Individual Learning Models Research Articles

Forest Fire Risk Prediction Based on Stacking Ensemble Learning for Yunnan Province of China

Forest fire risk prediction is essential for building a forest fire defense system. Ensemble learning methods can avoid the problem of difficult model selection for disaster susceptibility prediction and can significantly improve modeling accuracy. This study introduces a stacking ensemble learning model for predicting forest fire risks in Yunnan Province by integrating various data types, such as meteorological, topographic, vegetation, and human activity factors. A total of 70,274 fire points and an equal number of randomly selected nonfire points were used to develop the model, with 70% of the data allocated for training and the remaining 30% for testing. The stacking model combined four diverse machine learning methods: random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). We evaluated the model’s predictive performance using metrics like accuracy, area under the characteristic curve (AUC), and fire density (FD). The results demonstrated that the stacking fusion model exhibited remarkable accuracy with an AUC of 0.970 on the test set, significantly surpassing the performance of individual machine learning models, which had AUC values ranging from 0.935 to 0.953. Furthermore, the stacking fusion model effectively captured the maximum fire density in extremely high susceptibility areas, demonstrating enhanced generalization capabilities.

Heterogeneous ensemble machine learning to predict the asiaticoside concentration in centella asiatica urban

This study proposes a novel heterogeneous ensemble machine learning methodology to predict the concentration of asiaticoside in Centella asiatica (CA-CA) in the context of the lack of an effective prediction method capable of accurately estimating its quantity based on various growing environmental factors. The accurate prediction of the asi-aticoside concentration in CA-CA holds great significance in optimizing cultivation practices and improving the efficacy of the derived medicinal products. The presented approach aims to address this crucial need by employing a diverse ensemble of machine learning techniques. The proposed model integrates several machine learning tech-niques, including the standard long short-term memory (LSTM), gated recurrent unit (GRU), convolutional long short-term memory (ConvLSTM), and attention-based LSTM, by utilizing a differential evolution algorithm to optimize the ensemble model's weights. The developed model is called the heterogeneous ensemble machine learning model (He-ML). Experimental results demonstrate that the He-ML achieves an im-pressive root-mean-square error (RMSE) value of 4.76, which is up to 12.48 % lower than the RMSE. The findings highlight the advantages of employing an ensemble model over a single model, as the ensemble model achieves an RMSE value that is 14.67 % lower than that of the individual machine learning model. The utilization of differential evolution as the decision fusion strategy provides a notable improvement over the unweighted average approach. As a result, the RMSE value achieved is 8.46 % lower than that obtained with the unweighted average (UWA) technique.

Predicting dissolved oxygen level using Young's double-slit experiment optimizer-based weighting model

Accurate prediction of the dissolved oxygen level (DOL) is important for enhancing environmental conditions and facilitating water resource management. However, the irregularity and volatility inherent in DOL pose significant challenges to achieving precise forecasts. A single model usually suffers from low prediction accuracy, narrow application range, and difficult data acquisition. This study proposes a new weighted model that avoids these problems, which could increase the prediction accuracy of the DOL. The weighting constructs of the proposed model (PWM) included eight neural networks and one statistical method and utilized Young's double-slit experimental optimizer as an intelligent weighting tool. To evaluate the effectiveness of PWM, simulations were conducted using real-world data acquired from the Tualatin River Basin in Oregon, United States. Empirical findings unequivocally demonstrated that PWM outperforms both the statistical model and the individual machine learning models, and has the lowest mean absolute percentage error among all the weighted models. Based on two real datasets, the PWM can averagely obtain the mean absolute percentage errors of 1.0216%, 1.4630%, and 1.7087% for one-, two-, and three-step predictions, respectively. This study shows that the PWM can effectively integrate the distinctive merits of deep learning methods, neural networks, and statistical models, thereby increasing forecasting accuracy and providing indispensable technical support for the sustainable development of regional water environments.

Peningkatan Kemampuan Menulis Siswa Melalui Media Gambar di Kelas II UPTD SDN 145 Inpres Pampangan

This study aims to improve students' writing skills through image media by using an individual learning learning model in class II UPTD SDN 145 Inpres Pampangan. This research is Classroom Action Research, the subject of this study is class II which amounts to 32 students consisting of 19 boys and 13 girls. This research was carried out as many as 2 cycles, each cycle consists of four stages, namely action planning, action implementation, evaluation, and reflection. This research was conducted in the Teacher Training Professional Strengthening (P2K) activity which began on September 4-October 14 in the 2023/2024 school year. The results showed that the average score (mean) after applying image media in learning increased from 78% to 82%. Similarly, the completeness of student learning has increased from cycle I and cycle II. And from the results of the comparison of learning process activities in cycles I and II showed that there were several student activities that increased such as student attendance, students who paid attention to material discussion, students who asked questions and students who did practice questions. So it can be said that through image media can improve writing skills and learning outcomes of grade II students of UPTD SDN 145 Inpres Pampangan.

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

AbstractSoil moisture (SM) is a fundamental constituent of the terrestrial environment and the hydrological cycle. Owing to its significant influence on catchment hydrological responses, it can be utilized as an indicator of floods and droughts to aid early warning systems. This study aimed to develop a field‐scale method to estimate SM using parametric and machine learning‐based methods to compare whether advanced artificial intelligence methods can give similar results as traditional methods. Considering this, monthly observed SM data (from the top 10 cm), environmental covariates, and remotely sensed data from March 2019 to July 2021 for the Cathedral Peak Research Catchments VI and IX in South Africa were obtained. From the 241 observations obtained across 12 sites, 160 (∼66%) were used for model training, while the remaining 81 (∼34%) were used for model testing. Employing 10‐fold cross‐validation, the individual machine learning models (viz., support vector machine [SVM], random forest (RF), k‐nearest neighbor, classification and regression trees [Rpart], and generalized linear model) displayed a satisfactory performance (R2 = 0.52–0.79; root mean square error = 3.79–5.80). In the validation phase, the RF model displayed a superior performance, followed by the SVM. Subsequent SM estimation using the hybrid model produced satisfactory results in training (R2 = 0.90) and testing (R2 = 0.45). The results obtained from this study can aid in predicting SM variations in catchments with limited monitoring. Furthermore, this model can be applied in drought monitoring, forecasting, and informing agricultural management practices.

Stock market analysis using ensemble learning

A stock market or share market is the combination of shoppers and sellers of shares. Prediction of the stock market is a method for calculating the future value of a company's stock. Stock market can be regarded as a specific records of data mining as well as machine learning problem. The daily changes within the stock depends on the profits and losses and many people think that stock market is irregular and uncertain. Based on daily changes we can predict some movements in the stock. In the previous years, researchers have used many machine learning models to know the development of the stock market to enhance the accuracy. However researchers have implemented these machine learning models independently and compared the results of models. It is observed that the group of models produce quite much less noisy compared to independent models. Our vision is to study investment strategies to predict and analyses the stocks using machine learning models such as Random Forest, K Nearest Neighbor, Gaussian Naive bayes and Decision Tree. And these models are ensemble by using different techniques such as Voting Classifier, Adaboost classifier and Bagging Classifier to estimate their accuracies. From the results it is observed that ensemble approach gives maximum accuracy compared to individual machine learning models.

A generalized ensemble approach based on transfer learning for Braille character recognition

Braille character recognition(BCR) is a basic step in building and designing any Braille assistive technology. Each Braille character is represented by a 2 × 3 matrix of raised dots (called a cell), which can be read by touch. This study introduces a generalized recognition approach based on an ensemble of transfer learning models for BCR. The study experiments are performed on two benchmark English Braille datasets (handwritten Braille – Omniglot (HBO), and Braille character (BC)), and a new dataset of Arabic Braille characters collected by our group called Arabic Braille (AB). First, we investigate the performance of 17- transfer learning models on the three datasets. Then, we build three ensemble approaches based on majority voting from the most effective two, three, and four models in each dataset. The experimental results reveal that the ensemble of DarkNet-53, GoogleNet, SqueezeNet, and DenseNet-201 is a more generalizable ensemble approach for BCR. It achieves a higher F1 score and lesser generalization error (Etest) value than each individual transfer learning model. The F1 scores of the introduced ensemble reached 89.42%, 99.58%, and 97.11% on the HBO, BC, and AB datasets, respectively, with Etest values of 10.47%, 0.43%, and 3.23%. While the F1 scores of the DarkNet-53 which is the most effective single model on the three datasets are 87.54%, 99.14%, and 94.73, with Etest values of 12.79%, 0.85%, and 5.31%, respectively.

The Diagnosis of Malignant Pleural Effusion Using Tumor-Marker Combinations: A Cost-Effectiveness Analysis Based on a Stacking Model.

By incorporating the cost of multiple tumor-marker tests, this work aims to comprehensively evaluate the financial burden of patients and the accuracy of machine learning models in diagnosing malignant pleural effusion (MPE) using tumor-marker combinations. Carcinoembryonic antigen (CEA), carbohydrate antigen (CA)19-9, CA125, and CA15-3 were collected from pleural effusion (PE) and peripheral blood (PB) of 319 patients with pleural effusion. A stacked ensemble (stacking) model based on five machine learning models was utilized to evaluate the diagnostic accuracy of tumor markers. We evaluated the discriminatory accuracy of various tumor-marker combinations using the area under the curve (AUC), sensitivity, and specificity. To evaluate the cost-effectiveness of different tumor-marker combinations, a comprehensive score (C-score) with a tuning parameter w was proposed. In most scenarios, the stacking model outperformed the five individual machine learning models in terms of AUC. Among the eight tumor markers, the CEA in PE (PE.CEA) showed the best AUC of 0.902. Among all tumor-marker combinations, the PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA combination (C9 combination) achieved the highest AUC of 0.946. When w puts more weight on the cost, the highest C-score was achieved with the single PE.CEA marker. As w puts over 0.8 weight on AUC, the C-score favored diagnostic models with more expensive tumor-marker combinations. Specifically, when w was set to 0.99, the C9 combination achieved the best C-score. The stacking diagnostic model using PE.CEA is a relatively accurate and affordable choice in diagnosing MPE for patients without medical insurance or in a low economic level. The stacking model using the combination PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA is the most accurate diagnostic model and the best choice for patients without an economic burden. From a cost-effectiveness perspective, the stacking diagnostic model with PE.CA19-9 + PE.CA15-3 + PE.CEA combination is particularly recommended, as it gains the best trade-off between the low cost and high effectiveness.

PENERAPAN MODEL PEMBELAJARAN TEAM ASSISTED INDIVIDUALIZATION (TAI) UNTUK MENINGKATKAN HASIL BELAJAR IPAS PADA PESERTA DIDIK KELAS IV SD N CIWAKTU

In efforts to improve student learning outcomes, the teacher must improve the learning process carried out. One of the efforts that teachers can make in improving the learning process to be able to improve student learning outcomes is by applying the Team Assisted Individualization (TAI) learning model. The Team Assisted Individualization (TAI) learning model is an effort to improve the learning process because this learning model emphasizes guidance between group members to understand and solve the problems being studied. This research is a Classroom Action Research (CAR) which aims to improve the learning process so that it can improve student learning outcomes. This Classroom Action Research was conducted in class IV of SD N Ciwaktu in science learning. The results showed that the learning completeness in the pre-cycle was 42.85% of the total number of students. In cycle I the learning outcomes of students increased by 17.86% and the level of completeness of student learning outcomes in cycle I reached 60.71%. Whereas in cycle II the percentage of completeness of student learning outcomes obtained reached 89.28% with the frequency of students completing in cycle II as many as 25 students. Based on the results of the study, it was concluded that the application of the Team Assisted Individualization (TAI) model could improve the learning outcomes of fourth-grade students at SD N Ciwaktu in the Science subject.

Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach

Estimation of the ocean subsurface thermal structure (OSTS) is important for understanding thermodynamic processes and climate variability. In the present study, a novel multi-model ensemble machine learning (Ensemble-ML) model is developed to retrieve subsurface thermal structure in the Pacific Ocean by integrating sea surface data with Argo observations. The Ensemble-ML model integrates four individual machine learning models to enhance estimation accuracy and reliability. Our results exhibit good agreement between the satellite sea surface temperature (SST) and sea surface salinity (SSS) data and Argo observations, providing validation for the utilization of these datasets in the Ensemble-ML model. The Ensemble-ML model exhibits better performance compared to individual machine learning models, with an average root mean square error (RMSE) of 0.3273 °C and an average coefficient of determination (R²) of 0.9905. Notably, incorporating geographical information as input variables enhance model performance, emphasizing the importance of considering spatial context in OSTS estimation. The Ensemble-ML model accurately captures the spatial distribution of OSTS across depths and seasons in the Pacific Ocean, effectively reproducing critical temperature features while maintaining strong agreement with Argo observations. Nevertheless, its performance shows relative weakness within the thermocline layer and the equatorial Pacific region (spanning from 10°S to 10°N latitude), which are characterized by complex circulation systems. Despite these challenges, the Ensemble-ML model effectively reproduces the spatial distribution of OSTS of the Pacific Ocean. This indicates the potential of machine learning models, particularly ensemble models, for enhancing OSTS estimation in the Pacific Ocean and other regions, offering valuable insights for future research and applications in physical oceanography.

Online load-loss risk assessment based on stacking ensemble learning for power systems

Power systems faces significant uncertainty during operation owing to the increased integration of renewable energy into power grids and the expansion of the scale of power systems, these factors lead to higher load-loss risks; therefore, realization of a fast online load-loss risk assessment is crucial to ensuring the operational safety and reliability of power systems. This paper presents an online load-loss risk assessment method for power systems based on stacking ensemble learning. First, a traditional load-loss risk assessment method based on power flow analysis was constructed to generate risk samples. The label of the sample is load-loss risk assessment index and the features are multiple operational variables of the power system. And the recursive feature elimination using cross validation (RFECV) was adopted for feature selection. Second, four different machine learning models, including support vector regression (SVR), extremely randomized trees (ET), extreme gradient boosting (XGBoost) and elastic network (EN) were used to form a stacking ensemble learning model for sample training. Moreover, to further improve the model performance, the particle swarm optimization (PSO) algorithms was used for parameter optimization. Finally, based on this model, the online load-loss risk assessment of a power system was realized. The application of the proposed method on IEEE test systems demonstrated that the proposed method was more accurate than methods based on individual machine learning models, from which the stacking was designed, while still maintaining a significant advantage in terms of runtime compared to the traditional risk assessment method.

Adaptive personalized learning: Implementation of emerging technologies in higher education

The fourth industrial revolution determined the increase of active research on redesigning universities’ educational programs to modern technological advancement. Thus, the education system is faced with the challenge of applying an adaptive self-sufficient learning environment that meets labor market’s demands and students’ needs. The importance of adaptive personalized learning in the electronic information and educational environment is to create conditions that provide each student with possibilities to choose and compose learning content, methods and means. Moreover, students can manage their own learning and pace, and obtain interdisciplinary knowledge, professional and soft skills. The aim of this study was to design an adaptive personalized learning model in electronic information and educational environment for Russian higher education institution. This study examined US and Russian higher education institutions’ successful models of individual educational path, adaptive and/or personalized learning. The comparative and content analyses revealed various possibilities for using this model in an electronic information and educational environment. For instance, the model would be able to propose students individual learning path, analyze their level of educational materials’ assimilation and adapt learning programs to improve their performance.

Reliability of gamified reinforcement learning in densely sampled longitudinal assessments.

Reinforcement learning is a core facet of motivation and alterations have been associated with various mental disorders. To build better models of individual learning, repeated measurement of value-based decision-making is crucial. However, the focus on lab-based assessment of reward learning has limited the number of measurements and the test-retest reliability of many decision-related parameters is therefore unknown. In this paper, we present an open-source cross-platform application Influenca that provides a novel reward learning task complemented by ecological momentary assessment (EMA) of current mental and physiological states for repeated assessment over weeks. In this task, players have to identify the most effective medication by integrating reward values with changing probabilities to win (according to random Gaussian walks). Participants can complete up to 31 runs with 150 trials each. To encourage replay, in-game screens provide feedback on the progress. Using an initial validation sample of 384 players (9729 runs), we found that reinforcement learning parameters such as the learning rate and reward sensitivity show poor to fair intra-class correlations (ICC: 0.22-0.53), indicating substantial within- and between-subject variance. Notably, items assessing the psychological state showed comparable ICCs as reinforcement learning parameters. To conclude, our innovative and openly customizable app framework provides a gamified task that optimizes repeated assessments of reward learning to better quantify intra- and inter-individual differences in value-based decision-making over time.

Penggunaan Media Flashcard Untuk Meningkatkan Pengenalan Bentuk Huruf Siswa Kelas II Pada Mata Pelajaran Bahasa Indonesia di SD Inpres Malakaya Kabupaten Gowa

The main problem in this study is whether the use of flashcard media can improve letter recognition for second grade students in the Indonesian language subject at SD Inpres Malakaya, Gowa Regency. Based on the observations, several problems were found which were identified as the cause of the low recognition of letter shapes in class II students, namely the teacher still used one learning method and used less interesting media, so that learning became boring. This study aims to determine the use of flashcard media to improve letter recognition for class II students in the Indonesian language subject at SD Inpres Malakaya, Gowa Regency.This research uses Classroom Action Research (CAR) with individual learning models. CAR is carried out in two cycles where each cycle is held in three meetings with one evaluation. The subjects in this study were 25 grade II students of SD Inpres Malakaya, 15 male students and 10 female students.The data source is class II students, amounting to 25 students. CAR carried out in cycles I and II. The use of flashcard media in Indonesian subjects is proven to be able to improve the recognition of letter shapes for class II students. This can be seen from the results of the percentage of student completeness in cycle I by 24% and increased in cycle II by 92%. So it can be concluded that the use of flashcard media can improve the recognition of letter shapes for class II students of SD Inpres Malakaya, Gowa Regency.

An improved integration strategy for prediction of shear wave velocity using petrophysical logs: Integration of spatiotemporal and small sample nonlinear feature

Shear wave velocity (Vs) plays an important role in seismic data inversion and accurately determining petrophysical parameters. However, owing to the high acquisition cost of S-wave logging, Vs-related data cannot be obtained from all wells, especially the older ones. Therefore, developing an efficient and fast intelligent prediction method for Vs using existing petrophysical logging data is necessary. Owing to the advantages of conventional machine learning (CML) and deep learning (DL) methods, this study developed a data-driven integrated learning (IL) method for Vs prediction, which uses sensitive logging data as input, builds multiple sample sets, analyzes the performance differences of the CML and DL methods for predicting Vs in each dataset, and optimizes the support vector machine (SVM) in the CML method and convolutional neural network-long short-term memory (CNN-LSTM) in the DL method to construct an integrated CNN-LSTM-SVM neural network (ICLSNN). The spatiotemporal feature extraction capability of CNN-LSTM was used to effectively extract the deposition information of the formation from the logging data, and the small sample nonlinear feature extraction capability of SVM was used to alleviate the limitations of overfitting in DL for data-driven intelligent Vs prediction. The ICLSNN showed a higher Vs prediction accuracy than SVM or CNN-LSTM alone. The Vs prediction results of the single well in the discovery without training were better than those of the other IL and empirical models, proving the strong generalization applicability of the proposed model. This method compensates for the limitations of individual machine learning or DL models for Vs prediction and provides a novel research idea. The effective implementation of this scheme will facilitate the efficient prediction of Vs in areas with few wells.

Thyroid Nodules Classification using Weighted Average Ensemble and D-CRITIC based TOPSIS Methods for Ultrasound Images.

Thyroid disorders are prevalent worldwide and impact many people. The abnormal growth of cells in the thyroid gland region is very common and even found in healthy people. These abnormal cells can be cancerous or non-cancerous, so early detection of this disease is the only solution for minimizing the death rate or maximizing a patient's survival rate. Traditional techniques to detect cancerous nodules are complex and time-consuming; hence, several imaging algorithms are used to detect the malignant status of thyroid nodules timely. This research aims to develop computer-aided diagnosis tools for malignant thyroid nodule detection using ultrasound images. This tool will be helpful for doctors and radiologists in the rapid detection of thyroid cancer at its early stages. The individual machine learning models are inferior to medical datasets because the size of medical image datasets is tiny, and there is a vast class imbalance problem. These problems lead to overfitting; hence, accuracy is very poor on the test dataset. This research proposes ensemble learning models that achieve higher accuracy than individual models. The objective is to design different ensemble models and then utilize benchmarking techniques to select the best model among all trained models. This research investigates four recently developed image transformer and mixer models for thyroid detection. The weighted average ensemble models are introduced, and model weights are optimized using the hunger games search (HGS) optimization algorithm. The recently developed distance correlation CRITIC (D-CRITIC) based TOPSIS method is utilized to rank the models. Based on the TOPSIS score, the best model for an 80:20 split is the gMLP+ViT model, which achieved an accuracy of 89.70%, whereas using a 70:30 data split, the gMLP+FNet+Mixer-MLP has achieved the highest accuracy of 82.18% on the publicly available thyroid dataset. This study shows that the proposed ensemble models have better thyroid detection capabilities than individual base models for the imbalanced thyroid ultrasound dataset.

Deployment of the deep learning fusion method to emotional semantic evaluation of natural language

The emotional semantic evaluation of natural language plays a crucial role in sentiment analysis. Deep learning methods have shown great potential in capturing the complex relationships between words and emotions. This paper proposes a deep learning fusion method for deploying emotional semantic evaluation. The technique combines multiple deep learning architectures to capture local and global contextual information, including Bidirectional Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) networks, and Self-Attention mechanisms. Pretrained GloVe word embedding’s utilized to enhance word representation. A novel fusion layer combines the outputs of individual models; employing self-attention means to assign weights dynamically. This allows the model to weigh the importance of different representations in the final prediction. Benchmark movie review (MR) for sentiment analysis and emotion classification tasks are used to evaluate the proposed method. Experimental results demonstrate superior performance compared to individual deep learning models and traditional feature-based approaches. The proposed fusion method effectively captures the nuances of emotional semantics in natural language, leading to more accurate and nuanced evaluations.

A Fuzzy Ensemble-Based Deep learning Model for EEG-Based Emotion Recognition

Emotion recognition from EEG signals is a major field of research in cognitive computing. The major challenges involved in the task are extracting meaningful features from the signals and building an accurate model. This paper proposes a fuzzy ensemble-based deep learning approach to classify emotions from EEG-based models. Three individual deep learning models have been trained and combined using a fuzzy rank-based approach implemented using the Gompertz function. The model has been tested on two benchmark datasets: DEAP and AMIGOS. Our model has achieved 90.84% and 91.65% accuracies on the valence and arousal dimensions, respectively, for the DEAP dataset. The model also achieved accuracy above 95% on the DEAP dataset for the subject-dependent approach. On the AMIGOS dataset, our model has achieved state-of-the-art accuracies of 98.73% and 98.39% on the valence and arousal dimensions, respectively. The model achieved accuracies of 99.38% and 98.66% for the subject-independent and subject-dependent cases, respectively. The proposed model has provided satisfactory results on both DEAP and AMIGOS datasets and in both subject-dependent and subject-independent setups. Hence, we can conclude that this is a robust model for emotion recognition from EEG signals.

Scrutinization of land subsidence rate using a supportive predictive model: Incorporating radar interferometry and ensemble soft-computing

Land subsidence is a huge challenge that land and water resource managers are still facing. Radar datasets revolutionize the way and give us the ability to provide information about it, thanks to their low cost. But identifying the most important drivers need for the modeling process. Machine learning methods are especially top of mind amid the prediction studies of natural hazards and hit new heights over the last couple of years. Hence, putting an efficient approach like integrated radar-and-ensemble-based method into practice for land subsidence rate simulation is not available yet which is the main aim of this research. In this study, the number of 52 pairs of radar images were used to identify subsidence from 2014 to 2019. Then, using the simulated annealing (SA) algorithm the key variables affecting land subsidence were identified among the topographical parameters, aquifer information, land use, hydroclimatic variables, and geological and soil factors. Afterward, three individual machine learning models (including Support Vector Machine, SVM; Gaussian Process, GP; Bayesian Additive Regression Tree, BART) along with three ensemble learning approaches were considered for land subsidence rate modeling. The results indicated that the subsidence varies between 0 and 59 cm in this period. Comparing the Radar results with the permanent geodynamic station exhibited a very strong correlation between the ground station and the radar images (R2 = 0.99, RMSE = 0.008). Parsing the input data by the SA indicated that key drivers are precipitation, elevation, percentage of fine-grained materials in the saturated zone, groundwater withdrawal, distance to road, groundwater decline, and aquifer thickness. The performance comparison indicated that ensemble models perform better than individual models, and among ensemble models, the nonlinear ensemble approach (i.e., BART model combination) provided better performance (RMSE = 0.061, RSR = 0.42, R2 = 0.83, PBIAS = 2.2). Also, the distribution shape of the probability density function in the non-linear ensemble model is much closer to the observations. Results indicated that the presence of significant fine-grained materials in unconsolidated aquifer systems can clarify the response of the aquifer system to groundwater decline, low recharge, and subsequent land subsidence. Therefore, the interaction between these factors can be very dangerous and intensify subsidence.