A comprehensive comparison and analysis of machine learning algorithms including evaluation optimized for geographic location prediction based on Twitter tweets datasets

References

• Abdul Lateef, P. S., & Eranna, U. (2022). A simplified machine learning approach for recognizing human activity. International Journal of Electrical and Computer Engineering (IJECE), 9(5), 3465–27. https://doi.org/10.11591/ijece.v9i5.pp3465-3473
   Google Scholar
• Alpcan, T., Rubinstein, B. I. P. and Leckie, C. (2016). Large-scale strategic games and adversarial machine learning. 2016 IEEE 55th Conference on Decision and Control (CDC), pp. 4420–4426, https://doi.org/10.1109/CDC.2016.7798940.
   Google Scholar
• Bagchi, S., Tay, K. G., Huong, A., & Debnath, S. K. (2020). Image processing and machine learning techniques used in computer-aided detection system for mammogram screening-A review. International Journal of Electrical and Computer Engineering (IJECE), 10(3), 2336–2348. https://doi.org/10.11591/ijece.v10i3.pp2336-2348
   Google Scholar
• Bozkurt, S., Elibol, G., Gunal, S. and Yayan, U. (2015). A comparative study on machine learning algorithms for indoor positioning. 2015 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), Madrid, Spain, pp. 1–8, https://doi.org/10.1109/INISTA.2015.7276725.
   Google Scholar
• Burton, W. S., Myers, C. A., & Rullkoetter, P. J. (2021). Machine learning for rapid estimation of lower extremity muscle and joint loading during activities of daily living. Journal of Biomechanics, 123, 110439. https://doi.org/10.1016/j.jbiomech.2021.110439
   PubMed Web of Science ®Google Scholar
• Cheng, Z., Caverlee, J., and Lee, K. (2010). You are where you tweet: A content- based approach to geo-locating twitter users. Proceedings of the 19th ACM international conference on Information and knowledge management Toronto, Canada, (pp. 759–768).
   Google Scholar
• Chi, L., Lim, K. H., Alam, N., & J.Butler, C. (2016). Proceedings ofthe 2nd Workshop on Noisy User-generated Text, Osaka, Japan (pp. 227–234).
   Google Scholar
• DiMaio, S., Hanuschik, M., & Kreaden, U. (2011). The da Vinci Surgical System. In J. Rosen, B. Hannaford, & R. Satava (Eds.), Surgical Robotics (pp. 199–217). Springer US. https://doi.org/10.1007/978-1-4419-1126-1_9
   Google Scholar
• Dingler, T., & Pielot, M. (2019). I’ll be there for you: Quantifying Attentiveness towards Mobile Messaging. Proceedings of the MobileHCI'15, Copenhagen, Denmark, 15, 144–154. ACM. https://doi.org/10.1145/2785830.2785840
   Google Scholar
• Eisenstein, J., O’Connor, B., Smith, N. A., and Xing, E. (2010). A latent variable model for geographic lexical variation. Proceedings of the 2010 conference on empirical methods in natural language processing Cambridge, United States of America, (pp. 1277–1287).
   Google Scholar
• Frermann, L., & Lapata, M. (2016). A bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics, 4, 31–45. https://doi.org/10.1162/tacl_a_00081
   Google Scholar
• Goudarzi, M., Wu, H., Palaniswami, M., & Buyy a, R. (2021). An application placement technique for concurrent iot applications in edge and fog computing environments. IEEE Transactions on Mobile Computing, 20(4), 1298–1311. https://doi.org/10.1109/TMC.2020.2967041
   Web of Science ®Google Scholar
• Han, B., Cook, P., & Baldwin, T. (2014). Text-based twitter user geolocation prediction. The Journal of Artificial Intelligence Research, 49, 451–500. https://doi.org/10.1613/jair.4200
   Web of Science ®Google Scholar
• Heiser, G., Murray, T., & Klein, G. (2012). It’s time for trustworthy systems. IEEE Security & Privacy, 10(2), 67–70. https://doi.org/10.1109/MSP.2012.41
   Web of Science ®Google Scholar
• Inam, S., Mahmood, A., Khatoon, S., & Nawaz, N. (2022). Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction. Sustainability, 14(7317). https://doi.org/10.3390/su14127317
   Google Scholar
• Ismail, H. M., Harous, S., & Belkhouche, B. (2016). A comparative analysis of machine learning classifiers for twitter sentiment analysis. Research in Computing Science, 110(1), 71–83. https://doi.org/10.13053/rcs-110-1-6
   Google Scholar
• Khoudry, E., Belfqih, A., Ouaderhman, T., Boukherouaa, J., & Elmariami, F. (2020). A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6122–6138. https://doi.org/10.11591/ijece.v10i6.pp6122-6138
   Google Scholar
• Krantiwadmare. Logistic regression in machine learning. Medium, (2021, May 30). [Retrieved: February 4, 2023] [Online]. Available: https://medium.com/analytics-vidhya/logistic-regression-in-machine-learning-f3a90c13bb41.
   Google Scholar
• Lim, K. et al., (2018). “The Grass is Greener on the Other Side”, Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18. Available: https://doi.org/10.1145/3184558.3186337 [Retrieved July 10, 2021].
   Google Scholar
• Logunova, I. K-Nearest neighbors (KNN) algorithm for machine learning. Serokell Software Development Company, (2022, September 20). [Retrieved: July 10, 2023 2022] [Online]. Available: https://serokell.io/blog/knn-algorithm-in-ml.
   Google Scholar
• Mohammed, Z., Hanae, C., & Larbi, S. (2020). Comparative study on machine learning algorithms for early fire forest detection system using geodata. International Journal of Electrical and Computer Engineering (IJECE), 10(5), 5507–5513. https://doi.org/10.11591/ijece.v10i5.pp5507-5513
   Google Scholar
• Moumen, R., Chiheb, R., & Faizi, R. (2021). Real-time Arabic scene text detection using fully convolutional neural networks. International Journal of Electrical and Computer Engineering (IJECE), 11(2), 1634–1640. https://doi.org/10.11591/ijece.v11i2.pp1634-1640
   Google Scholar
• Pavalanathan, U. and Eisenstein, J. (2015). Confounds and consequences in tagged twitter data. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal (pp. 2138–2148).
   Google Scholar
• Pennington, J., Socher, R., and Manning, C. D. (2014). Glove: Global vectors for word representation. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Doha, Qatar, (pp. 1532–1543).
   Google Scholar
• Procter, S., & Secco, E. (2021). Design of a Biomimetic BLDC Driven Robotic Arm for Teleoperation & Biomedical Applications. Journal of Human, Earth, and Future, 2(4), 345–354. https://doi.org/10.28991/HEF-2021-02-04-03
   Google Scholar
• Rahimi, A., Cohn, T., & Baldwin, T. (2015) Twitter user geolocation using a unified text and network prediction model. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers), Beijing, China. Association for Computational Linguistics.
   Google Scholar
• Rahimi, A., Cohn, T., and Baldwin, T. (2018). Semi-supervised user geolocation via graph convolutional Networks. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia (pp. 2009–2019).
   Google Scholar
• Ranjan, R., Rana, O., Nepal, S., Yousif, M., James, P., Wen, Z., Barr, S., Watson, P., Jayaraman, P. P., Georgakopoulos, D., Villari, M., Fazio, M., Garg, S., Buyya, R., Wang, L., Zomaya, A. Y., & Dustdar, S. (2018). The next grand challenges: Integrating the internet of things and data science. IEEE Cloud Computing, 5(3), 12–26. May Jun. https://doi.org/10.1109/MCC.2018.032591612
   Web of Science ®Google Scholar
• Ren, Y. (2021). Python machine learning: Machine learning and deep learning with python (Book Review). International Journal of Knowledge-Based Organizations, 11(1), 67–70. https://doi.org/10.4018/IJKBO
   Google Scholar
• Samadi, M. (2011) Problems in the translation of legal terms from persian into english. The International Journal - Language Society and Culture, 3(33), 108–114. .
   Google Scholar
• Sharrab, Y. O., Alsmirat, M., Hawashin, B., & Sarhan, N. (2021). Machine learning- based energy consumption modeling and comparing of H.264 and google VP8 encoders. International Journal of Electrical and Computer Engineering (IJECE), 11(2), 1303–1310. https://doi.org/10.11591/ijece.v11i2.pp1303-1310
   Google Scholar
• Wang, Y., Dai, B., Kong, L., Erfani, S. M., Bailey, J., &, and Zha, H. (2018). Learning deep hidden nonlinear dynamics from aggregate data. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence. UAI 2018, Monterey, USA, 1, 83–92.
   Google Scholar
• Wang, P., Fan, E., & Wang, P. (2020). Comparative Analysis of Image Classification Algorithms Based on Traditional Machine Learning and Deep Learning. Pattern recognition letters, 1. https://doi.org/10.1016/j.patrec.2020.07.042
   Google Scholar
• Yun, J., & Woo, J. (2020). A comparative analysis of deep learning and machine learning on detecting movement directions using PIR Sensors. IEEE Internet of Things Journal, 7(4), 2855–2868. https://doi.org/10.1109/JIOT.2019.2963326
   Web of Science ®Google Scholar
• Zalk, M., Bosua, R., & Sharma, R. (2011). Improving knowledge sharing behaviour within organizations: Towards a mode. Proceedings of the 19th European Conference on Information Systems, ECIS 2011, Helsinki, Finland. AISeL. http://aisel.aisnet.org/ecis2011/212
   Google Scholar
• Zhao, P., Adnan, K. A., Lyu, X., Wei, S. and Sinnott, R. O. (2020). Estimating the size of crowds through deep learning. 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), pp. 1–8, https://doi.org/10.1109/CSDE50874.2020.9411377.
   Google Scholar