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Fuzzy Side Information Clustering-Based Framework for Effective Recommendations

Mohammed Wasid, Rashid Ali
2019 Computing and informatics  
However, incorporation of side information into traditional two-dimensional recommender systems would increase the dimensionality and complexity of the system.  ...  Additionally, we use fuzzy sets to repre-  ...  Nilashi et al. in [26] proposed a technique to solve scalability and sparsity problems of multi-criteria recommender systems using dimensionality reduction and Neuro-Fuzzy techniques.  ... 
doi:10.31577/cai_2019_3_597 fatcat:4kvkryz34bbexphitzlulvpclm

Adaptive-surrogate based on a neuro-fuzzy network and granular computing

Israel Cruz-Vega, Mauricio Garcia-Limon, Hugo Jair Escalante
2014 Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14  
In this paper, with the aim of granular computing as a method of grouping data, such information is exploited to obtain knowledge of the structure and parameters of individuals and then, design a Neuro-Fuzzy  ...  We implement this adaptive surrogate in a genetic algorithm and show its performance using benchmark functions.  ...  This paper introduces a surrogate modeling approach based on granular computing and a Neuro Fuzzy Network.  ... 
doi:10.1145/2576768.2598376 dblp:conf/gecco/Cruz-VegaGE14 fatcat:xxcukja5jrewjpr43wl6ioqe7q


Dragan Pamucar, Goran Cirovic
2018 Decision Making: Applications in Management and Engineering  
The adaptive neuro-fuzzy network is trained to select an optimal road route on the basis of standard and additional criteria.  ...  In the DRG system for the choice of road route, the experiential knowledge of drivers and dispatchers is accumulated in a neuro-fuzzy network which has the capability of generalizing a solution.  ...  Acknowledgements The work reported in this paper is a part of the investigation within the research project TR 36017 supported by the Ministry for Science and Technology, Republic of Serbia.  ... 
doi:10.31181/dmame180113p fatcat:jconf34lwfffnfzweksuw732pq

Spatial modeling of environmental vulnerability of marine finfish aquaculture using GIS-based neuro-fuzzy techniques

Juan Moreno Navas, Trevor C. Telfer, Lindsay G. Ross
2011 Marine Pollution Bulletin  
Neuro-fuzzy techniques within GIS modeling classify vulnerability of coastal regions appropriately and have a role in policy decisions for aquaculture site selection.  ...  Subsequent incorporation into environmental vulnerability models, based on neuro-fuzzy techniques, highlighted localities particularly vulnerable to aquaculture development.  ...  Richard Conner and Mr. Asmund Andersen for helpful comments and for the assistance in the questionnaire.  ... 
doi:10.1016/j.marpolbul.2011.05.019 pmid:21683421 fatcat:mur624ugz5fb7gnowy2oc5cpee

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

Adetokunbo MacGregor John-Otumu, Godswill U. Ogba, Obi C. Nwokonkwo
2020 Journal of Advances in Science and Engineering  
aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients  ...  This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the  ...  Neuro-Fuzzy Hybrid Systems Hybrid systems can be referred to as the integration of the weaknesses and the strengths of two or more techniques to resolve a common drawback in order to yield a better result  ... 
doi:10.37121/jase.v3i1.83 fatcat:2kb3zwrpunejlcbw26x7xznj6m

A new similarity-based multi-criteria recommendation algorithm based on autoencoders

2021 Turkish Journal of Electrical Engineering and Computer Sciences  
Multi-criteria recommender systems provide more 9 personalized and accurate predictions compared to traditional recommender systems.  ...  Multi-criteria recommender systems are an extension of traditional 8 recommender systems that utilize multi-criteria-based user preferences.  ...  Then using this model, predictions can be produced 10 when a request is occurred for a real-time multi-criteria recommender system.  ... 
doi:10.3906/elk-2107-145 fatcat:n4kycyumu5gi7moperbxrxf5tm

Artificial intelligence in recommender systems

Qian Zhang, Jie Lu, Yaochu Jin
2020 Complex & Intelligent Systems  
It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer  ...  This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of  ...  In fuzzy network techniques, fuzzy rules are extracted using the adaptive neuro-fuzzy inference system (ANFIS) to alleviate the data sparsity issue in CF and predict user preferences, especially for multi-criteria  ... 
doi:10.1007/s40747-020-00212-w fatcat:ev3cyoy2mjeuhmq3rymkx2shsy

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Zs.J. Viharos, K.B. Kis
2015 Measurement (London)  
Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems.  ...  Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their  ...  Acknowledgments The authors acknowledge the support of grants of the Fraunhofer Project Center for Production Management and Informatics at SZTAKI, Budapest, Hungary and the Highly industrialised region  ... 
doi:10.1016/j.measurement.2015.02.001 fatcat:qwifvzufhfaf3cucu3l6zhqfei

Artificial Neural Networks and Particle Swarm Optimization Algorithms for Preference Prediction in Multi-Criteria Recommender Systems

Mohamed Hamada, Mohammed Hassan
2018 Informatics  
The empirical results of the study show that the proposed model has a higher prediction accuracy than the corresponding traditional recommendation technique and other multi-criteria recommender systems  ...  They make personalized recommendations to online users using various data mining and filtering techniques.  ...  Mohamed Hamada is the research advisor, who proposed the structure of the paper and helped in proofreading and improving the quality of the article.  ... 
doi:10.3390/informatics5020025 fatcat:cu6s5glrnjgm5nzqooqkoow3me

A Review of Fault Diagnosing Methods in Power Transmission Systems

Ali Raza, Abdeldjabar Benrabah, Thamer Alquthami, Muhammad Akmal
2020 Applied Sciences  
Feature extractions, transformations with dimensionality reduction methods are discussed.  ...  A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results.  ...  The authors are thankful to the Department of Electrical Engineering, The University of Lahore-Pakistan, Ecole Militaire Polytechnique, Algiers-Algeria, King Abdulaziz Univeristy, Jeddah-Saudi Arabia and  ... 
doi:10.3390/app10041312 fatcat:pkcuditcvzbgbost4ydbnih2g4

Hybrid functional networks for PVT characterisation

Munirudeen A. Oloso, Mohamed G. Hassan, Mohamed Bader-El-Den, James M. Buick
2017 2017 Intelligent Systems Conference (IntelliSys)  
Platform (Presenter: Gil Hurwitz, United States) 396 -Improving Prediction Accuracy of Multi- Criteria Recommender Systems using Adaptive Genetic Algorithms (Presenter: Mohammed Hassan, Japan) 83  ...  257 -The Detection of Security Threat Level by Analyzing Certain Human Emotions using Neuro-Fuzzy Techniques (Presenter: Mohammad Malkawi, Jordan) 359 -Quantitative Model for Dynamic Propagation and Countermeasure  ... 
doi:10.1109/intellisys.2017.8324242 fatcat:wly6p5kx5jezvjd6jqykurrqqi

CANFIS—a computer aided diagnostic tool for cancer detection

Latha Parthiban, R. Subramanian
2009 Journal of Biomedical Science and Engineering  
In this investigation, an approach using Coactive Neuro-Fuzzy Inference System (CANFIS) as diagnosis system for breast cancer has been proposed on Wisconsin Breast Cancer Data (WBCD).  ...  resolved with a human like decision-making process using Artificial Intelligence (AI) algorithms.  ...  Each system shows the PPV within the range from less than 60% (AdaBoost) up to over 95% (Neuro-Fuzzy Hybrid Models).  ... 
doi:10.4236/jbise.2009.25048 fatcat:eu65rncryfcnpcju52mn4jplkm

Review of soft computing models in design and control of rotating electrical machines

Adrienn Dineva, Amir Mosavi, Sina Faizollahzadeh Ardabili, Istvan Vajda, Shahab Shamshirband, Timon Rabczuk, Kwok-Wing Chau
2020 Zenodo  
From this perspective, a wide range of energy management systems are highly relying on the advancement of soft computing techniques used for rotating electrical machines.  ...  Through a novel taxonomy of techniques and applications the most important advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines  ...  The such composed system is called fuzzy neural, neural fuzzy, neuro-fuzzy or fuzzy-neuro network, or ANFIS in which for instance neural networks can be used to tune membership functions of fuzzy systems  ... 
doi:10.5281/zenodo.4056793 fatcat:44twbmjbdjdilfe3quknwql6pi

Comparison Between Pid Controllers for Gryphon Robot Optimized With Neuro-Fuzzy System and Three Intelligent Optimizationalgorithms

Somayyeh Nalan Ahmadabad, Maryam Kouzehgar, Fatemeh Masoudnia
2013 International Journal of Control Theory and Computer Modeling  
In this paper three intelligent evolutionary optimization approaches to design PID controller for a Gryphon Robot are presented and compared to the results of a neuro-fuzzy system applied.  ...  Simulation results show that FNN has a remarkable effect on decreasing the amount of settling time and rise-time and eliminating of steady-state error while the SFL algorithm performs better on steady-state  ...  Any Neuro-Fuzzy system is a Neural Network which learns how to classify data using Fuzzy rules and Fuzzy sets.  ... 
doi:10.5121/ijctcm.2013.3604 fatcat:33knotx4erdcjdhc2xt6cizgbq

Computationally intelligent modeling and control of fluidized bed combustion process

Cojbasic Zarko, Nikolic Vlastimir, Ciric Ivan, Cojbasic Ljubica
2011 Thermal Science  
Proposed adaptive neuro-fuzzy-genetic modelling and intelligent control strategies provide for efficient combining of available expert knowledge with experimental data.  ...  Finally, fuzzy and conventional process control systems for fuel flow and primary air flow regulation based on developed models and optimized by genetic algorithms have also been developed.  ...  basic architecture and the hybrid learning algorithm of adaptive neuro-fuzzy inference system (ANFIS) [9] , modified mountain clustering (MMC) technique for initial neuro-fuzzy model structure determination  ... 
doi:10.2298/tsci101205031c fatcat:djwuzlyi4rarzf6ahx5eckg3hu
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