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Adaptive Neuro-Fuzzy model for path loss prediction in the VHF band

https://doi.org/10.1109/iccni.2017.8123768
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/b978-0-7506-5100-4.x5000-1openalex-snapshot
Advanced Control Engineering
inferredcites →https://doi.org/10.1109/act.2009.98openalex-snapshot
Secure Path Loss Prediction in Fringe Areas Using Fuzzy Logic Approach
inferredcites →https://doi.org/10.1109/apmc.2005.1606797openalex-snapshot
Microwave Line-of-Sight Path Loss Prediction on Urban street by Fuzzy Logic Model
inferredcites →https://doi.org/10.1109/eurocon.2013.6625043openalex-snapshot
Error bounds of empirical path loss models at VHF/UHF bands in Kwara State, Nigeria
inferredcites →https://doi.org/10.1109/hnicem.2015.7393223openalex-snapshot
Neural network-based path loss prediction for digital TV macrocells
inferredcites →https://doi.org/10.1109/nafips.2007.383861openalex-snapshot
Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks
inferredcites →https://doi.org/10.1109/spices.2015.7091360openalex-snapshot
Optimal power control for cognitive radio in spectrum distribution using ANFIS
inferredcites →https://doi.org/10.1109/sta.2015.7505236openalex-snapshot
Neural network approach to model the propagation path loss for great Tripoli area at 900, 1800, and 2100 MHz bands
inferredcites →https://doi.org/10.1109/tencon.2006.343758openalex-snapshot
Muti-Layer Fuzzy Logic Sets for Mobile Path Loss in Forests
inferredcites →https://doi.org/10.2528/pierb13011306openalex-snapshot
ON THE STUDY OF EMPIRICAL PATH LOSS MODELS FOR ACCURATE PREDICTION OF TV SIGNAL FOR SECONDARY USERS
inferredcites →https://doi.org/10.3906/elk-0904-18openalex-snapshot
Fuzzy adaptive neural network approach to path loss prediction in urban areas at GSM-900 band

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Ann-Based Model for Multiband Path Loss Prediction in Built-Up Environments
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An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks
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Support Vector Machine for Path Loss Predictions in Urban Environment
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Path loss predictions for fringe areas using adaptive neuro-fuzzy inference system
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Data on the key performance indicators for quality of service of GSM networks in Nigeria
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Path loss dataset for modeling radio wave propagation in smart campus environment
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Comparative assessment of data obtained using empirical models for path loss predictions in a university campus environment
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Exploration of daily Internet data traffic generated in a smart university campus
inferred← citeshttps://doi.org/10.1016/j.jestch.2018.05.013openalex-snapshot
Path loss predictions for multi-transmitter radio propagation in VHF bands using Adaptive Neuro-Fuzzy Inference System
inferred← citeshttps://doi.org/10.1016/j.sciaf.2022.e01350openalex-snapshot
ANN-based model for multiband path loss prediction in built-up environments
inferred← citeshttps://doi.org/10.1080/1448837x.2025.2460285openalex-snapshot
Characterization of path Loss in the tetra band for smart city project using sparse convAutoNet-driven regression random machines
inferred← citeshttps://doi.org/10.1109/access.2019.2921411openalex-snapshot
Path Loss Predictions in the VHF and UHF Bands Within Urban Environments: Experimental Investigation of Empirical, Heuristics and Geospatial Models
inferred← citeshttps://doi.org/10.1109/access.2019.2947009openalex-snapshot
Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel
inferred← citeshttps://doi.org/10.1109/access.2020.3035209openalex-snapshot
Artificial Neural Network Based Path Loss Prediction for Wireless Communication Network
inferred← citeshttps://doi.org/10.1109/comcas44984.2019.8958229openalex-snapshot
Modelling Large-Scale Signal Fading in Urban Environment Based on Fuzzy Inference System
inferred← citeshttps://doi.org/10.1109/icassp.2019.8682491openalex-snapshot
Deep Learning Propagation Models over Irregular Terrain
inferred← citeshttps://doi.org/10.1109/icias49414.2021.9642585openalex-snapshot
A Hybrid Combination of a Convolutional Neural Network with a Regression Model for Path Loss Prediction Using Tiles of 2D Satellite Images
inferred← citeshttps://doi.org/10.1109/imitec50163.2020.9334133openalex-snapshot
A Model for Wireless Signal Path Loss using Radiosity Technique
inferred← citeshttps://doi.org/10.1109/ited56637.2022.10051448openalex-snapshot
Application of Machine Learning Algorithms to Path Loss Modeling: A Review
inferred← citeshttps://doi.org/10.1109/nigercon54645.2022.9803057openalex-snapshot
Effect of Training Algorithms and Network Architecture on the Performance of Multi-Band ANN-Based Path Loss Prediction Model
inferred← citeshttps://doi.org/10.1109/nigercon54645.2022.9803159openalex-snapshot
Efficacy of some Unpopular PathLoss Propagation Models in the VHF and UHF Bands
inferred← citeshttps://doi.org/10.1109/prai59366.2023.10332091openalex-snapshot
An Efficient Wave Propagation Prediction Model Generated by CycleGAN with Ray Tracing Data
inferred← citeshttps://doi.org/10.1109/tbc.2023.3311335openalex-snapshot
An SVR-Based Radio Propagation Prediction Model for Terrestrial FM Broadcasting Services in Beijing and Its Surrounding Area
inferred← citeshttps://doi.org/10.1109/wcnc57260.2024.10570994openalex-snapshot
Clutter Loss Prediction Models for Satellite-Ground Communication Based on Neural Networks
inferred← citeshttps://doi.org/10.11113/elektrika.v18n1.140openalex-snapshot
ANFIS Model for Path Loss Prediction in the GSM and WCDMA Bands in Urban Area
inferred← citeshttps://doi.org/10.11113/elektrika.v19n3.232openalex-snapshot
A Quasi-Moment-Method-Based Calibration of Basic Pathloss Models
inferred← citeshttps://doi.org/10.1155/2021/6619364openalex-snapshot
Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis
inferred← citeshttps://doi.org/10.11591/ijaas.v11.i3.pp277-286openalex-snapshot
Digital terrestrial television in Nigeria: A technical review of path loss modeling and optimization techniques
inferred← citeshttps://doi.org/10.15866/irecap.v13i5.23345openalex-snapshot
Designing and Investigating the Effect of Single-Intermediate and 2-Intermediate Layers of Vanilla Neural Networks in Electromagnetic Path Loss Prediction
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3514673/v1openalex-snapshot
Investigation and Modelling of Path Losses associated with Digital Terrestrial Television Communication Link Within the UHF band in Nigeria
inferred← citeshttps://doi.org/10.25077/jnte.v12n1.1052.2023openalex-snapshot
Path Loss Prediction Accuracy Based On Random Forest Algorithm in Palembang City Area
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Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments
inferred← citeshttps://doi.org/10.3390/app14177545openalex-snapshot
Machine-Learning-Based Path Loss Prediction for Vehicle-to-Vehicle Communication in Highway Environments
inferred← citeshttps://doi.org/10.3390/app9091908openalex-snapshot
Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
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Advanced Information Processing Methods and Their Applications
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The Current Progress and Future Prospects of Path Loss Model for Terrestrial Radio Propagation
inferred← citeshttps://doi.org/10.36909/jer.10457openalex-snapshot
Effect of membership functions and data size on the performance of ANFIS-based model for predicting path losses in the VHF and UHF bands
inferred← citeshttps://doi.org/10.4236/ojapps.2022.126068openalex-snapshot
Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models
inferred← citeshttps://doi.org/10.56532/mjsat.v3i3.163openalex-snapshot
Computation of VHF Signal Strength for Point to Area Network using Machine Learning Modeling Techniques
inferred← citeshttps://doi.org/10.62341/ammr2324openalex-snapshot
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