Relevant bibliographies by topics / Neuro-genetic hybrid system

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Neuro-genetic hybrid system'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Neuro-genetic hybrid system"

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Adithya Nallamuthu, Suresh. "A Hybrid Genetic-Neuro Algorithm for Cloud Intrusion Detection System." Journal of Computational Science and Intelligent Technologies 1, no. 2 (2020): 15–25. http://dx.doi.org/10.53409/mnaa.jcsit20201203.

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The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) is used for attack detection and classification. The Canadian Institute for Cyber-secu
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Suryanita, Reni, Mardiyono Mardiyono, and Azlan Adnan. "Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 4 (2017): 1830. http://dx.doi.org/10.12928/telkomnika.v15i4.6006.

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Reni, Suryanita, Mardiyono, and Adnan Azlan. "Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 4 (2017): 1830–40. https://doi.org/10.12928/TELKOMNIKA.v15i4.6006.

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The natural disaster and design mistake can damage the bridge structure. The damage caused a severe safety problem to human.The study aims to develop the intelligent system for bridge health monitoring due to earthquake load. The Genetic Algorithm method in Neuro-Genetic hybrid has applied to optimize the acceptable Neural Network weight.The acceleration, displacement and time history of the bridge structural responses are used as the input, while the output is the damage level of the bridge. The system displays the alert warning of decks based on result prediction of Neural Network analysis.
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Shehab, Abdulhabib Alzaeemi, Sathasivam Saratha, and Velavan Muraly. "Hybrid Genetic Algorithm Model in Neuro Symbolic Integration." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 2144–49. https://doi.org/10.35940/ijeat.D8761.049420.

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The development of artificial neural network and logic programming plays an important part in neural network studies. Genetic Algorithm (GA) is one of the escorted randomly searching technicality that uses evolutional concepts of the natural election as a stimulus to solve the computational problems. The essential purposes behind the studies of the evolutional system is for developing adaptive search techniques which are robust. In this paper, GA is merged with agent based modeling (ABM) by using specified proceedings to optimise the states of neurons and energy function in the Hopfield neural
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Varnamkhasti, M. Jalali. "A hybrid of adaptive neuro-fuzzy inference system and genetic algorithm." Journal of Intelligent & Fuzzy Systems 25, no. 3 (2013): 793–96. http://dx.doi.org/10.3233/ifs-120685.

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Lee, Gooyong, Sangeun Lee, and Heekyung Park. "Improving applicability of neuro-genetic algorithm to predict short-term water level: a case study." Journal of Hydroinformatics 16, no. 1 (2013): 218–30. http://dx.doi.org/10.2166/hydro.2013.011.

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This paper proposes a practical approach of a neuro-genetic algorithm to enhance its capability of predicting water levels of rivers. Its practicality has three attributes: (1) to easily develop a model with a neuro-genetic algorithm; (2) to verify the model at various predicting points with different conditions; and (3) to provide information for making urgent decisions on the operation of river infrastructure. The authors build an artificial neural network model coupled with the genetic algorithm (often called a hybrid neuro-genetic algorithm), and then apply the model to predict water level
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Wonsathan, Rati, Isaravuth Seedadan, Nittaya Nunloon, and Jesadapong Kitibut. "Prediction of Evaluation Learning by Using Neuro-Fuzzy System." Advanced Materials Research 931-932 (May 2014): 1482–87. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1482.

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Artificial intelligent techniques are being actively applied in many applications. With their powerful learning capability of neural networks and reducing the optimizing search space by prior knowledge rules of Fuzzy systems have been proven to be rather efficiency. In this research, the hybrid Neuro-Fuzzy system (NF) is proposed to be utilized as a predictor of the Grade Point Average (GPA) of students for future planning where the Radial Basis Function (RBF) is implemented as a neuro-fuzzy system. The NFs parameters consisted of centre and width of the Gaussian membership function and weight
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Alameer, Zakaria, Mohamed Abd Elaziz, Ahmed A. Ewees, Haiwang Ye, and Zhang Jianhua. "Forecasting Copper Prices Using Hybrid Adaptive Neuro-Fuzzy Inference System and Genetic Algorithms." Natural Resources Research 28, no. 4 (2019): 1385–401. http://dx.doi.org/10.1007/s11053-019-09473-w.

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Olayode, Isaac Oyeyemi, Lagouge Kwanda Tartibu, and Frimpong Justice Alex. "Comparative Study Analysis of ANFIS and ANFIS-GA Models on Flow of Vehicles at Road Intersections." Applied Sciences 13, no. 2 (2023): 744. http://dx.doi.org/10.3390/app13020744.

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In the last two decades the efficient traffic-flow prediction of vehicles has been significant in curbing traffic congestions at freeways and road intersections and it is among the many advantages of applying intelligent transportation systems in road intersections. However, transportation researchers have not focused on prediction of vehicular traffic flow at road intersections using hybrid algorithms such as adaptive neuro-fuzzy inference systems optimized by genetic algorithms. In this research, we propose two models, namely the adaptive neuro-fuzzy inference system (ANFIS) and the adaptive
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Lavanya, K., M. A. Saleem Durai, and N. Ch S. N. Iyengar. "A Hybrid Model for Rice Disease Diagnosis Using Entropy Based Neuro Genetic Algorithm." International Journal of Agricultural and Environmental Information Systems 7, no. 2 (2016): 52–69. http://dx.doi.org/10.4018/ijaeis.2016040103.

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Disease prediction is often characterized by a high degree of fuzziness and uncertainty. This may reside in the imperfect and complex nature of symptoms that aids in diagnosis.. For precise rice disease diagnosis, domain knowledge of expertise pathologists along with clinically screened database of crop symptoms is considered as knowledge base. The hybrid method pre treats the crop symptoms for removal of noise and redundancy. It forms as target data for rice disease diagnostic model. The Entropy assisted GEANN algorithm reduces the n- dimensionality of diagnostic symptoms and optimizes the ta
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Dissertations / Theses on the topic "Neuro-genetic hybrid system"

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Васильєва, Тетяна Анатоліївна, Татьяна Анатольевна Васильева, Tetiana Anatoliivna Vasylieva, and O. Skrynnyk. "Neuro-Genetic Hybrid System for Management of Organizational Development Measures." Thesis, RWTH Aachen University, 2020. https://essuir.sumdu.edu.ua/handle/123456789/85490.

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Current practical experience in measuring the effectiveness of organizational development activities is largely based on the evaluation of surveys. In this paper we present an approach based on an artificial neural network with elements of a fuzzy approach and a genetic algorithm to control organizational development. Based on genetic algorithms, the organizational development measures are initiated, selected, combined or mutated with the goal of finding the best possible solution for each concrete case. Since many variables have the uncertain set of their values, the use of a hybrid neuro-fuz
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Burton, Anthony Richard. "A hybrid neuro-genetic pattern evolution system applied to musical composition." Thesis, University of Surrey, 1998. http://epubs.surrey.ac.uk/2875/.

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Book chapters on the topic "Neuro-genetic hybrid system"

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Vasundara, M., K. P. Padamanaban, M. Sabareeswaran, and K. Kalaivanan. "Neuro-Genetic Hybrid System for 2D Fixture Layout." In Lecture Notes in Mechanical Engineering. Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-1007-8_16.

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Grądzki, Piotr. "Applying Genetic Algorithm for Training Hybrid Neuro-Fuzzy Classifier — the nfgClass System." In Neural Networks and Soft Computing. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1902-1_78.

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Tabiti, Alae eddine. "Neuro-Genetic System: A Hybrid System of CNN-BiLSTM Optimized by Genetic Algorithm for Road Accident Severity Prediction." In Knowledge Science, Engineering and Management. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5495-3_3.

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Kour, Haneet, Jatinder Manhas, and Vinod Sharma. "Hybrid System Based on Genetic Algorithm and Neuro-Fuzzy Approach for Neurodegenerative Disease Forecasting." In Proceedings of Data Analytics and Management. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6289-8_27.

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Zha, Xuan F. "An Integrated Intelligent System Model and Its Applications in Virtual Product Design and Development." In Virtual Technologies. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-955-7.ch028.

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In this Chapter, a novel integrated intelligent framework is first proposed for virtual engineering design and development based on the soft computing and hybrid intelligent techniques. Then, an evolutionary neuro-fuzzy (EFNN) model is developed and used for supporting modeling, analysis and evaluation, and optimization tasks in the design process, which combines fuzzy logic with neural networks and genetic algorithms. The developed system HIDS-EFNN provides a unified integrated intelligent environment for virtual engineering design and simulation. The focus of this Chapter is to present a hyb
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Lavanya, K., M. A. Saleem Durai, and N. Ch S. N. Iyengar. "A Hybrid Model for Rice Disease Diagnosis Using Entropy Based Neuro Genetic Algorithm." In Fuzzy Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch012.

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Disease prediction is often characterized by a high degree of fuzziness and uncertainty. This may reside in the imperfect and complex nature of symptoms that aids in diagnosis.. For precise rice disease diagnosis, domain knowledge of expertise pathologists along with clinically screened database of crop symptoms is considered as knowledge base. The hybrid method pre treats the crop symptoms for removal of noise and redundancy. It forms as target data for rice disease diagnostic model. The Entropy assisted GEANN algorithm reduces the n- dimensionality of diagnostic symptoms and optimizes the ta
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Lahmiri, Salim. "Prediction of International Stock Markets Based on Hybrid Intelligent Systems." In Handbook of Research on Innovations in Information Retrieval, Analysis, and Management. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8833-9.ch004.

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This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to b
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Dracopoulos, Dimitris C., and Dimitrios Effraimidis. "Evolutionary Control of Helicopter Hovering Based on Genetic Programming." In Intelligent Technologies and Techniques for Pervasive Computing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4038-2.ch001.

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Computational intelligence techniques such as neural networks, fuzzy logic, and hybrid neuroevolutionary and neuro-fuzzy methods have been successfully applied to complex control problems in the last two decades. Genetic programming, a field under the umbrella of evolutionary computation, has not been applied to a sufficiently large number of challenging and difficult control problems, in order to check its viability as a general methodology to such problems. Helicopter hovering control is considered a challenging control problem in the literature and has been included in the set of benchmarks
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Castellano, G. "On the Use of Soft Computing Techniques for Web Personalization." In Business Web Strategy. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-024-0.ch018.

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Due to the growing variety and quantity of information available on the Web, there is urgent need for developing Web-based applications capable of adapting their services to the needs of the users. This is the main rationale behind the flourishing area of Web personalization that finds in soft computing (SC) techniques a valid tool to handle uncertainty in Web usage data and develop Web-based applications tailored to user preferences. The main reason for this success seems to be the synergy resulting from SC paradigms, such as fuzzy logic, neural networks, and genetic algorithms. Each of these
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Conference papers on the topic "Neuro-genetic hybrid system"

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Devi, P., M. Manju, and V. Kavitha. "A hybrid Fuzzy rule based Neuro-Genetic approach for fingerprint recognition system." In 2010 2nd International Conference on Advanced Computing (ICoAC). IEEE, 2010. http://dx.doi.org/10.1109/icoac.2010.5725353.

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Krishnaji, Andey, and Allam Appa Rao. "Implementation of a hybrid Neuro Fuzzy Genetic System for improving protein secondary structure prediction." In 2012 National Conference on Computing and Communication Systems (NCCCS). IEEE, 2012. http://dx.doi.org/10.1109/ncccs.2012.6413011.

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Krishnaji, Andey, and Allam Appa Rao. "An improved Hybrid Neuro Fuzzy Genetic System (I-HNFGS) for protein secondary structure prediction from amino acid sequence." In 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2013. http://dx.doi.org/10.1109/icacci.2013.6637351.

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Santika, Gayatri Dwi, Diah Ayu Retnani Wulandari, and Fitriyana Dewi. "Quality Assessment Level of Quality of Cocoa Beans Export Quality using Hybrid Adaptive Neuro - Fuzzy Inference System (ANFIS) and Genetic Algorithm." In 2018 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, 2018. http://dx.doi.org/10.1109/icecos.2018.8605189.

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Coronado, Luis Enrique, Raul Chavez-Romero, Mauro Maya, Antonio Cardenas, and Davide Piovesan. "Combining Genetic Algorithms and Extended Kalman Filter to Estimate Ankle’s Muscle-Tendon Parameters." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9781.

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This work proposes a set of simulation and experimental measurements to estimate muscle biomechanical parameter during human quiet standing. Understanding the mechanisms involved in postural stability is indispensable to improve the knowledge of how humans can regain balance against possible disturbances. Postural stability requires the ability to compensate the movement of the body’s center of gravity caused by external or internal perturbations. This paper describes the implementation of a hybrid parameter-estimation approach to infer the features of the human neuro-mechanical system during
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Redouane, Kheireddine, and Ashkan Jahanbani Ghahfarokhi. "Hybrid Optimization Framework for Well-Placement Using Gradient-Free Algorithm and Physics-Informed Artificial Intelligence." In SPE Reservoir Simulation Conference. SPE, 2025. https://doi.org/10.2118/223867-ms.

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Abstract Accurate well placement plays essential role in increasing the field recovery/storage while reducing the operational costs. This task is complex, requiring robust solutions that can handle optimization problems efficiently. Despite numerous existing solutions, there remains a need for a fast, highly accurate, computer-aided optimization tool. In this paper, we developed an auto-adaptive workflow that leverages a complex interplay between machine learning (ML), physics of fluid-flow and Gradient-Free algorithm to enhance the performance of solving well-placement problems. We developed
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Zhang, Zhibing. "An Efficient Neuro-Fuzzy-Genetic Data Mining Framework Based on Computational Intelligence." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.148.

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Mohammadian, Mohsen, S. M. Taghi Bathaee, and S. M. Mehdi Ansarey M. "Neuro-genetic energy management for hybrid fuel cell power train." In 2004 IEEE Conference on Cybernetics and Intelligent Systems. IEEE, 2004. http://dx.doi.org/10.1109/iccis.2004.1460733.

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Yakzan, Adnan, Roger Green, and Evor Hines. "A Neuro-Genetic Hybrid Algorithm Utilizing Outdoors LOS Optical Wireless Channels." In 2012 4th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2012). IEEE, 2012. http://dx.doi.org/10.1109/cicsyn.2012.18.

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Fabro, J. A., F. Neves, and L. V. R. Arruda. "A proposal of a fuzzy-neuro predictive control, tuned by genetic algorithms, with application to the start-up control of a distillation column." In Fifth International Conference on Hybrid Intelligent Systems (HIS'05). IEEE, 2005. http://dx.doi.org/10.1109/ichis.2005.15.

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