Search Results - (( waste decision tree algorithm ) OR ( java implication based algorithm ))

Waste management using machine learning and deep learning algorithms by Sami, Khan Nasik, Amin, Zian Md Afique, Hassan, Raini

“…For our research we did the comparisons between three Machine Learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Decision Tree, and one Deep Learning algorithm called Convolutional Neural Network (CNN), to find the optimal algorithm that best fits for the waste classification solution. …”
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Sales prediction for Adha Station by using predictive analytics by Mohd Mokhid, Muhammad Amier Latieff

“…Additionally, pre-processing is conducted using the RapidMiner application prior to mapping the cleaned data with three distinct algorithms for predictive analysis: Decision Tree, Random Forest, and Multiple Linear Regression techniques. …”
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Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud by Shariff, S. Sarifah Radiah, Hud, Hady

“…This research attempts to solve the problem statement on how to forecast the daily optimal quantity of a perishable product during the new norm post-pandemic, ensuring minimal unsold items are discarded as waste. Originality/value Overall, this research provides initial insights into adopting Machine Learning algorithms in making better-informed managerial decisions among SMEs in Malaysia…”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management by almahameed, Bader aldeen, Bisharah, Majdi

“…This study examines the utilization of different Machine Learning algorithms, such as Linear Regression, Decision Trees, Support Vector Machines (SVM), Gradient Boosting, Random Forest, K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN) Regression, and Particle Swarm Optimization (PSO), in the domain of predictive modeling and cost optimization in the field of construction project management. …”
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Predicting the rutting parameters of nanosilica/waste denim fiber composite asphalt binders using the response surface methodology and machine learning methods by Al-Sabaeei, Abdulnaser M., Alhussian, Hitham, Abdulkadir, Said Jadid, Giustozzi, Filippo, Mohd Jakarni, Fauzan, Md Yusoff, Nur Izzi

“…Of the evaluated ML models, Decision Tree Regression (DTR) shows the best performance in predicting Jnr and %R, with the highest R2 of 0.99 and smallest root mean square error (RMSE) of <1%, which indicates its ability to represent the experimental MSCR parameters accurately. …”
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Prediction on the mechanical strength of coal ash concrete using artificial neural network by Muhammad Nor Syahrul, Zaimi, Nur Farhayu, Ariffin, Sharifah Maszura, Syed Mohsin, Abdul Muiz, Hasim, Nurul Natasha, Nasrudin

“…The prediction incorporates the decision tree, linear regression (LR), and random forest through soft voting. …”
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A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee by Wong , Wen Yee

“…The proposed deep learning model renders faster without the use of SMOTE. Any resampling algorithm is not a necessity in the case of this proposed algorithm. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

The predictive machine learning model of a hydrated inverse vulcanized copolymer for effective mercury sequestration from wastewater by Ghumman, A.S.M., Shamsuddin, R., Abbasi, A., Ahmad, M., Yoshida, Y., Sami, A., Almohamadi, H.

“…A predictive machine learning model was also developed to predict the amount of mercury removed () using GPR, ANN, Decision Tree, and SVM algorithms. Hyperparameter and loss function optimization was also carried out to reduce the prediction error. …”
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