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All Journal Brilliance: Research of Artificial Intelligence
John, Anagu Emmanuel
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Decision Sciences, Operations Research & Management Mathematics Other

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Comparative Analysis Of Machine Learning Models For Greenhouse Microclimate Prediction Cletus, Felicia; John, Anagu Emmanuel
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3783

Abstract

The research assesses the effectiveness of these models as Bi-LSTM, ANN, GBM, and RF in predicting microclimate factors like temperature, humidity, and CO2 levels. It also highlights the constraints associated with employing machine learning models for greenhouse microclimate prediction and suggests potential areas for future investigation. The findings indicate that both ensemble techniques (Gradient Boosting Machine and Random Forest) and deep learning frameworks (ANN and BI-LSTM) performed well during the assessment. While both ensemble methods exhibited impressive results, Gradient Boosting Machine (GBM) slightly surpassed Random Forest (RF) across various evaluation criteria. GBM attained a notable R-squared value of 0.9998, signifying its robust fit and capacity to elucidate data variability, in addition to a Root Mean Squared Error (RMSE) of 0.0079 and Mean Absolute Error (MAE) of 0.0001. RF demonstrated similar outcomes, with an R-squared value of 0.9999. Conversely, ANN outperformed BI-LSTM in terms of R-squared values and MAE, displaying an R-squared value of 0.999999 and a MAE of 0.0079. An analysis of the sensitivity of the ANN model revealed that altering the average indoor relative humidity in the first sensor had the greatest impact on the prediction outcome among other variables. Assessing and ranking the importance of each feature used in training the RF and GBM models indicated that the average relative humidity in the second sensor held the highest significance, with any modification to it likely to notably influence the prediction outcome. These results support the notion that machine learning algorithms serve as effective predictive tools, offering valuable insights for enhancing greenhouse operations. Future research should focus on practical implications and real-world applications, particularly in optimizing hyperparameters.
Machine Learning System for Predicting Student Suitability for University Courses Wella, Rande; Sandra, Ahmadu A.; John, Anagu Emmanuel
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.5774

Abstract

The accuracy of student-course predictions determines both university learning success and career guidance process. When selecting students for courses using conventional methods universities fail to weigh all important factors which results in mismatching student-enrollment decisions. This research constructs a web-based machine learning framework to assess which university classes students would succeed in through fundamental attributes. The research conducts its analysis using background and academic data obtained from 2000 Taraba Secondary School students. The primary targets of this project involve understanding the course suitability determinants among students followed by building a machine learning model and creating a web-based interface and conducting a test on model effectiveness. Three machine learning algorithms—Random Forest, Decision Tree, and Support Vector Machine (SVM). A 95% accuracy emerged as the best outcome from using Random Forest whereas Support Vector Machine (SVM) achieved 93% accuracy and Decision Tree produced 92%. The predictive abilities for matching students to academic courses improve significantly through implementing machine learning algorithms. Students obtain automatic recommendations immediately after entering their student data on the platform. The new student guidance system shows students to proper courses ahead of time thus reducing curricular mismatches and boosting their academic outcomes. Future researchers must build adaptive learning models and real-time data updating functions to enhance accuracy and scalability levels in their work.
Comparative Analysis Of Machine Learning Models For Greenhouse Microclimate Prediction Cletus, Felicia; John, Anagu Emmanuel
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3783

Abstract

The research assesses the effectiveness of these models as Bi-LSTM, ANN, GBM, and RF in predicting microclimate factors like temperature, humidity, and CO2 levels. It also highlights the constraints associated with employing machine learning models for greenhouse microclimate prediction and suggests potential areas for future investigation. The findings indicate that both ensemble techniques (Gradient Boosting Machine and Random Forest) and deep learning frameworks (ANN and BI-LSTM) performed well during the assessment. While both ensemble methods exhibited impressive results, Gradient Boosting Machine (GBM) slightly surpassed Random Forest (RF) across various evaluation criteria. GBM attained a notable R-squared value of 0.9998, signifying its robust fit and capacity to elucidate data variability, in addition to a Root Mean Squared Error (RMSE) of 0.0079 and Mean Absolute Error (MAE) of 0.0001. RF demonstrated similar outcomes, with an R-squared value of 0.9999. Conversely, ANN outperformed BI-LSTM in terms of R-squared values and MAE, displaying an R-squared value of 0.999999 and a MAE of 0.0079. An analysis of the sensitivity of the ANN model revealed that altering the average indoor relative humidity in the first sensor had the greatest impact on the prediction outcome among other variables. Assessing and ranking the importance of each feature used in training the RF and GBM models indicated that the average relative humidity in the second sensor held the highest significance, with any modification to it likely to notably influence the prediction outcome. These results support the notion that machine learning algorithms serve as effective predictive tools, offering valuable insights for enhancing greenhouse operations. Future research should focus on practical implications and real-world applications, particularly in optimizing hyperparameters.
Co-Authors Cletus, Felicia Sandra, Ahmadu A. Wella, Rande