The objective of this work is to determine the true owner of a land—public or private—in the region of Kumasi (Ghana). For this purpose, we applied different machine learning methods to the ZEF (Center for Development Research) dataset on Land plots in Kumasi. Machine learning is a field in artificial intelligence that has been applied to many domains successfully in other continents. The machine learning algorithms considered are Polynomial Regression, Softmax Regression, Na？ve Bayes, Bagging, Neural Network, Decision Tree, Random Forest, k-Nearest Neighbor and Support Vector Machines. In addition, we designed a rule-based convolution algorithm from these aforementioned algorithms, which performs excellently on the given dataset. In fact, our proposed model performs better than the cited models on the Kumasi dataset. This research is not trivial given the conflicts generated by land ownership issues in the sub-Saharan Africa region as a whole and in Ghana particularly. Therefore, we hope this work will help buyers be more alert in ascertaining the true ownership of a land before purchasing it.
References
[1]
Boafo-Anang, M., Akrofi, E.O. and Shitsi, F.J. (2021) Land Disputes and Conflicting Land-Court Judgement Resolution in Ghana: Perspectives of Regulatory Stakeholders. African Journal on Land Policy and Geospatial Sciences, 4, 331-342.
[2]
Sabogu, A., Nasse, T. and Kanto, I. (2020) Understanding Land Conflicts and Food Security in West Africa: Triggers and Perspectives from Dorimon in Ghana. International Journal of Economics, Business and Management Research, 4, 84-108.
Habehh, H. and Gohel, S. (2021) Machine Learning in Healthcare. Current Genomics, 22, 291-300. https://doi.org/10.2174/1389202922666210705124359
[5]
Ersozlu, Z., Taheri, S. and Koch, I. (2024) A Review of Machine Learning Methods Used for Educational Data. Education and Information Technologies, 29, 22125-22145. https://doi.org/10.1007/s10639-024-12704-0
[6]
Kelly, B.T. and Xiu, D.C. (2023) Financial Machine Learning. Becker Friedman Institute for Economics-Working Paper, No. 2023-100.
[7]
Chopra, V. and Priyadarshi, D. (2019) Role of Machine Learning in Manufacturing Sector. International Journal of Recent Technology and Engineering (IJRTE), 8, 2320-2328. https://doi.org/10.35940/ijrte.d8191.118419
[8]
Yaokumah, W., Appati, J.K. and Kumah, D. (2021) Machine Learning Methods for Detecting Internet-Of-Things (IoT) Malware. International Journal of Cognitive Informatics and Natural Intelligence, 15, 1-18. https://doi.org/10.4018/ijcini.286768
Evgeniou, T. and Pontil, M. (2001) Support Vector Machines: Theory and Applications. In: Paliouras, G., Karkaletsis, V. and Spyropoulos, C.D., Eds., Machine Learning and Its Applications, Springer, 249-257. https://doi.org/10.1007/3-540-44673-7_12
[11]
Aeeneh, S., Zlatanov, N. and Yu, J. (2025) New Bounds on the Accuracy of Majority Voting for Multiclass Classification. IEEE Transactions on Neural Networks and Learning Systems, 36, 6014-6028. https://doi.org/10.1109/tnnls.2024.3387544
[12]
Halder, R.K., et al. (2024) Enhancing K-Nearest Neighbor Algorithm: A Comprehensive Review and Performance Analysis of Modifications. Journal of Big Data, 11, Article No. 113.
[13]
Kumari, K. and Yadav, S. (2018) Linear Regression Analysis Study. Journal of the Practice of Cardiovascular Sciences, 4, 33-36. https://doi.org/10.4103/jpcs.jpcs_8_18
Lingaraj, H. (2016) A Study on Genetic Algorithm and Its Applications. International Journal of Computer Sciences and Engineering, 4, 139-143.
[16]
Shakya, A.K., Pillai, G. and Chakrabarty, S. (2023) Reinforcement Learning Algorithms: A Brief Survey. Expert Systems with Applications, 231, Article ID: 120495. https://doi.org/10.1016/j.eswa.2023.120495
[17]
Breiman, L. (1996) Bagging Predictors. Machine Learning, 24, 123-140. https://doi.org/10.1023/a:1018054314350
[18]
Al-Bashiri, H., Abdulgabber, M.A., Romli, A. and Hujainah, F. (2017) Collaborative Filtering Recommender System: Overview and Challenges. Advanced Science Letters, 23, 9045-9049. https://doi.org/10.1166/asl.2017.10020
[19]
Southworth, J., Smith, A.C., Safaei, M., Rahaman, M., Alruzuq, A., Tefera, B.B., et al. (2024) Machine Learning versus Deep Learning in Land System Science: A Decision-Making Framework for Effective Land Classification. Frontiers in Remote Sensing, 5, Article 1374862. https://doi.org/10.3389/frsen.2024.1374862
[20]
Wang, J., Bretz, M., Dewan, M.A.A. and Delavar, M.A. (2022) Machine Learning in Modelling Land-Use and Land Cover-Change (LULCC): Current Status, Challenges and Prospects. Science of the Total Environment, 822, Article ID: 153559. https://doi.org/10.1016/j.scitotenv.2022.153559
