assessment of the digital characteristics of urine flow is often ambiguous, they are not taken into consideration by some doctors and often remain without due attention. Today there is a tendency in medicine to objectify by quantification of clinical parameters. The main technology used to solve the tasks of data processing and analysis, as well as their classi

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4

Mardin, A. "Neural Network Learning and Expert Systems." AI Communications 7, no. 3-4 (1994): 238–40. http://dx.doi.org/10.3233/aic-1994-73-412.

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5

Castillo, E., A. Cobo, J. M. Gutiérrez, A. Iglesias, and H. Sagástegui. "Causal Network Models in Expert Systems." Computer-Aided Civil and Infrastructure Engineering 9, no. 5 (1994): 315–28. http://dx.doi.org/10.1111/j.1467-8667.1994.tb00339.x.

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6

Kerr, Sandra M. "Neural network learning and expert systems." Artificial Intelligence in Engineering 8, no. 4 (1993): 315–16. http://dx.doi.org/10.1016/0954-1810(93)90016-9.

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7

Hunt, John. "Neural network learning and expert systems." Engineering Applications of Artificial Intelligence 9, no. 4 (1996): 465. http://dx.doi.org/10.1016/0952-1976(96)81679-4.

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8

Luna, Francesco. "Neural network learning and expert systems." Journal of Economic Dynamics and Control 19, no. 4 (1995): 741–45. http://dx.doi.org/10.1016/0165-1889(95)90003-9.

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9

Gao, Shengxiang, Zhengtao Yu, Linbin Shi, Xin Yan, and Haixia Song. "A Method to Review Expert Recommendation Using Topic Relevance and Expert Relationship." International Journal of Cooperative Information Systems 27, no. 01 (2018): 1741004. http://dx.doi.org/10.1142/s0218843017410040.

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In the process of recommending review experts to projects, in order to effectively make use of the relevance among topics and the relationship among experts, a new method is proposed for review expert recommendation using topic relevance and expert relationship. In this method, firstly, the relevance among topics and the relationships among experts are used to respectively construct the Markov network of topics and the Markov network of experts. Next, the maximum topic clique is extracted from the topic Markov network and the maximum expert clique is extracted from the expert Markov network; t

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10

Lv, Tai Fu. "Research on High-Density Network Intrusion Features Detection Algorithm." Applied Mechanics and Materials 644-650 (September 2014): 1054–57. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1054.

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Research on high-density network intrusion features problems, which improves the detection accuracy. For high-density network, an intrusion feature detection system based on intelligent expert systems and neural networks in is proposed. First, use expert systems for known high-density network intrusion detection. Use the neural network expert system to detect those which cannot find the unknown high-density network intrusion. Finally test results using neural network expert system rule library to be updated. Experimental results show that this method can effectively detect high-density network

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11

Pocebneva, Irina, Vadim Belousov, Irina Fateeva, Vitaly Lukinov, and Tatyana Folomeeva. "Algorithms of hierarchical mixture of opinions of experts in problems of synthesis of information management systems city development." MATEC Web of Conferences 170 (2018): 01120. http://dx.doi.org/10.1051/matecconf/201817001120.

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In the present article we will consider a class of associative machines with dynamic structure where the entrance signal exerts direct impact on the mechanism of association of output signals of experts. At the same time we are interested in such group of expert decisions at which separate expert responses unite not linearly through hierarchically organized lock networks. Hierarchical mixture of opinions of experts, along with simple mixture are examples of modular networks: neural network of a module if the calculations executed by it can be distributed on several subsystems processing differ

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12

Coffield, David. "Expert systems applications in integrated network management." Computer Communications 13, no. 5 (1990): 312. http://dx.doi.org/10.1016/0140-3664(90)90019-d.

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13

Coffield, David. "Expert systems applications in integrated network management." Knowledge-Based Systems 3, no. 2 (1990): 124. http://dx.doi.org/10.1016/0950-7051(90)90011-6.

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14

Nguyen, Dat-Dao. "Designing Optimal Knowledge Base for Neural Expert Systems." International Business Research 10, no. 6 (2017): 137. http://dx.doi.org/10.5539/ibr.v10n6p137.

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One of the limitations of conventional expert systems and traditional machine induction methods in capturing human expertise is in their requirement of a large pool of structured samples from a multi-criteria decision problem domain. Then the experts may have difficulty in expressing explicitly the rules on how each decision was reached. To overcome these shortcomings, this paper reports on the design of an optimal knowledge base for machine induction with the integration of Artificial Neural Network (ANN) and Expert Systems (ES). In this framework, an orthogonal plan is used to define an opti

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15

Pfau-Wagenbauer, Monika, and Wolfgang Nejdl. "Model/heuristic-based alarm processing for power systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 7, no. 1 (1993): 65–78. http://dx.doi.org/10.1017/s089006040000007x.

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This paper describes an intelligent alarm processing expert system which is integrated in a large Supervisory Control and Data Acquisition system for power distribution networks. The expert system works as an operator support tool by diagnosing network disturbances and device malfunctions. The expert system is based on a hierarchic, multi-level problem-solving architecture, integrating both model-based and heuristic techniques acting upon an object-oriented data structure. Several enhancements have been designed and implemented to enable the system to perform its task online and real-time. The

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16

Krotova, Svetlana, Irina Remizova, Elena Ovchinnikova, Aleksei Ilin, and Iurii Kozhubaev. "Basic methods of developing an expert system to assess the efficiency of power supply networks." E3S Web of Conferences 371 (2023): 03050. http://dx.doi.org/10.1051/e3sconf/202337103050.

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The paper presents the basic methods for developing an expert system to assess the efficiency of power supply networks. The existing expert systems and possibility of their use to assess the efficiency of power supply systems have been analyzed. Mathematical models for network expertise, in particular, methods of the theory of choice and decision making were described. Decision-making algorithm for power grid based on differential evolution method is considered. The algorithm to calculate the reduced costs in designing power supply networks, allowing to develop the optimal expert system for co

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17

Goodman, Rodney M., John Miller, Padhraic Smyth, and Hayes Latin. "Real Time Autonomous Expert Systems in Network Management." Office Technology and People 5, no. 3 (1989): 205–28. http://dx.doi.org/10.1108/eum0000000003536.

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18

Gomide, Fernando. "Fuzzy engineering expert systems with neural network applications." Fuzzy Sets and Systems 140, no. 2 (2003): 397–98. http://dx.doi.org/10.1016/s0165-0114(03)00261-6.

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19

Ramamoorthy, P. A., and S. Ho. "A neural network approach for implementing expert systems." Neural Networks 1 (January 1988): 44. http://dx.doi.org/10.1016/0893-6080(88)90086-x.

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20

Bernstein, L., and C. M. Yuhas. "Expert systems in network management-the second revolution." IEEE Journal on Selected Areas in Communications 6, no. 5 (1988): 784–87. http://dx.doi.org/10.1109/49.627.

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21

Lirov, Yuval, and Benjamin Melamed. "Distributed expert systems for queueing network capacity planning." Annals of Operations Research 39, no. 1 (1992): 137–55. http://dx.doi.org/10.1007/bf02060939.

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22

DEDIĆ, Luka, and Miroslav VUJIĆ. "A Review of Expert Systems Integration in Signal Plan Optimisation." Promet - Traffic&Transportation 36, no. 5 (2024): 797–817. http://dx.doi.org/10.7307/ptt.v36i5.601.

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In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectori

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23

Marra, Rose M., and David H. Jonassen. "Transfer Effects of Semantic Networks on Expert Systems: Mindtools at Work." Journal of Educational Computing Research 26, no. 1 (2002): 1–23. http://dx.doi.org/10.2190/wmln-n7jc-21pa-47th.

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Semantic networks and expert systems can support learning and critical thinking as Mindtools. Mindtools are computer-based tools that function as intellectual partners with the learner in order to engage and facilitate critical thinking and higher-order learning. Semantic networks and expert systems in particular are cognitive reflection tools that help learners to build a representation of what they know by designing their own knowledge bases. Semantic networks have been used as a knowledge elicitation tool for expert system construction; however, the effects of using these tools together has

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24

Chen, Wenyu, Weimin Li, Lei Shao, Tao Zhang, and Xi Wang. "Large-scale group-hierarchical DEMATEL method for complex systems." PLOS ONE 18, no. 12 (2023): e0288326. http://dx.doi.org/10.1371/journal.pone.0288326.

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Existing Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods are mostly suitable for simple systems with fewer factors, and lack effective integration of expert knowledge and experience from large-scale group populations, resulting in a potential compromise of the quality of the initial direct relation (IDR) matrix. To make DEMATEL better suited for the identification of critical factors in complex systems, this paper proposes a hierarchical DEMATEL method for large-scale group decision-making. Considering the limitations of expert knowledge and experience, a method based on expe

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25

Heckerman, E. D., and N. B. Nathwani. "Toward Normative Expert Systems: Part II Probability-Based Representations for Efficient Knowledge Acquisition and Inference." Methods of Information in Medicine 31, no. 02 (1992): 106–16. http://dx.doi.org/10.1055/s-0038-1634868.

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Abstract:We address practical issues concerning the construction and use of decision-theoretic or normative expert systems for diagnosis. In particular, we examine Pathfinder, a normative expert system that assists surgical pathologists with the diagnosis of lymph-node diseases, and discuss the representation of dependencies among pieces of evidence within this system. We describe the belief network, a graphical representation of probabilistic dependencies. We see how Pathfinder uses a belief network to construct differential diagnosis efficiently, even when there are dependencies among pieces

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26

Miller, CDF. "Expert systems." Data Processing 27, no. 6 (1985): 49. http://dx.doi.org/10.1016/0011-684x(85)90281-3.

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27

Afrianto, Eko, and Ekatri Ayuningsih. "Expert System for Troubleshooting Web-Based Lan Network Using Forward Chaining Method." Journal of Intelligent Systems and Information Technology 2, no. 1 (2025): 32–42. https://doi.org/10.61971/jisit.v2i1.138.

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This research develops an expert system for troubleshooting LAN networks using the forward chaining method at SMK PGRI Lumajang. With numerous devices connected simultaneously, including computers, tablets, and smart devices, the network may become congested, leading to slow data transfer and connectivity issues. This is especially problematic during peak hours when students and staff access online resources concurrently. The forward chaining method is an artificial intelligence approach in which the system uses given facts to reach a solution. Expert systems have become an important instrumen

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28

Boyle, C. D. B., and J. F. Schuette. "Debugging expert systems using a dynamically created hypertext network." Expert Systems with Applications 4, no. 4 (1992): 423–24. http://dx.doi.org/10.1016/0957-4174(92)90137-h.

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29

Callahan, Paul H. "Expert Systems for AT&T Switched Network Maintenance." AT&T Technical Journal 67, no. 1 (1988): 93–103. http://dx.doi.org/10.1002/j.1538-7305.1988.tb00237.x.

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30

Ritter, Noah, and Jeremy Straub. "Implementation of Hardware-Based Expert Systems and Comparison of Their Performance to Software-Based Expert Systems." Machines 9, no. 12 (2021): 361. http://dx.doi.org/10.3390/machines9120361.

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Expert systems are a form of highly understandable artificial intelligence that allow humans to trace the decision-making processes that are used. While they are typically software implemented and use an iterative algorithm for rule-fact network processing, this is not the only possible implementation approach. This paper implements and evaluates the use of hardware-based expert systems. It shows that they work accurately and can be developed to parallel software implementations. It also compares the processing speed of software and hardware-based expert systems, showing that hardware-based sy

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31

van de Riet, R. P. "Expert database systems." Future Generation Computer Systems 2, no. 3 (1986): 191–99. http://dx.doi.org/10.1016/0167-739x(86)90015-4.

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32

Zemanek, Heinz. "Building expert systems." Future Generation Computer Systems 2, no. 4 (1986): 262–63. http://dx.doi.org/10.1016/0167-739x(86)90026-9.

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33

WEIGEND, ANDREAS S., MORGAN MANGEAS, and ASHOK N. SRIVASTAVA. "NONLINEAR GATED EXPERTS FOR TIME SERIES: DISCOVERING REGIMES AND AVOIDING OVERFITTING." International Journal of Neural Systems 06, no. 04 (1995): 373–99. http://dx.doi.org/10.1142/s0129065795000251.

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In the analysis and prediction of real-world systems, two of the key problems are nonstationarity (often in the form of switching between regimes), and overfitting (particularly serious for noisy processes). This article addresses these problems using gated experts, consisting of a (nonlinear) gating network, and several (also nonlinear) competing experts. Each expert learns to predict the conditional mean, and each expert adapts its width to match the noise level in its regime. The gating network learns to predict the probability of each expert, given the input. This article focuses on the ca

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34

Pederson, Shane P. "Probabilistic Networks and Expert Systems." Technometrics 43, no. 1 (2001): 108–9. http://dx.doi.org/10.1198/tech.2001.s564.

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35

Ruppert, David. "Probabilistic Networks and Expert Systems." Journal of the American Statistical Association 96, no. 456 (2001): 1524. http://dx.doi.org/10.1198/jasa.2001.s424.

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36

Looney, Carl G. "Neural networks as expert systems." Expert Systems with Applications 6, no. 2 (1993): 129–36. http://dx.doi.org/10.1016/0957-4174(93)90003-o.

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37

Jones, Russell. "Commercial expert systems." Data Processing 28, no. 3 (1986): 115–19. http://dx.doi.org/10.1016/0011-684x(86)90002-x.

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38

Bekturdiev, Sanjarbek Sharifboyevich, and Sadoqat Abdusalom qizi Tursunova. "THE PLACE OF ARTIFICIAL NEURAL NETWORK MODELS IN EXPERT SYSTEMS." Involta Scientific Journal 3, no. 12 (2024): 21–28. https://doi.org/10.5281/zenodo.14507255.

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Understanding the evolution of artificial neural networks and their introduction into expert systems provides a foundational knowledge of their significance. Key milestones in the development of ANNs and their initial applications in expert systems are crucial for appreciating their current role and potential.  Delving into the workings of ANNs, including their structure (neurons, layers, activation functions) and learning processes (supervised, unsupervised, reinforcement learning), gives insight into why these models are effective. This section will cover how ANNs process information, l

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39

González Pérez, Pedro Pablo, and José Negrete Martínez. "REDSIEX: A Cooperative Network of Expert Systems with Blackboard Architectures." Expert Systems 14, no. 4 (1997): 180–89. http://dx.doi.org/10.1111/1468-0394.00055.

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40

Sarkar, Sumit, and Ishwar Murthy. "Criteria to evaluate approximate belief network representations in expert systems." Decision Support Systems 15, no. 4 (1995): 323–50. http://dx.doi.org/10.1016/0167-9236(94)00045-x.

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41

Maybury, Mark. "Knowledge on Demand: Knowledge and Expert Discovery." JUCS - Journal of Universal Computer Science 8, no. (5) (2002): 491–505. https://doi.org/10.3217/jucs-008-05-0491.

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This article outlines new technologies in the areas of automated expertise finding, expert network discover, virtual place-based collaboration, and automated question answering. We illustrate each of these areas with implemented and in some cases empirically evaluated systems. Collectively, these illustrate new methods for automatic discovery of knowledge, experts, and communities in an effective and efficient manner.

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42

Vivek, Bairy, and Jorepalli Sunil. "Intent-Based Networking with AI: Towards Fully Autonomous Network Operations." Applied Science and Engineering Journal for Advanced Research 4, no. 2 (2025): 39–44. https://doi.org/10.5281/zenodo.15347801.

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This research explored the integration of Artificial Intelligence (AI) into Intent-Based Networking (IBN) systems with the goal of enabling fully autonomous network operations. Utilizing a qualitative research design, the study involved expert interviews and system behavior observations within simulated environments. The AI-driven IBN prototype was developed with components for natural language intent recognition, policy translation, and autonomous fault management. The findings indicated high accuracy in interpreting user intents (93.7%), efficient policy deployment, and significant reduction

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43

de Rosa, Francesca, and Alessandro De Gloria. "An Analytical Game for Knowledge Acquisition for Maritime Behavioral Analysis Systems." Applied Sciences 10, no. 2 (2020): 591. http://dx.doi.org/10.3390/app10020591.

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The use of Bayesian networks for behavioral analysis is gaining attention. The design of such algorithms often makes use of expert knowledge. The knowledge is collected and organized during the knowledge acquisition design task. In this paper, we discuss how analytical games can be exploited as knowledge acquisition techniques in order to collect information useful to intelligent systems design. More specifically, we introduce a recently developed method, called the MARISA (MARItime Surveillance knowledge Acquisition) Game. The aim of this game is to ease the elicitation from domain experts of

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44

Avogadro, Paolo, Silvia Calegari, and Matteo Alessandro Dominoni. "Expert students in social learning management systems." Interactive Technology and Smart Education 13, no. 3 (2016): 202–17. http://dx.doi.org/10.1108/itse-06-2016-0018.

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Purpose A social learning management system (social LMS) is a tool which favors social interactions and allows scholastic institutions to supervise and guide the learning process. The inclusion of the social feature to a “normal” LMS leads to the creation of educational social networks (EduSN), where the students interact and learn. The advantages derived from an augmented student-student interaction are counterbalanced by the difficulty to control the quality of the information. For this reason, the purpose of this study is to understand who is a source of reliable and high quality knowledge

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45

Abdelrahim, Ashraf M., and K. P. George. "Artificial Neural Network for Enhancing Selection of Pavement Maintenance Strategy." Transportation Research Record: Journal of the Transportation Research Board 1699, no. 1 (2000): 16–22. http://dx.doi.org/10.3141/1699-03.

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In the selection of an economical treatment for rehabilitation of a deteriorated pavement section, decision makers usually encounter various situations. Factors affecting selection of a flexible pavement maintenance strategy may include distress conditions, traffic volume, and road class, among others. Traditionally engineers make their selection on the basis of their experience and judgment and past maintenance data. Experts’ judgments are usually compromised in a group discussion to construct decision trees or decision matrices or even to develop knowledge-based expert systems. An artificial

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46

Place, John F., Alain Truchaud, Kyoichi Ozawa, Harry Pardue, and Paul Schnipelsky. "Use of artificial intelligence in analytical systems for the clinical laboratory." Journal of Automatic Chemistry 17, no. 1 (1995): 1–15. http://dx.doi.org/10.1155/s1463924695000010.

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The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI), both as expert systems and as neural networks.This paper considers the role of software in system operation, control and automation, and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engi

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47

Zahnstecher, Brian. "Derating the 5G Network [Expert View]." IEEE Power Electronics Magazine 8, no. 1 (2021): 69–73. http://dx.doi.org/10.1109/mpel.2020.3047288.

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48

Van De Riet, Reind P. "Problems with expert systems?" Future Generation Computer Systems 3, no. 1 (1987): 11–16. http://dx.doi.org/10.1016/0167-739x(87)90039-2.

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49

Zotov, V. V. "Organization of Expert Activity on Digital Network Platforms." Administrative Consulting, no. 11 (December 23, 2022): 55–66. http://dx.doi.org/10.22394/1726-1139-2022-11-55-66.

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Currently, expert-analytical activity is being developed on digital network platforms as sociotechnical systems that organize algorithmized relationships between actors and actant in the field of assessing a problem situation and making decisions to resolve it. The lag in the development of expert and analytical activities in the public administration system of the region leads to a slowdown in the processes of their socio-economic development. The purpose of the presented work was to substantiate the need of the regional management system for a conceptual model of smart configuration of exper

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50

Zotov, Vitaly V. "Organization of Expert Activity on Digital Network Platforms." Administrative Consulting, no. 11 (167) (June 7, 2022): 55–66. https://doi.org/10.22394/1726-1139-2022-11-55-66.

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Currently, expert-analytical activity is being developed on digital network platforms as sociotechnical systems that organize algorithmized relationships between actors and actant in the field of assessing a problem situation and making decisions to resolve it. The lag in the development of expert and analytical activities in the public administration system of the region leads to a slowdown in the processes of their socio-economic development. The purpose of the presented work was to substantiate the need of the regional management system for a conceptual model of smart configuration of

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