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

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

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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Tung, Navpreet Singh, and Sandeep Chakravorty. "Neuro Inspired Genetic Hybrid Algorithm for Active Power Dispatch Planning Problem in Small Scale System." International Journal of Hybrid Information Technology 8, no. 9 (2015): 171–84. http://dx.doi.org/10.14257/ijhit.2015.8.9.17.

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Nazerian, Morteza, Fateme Naderi, Ali Partovinia, Antonios N. Papadopoulos, and Hamed Younesi-Kordkheili. "Developing adaptive neuro-fuzzy inference system-based models to predict the bending strength of polyurethane foam-cored sandwich panels." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 236, no. 1 (2021): 3–22. http://dx.doi.org/10.1177/14644207211024278.

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The aim of this paper was to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) and to predict the flexural strength of the sandwich panels made with thin medium density fiberboard as surface layers, and polyurethane foam as a core layer, by applying metaheuristic optimization methods. For this purpose, various models, namely ant colony optimization for the continuous domain (ACOR), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) were applied and compared, as different efficient bio-inspired paradigms, to assess their suitabi

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Elbaz, Khalid, Shui-Long Shen, Annan Zhou, Da-Jun Yuan, and Ye-Shuang Xu. "Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm." Applied Sciences 9, no. 4 (2019): 780. http://dx.doi.org/10.3390/app9040780.

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The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prio

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P, Muneeshwari, and Kishanthini M. "A New Framework for Anomaly Detection in NSL-KDD Dataset using Hybrid Neuro-Weighted Genetic Algorithm." Journal of Computational Science and Intelligent Technologies 1, no. 1 (2020): 29–36. http://dx.doi.org/10.53409/mnaa.jcsit1105.

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There are an increasing number of security threats to the Internet and computer networks. For new kinds of attacks constantly emerging, a major challenge is the development of versatile and innovative security-oriented approaches. Anomaly-based network intrusion detection techniques are in this sense a valuable tool for defending target devices and networks from malicious activities. With testing dataset, this work was able to use the NSL-KDD data collection, the binary and multiclass problems. With that inspiration, data mining techniques are used to offer an automated platform for network at

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Bae, Chul-Ho, Yul Chu, Hyun-Jun Kim, Jung-Hwan Lee, and Myung-Won Suh. "A study on maintenance reliability allocation of urban transit brake system using hybrid neuro-genetic technique." Journal of Mechanical Science and Technology 21, no. 1 (2007): 32–47. http://dx.doi.org/10.1007/bf03161710.

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Vo Thanh Ha. "Hybrid Intelligent Controller Design for Three-Disc AFPMSM in Electric Vehicles." Journal of Information Systems Engineering and Management 10, no. 51s (2025): 464–81. https://doi.org/10.52783/jisem.v10i51s.10413.

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Abstract: This study introduces a torque distribution control system for a three-disc axial flux permanent magnet synchronous motor (AFPMSM) using a genetic algorithm-optimized back-propagation neural network (BP_ANN_GA) and an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based torque control. These controllers enhance torque accuracy, reduce energy losses, improve motor stability and performance, and meet the demands of modern EVs by advancing efficient transmission technology. The BP_ANN_GA controller predicts torque distribution (Tm1, Tm2, Tm3) based on speed and required torque (T*m), wit

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Manoharan, Neelamegam, Subhransu Sekhar Dash, Kurup Sathy Rajesh, and Sidhartha Panda. "Automatic Generation Control by Hybrid Invasive Weed Optimization and Pattern Search Tuned 2-DOF PID Controller." International Journal of Computers Communications & Control 12, no. 4 (2017): 533. http://dx.doi.org/10.15837/ijccc.2017.4.2751.

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A hybrid invasive weed optimization and pattern search (hIWO-PS) technique is proposed in this paper to design 2 degree of freedom proportionalintegral- derivative (2-DOF-PID) controllers for automatic generation control (AGC) of interconnected power systems. Firstly, the proposed approach is tested in an interconnected two-area thermal power system and the advantage of the proposed approach has been established by comparing the results with recently published methods like conventional Ziegler Nichols (ZN), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), genetic a

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Le, Lu Minh, Hai-Bang Ly, Binh Thai Pham, et al. "Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression." Materials 12, no. 10 (2019): 1670. http://dx.doi.org/10.3390/ma12101670.

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This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as

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Joseph, A. K., and G. Radhamani. "Hybrid Test Case Optimization Approach Using Genetic Algorithm With Adaptive Neuro Fuzzy Inference System for Regression Testing." Journal of Testing and Evaluation 45, no. 6 (2017): 20160137. http://dx.doi.org/10.1520/jte20160137.

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Santos, Edison Conde Perez dos, Carlos Alberto Nunes Cosenza, and José Carlos Cesar Amorim. "Hybrid sediment transport model for the “linguado” channel, state of Santa Catarina, Brazil." Independent Journal of Management & Production 8, no. 4 (2017): 1210. http://dx.doi.org/10.14807/ijmp.v8i4.645.

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This study involves an assessment of various artificial intelligence-related techniques which aim to produce a more robust system for sediment transport modeling. The intelligent systems developed in this research are directly applicable to academic knowledge and use data from a report on "water circulation assessment in the “Linguado” Channel and Babitonga Bay ,”Santa Catarina”, Brazil, developed by Military Engineering Institute (IME). The solution employed for sediment transport was built using an intelligent system from the conception of two hybrid models. The first was a Neuro-Fuzzy (ANFI

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Lassoued, Hela, Raouf Ketata, and Hajer Ben Mahmoud. "Optimal Neuro Fuzzy Classification for Arrhythmia Data Driven System." International Journal of Innovative Technology and Exploring Engineering 11, no. 1 (2021): 70–80. http://dx.doi.org/10.35940/ijitee.a9628.1111121.

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This paper presents a data driven system used for cardiac arrhythmia classification. It applies the Neuro-Fuzzy Inference System (ANFIS) to classify MIT-BIH arrhythmia database electrocardiogram (ECG) recordings into five (5) heartbeat types. In fact, in order to obtain the input feature vector from recordings, a time scale method based on a Discrete Wavelet Transform (DWT) was investigated. Then, the time scale features are selected by applying the Principal Component Analysis (PCA). Therefore, the selected input feature vectors are classified by the Neuro-Fuzzy method. However, the ANFIS con

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Hela, Lassoued, Ketata Raouf, and Ben Mahmoud Hajer. "Optimal Neuro-Fuzzy Classification for Arrhythmia Data Driven System." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 1 (2021): 70–80. https://doi.org/10.35940/ijitee.A9628.1111121.

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This paper presents a data driven system used for cardiac arrhythmia classification. It applies the Neuro-Fuzzy Inference System (ANFIS) to classify MIT-BIH arrhythmia database electrocardiogram (ECG) recordings into five (5) heartbeat types. In fact, in order to obtain the input feature vector from recordings, a time scale method based on a Discrete Wavelet Transform (DWT) was investigated. Then, the time scale features are selected by applying the Principal Component Analysis (PCA). Therefore, the selected input feature vectors are classified by the Neuro-Fuzzy method. However, the ANFIS con

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Oghorodi, D., E. J. Atajeromavwo, A. E. Okpako, et al. "A Cutting-Edge Approach to Predictive Precision in Oncology Using a Geneto-Neuro-Fuzzy Hybrid Model." AFRICAN JOURNAL OF APPLIED RESEARCH 11, no. 1 (2025): 766–85. https://doi.org/10.26437/ajar.v11i1.880.

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Purpose: This study introduces a pioneering hybrid model that combines genetic algorithms, neuro-fuzzy logic, and mobile agent technology to enhance predictive precision for early-stage prostate cancer diagnosis. Design/Methodology/Approach: One hundred and twenty records of prostate cancer patients were initially collected from the Delta State University Teaching Hospital, Oghara, Nigeria. Each patient’s record included relevant data on prostate disease, such as age, PSA levels, clinical history, symptom severity, biopsy results, and other demographic and clinical factors. This data was extra

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Dziwiński, Piotr, Łukasz Bartczuk, and Józef Paszkowski. "A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm." Journal of Artificial Intelligence and Soft Computing Research 10, no. 2 (2020): 95–111. http://dx.doi.org/10.2478/jaiscr-2020-0007.

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AbstractThe social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different sys

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BHATTACHARYA, MAHUA, NAVEEN SHARMA, VAIBHAV GOYAL, SAGAR BHATIA, and ARPITA DAS. "A STUDY ON GENETIC ALGORITHM BASED HYBRID SOFTCOMPUTING MODEL FOR BENIGNANCY/MALIGNANCY DETECTION OF MASSES USING DIGITAL MAMMOGRAM." International Journal of Computational Intelligence and Applications 10, no. 02 (2011): 141–65. http://dx.doi.org/10.1142/s1469026811003033.

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In present works authors have developed a computerized classification procedure for tumor mass in breasts using digital mammogram. The process implements genetic algorithm and hybrid neuro-fuzzy approaches to classify tumor masses into benign and malignant group in order to assist the physicians for treatment planning. The classification process is based on accurate analysis of shape and margin of tumor mass appearing in breast. The shape features using Fourier descriptors introduce a large number of feature vectors. Thus, to classify different boundaries, a standard multilayer preceptor needs

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Akinyokun, Oluwole Charles, Emem Etok Akpan, and Udoinyang Godwin Inyang. "Design of a hybrid intelligent system for the management of flood disaster risks." Artificial Intelligence Research 8, no. 1 (2019): 14. http://dx.doi.org/10.5430/air.v8n1p14.

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The frequency of occurrence and intensity of floods is a huge threat to environment, human existence, critical infrastructure and economy. Flood risk assessments depend on probabilistic approaches and suffer from non-existence of appropriate indices of acceptable risk, dearth of information and pieces of knowledge for explicit view and understanding of the characteristics and severity level of flood hazard. This paper proposes a hybridized intelligent framework comprising fuzzy logic (FL), neural network and genetic algorithm for clustering and visualization of flood data, prediction and class

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Zaychenko, Yuriy, and Tetiana Starovoit. "A hybrid model of artificial intelligence integrated into GIS for predicting accidents in water supply networks." System research and information technologies, no. 2 (June 28, 2024): 52–67. http://dx.doi.org/10.20535/srit.2308-8893.2024.2.04.

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The search for an effective and reliable model for predicting accidents on water supply networks by determining their exact locations has always been important for effectively managing water distribution systems. This study, based on the adaptive neuro-fuzzy logical inference system (ANFIS) model, was developed to predict accidents in the city of Kyiv (Ukraine) water supply network. The ANFIS model was combined with genetic algorithms and swarm optimization (ACO) methods and integrated into a GIS to visualize results and determine locations. Forecasts were evaluated according to the following

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Liu, Xinni, Sadaam Hadee Hussein, Kamarul Hawari Ghazali, Tran Minh Tung, and Zaher Mundher Yaseen. "Optimized Adaptive Neuro-Fuzzy Inference System Using Metaheuristic Algorithms: Application of Shield Tunnelling Ground Surface Settlement Prediction." Complexity 2021 (March 10, 2021): 1–15. http://dx.doi.org/10.1155/2021/6666699.

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Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement (GS) is a crucial concern for tunnelling problems, and the adequate predictive model can be a vital tool for tunnel designers to simulate the ground settlement accurately. This study proposes relatively new hybrid artificial intelligence (AI) models to predict the ground settlement of earth pressure balance (EPB) shield tunnelling in the Bangkok MRTA project. The predictive models were

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Dam dam, Hai Phu Nguyen, Dung Quang Vu, Indra Prakash, and Binh Thai Pham. "Using GA-ANFIS machine learning model for forecasting the load bearing capacity of driven piles." Journal of Science and Transport Technology 3, no. 2 (2023): 26–33. http://dx.doi.org/10.58845/jstt.utt.2023.en.3.2.26-33.

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This paper is aimed to apply hybrid machine learning model namely GA-ANFIS, which is a combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA), for the prediction of total bearing capability of driven piles. A database of 95 Pile Driving Analyzer (PDA) tests carried out at the win power project in Hoa Binh province, Vietnam was used to develop hybrid model. The database was split into 70:30 ratio for training (70%) and validating (30%) model. Accuracy of the model was evaluated using statistical standard indicators: Coefficient of determination (R2), Mean Absolu

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Mabdeh, Ali Nouh, A’kif Al-Fugara, Khaled Mohamed Khedher, Muhammed Mabdeh, Abdel Rahman Al-Shabeeb, and Rida Al-Adamat. "Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms." Sustainability 14, no. 15 (2022): 9446. http://dx.doi.org/10.3390/su14159446.

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Support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) are two well-known and powerful artificial intelligence techniques which have been frequently used for hazard mapping. So far, a plethora of hybrid models have been developed using a combination of either the SVR or ANFIS and evolutionary algorithms, but there are only a handful of studies that compare the performance of these models when integrated with evolutionary algorithms, especially in forest fire susceptibility mapping (FFSM). The aim of this study was to compare performance of ANFIS-, and SVR-based e

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Mohammed, Mariamme, Ahmad Sharafati, Nadhir Al-Ansari, and Zaher Mundher Yaseen. "Shallow Foundation Settlement Quantification: Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model." Advances in Civil Engineering 2020 (February 22, 2020): 1–14. http://dx.doi.org/10.1155/2020/7381617.

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Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ra

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Riny, Sulistyowati, Agus Sujono Hari, Candra Riawan Dedet, Seto Wibowo Rony, and Ashari Mochamad. "Prototype and monitoring system of phasor measurement unit based on the internet of things." Prototype and monitoring system of phasor measurement unit based on the internet of things 30, no. 1 (2023): 14–23. https://doi.org/10.11591/ijeecs.v30.i1.pp14-23.

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This research resulting the method to reduce phasor measurement unit (PMU) amount and optimization of PMU replacement using a combination of Integer linear k-means. The first step of modeling is using a lot of PMUs that are optimized at Bendul Merisi network using integer linear k-means clustering for achieving an optimum solution of amount and replacement of PMU to be installed. The second step is estimating the uninstalled bus's power and voltage. PMU is using modified adaptive neuro-fuzzy inference system (ANFIS) of hybrid particle swarm optimization (PSO)-genetic algorithm (GA). The th

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Farajpanah, Hiwa, Morteza Lotfirad, Arash Adib, et al. "Ranking of hybrid wavelet-AI models by TOPSIS method for estimation of daily flow discharge." Water Supply 20, no. 8 (2020): 3156–71. http://dx.doi.org/10.2166/ws.2020.211.

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Abstract This research uses the multi-layer perceptron–artificial neural network (MLP-ANN), radial basis function–ANN (RBF-ANN), least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5T), gene expression programming (GEP), genetic programming (GP) and Bayesian network (BN) with five types of mother wavelet functions (MWFs: coif4, db10, dmey, fk6 and sym7) and selects the best model by the TOPSIS method. The case study is the Navrood watershed in the north of Iran and the considered parameters are daily flow discharge, temperature and precip

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Eghbal, F., M. Ehsanifar, M. Mirhosseini, and H. Mazaheri. "Developing a Hybrid Model for Predicting Financial Performance of Iranian Construction Companies Based on Genetic Algorithm and Adaptive Neuro-Fuzzy Inference System." International Journal of Engineering 38, no. 11 (2025): 2697–712. https://doi.org/10.5829/ije.2025.38.11b.18.

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Jalalkamali, Amir. "Hybrid Linear Moments and ANFIS-GA to Predict Groundwater Salinity." Current World Environment 11, no. 3 (2016): 767–77. http://dx.doi.org/10.12944/cwe.11.3.11.

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There is, unfortunately, a lack of exhaustive qualitative and quantitative information about Iran groundwater resources. That is why various models are used in estimation of qualitative and quantitative groundwater parameters. The present paper presents a comparison of the hybrid of Adaptive Neuro Fuzzy Inference System (ANFIS) with Genetic Algorithm (GA) model and L-moments regarding their power and efficiency in regional and at-site anticipation of salinity of groundwater at Kerman plain. In doing so, electrical conductivity is considered the dependent variable, while, through regression ana

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Sulistyowati, Riny, Hari Agus Sujono, Dedet Chandra Riawan, Rony Seto Wibowo, and Mochamad Ashari. "Prototype and monitoring system of phasor measurement unit based on the internet of things." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 1 (2023): 14. http://dx.doi.org/10.11591/ijeecs.v30.i1.pp14-23.

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This research resulting the method to reduce phasor measurement unit (PMU) amount and optimization of PMU replacement using a combination of Integer linear k-means. The first step of modeling is using a lot of PMUs that are optimized at Bendul Merisi network using integer linear k-means clustering for achieving an optimum solution of amount and replacement of PMU to be installed. The second step is estimating the uninstalled bus's power and voltage. PMU is using modified adaptive neuro-fuzzy inference system (ANFIS) of hybrid particle swarm optimization (PSO)-genetic algorithm (GA). The third

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Chandra Sekhar, J. N., and G. V. Marutheswar. "Direct Torque Control of Induction Motor Using Enhanced Firefly Algorithm — ANFIS." Journal of Circuits, Systems and Computers 26, no. 06 (2017): 1750092. http://dx.doi.org/10.1142/s021812661750092x.

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In this paper, the hybrid direct torque control (DTC) technique is proposed for controlling the speed of the induction motor (IM). The hybrid technique is the combination of an enhanced firefly algorithm (FA) and the adaptive neuro fuzzy inference system (ANFIS) technique. The performance of the FA is improved by updating the randomized parameter. Here, the genetic algorithm (GA) is utilized for updating the parameter and improved the performance of the FA. Initially, the actual torque and the change of toque are applied to the input of the enhanced FA and form the electromagnetic torque as a

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Kuang, Mingjun, Qingwen Hou, Jindong Wang, Jianping Guo, and Zhengjun Wei. "GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management." Machines 13, no. 7 (2025): 624. https://doi.org/10.3390/machines13070624.

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This study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the nonlinear and disturbance-sensitive dynamics of SPAD systems, a performance-oriented dataset is constructed through multi-scenario simulations using settling time, overshoot, and steady-state error as fitness metrics. The genetic algorithm (GA) facilitates broad exploration of the proportional–integral–derivative (PID) controller parameter space wh

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Lutfy, O. F., Mohd S. B. Noor, M. H. Marhaban, and K. A. Abbas. "A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 223, no. 3 (2008): 309–21. http://dx.doi.org/10.1243/09596518jsce683.

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This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. The GA, wi

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Olaosebikan Abidoye Olafadehan and Victor Ehigimetor Bello. "Comparative Studies of RSM, RSM–GA and ANFILS for Modeling and Optimization of Naphthalene Adsorption on Chitosan–CTAB–Sodium Bentonite Clay Matrix." Journal of Applied Science & Process Engineering 9, no. 2 (2022): 1242–80. http://dx.doi.org/10.33736/jaspe.4749.2022.

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The aim of this article was to compare the predictive abilities of the optimization techniques of response surface methodology (RSM), the hybrid of RSM–genetic algorithm (RSM–GA) and adaptive neuro-fuzzy interference logic system (ANFILS) for design responses of % removal of naphthalene and adsorption capacity of the synthesized composite nanoparticles of chitosan–cetyltrimethylammonium bromide (CTAB)–sodium bentonite clay. The process variables considered were surfactant concentration, , activation time, , activation temperature, , and chitosan dosage, . The ANFILS models showed better modeli

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Jasmine, Mansura, Abdolmajid Mohammadian, and Hossein Bonakdari. "On the Prediction of Evaporation in Arid Climate Using Machine Learning Model." Mathematical and Computational Applications 27, no. 2 (2022): 32. http://dx.doi.org/10.3390/mca27020032.

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Evaporation calculations are important for the proper management of hydrological resources, such as reservoirs, lakes, and rivers. Data-driven approaches, such as adaptive neuro fuzzy inference, are getting popular in many hydrological fields. This paper investigates the effective implementation of artificial intelligence on the prediction of evaporation for agricultural area. In particular, it presents the adaptive neuro fuzzy inference system (ANFIS) and hybridization of ANFIS with three optimizers, which include the genetic algorithm (GA), firefly algorithm (FFA), and particle swarm optimiz

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Inyang, Udoinyang Godwin, Emem Etok Akpan, and Oluwole Charles Akinyokun. "A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification." International Journal of Computational Intelligence and Applications 19, no. 02 (2020): 2050012. http://dx.doi.org/10.1142/s1469026820500121.

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Communities globally experience devastating effects, high monetary loss and loss of lives due to incidents of flood and other hazards. Inadequate information and awareness of flood hazard make the management of flood risks arduous and challenging. This paper proposes a hybridized analytic approach via unsupervised and supervised learning methodologies, for the discovery of pieces of knowledge, clustering and prediction of flood severity levels (FSL). A two-staged unsupervised learning based on [Formula: see text]-means and self-organizing maps (SOM) was performed on the unlabeled flood dataset

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Yaseen, Zaher, Isa Ebtehaj, Sungwon Kim, et al. "Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis." Water 11, no. 3 (2019): 502. http://dx.doi.org/10.3390/w11030502.

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In this research, three different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are integrated with the adaptive neuro-fuzzy inference system (ANFIS) model. The developed hybrid models are proposed to forecast rainfall time series. The capability of the proposed evolutionary hybrid ANFIS was compared with the conventional ANFIS in forecasting monthly rainfall for the Pahang watershed, Malaysia. To select the optimal model, sixteen different combinations of six different lag attributes taking into account the ef

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Debiche, Fatiha, Mohammed Amin Benbouras, Alexandru-Ionut Petrisor, Lyes Mohamed Baba Ali, and Abdelghani Leghouchi. "Advancing Landslide Susceptibility Mapping in the Medea Region Using a Hybrid Metaheuristic ANFIS Approach." Land 13, no. 6 (2024): 889. http://dx.doi.org/10.3390/land13060889.

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Landslides pose significant risks to human lives and infrastructure. The Medea region in Algeria is particularly susceptible to these destructive events, which result in substantial economic losses. Despite this vulnerability, a comprehensive landslide map for this region is lacking. This study aims to develop a novel hybrid metaheuristic model for the spatial prediction of landslide susceptibility in Medea, combining the Adaptive Neuro-Fuzzy Inference System (ANFIS) with four novel optimization algorithms (Genetic Algorithm—GA, Particle Swarm Optimization—PSO, Harris Hawks Optimization—HHO, a

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Kabengele, Kantu T., Isaac O. Olayode, and Lagouge K. Tartibu. "Analysis of the Performance of a Hybrid Thermal Power Plant Using Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Approaches." Applied Sciences 13, no. 21 (2023): 11874. http://dx.doi.org/10.3390/app132111874.

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The hybridization of conventional thermal power plants by the incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation,

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Nagaraju, Gowtham, and Shobha Shankar. "Power quality improvement of wind energy conversion system with unified power quality controller: A hybrid control model." Transactions of the Institute of Measurement and Control 42, no. 11 (2020): 1997–2010. http://dx.doi.org/10.1177/0142331220903662.

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The real problems in diminution of power quality (PQ) occur due to the rapid growth of nonlinear load are leading to a sudden decrease of source voltage for a few seconds. All these problems can be compensated by unified power quality controller (UPQC). The proposed research is based on designing a wind energy conversion system (WECS) fed to the dc-link capacitor of UPQC so as to maintain proper voltage across it and operate the UPQC for PQ improvement. The proposed research utilizes two techniques for enhancing the performance of UPQC known as integrated ant lion optimizer (IALO)-adaptive neu

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AlAyyash, Saad, A’kif Al-Fugara, Rania Shatnawi, Abdel Rahman Al-Shabeeb, Rida Al-Adamat, and Hani Al-Amoush. "Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping." Sustainability 15, no. 3 (2023): 2499. http://dx.doi.org/10.3390/su15032499.

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The groundwater contained in aquifers is among the most important water supply resources, especially in semi-arid and arid regions worldwide. This study aims to evaluate and compare the prediction capability of two well–known models, support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), combined with a genetic algorithm (GA), invasive weed optimization (IWO), and teaching–learning-based optimization (TLBO) algorithms in groundwater potential mapping (GPM) the Azraq Basin in Jordan. The hybridization of the SVM and ANFIS models with the GA, IWO, and TLBO algorithms res

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Jadidi, Aydin, Raimundo Menezes, Nilmar de Souza, and Antonio Cezar de Castro Lima. "Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model." Energies 12, no. 10 (2019): 1891. http://dx.doi.org/10.3390/en12101891.

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Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy I

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Ramalingam, Chithambaramani, and Prakash Mohan. "An Efficient Applications Cloud Interoperability Framework Using I-Anfis." Symmetry 13, no. 2 (2021): 268. http://dx.doi.org/10.3390/sym13020268.

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Cloud interoperability provides cloud services such as Software as a Service (SaaS) or customer system to communicate between the cloud providers. However, one of the most important barriers for existing researches was to adopt the application’s or data’s in cloud computing environments so as to obtain efficient cloud interoperability. This paper focuses on reliable cloud interoperability with a heterogeneous cloud computing resource environment with the objective of providing unilateral provision computing capabilities of a cloud server without the help of human interaction and allowing prope

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Mustafa, Rashid, Pijush Samui, and Sunita Kumari. "Reliability Analysis of Gravity Retaining Wall Using Hybrid ANFIS." Infrastructures 7, no. 9 (2022): 121. http://dx.doi.org/10.3390/infrastructures7090121.

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Gravity retaining walls are a vital structure in the area of geotechnical engineering, and academicians in earlier studies have conveyed substantial uncertainties involved in calculating the factor of safety against overturning, using a deterministic approach. Hence, to enhance the accuracy and eliminate the uncertainties involved, artificial intelligence (AI) was used in the present research. The main aim of this study is to propose a high-performance machine learning (ML) model to determine the factor of safety (FOS) of gravity retaining walls against overturning. The projected methodology i

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