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[1]
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Characterization of path Loss in the tetra band for smart city project using sparse convAutoNet-driven regression random machines
Australian Journal of Electrical and Electronics Engineering,
2025
DOI:10.1080/1448837X.2025.2460285
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[2]
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Performance Evaluation of GeoAI-Based Approach for Path Loss Prediction in Cellular Communication Networks
Wireless Personal Communications,
2024
DOI:10.1007/s11277-024-11554-w
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[3]
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3D printing of biodegradable polymers and their composites – Current state-of-the-art, properties, applications, and machine learning for potential future applications
Progress in Materials Science,
2024
DOI:10.1016/j.pmatsci.2024.101336
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[4]
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Received Signal Strength Indicator Prediction for Mesh Networks in a Real Urban Environment Using Machine Learning
IEEE Access,
2024
DOI:10.1109/ACCESS.2024.3492706
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[5]
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Comparative analysis and optimization of path loss models for small cell wireless communication systems
Cogent Engineering,
2024
DOI:10.1080/23311916.2024.2402894
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[6]
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Path Loss Prediction on Earth-Space Link Using Statistical and Time Series Approach at Ka-Band in Abuja, North Central Nigeria
IOP Conference Series: Earth and Environmental Science,
2024
DOI:10.1088/1755-1315/1428/1/012018
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[7]
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A Survey: Network Feature Measurement Based on Machine Learning
Applied Sciences,
2023
DOI:10.3390/app13042551
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[8]
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Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review
Future Internet,
2023
DOI:10.3390/fi15110362
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[9]
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On the interpretability of machine learning regression for path-loss prediction of millimeter-wave links
Expert Systems with Applications,
2023
DOI:10.1016/j.eswa.2022.119324
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[10]
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On the interpretability of machine learning regression for path-loss prediction of millimeter-wave links
Expert Systems with Applications,
2023
DOI:10.1016/j.eswa.2022.119324
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[11]
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Spectrum sensing-focused cognitive radio network for 5G revolution
Frontiers in Environmental Science,
2023
DOI:10.3389/fenvs.2023.1113832
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[12]
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Application of Intelligent Digital Technology in Load Forecasting of New Power Systems
2023 International Conference on Network, Multimedia and Information Technology (NMITCON),
2023
DOI:10.1109/NMITCON58196.2023.10276027
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[13]
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Comparison of machine learning path loss model for wireless sensor networks in cassava crops
2023 IEEE Colombian Caribbean Conference (C3),
2023
DOI:10.1109/C358072.2023.10436224
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[14]
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Anchor link prediction across social networks based on multiple consistency
Knowledge-Based Systems,
2022
DOI:10.1016/j.knosys.2022.109939
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[15]
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Anchor link prediction across social networks based on multiple consistency
Knowledge-Based Systems,
2022
DOI:10.1016/j.knosys.2022.109939
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[16]
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Improving Path Loss Prediction Using Environmental Feature Extraction from Satellite Images: Hand-Crafted vs. Convolutional Neural Network
Applied Sciences,
2022
DOI:10.3390/app12157685
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[17]
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A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems
Mathematical and Computational Applications,
2022
DOI:10.3390/mca27050077
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[18]
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Path Loss Prediction in Tropical Regions using Machine Learning Techniques: A Case Study
Electronics,
2022
DOI:10.3390/electronics11172711
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[19]
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Predicting the Frequency Bands and the Path Loss in Wireless Communication Systems using Random Forests
2022 3rd International Conference on Smart Electronics and Communication (ICOSEC),
2022
DOI:10.1109/ICOSEC54921.2022.9951963
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