Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks

[EN] In this paper, an optimal model is developed for path loss predictions using the Feed-Forward Neural Network (FFNN) algorithm. Drive test measurements were carried out in Canaanland Ota, Nigeria and Ilorin, Nigeria to obtain path loss data at varying distances from 11 different 1,800 MHz base station transmitters. Single-layered FFNNs were trained with normalized terrain profile data (longitude, latitude, elevation, altitude, clutter height) and normalized distances to produce the corresponding path loss values based on the Levenberg-Marquardt algorithm. The number of neurons in the hidden layer was varied (1-50) to determine the Artificial Neural Network (ANN) model with the best prediction accuracy. The performance of the ANN models was evaluated based on different metrics: Mean Absolute error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), standard deviation, and regression coefficient (R). Results of the machine learning processes show that the FNN architecture adopting a tangent activation function and 48 hidden neurons produced the least prediction error, with MAE, MSE, RMSE, standard deviation, and R values of 4.21 dB, 30.99 dB, 5.56 dB, 5.56 dB, and 0.89, respectively. Regarding generalization ability, the predictions of the optimal ANN model yielded MAE, MSE, RMSE, standard deviation, and R values of 4.74 dB, 39.38 dB, 6.27 dB, 6.27 dB, and 0.86, respectively, when tested with new data not previously included in the training process. Compared to the Hata, COST 231, ECC-33, and Egli models, the developed ANN model performed better in terms of prediction accuracy and generalization ability.This work was supported by Covenant University [grant number CUCRID-SMARTCU-000343].Popoola, SI.; Adetiba, E.; Atayero, AA.; Faruk, N.; Tavares De Araujo Cesariny Calafate, CM. (2018). Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks. Cogent Engineering. 5:1-19. https://doi.org/10.1080/23311916.2018.1444345S1195Adetiba, E., Iweanya, V. C., Popoola, S. I., Adetiba, J. N., & Menon, C. (2017). Automated detection of heart defects in athletes based on electrocardiography and artificial neural network. Cogent Engineering, 4(1). doi:10.1080/23311916.2017.1411220Adetiba, E., & Olugbara, O. O. (2015). Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features. The Scientific World Journal, 2015, 1-17. doi:10.1155/2015/786013Adeyemo, Z. K., Ogunremi, O. K., & Ojedokun, I. A. (2016). Optimization of Okumura-Hata Model for Long Term Evolution Network Deployment in Lagos, Nigeria. International Journal on Communications Antenna and Propagation (IRECAP), 6(3), 146. doi:10.15866/irecap.v6i3.9012Akhoondzadeh-Asl, L., & Noori, N. (2007). Modification and Tuning of the Universal Okumura-Hata Model for Radio Wave Propagation Predictions. 2007 Asia-Pacific Microwave Conference. doi:10.1109/apmc.2007.4554925Al Salameh, M. S., & Al-Zu’bi, M. M. (2015). Prediction of radiowave propagation for wireless cellular networks in Jordan.Paper presented at the Knowledge and Smart Technology (KST), 2015 7th International Conference on.Alamoud, M. A., & Schutz, W. (2012). Okumura-hata model tuning for TETRA mobile radio networks in Saudi Arabia. 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). doi:10.1109/ictea.2012.6462901Armenta, A., Serrano, A., Cabrera, M., & Conte, R. (2011). The new digital divide: the confluence of broadband penetration, sustainable development, technology adoption and community participation. Information Technology for Development, 18(4), 345-353. doi:10.1080/02681102.2011.625925Begovic, P., Behlilovic, N., & Avdic, E. (2012). Applicability evaluation of Okumura, Ericsson 9999 and winner propagation models for coverage planning in 3.5 GHZ WiMAX systems.Erceg, V., Greenstein, L. J., Tjandra, S. Y., Parkoff, S. R., Gupta, A., Kulic, B., … Bianchi, R. (1999). An empirically based path loss model for wireless channels in suburban environments. IEEE Journal on Selected Areas in Communications, 17(7), 1205-1211. doi:10.1109/49.778178Farhoud, M., El-Keyi, A., & Sultan, A. (2013). Empirical correction of the Okumura-Hata model for the 900 MHz band in Egypt. 2013 Third International Conference on Communications and Information Technology (ICCIT). doi:10.1109/iccitechnology.2013.6579585Faruk, N., Adediran, Y. A., & Ayeni, A. A. (2013). Error bounds of empirical path loss models at VHF/UHF bands in Kwara State, Nigeria. Eurocon 2013. doi:10.1109/eurocon.2013.6625043Faruk, N., Ayeni, A., & Adediran, Y. A. (2013). ON THE STUDY OF EMPIRICAL PATH LOSS MODELS FOR ACCURATE PREDICTION OF TV SIGNAL FOR SECONDARY USERS. Progress In Electromagnetics Research B, 49, 155-176. doi:10.2528/pierb13011306Hata, M. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transactions on Vehicular Technology, 29(3), 317-325. doi:10.1109/t-vt.1980.23859Hufford, G. A. (1952). An integral equation approach to the problem of wave propagation over an irregular surface. Quarterly of Applied Mathematics, 9(4), 391-404. doi:10.1090/qam/44350Ibhaze, A. E., Ajose, S. O., Atayero, A. A.-A., & Idachaba, F. E. (2016). Developing smart cities through optimal wireless mobile network.Paper presented at the emerging technologies and innovative business practices for the transformation of societies (EmergiTech), IEEE international conference on.Luebbers, R. (1984). Propagation prediction for hilly terrain using GTD wedge diffraction. IEEE Transactions on Antennas and Propagation, 32(9), 951-955. doi:10.1109/tap.1984.1143449Matthews, V. O., Osuoyah, Q., Popoola, S. I., Adetiba, E., & Atayero, A. A. (2017, July 5–7). C-BRIG: A network architecture for real-time information exchange in smart and connected campuses. In Lecture notes in engineering and computer science: Proceedings of the world congress on engineering 2017 (pp. 398–401). London.Medeisis, A., & Kajackas, A. (s. f.). On the use of the universal Okumura-Hata propagation prediction model in rural areas. VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026). doi:10.1109/vetecs.2000.851585Mohtashami, V., & Shishegar, A. A. (2012). Modified wavefront decomposition method for fast and accurate ray-tracing simulation. IET Microwaves, Antennas & Propagation, 6(3), 295. doi:10.1049/iet-map.2011.0264Nimavat, V. D., & Kulkarni, G. (2012). Simulation and performance evaluation of GSM propagation channel under the urban, suburban and rural environments.Paper presented at the communication, information & computing technology (ICCICT), 2012 international conference on.. O. F. O. (2014). RADIO FREQUENCY OPTIMIZATION OF MOBILE NETWORKS IN ABEOKUTA, NIGERIA FOR IMPROVED QUALITY OF SERVICE. International Journal of Research in Engineering and Technology, 03(08), 174-180. doi:10.15623/ijret.2014.0308027Phillips, C., Sicker, D., & Grunwald, D. (2013). A Survey of Wireless Path Loss Prediction and Coverage Mapping Methods. IEEE Communications Surveys & Tutorials, 15(1), 255-270. doi:10.1109/surv.2012.022412.00172Popoola, S. I., Atayero, A. A., Badejo, J. A., John, T. M., Odukoya, J. A., & Omole, D. O. (2018). Learning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university. Data in Brief, 17, 76-94. doi:10.1016/j.dib.2017.12.059Popoola, S. I., Atayero, A. A., & Faruk, N. (2018). Received signal strength and local terrain profile data for radio network planning and optimization at GSM frequency bands. Data in Brief, 16, 972-981. doi:10.1016/j.dib.2017.12.036Popoola, S. I., Atayero, A. A., Faruk, N., & Badejo, J. A. (2018). Data on the key performance indicators for quality of service of GSM networks in Nigeria. Data in Brief, 16, 914-928. doi:10.1016/j.dib.2017.12.005Popoola, S. I., Atayero, A. A., Faruk, N., Calafate, C. T., Adetiba, E., & Matthews, V. O. (2017, July 5–7). Calibrating the standard path loss model for urban environments using field measurements and geospatial data.Paper presented at the Lecture notes in engineering and computer science: Proceedings of the world congress on engineering 2017 (pp. 513–518). London.Popoola, S. I., Atayero, A. A., Faruk, N., Calafate, C. T., Olawoyin, L. A., & Matthews, V. O. (2017). Standard propagation model tuning for path loss predictions in built-up environments.Paper presented at the International Conference on Computational Science and Its Applications.Popoola, S. I., Atayero, A. A., Okanlawon, T. T., Omopariola, B. I., & Takpor, O. A. (2018). Smart campus: Data on energy consumption in an ICT-driven university. Data in Brief, 16, 780-793. doi:10.1016/j.dib.2017.11.091Popoola, S. I., Badejo, J. A., Ojewande, S. O., & Atayero, A. (2017, October 25–27). Statistical evaluation of quality of service offered by GSM network operators in Nigeria. In Lecture notes in engineering and computer science: Proceedings of the world congress on engineering and computer science 2017 (pp. 69–73). San Francisco.Popoola, S. I., Misra, S., & Atayero, A. A. (2018). Outdoor path loss predictions based on extreme learning machine. Wireless Personal Communications, 1–20.Rath, H. K., Verma, S., Simha, A., & Karandikar, A. (2016). Path Loss model for Indian terrain-empirical approach.Paper presented at the communication (NCC), 2016 twenty second national conference on.Salman, M. A., Popoola, S. I., Faruk, N., Surajudeen-Bakinde, N., Oloyede, A. A., & Olawoyin, L. A. (2017). Adaptive neuro-fuzzy model for path loss prediction in the VHF band.Paper presented at the computing networking and informatics (ICCNI), 2017 international conference on.Schneider, I., Lambrecht, F., & Baier, A. (s. f.). Enhancement of the Okumura-Hata propagation model using detailed morphological and building data. Proceedings of PIMRC ’96 - 7th International Symposium on Personal, Indoor, and Mobile Communications. doi:10.1109/pimrc.1996.567508Sotiroudis, S. P., & Siakavara, K. (2015). Mobile radio propagation path loss prediction using Artificial Neural Networks with optimal input information for urban environments. AEU - International Journal of Electronics and Communications, 69(10), 1453-1463. doi:10.1016/j.aeue.2015.06.014Zelley, C. A., & Constantinou, C. C. (1999). A three-dimensional parabolic equation applied to VHF/UHF propagation over irregular terrain. IEEE Transactions on Antennas and Propagation, 47(10), 1586-1596. doi:10.1109/8.80590

Synthesis, anticancer, antitubercular and antimicrobial activity of 6-carbethoxy-5-(3' chlorophenyl)-3-aryl-2-cyclohexenones and 6-aryl-4-(3'-chlorophenyl)-3-oxo-2,3a,4,5-tetrahydro-¹<i>H</i>-indazoles

1497-15016-Carbethoxy-5-(3'-chlorophenyl)-3-aryl-2-cyclohexenones 2a-j are obtained from the chalcones 1a-j by Micheal addition of ethyl acetoacetate, followed by internal Claisen condensation. Reaction of 2a-j with hydrazine hydrate affords the corresponding 6-aryl-4-(3' -chlorophenyl)-3-oxo-2, 3a-4,5-tetrahydro-1H-indazoles 3a-j. The synthesized compounds have been evaluated for their anticancer, antitubercular and antimicrobial activity

Synthesis and antimicrobial activity of coumarin derivatives metal complexes: An in vitro evaluation

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