RETRACTED ARTICLE: Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural …

PM Shakeel, A Tolba, Z Al-Makhadmeh… - Neural Computing and …, 2020 - Springer
Today, most of the people are affected by lung cancer, mainly because of the genetic
changes of the tissues in the lungs. Other factors such as smoking, alcohol, and exposure to …

A UHF path loss model using learning machine for heterogeneous networks

M Ayadi, AB Zineb, S Tabbane - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
In this paper, we present and evaluate a new propagation model for heterogeneous
networks. The designed model is multiband, multienvironment, and is usable for short and …

Cellular network radio propagation modeling with deep convolutional neural networks

X Zhang, X Shu, B Zhang, J Ren, L Zhou… - Proceedings of the 26th …, 2020 - dl.acm.org
Radio propagation modeling and prediction is fundamental for modern cellular network
planning and optimization. Conventional radio propagation models fall into two categories …

Centimeter and millimeter-wave propagation characteristics for indoor corridors: results from measurements and models

FD Diba, MA Samad, DY Choi - IEEE Access, 2021 - ieeexplore.ieee.org
The millimeter-wave (mm-wave) frequency band is projected to play a critical role in next-
generation wireless networks owing to its large available bandwidth. Despite the theoretical …

[HTML][HTML] ANN-based model for multiband path loss prediction in built-up environments

N Faruk, QR Adebowale, IFY Olayinka, KS Adewole… - Scientific African, 2022 - Elsevier
Path loss propagation models are critically needed for optimum planning and deployment of
wireless communication networks. However, the complexity exhibited by the propagated …

Prediction of neurological disorders using optimized neural network

PR Kshirsagar, SG Akojwar - 2016 International Conference on …, 2016 - ieeexplore.ieee.org
In the earth there is distressing number of people who suffer from neurological disorders.
Electroencephalogram EEG signal are chaotic time series signals and tends to change …

An approach to modernization of the Hat and COST 231 model for improvement of electromagnetic compatibility in premises for navigation and motion control …

DI Bakhtiiarov, GF Konakhovych… - 2018 IEEE 5th …, 2018 - ieeexplore.ieee.org
Research of the law of fading of an electromagnetic field in real conditions of operation
depending on frequency of radiation is presented. Methods of approximation which were …

Performance evaluation of dynamic neural networks for mobile radio path loss prediction

A Bhuvaneshwari, R Hemalatha… - 2016 IEEE Uttar …, 2016 - ieeexplore.ieee.org
The prediction of path loss for the mobile radio signals is an important part in the design
phase of the wireless cellular networks. In the process of modelling the path loss, the GSM …

Artificial intelligence–based optimization of reverse osmosis systems operation performance

S Nazif, E Mirashrafi, B Roghani… - Journal of …, 2020 - ascelibrary.org
In recent years, reverse osmosis (RO) systems have been highly utilized in industrial
processes. One of the most important operational issues of these systems is membrane …

Linear discriminant analysis based genetic algorithm with generalized regression neural network–a hybrid expert system for diagnosis of diabetes

J Jayashree, SA Kumar - Programming and Computer Software, 2018 - Springer
Among the applications enabled by expert systems, disease diagnosis is a particularly
important one. Nowadays, diabetes is found to be a complex health issue in human life …