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ABSTRACT
This work reviews Machine Learning applications in the sustainable utilization of waste materials as energy source so that analysis of the past works exposed the lack of reviewing study. To solve it, the origin of waste biomass raw materials is explained, and the application of Machine Learning in this section is scrutinized. After analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in waste biomass production areas to enhance the quality and quantity of production, improve the predictions, diminish the losses, as well as increase storage and transformation conditions. The positive effects and application with the utilized algorithms and other effective information are collected in this work for the first time. According to the statistical analysis, in 20% out of the studies conducted about the application of Machine Learning and Deep Learning in waste biomass raw materials, Artificial Neural Network (ANN) algorithm has been applied. Afterward, the Super Vector Machine (SVM) and Random Forest (RF) are the second and third most-utilized algorithms applied in 15% and 14% of studies. Meanwhile, 27% of studies focused on the applications of Machine Learning and Deep Learning in the Forest wastes.
Abbreviations
| Abbreviation | = | Meaning |
| ML | = | Machine Learning |
| DL | = | Deep Learning |
| ANN | = | Artificial Neural Network |
| IoT | = | Internet of Things |
| SVM | = | Super Vector Machine |
| NB | = | Naive Bayes |
| KNN | = | K-nearest Neighbor |
| DT | = | Decision Tree |
| RF | = | Random Forest |
| ANFIS | = | Adaptive Network Fuzzy Inference System |
| XGBoost | = | Extreme Gradient Boosting |
| GAM | = | Generalized Additive Model |
| RNN | = | Recurrent Neural Network |
| MLR | = | Multiple Linear Regression |
| RBNN | = | Radial-basis Neural Network |
| SMOR | = | Sequential Minimal Optimization Regression |
| LDA | = | Linear Discriminant Analysis |
| FRBS | = | Fuzzy Rule-based Systems |
| DBN | = | Deep Belief Network |
| CL | = | Classification Trees |
| C | = | Carbon |
| O | = | Oxygen |
| S | = | Sulphur |
| A | = | Ash |
| K | = | Potassium |
| P | = | Phosphorus |
| Ca | = | Calcium |
| Zn | = | Zink |
| CO2 | = | Carbon Dioxide |
| NIR | = | Near Infrared |
| RBF | = | Radial Basis Function |
| ET | = | Extra Trees |
| SPA | = | Successive Projection Algorithm |
| LRM | = | Linear Regression Model |
| CRBM | = | Conditional Restricted Boltzmann Machine |
| GA | = | Genetic Algorithm |
| RO | = | Reverse Osmosis |
| t-SNE | = | t-Distributed Stochastic Neighbor Embedding |
| DBSCAN | = | Density-Based Spatial Clustering of Applications with Noise |
| GAN | = | Generative Adversial Network |
| GRU | = | Gated Recurrent Units |
| SMR | = | Stepwise Multiple Regression |
| LSTM | = | Long Short Term Memory |
| CNN | = | Convolutional Neural Network |
| MLP | = | Multilayer Perceptron |
| FPN Mask | = | Feature Pyramid Network Mask |
| GP | = | Gaussian Process |
| DNN | = | Deep Neural Network |
| PR | = | Polynomial Regression |
| GBDT | = | Gradient Boosting Decision Tree |
| AdaBoost | = | Adaptive boosting |
| PLSDA | = | Partial Least Square Discriminant Analysis |
| RCCN | = | Region-based CNN |
| PGM | = | Probabilistic Graphical Models |
| GPR | = | Gaussian Processes Regression |
| BNN | = | Bayesian Neural Network |
| LR | = | Logistics Regression |
| PLS-DA | = | Partial Least Squares Discriminant Analysis |
| BRT | = | Boosted Regression Tree |
| GMMs | = | Gaussian Mixture Models |
| LSSVR | = | Least-Squares Support Vector Regression |
| GBM | = | Generalized Boosted Model |
| H | = | Hudrogen |
| N | = | Nitrogen |
| Cl | = | chlorine |
| Pb | = | Lead (Plumbum) |
| Na | = | Sodium |
| Mg | = | Magnesium |
| Si | = | Silica |
| HHV | = | Higher Heating Value |
| PMF | = | Positive Matrix Factorization |
| PLS | = | Partial Least Squares |
| KRR | = | Kernel Ridge Regression |
| MARS | = | Multivariate Adaptive Regression Splines |
| CARS | = | Competitive Adaptive Reweighted Sampling |
| SVR | = | Supper Vector Regression |
| PCA | = | Principal Component Analysis |
| DO | = | Dissolved Oxygen |
| NF | = | Nano-filtration |
| LSA | = | Latent Semantic Analysis |
| GNN | = | Graph Neural Networks |
| GAT | = | Graph Attention Network |
| LRLS | = | Kernel-based Regularized Least Squares |
| GLM | = | Generalized Linear Model |
Disclosure statement
No potential conflict of interest was reported by the author(s).