Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2023, Cilt: 3 Sayı: 1, 36 - 44, 15.02.2023

Fatin Aljarah Aydın Çetin

https://doi.org/10.54569/aair.1200695

Öz

Kaynakça

  • Varila M., “What Is Potable Water? Your Guide to Understanding Types of Water”, viralrang, 2020. [Online]. Available: https://viralrang.com/what-is-potable-water-your-guide-to-understanding-types-of-water/#. [Accessed: Nov 8, 2022]
  • UNECE, “miyah alshrob,” who, (2022). [Online]. Available: https://www.who.int/ar/news-room/fact-sheets/detail/drinking-water. [Accessed: Oct 19, 2022].
  • Fluence news team, “What Is Potable Water?”, fluencecorp, 2019. [Online]. Available: https://tinyurl.com/2qj936u9. [Accessed: Nov 8, 2022].
  • World Health Organization, “Preventing diarrhoea through better water, sanitation and hygiene: exposures and impacts in low- and middle-income countries,” World Health Organization (Report), Villars-sous-Yens, Switzerland, ‎2014.
  • World Health Organization, “Diarrhoeal disease,” who, 2017. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/diarrhoeal-disease. [Accessed: Dec 3, 2022].
  • Li D., Liu S., “System and Platform for Water Quality Monitoring Chapter 3,” in Water Quality Monitoring and Management, China: Academic Press, 2019, p. 101.
  • Edition F., Guidelines for Drinking-water Quality - 4th ED., Malta: World Health Organization WHO Library Cataloguing, 2011.
  • Al safaw Y.A., R. Al Shanouna R.A.A., Messer, N., “Takeem hasaas naweet almeah w hesab muamel WQI le baaz masader almeah fe karyat abo marya kazaa talefar\ muhafazat nainawa,” Journal of Education and Science, 27( 3), 87, 2018.
  • Al Safawi A. Y. T., “Tatbik almuasher alkndy (WQI CCME) le takeem jawdet almeyah le agrad alshrub: dirasat halat jawdet almeyah aljawfeia fe nahiat almehalabia\ muhafazat nainawe,” Journal of Rafidain Sciences, 27(4), 199, 2018.
  • Dilip P.V., Dnyaneshwar, M. S., Rajendra, L. D., Suresh, N. P., “Assessment of Ground Water Quality In Gajanan Colony, Ahmednagar. By Water Quality Index (WQI),” in Second Shri Chhatrapati Shivaji Maharaj QIP Conference on Engineering Innovations, Ahmednagar, India, 105, 2019, ISSN: 2581- 4230.
  • Ajayi O.O, Bagula A.B, Maluleke H.C., “Water Net: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes”, IEEE Access, 10, 48318- 48337. 2022, doi: 10.1109/ACCESS.2022.3172274, 2022.
  • Aldhyani T.H.H., Al-Yaari M., Al kahtani H., “Water Quality Prediction Using Artificial Intelligence Algorithms,” Applied Bionics and Biomechanics, vol.2020, 1-10. doi: 10.1155/2020/6659314, 2020.
  • Nasir N, Kansal A, Aishalton O, “Water quality classification using machine learning algorithms”, Journal of Water Process Engineering, vol.48. doi: 10.1016/j.jwpe.2022.102920, 2022.
  • Wang L, Zhu Z, Sassoubre L, “improving the robustness of beach water quality modeling using an ensemble machine learning approach”, Science of the Total Environment, 765, 1-4, doi: 10.1016/j.scitotenv.2020.142760, 2021.
  • Rosly R, Makhtar M, Awang M.K, “Comparison of Ensemble Classifiers for Water Quality Dataset,” in Proceedings of the UniSZA Research Conference 2015 (URC ’15), Terengganu, Malaysia, 1-6, 2015
  • Mogaraju J.K, “Application of machine learning algorithms in the investigation of groundwater quality parameters over YSR district, India,” Turkish Journal of Engineering, 7(1), 64 - 72. doi: 10.31127/tuje.1032314, 2023.
  • El Bilali A, Taleb A, Brouziyne Y, “Groundwater quality forecasting using machine learning algorithms for irrigation purposes”, Agricultural Water Management, 245, 106625. doi: 10.1016/j.agwat.2020.106625 , 2021.
  • Abdul Malek N.H, Wan Yaacob W.F, Md nasir S.A, “Prediction of Water Quality Classification of the Kelantan River Basin, Malaysia, Using Machine Learning Techniques”, Water, 14(7), 1067. doi: 10.3390/w14071067, 2022
  • Al-Musawi N, “Prediction and Assessment of Water Quality Index Using Neural Network Model and Gis Case Study: Tigris River in Baghdad City”, Applied Research Journal, 3(11), 343-353, 2018.
  • Talat R.A, Al-Assaf A.Y, Al-Saffawi A.Y.T, “Valuation of water quality for drinking and domestic purposes using WQI: Case study for groundwater of Al-Jameaa and Al-Zeraee quarters in Mosul city/Iraq”, Journal of Physics Conference Series, 1294(7). doi: 10.1088/1742-6596/1294/7/072011, 2019
  • Safawi A.Y.T.A, “tatbiq al muasher al kanadi(WQI CCME) le taqeem javed almeyah le agrade alshrub”, in The third Scientific Conference of life sciences, Iraq, 27(5), 193-202, 2019.
  • Mahmood A, “Evaluation of raw water quality in Wassit governorate by Canadian water quality index”, in Environmental Engineering and Sustainable Development, Iraq, 162, 1-8. 2018, doi: 10.1051/matecconf/201816205020.
  • Mosavi A, Ozturk P, Chau K, “Flood Prediction Using Machine Learning Models: Literature Review”, Water, 10(11), 1536. doi: 10.3390/w10111536, 2018.
  • Chen Y, Song L, Liu Y, “A Review of the Artificial Neural Network Models for Water Quality Prediction,” Applied Sciences, 10(17), 5776. doi: 10.3390/app10175776, 20 8 2020.
  • Koranga M., Pant P, Pant D, “SVM Model to Predict the Water Quality Based on Physicochemical Parameters,” International Journal of Mathematical, Engineering and Management Sciences, 6(2), 645-659. doi: 10.33889/IJMEMS.2021.6.2.040, 2021
  • Al-Adhaileh M. H, Alsaade F. W, “Modelling and Prediction of Water Quality by Using Artificial Intelligence,” Sustainability, 13(8), 4259. doi: 10.3390/su13084259, 2021
  • Park S, Jung S, Lee H, “Large-Scale Water Quality Prediction Using Federated Sensing,” Sensors, 21(4), 1462. doi: 10.3390/s21041462, 2021.
  • Kadiwal A., “Water Quality, Drinking water potability,” Kaggle, 2019. [Online]. Available: https://www.kaggle.com/datasets/adityakadiwal/water-potability. [Accessed: March 9, 2022].
  • Pérez F, Granger B, “jupytercon,” jupyter, 2014. [Online]. Available: https://jupyter.org/. [Accessed: March 5, 2022].
  • Scikit-learn authors, “1. Supervised learning,” scikit-learn, 2022. [Online]. Available: https://scikit-learn.org/stable/supervised_learning.html#supervised-learning. [Accessed: April 10, 2022].
  • Developers, “NumPy 1.23.0 released,” numpy, 2022. [Online]. Available: https://numpy.org. [Accessed: April 10, 2022].
  • Developers, “pandas: powerful Python data analysis toolkit,” pypi, 2022. [Online]. Available: https://pypi.org/project/pandas/. [Accessed: April 10, 2022].
  • Developers, “seaborn: statistical data visualization,” seaborn, 2021. [Online]. Available: https://seaborn.pydata.org/. [Accessed: April 10, 2022].
  • Developers, “Matplotlib: Visualization with Python,” matplotlib, 2022. [Online]. Available: https://matplotlib.org/. [Accessed: April 10, 2022].
  • Brownlee, J., “What is the Difference Between a Parameter and a Hyperparameter?,” machine learning mastery, 26 6 2017. [Online]. Available: https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/. [Accessed; June 10, 2022].
  • Yıldırım S, “6 Must-Know Parameters for Machine Learning Algorithms,” towards data science, 2022. [Online]. Available: https://towardsdatascience.com/6-must-know-parameters-for-machine-learning-algorithms-ed52964bd7a9. [Accessed: June 10, 2022].
  • Yıldırım S, “L1 and L2 Regularization — Explained,” towardsdatascience, 2020. [Online]. Available: https://towardsdatascience.com/l1-and-l2-regularization-explained-874c3b03f668. [Accessed: June 10, 2022].
  • Developers, “sklearn.ensemble.HistGradientBoostingClassifier,” scikit-learn, 2022. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html. [Accessed: June 11, 2022].
  • DigitalSreeni, Director, 184 - Scheduling learning rate in keras. [Video]. United States: Site: YouTube, 2020. URL: https://youtu.be/drcagR2zNpw.
  • Developers, “Sklearn.tree.DecisionTreeClassifier,” scikit-learn, 2022. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html [Accessed: June 11, 2022].
  • Bhatt B, Director, Decision Tree Hyperparameters : max_depth, min_samples_split, min_samples_leaf, max_features. [Video]. India: Site: YouTube, 2019. URL: https://www.youtube.com/watch?v=XABw4Y3GBR4&t=365s.
  • Paper D, “Scikit-Learn Classifier Tuning from Complex Training Sets,” in Hands-on Scikit-Learn for Machine Learning Applications, Logan, UT, USA, Apress, Berkeley, CA, 2020. doi: 10.1007/978-1-4842-5373-1_6.
  • Alwanas A.A.H, Al-Musawi A.A, Salih S.Q, “Load-carrying capacity and mode failure simulation of beam-column joint connection: Application of self-tuning machine learning model,” Engineering Structures, 194, 220-229. doi: c10.1016/j.engstruct.2019.05.048, 2019.
  • Tung T. M, Yaseen Z. M, “A survey on river water quality modelling using artificial intelligence models: 2000--2020”, Journal of Hydrology, vol. 585, 124670. doi: 10.1016/j.jhydrol.2020.124670, 2020.
  • QI C, Huang S, Wang X, “Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN,” IEEE Access, vol. 8, 188070. doi: 10.1109/ACCESS.2020.3030878, 2020.
  • Soumik S.K, “How to Calculate Confusion Matrix Manually.”, medium, (2020). [Online]. Available: https://medium.com/analytics-vidhya/how-to-calculate-confusion-matrix-manually-14292c802f52. [Accessed: June 22, 2022].
  • Ho J.Y, Afana H.A, El-Shafie A.H, “Towards a time and cost-effective approach to water quality index class,” Journal of Hydrology, vol. 575, 148-165. doi: 10.1016/j.jhydrol.2019.05.016, 2019.
  • Atha R, “Building Classification Model with Python,” medium, (2021). [Online]. Available: https://medium.com/analytics-vidhya/building-classification-model-with-python-9bdfc13faa4b. [Accessed: June 22, 2022].
  • Sasaki Y., “The truth of the F-measure,” School of Computer Science, University of Manchester, 2007.
  • Wiryaseputra M, “Water Quality Prediction Using Machine Learning Classification Algorithm”, International Journal of Scientific & Engineering Research, 8(9). doi: 10.14299/000000, 2022.

Prediction of Water Quality with Ensemble Learning Algorithms

Yıl 2023, Cilt: 3 Sayı: 1, 36 - 44, 15.02.2023

Fatin Aljarah Aydın Çetin

https://doi.org/10.54569/aair.1200695

Öz

As monitoring and control of the quality of the water is one of the most important issues in the world since only 74% of the world's population use safely managed water where the water is treated well to reach the minimum limit of safety and quality standards. For observation of the water potability and to take immediate actions to improve the water quality, real-time monitoring and classification process are required. However, monitoring and controlling the quality of the water is not an easy task since it has many requirements such as the collection and analysis of data and measures to be taken. In this paper, we focus on applying machine learning for evaluation of the water quality. We have chosen five ensemble learning algorithms namely, Adaptive Boosting, Random Forest, Extra trees classifier, Gradient Boosting, and Stacking Classifier to evaluate their classification performances in defining the water quality. Results reveal that the Stacking Classifier has the highest performance among the five classifiers that we have studied.

Anahtar Kelimeler

Water potability artificial Intelligent production WQ machine learning ML

Kaynakça

  • Varila M., “What Is Potable Water? Your Guide to Understanding Types of Water”, viralrang, 2020. [Online]. Available: https://viralrang.com/what-is-potable-water-your-guide-to-understanding-types-of-water/#. [Accessed: Nov 8, 2022]
  • UNECE, “miyah alshrob,” who, (2022). [Online]. Available: https://www.who.int/ar/news-room/fact-sheets/detail/drinking-water. [Accessed: Oct 19, 2022].
  • Fluence news team, “What Is Potable Water?”, fluencecorp, 2019. [Online]. Available: https://tinyurl.com/2qj936u9. [Accessed: Nov 8, 2022].
  • World Health Organization, “Preventing diarrhoea through better water, sanitation and hygiene: exposures and impacts in low- and middle-income countries,” World Health Organization (Report), Villars-sous-Yens, Switzerland, ‎2014.
  • World Health Organization, “Diarrhoeal disease,” who, 2017. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/diarrhoeal-disease. [Accessed: Dec 3, 2022].
  • Li D., Liu S., “System and Platform for Water Quality Monitoring Chapter 3,” in Water Quality Monitoring and Management, China: Academic Press, 2019, p. 101.
  • Edition F., Guidelines for Drinking-water Quality - 4th ED., Malta: World Health Organization WHO Library Cataloguing, 2011.
  • Al safaw Y.A., R. Al Shanouna R.A.A., Messer, N., “Takeem hasaas naweet almeah w hesab muamel WQI le baaz masader almeah fe karyat abo marya kazaa talefar\ muhafazat nainawa,” Journal of Education and Science, 27( 3), 87, 2018.
  • Al Safawi A. Y. T., “Tatbik almuasher alkndy (WQI CCME) le takeem jawdet almeyah le agrad alshrub: dirasat halat jawdet almeyah aljawfeia fe nahiat almehalabia\ muhafazat nainawe,” Journal of Rafidain Sciences, 27(4), 199, 2018.
  • Dilip P.V., Dnyaneshwar, M. S., Rajendra, L. D., Suresh, N. P., “Assessment of Ground Water Quality In Gajanan Colony, Ahmednagar. By Water Quality Index (WQI),” in Second Shri Chhatrapati Shivaji Maharaj QIP Conference on Engineering Innovations, Ahmednagar, India, 105, 2019, ISSN: 2581- 4230.
  • Ajayi O.O, Bagula A.B, Maluleke H.C., “Water Net: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes”, IEEE Access, 10, 48318- 48337. 2022, doi: 10.1109/ACCESS.2022.3172274, 2022.
  • Aldhyani T.H.H., Al-Yaari M., Al kahtani H., “Water Quality Prediction Using Artificial Intelligence Algorithms,” Applied Bionics and Biomechanics, vol.2020, 1-10. doi: 10.1155/2020/6659314, 2020.
  • Nasir N, Kansal A, Aishalton O, “Water quality classification using machine learning algorithms”, Journal of Water Process Engineering, vol.48. doi: 10.1016/j.jwpe.2022.102920, 2022.
  • Wang L, Zhu Z, Sassoubre L, “improving the robustness of beach water quality modeling using an ensemble machine learning approach”, Science of the Total Environment, 765, 1-4, doi: 10.1016/j.scitotenv.2020.142760, 2021.
  • Rosly R, Makhtar M, Awang M.K, “Comparison of Ensemble Classifiers for Water Quality Dataset,” in Proceedings of the UniSZA Research Conference 2015 (URC ’15), Terengganu, Malaysia, 1-6, 2015
  • Mogaraju J.K, “Application of machine learning algorithms in the investigation of groundwater quality parameters over YSR district, India,” Turkish Journal of Engineering, 7(1), 64 - 72. doi: 10.31127/tuje.1032314, 2023.
  • El Bilali A, Taleb A, Brouziyne Y, “Groundwater quality forecasting using machine learning algorithms for irrigation purposes”, Agricultural Water Management, 245, 106625. doi: 10.1016/j.agwat.2020.106625 , 2021.
  • Abdul Malek N.H, Wan Yaacob W.F, Md nasir S.A, “Prediction of Water Quality Classification of the Kelantan River Basin, Malaysia, Using Machine Learning Techniques”, Water, 14(7), 1067. doi: 10.3390/w14071067, 2022
  • Al-Musawi N, “Prediction and Assessment of Water Quality Index Using Neural Network Model and Gis Case Study: Tigris River in Baghdad City”, Applied Research Journal, 3(11), 343-353, 2018.
  • Talat R.A, Al-Assaf A.Y, Al-Saffawi A.Y.T, “Valuation of water quality for drinking and domestic purposes using WQI: Case study for groundwater of Al-Jameaa and Al-Zeraee quarters in Mosul city/Iraq”, Journal of Physics Conference Series, 1294(7). doi: 10.1088/1742-6596/1294/7/072011, 2019
  • Safawi A.Y.T.A, “tatbiq al muasher al kanadi(WQI CCME) le taqeem javed almeyah le agrade alshrub”, in The third Scientific Conference of life sciences, Iraq, 27(5), 193-202, 2019.
  • Mahmood A, “Evaluation of raw water quality in Wassit governorate by Canadian water quality index”, in Environmental Engineering and Sustainable Development, Iraq, 162, 1-8. 2018, doi: 10.1051/matecconf/201816205020.
  • Mosavi A, Ozturk P, Chau K, “Flood Prediction Using Machine Learning Models: Literature Review”, Water, 10(11), 1536. doi: 10.3390/w10111536, 2018.
  • Chen Y, Song L, Liu Y, “A Review of the Artificial Neural Network Models for Water Quality Prediction,” Applied Sciences, 10(17), 5776. doi: 10.3390/app10175776, 20 8 2020.
  • Koranga M., Pant P, Pant D, “SVM Model to Predict the Water Quality Based on Physicochemical Parameters,” International Journal of Mathematical, Engineering and Management Sciences, 6(2), 645-659. doi: 10.33889/IJMEMS.2021.6.2.040, 2021
  • Al-Adhaileh M. H, Alsaade F. W, “Modelling and Prediction of Water Quality by Using Artificial Intelligence,” Sustainability, 13(8), 4259. doi: 10.3390/su13084259, 2021
  • Park S, Jung S, Lee H, “Large-Scale Water Quality Prediction Using Federated Sensing,” Sensors, 21(4), 1462. doi: 10.3390/s21041462, 2021.
  • Kadiwal A., “Water Quality, Drinking water potability,” Kaggle, 2019. [Online]. Available: https://www.kaggle.com/datasets/adityakadiwal/water-potability. [Accessed: March 9, 2022].
  • Pérez F, Granger B, “jupytercon,” jupyter, 2014. [Online]. Available: https://jupyter.org/. [Accessed: March 5, 2022].
  • Scikit-learn authors, “1. Supervised learning,” scikit-learn, 2022. [Online]. Available: https://scikit-learn.org/stable/supervised_learning.html#supervised-learning. [Accessed: April 10, 2022].
  • Developers, “NumPy 1.23.0 released,” numpy, 2022. [Online]. Available: https://numpy.org. [Accessed: April 10, 2022].
  • Developers, “pandas: powerful Python data analysis toolkit,” pypi, 2022. [Online]. Available: https://pypi.org/project/pandas/. [Accessed: April 10, 2022].
  • Developers, “seaborn: statistical data visualization,” seaborn, 2021. [Online]. Available: https://seaborn.pydata.org/. [Accessed: April 10, 2022].
  • Developers, “Matplotlib: Visualization with Python,” matplotlib, 2022. [Online]. Available: https://matplotlib.org/. [Accessed: April 10, 2022].
  • Brownlee, J., “What is the Difference Between a Parameter and a Hyperparameter?,” machine learning mastery, 26 6 2017. [Online]. Available: https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/. [Accessed; June 10, 2022].
  • Yıldırım S, “6 Must-Know Parameters for Machine Learning Algorithms,” towards data science, 2022. [Online]. Available: https://towardsdatascience.com/6-must-know-parameters-for-machine-learning-algorithms-ed52964bd7a9. [Accessed: June 10, 2022].
  • Yıldırım S, “L1 and L2 Regularization — Explained,” towardsdatascience, 2020. [Online]. Available: https://towardsdatascience.com/l1-and-l2-regularization-explained-874c3b03f668. [Accessed: June 10, 2022].
  • Developers, “sklearn.ensemble.HistGradientBoostingClassifier,” scikit-learn, 2022. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html. [Accessed: June 11, 2022].
  • DigitalSreeni, Director, 184 - Scheduling learning rate in keras. [Video]. United States: Site: YouTube, 2020. URL: https://youtu.be/drcagR2zNpw.
  • Developers, “Sklearn.tree.DecisionTreeClassifier,” scikit-learn, 2022. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html [Accessed: June 11, 2022].
  • Bhatt B, Director, Decision Tree Hyperparameters : max_depth, min_samples_split, min_samples_leaf, max_features. [Video]. India: Site: YouTube, 2019. URL: https://www.youtube.com/watch?v=XABw4Y3GBR4&t=365s.
  • Paper D, “Scikit-Learn Classifier Tuning from Complex Training Sets,” in Hands-on Scikit-Learn for Machine Learning Applications, Logan, UT, USA, Apress, Berkeley, CA, 2020. doi: 10.1007/978-1-4842-5373-1_6.
  • Alwanas A.A.H, Al-Musawi A.A, Salih S.Q, “Load-carrying capacity and mode failure simulation of beam-column joint connection: Application of self-tuning machine learning model,” Engineering Structures, 194, 220-229. doi: c10.1016/j.engstruct.2019.05.048, 2019.
  • Tung T. M, Yaseen Z. M, “A survey on river water quality modelling using artificial intelligence models: 2000--2020”, Journal of Hydrology, vol. 585, 124670. doi: 10.1016/j.jhydrol.2020.124670, 2020.
  • QI C, Huang S, Wang X, “Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN,” IEEE Access, vol. 8, 188070. doi: 10.1109/ACCESS.2020.3030878, 2020.
  • Soumik S.K, “How to Calculate Confusion Matrix Manually.”, medium, (2020). [Online]. Available: https://medium.com/analytics-vidhya/how-to-calculate-confusion-matrix-manually-14292c802f52. [Accessed: June 22, 2022].
  • Ho J.Y, Afana H.A, El-Shafie A.H, “Towards a time and cost-effective approach to water quality index class,” Journal of Hydrology, vol. 575, 148-165. doi: 10.1016/j.jhydrol.2019.05.016, 2019.
  • Atha R, “Building Classification Model with Python,” medium, (2021). [Online]. Available: https://medium.com/analytics-vidhya/building-classification-model-with-python-9bdfc13faa4b. [Accessed: June 22, 2022].
  • Sasaki Y., “The truth of the F-measure,” School of Computer Science, University of Manchester, 2007.
  • Wiryaseputra M, “Water Quality Prediction Using Machine Learning Classification Algorithm”, International Journal of Scientific & Engineering Research, 8(9). doi: 10.14299/000000, 2022.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Araştırma Makalesi
Yazarlar

Fatin Aljarah 0000-0001-6138-6849

Aydın Çetin 0000-0002-8669-823X

Erken Görünüm Tarihi 13 Şubat 2023
Yayımlanma Tarihi 15 Şubat 2023
Kabul Tarihi 8 Aralık 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 3 Sayı: 1

Kaynak Göster

IEEE F. Aljarah ve A. Çetin, “Prediction of Water Quality with Ensemble Learning Algorithms”, Adv. Artif. Intell. Res., c. 3, sy. 1, ss. 36–44, 2023, doi: 10.54569/aair.1200695.
 Kapak Resmi İndir

88x31.png
Advances in Artificial Intelligence Research is an open access journal which means that the content is freely available without charge to the user or his/her institution. All papers are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows users to distribute, remix, adapt, and build upon the material in any medium or format for non-commercial purposes only, and only so long as attribution is given to the creator.

Graphic design @ Özden Işıktaş