Learning Model In Terms Research Articles

The Effectiveness of the Jigsaw and Traditional Teaching Methods on Math Anxiety, Attitude and Performance

This study was conducted to compare the effectiveness of cooperative learning on mathematics anxiety, attitudes towards mathematics, and mathematics performance of the 12th-class Private school students of Herat City by using a quasi-experimental study. The statistical population included 650 individuals who were all high school students. 40 individuals were selected in two 20-individual groups by the multistage cluster random sampling method. The Math Anxiety Scale (Baloglu & Zelhart, 2007), the Mathematics Attitude Scale (Fennema-Sherman, 2000), and teacher-made tests of mathematics academic performance were used to collect data. The Jigsaw cooperative learning model was used to monitor the effectiveness of the presence or absence of cooperative learning, and the data were analyzed using SPSS software and one-way covariance analysis (ANOVA). In this study, three hypotheses were formulated, and their results indicated that there is a significant difference between the Jigsaw cooperative and traditional learning models in terms of math anxiety, attitudes towards math, and mathematics performance of the 12th-class Private school students of Herat City. It is concluded that the Jigsaw cooperative learning model can be used as one of the effective methods in schools to reduce mathematics anxiety, foster a positive attitude towards mathematics, and enhance the 12th-class Private school students’ mathematics performance.

G-LASSO/G-SCAD/G-MCP penalized trinomial logit dynamic models predict up trends, sideways trends and down trends for stock returns

Correctly predicting up trends, sideways trends and down trends for stock returns is vitally important in financial market. Group penalized trinomial logit dynamic models can not only provide three-class label information and three-class probability estimations, but also enhance three-class prediction performance by shrinking group coefficients to bypass multi-collinearity and over-fitting. In this paper we propose G-LASSO/G-SCAD/G-MCP penalized trinomial logit dynamic models with 24 technical indicators to predict up trends, sideways trends and down trends for stock returns, develop group coordinate descent algorithm (GCD) to complete group selection and group estimation simultaneously, establish the relative optimal Bayes classifier to identify class labels, introduce three-class confusion matrix and hypervolume under the ROC manifold (HUM) to assess the three-class prediction performance to 15 methods. Experiment results show that G-LASSO/G-SCAD/G-MCP penalized trinomial logit dynamic models predict better than LASSO/SCAD/MCP penalized trinomial logit dynamic models, 6 deep learning models and 3 machine learning models in terms of Accuracy and HUM. In particular, the highest prediction Accuracy (the average Accuracy) from G-LASSO penalized method outperforms 3-Layer Long Short-Term Memory (LSTM) and Random Forest (RF) for 4.76% and 10.29% (8.16% and 11.78%), respectively. Moreover, compared with LASSO/SCAD/MCP penalized methods, G-LASSO/G-SCAD/G-MCP penalized methods enhance 5.88% Accuracy and only require 27.29% Predicted Time. The proposed G-LASSO/G-SCAD/G-MCP penalized trinomial logit dynamic models can not only be extended as more general three-class prediction method by replacing 24 technical indicators by some factors influencing three-class response variable, but also be directly extended as more efficient multi-category prediction method by replacing three-class response variable by multi-category response variable.

A study on the DAM-EfficientNet hail rapid identification algorithm based on FY-4A_AGRI

Hail, a highly destructive weather phenomenon, necessitates critical identification and forecasting for the protection of human lives and properties. The identification and forecasting of hail are vital for ensuring human safety and safeguarding assets. This research proposes a deep learning algorithm named Dual Attention Module EfficientNet (DAM-EfficientNet), based on EfficientNet, for detecting hail weather conditions. DAM-EfficientNet was evaluated using FY-4A satellite imagery and real hail fall records, achieving an accuracy of 98.53% in hail detection, a 97.92% probability of detection, a false alarm rate of 2.08%, and a critical success index of 95.92%. DAM-EfficientNet outperforms existing deep learning models in terms of accuracy and detection capability, with fewer parameters and computational needs. The results validate DAM-EfficientNet’s effectiveness and superior performance in hail weather detection. Case studies indicate that the model can accurately forecast potential hail-affected areas and times. Overall, the DAM-EfficientNet model proves to be effective in identifying hail weather, offering robust support for weather disaster alerts and prevention. It holds promise for further enhancements and broader application across more data sources and meteorological parameters, thereby increasing the precision and timeliness of hail forecasting to combat hail disasters and boost public safety.

Crop Infestation Classification Using MIL-Attention Based CNN

Agriculture constantly faces various challenges including attacks from new pests and insects. With large farm sizes and plummeting manpower in the agricultural sector, it becomes challenging to continuously monitor crops for pest infestation. In this research paper, a specific type of pest attack known as the white fly attack has been investigated which affects a variety of crops. This paper presents a multiple instance learning based deep learning approach based on Convolutional Neural Networks for the detection of whitefly pests. A comparative analysis with conventional machine learning and deep learning techniques has also been presented. The performance of the proposed system has been evaluated in terms of the classification accuracy. The experimental results obtained show that the proposed technique attains a classification accuracy of 98.5% and outperforms both separate feature trained machine learning models and well as baseline deep learning models in terms of classification accuracy. Keywords: Precision Agriculture, Whitefly Pest Detection, Feature Extraction, Machine Learning, Deep Learning, Multi Instance Learning (MIL), Classification Accuracy.

Research on State of Health Estimation of Lithium Batteries Based on Electrochemical Impedance Spectroscopy and CNN-VIT Models

Abstract With the development of electric vehicles, the demand for lithium-ion batteries has been increasing annually. Accurately estimating the state of health (SOH) of lithium-ion batteries is crucial for their efficient and reliable use. Most of the existing research on SOH estimation is based on parameters such as current, voltage, and temperature, which are prone to fluctuations. Estimating the SOH of lithium-ion batteries based on electrochemical impedance spectroscopy (EIS) and data-driven approaches has been proven effective. In this paper, we explore a novel SOH estimation model for lithium batteries based on EIS and Convolutional Neural Network (CNN)-Vision Transformer (VIT). The EIS data are treated as a grayscale image, eliminating the need for manual feature extraction and simultaneously capturing both local and global features in the data. To validate the effectiveness of the proposed model, a series of simulation experiments are conducted, comparing it with various traditional machine learning models in terms of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The simulation results demonstrate that the proposed model performs best overall in the testing dataset at three different temperatures. This confirms that the model can accurately and stably estimate the SOH of lithium-ion batteries without requiring manual feature extraction and knowledge of battery aging temperature.

Deep graph tensor learning for temporal link prediction

Knowledge discovery on dynamic graphs has received much attention in recent years. As a key task of dynamic graph research, the goal of temporal link prediction is to accurately predict the time-varying links in dynamic networks. Uncertainty in link emergence is a major challenge in this research, as it is not easy to learn stable and reliable link-level feature representations, which are usually readily available on static graphs. In order to adapt to the ever-changing graph structure, this paper proposes to construct a deep graph tensor learning model, which can capture the contextual characteristics of graph evolution from both the graph structure (spatial) mode and the link sequence (temporal) mode. Therefore, compared to link prediction on static graphs, temporal link prediction can benefit more from the link-level embedding representations coupled with spatio-temporal features. The experimental results on seven public dynamic graph datasets show that the prediction accuracy obtained by the new model is overall better than competing models such as GC-LSTM, EvolveGCN, and HTGN. In the meantime, as a result of getting rid of the traditional RNN learning paradigm, the new model is also significantly better than the traditional temporal graph learning model in terms of training efficiency.

Shallow and Deep Learning Models for Vessel Motions Forecasting during Adverse Weather Conditions

Accurately forecasting vessel motions is a critical step towards achieving fast and accurate intelligent vessel control systems. Intelligent vessel control relies on accurate predictions of vessel motion to make informed decisions regarding control, maneuvering, and positioning, particularly during times of exogenous loading caused by adverse weather conditions. Hence, by accurately forecasting vessel motion accurately, the control system can anticipate potential issues (i.e., excessive trim or roll) and prescribe corrective actions before they become problematic. In this study, the authors propose two approaches to address the problem of vessel motion forecasting. The first approach relies on classical shallow learning models, whereas the second approach involves the use of state-of-the-art deep learning models for improved accuracy at further forecast horizons. Unlike shallow models, deep models can learn the required features directly from the data and therefore do not require a priori knowledge or additional features engineering. By leveraging deep learning models, the authors show that vessel motions can be forecasted further into the future without a significant loss in accuracy, thereby improving the overall effectiveness of the intelligent vessel control system. To support their statements, the authors use real operational data and compare the performance of the shallow and deep learning models. The results show that deep learning outperforms shallow learning models in terms of accuracy without a significant increase in the computational demand. Additionally, the authors demonstrate that their models remain accurate even under adverse weather conditions, indicating that they have practical applicability for vessel motions forecasting and can potentially improve the overall effectiveness of intelligent vessel control systems.

Fault diagnosis method for Small modular reactor based on transfer learning and an improved DCNN model

Deep convolutional neural networks (DCNN) are widely applied in the realm of deep learning. This paper presents a novel approach that combines transfer learning techniques with a hybrid domain attention mechanism module to enhance and refine the DCNN architecture, consequently boosting its performance. The focus of this study is the application of the improved DCNN model to fault diagnosis within small modular reactor. We hope to improve the fault monitoring and diagnosis capabilities of small modular reactors through algorithm improvements, to enhance their safety. Comparative results demonstrate that the improved model surpasses other deep learning models in terms of convergence rate, recognition accuracy, and model size, evidencing robust generalisation capabilities.

A kinetic-assisted growth curve prediction method for Chlamydomonas reinhardtii incorporating transfer learning

Traditional predictions of microalgal growth states rely on empirical or easily implementable kinetic models, leading to significant biases and elevated cost. This study proposes a kinetic-assisted machine learning method for predicting the growth curve of microalgal biomass under small sample conditions. Firstly, a microalgae growth kinetic model is constructed based on the logistic model. A two-stage kinetic fitting strategy is specified to account for the light–dark ratio. The Box-Behnken method is employed for experimental design. Then, using Two-stage TrAdaboost.R2 algorithm, the kinetic model is utilized as the source domain, and the experimental design data serves as the target domain for training machine learning models. The results indicate that the proposed method outperforms a single machine learning model in terms of prediction and has the potential to rapidly estimate microalgal growth trends under different conditions and accurately predict harvested biomass, potentially reducing the need for laborious, expensive, and time-consuming laboratory trials.

Permeability and porosity of light-weight concrete with plastic waste aggregate: Experimental study and machine learning modelling

The plastic waste has huge potential being used as the aggregate of concrete but the premise requires to have a sound understanding about the seepage mechanical properties of the light-weight concrete. In this study, initially, the physical experiment was conducted to measure the permeability and porosity of light-weight concrete with various amount of plastic waste aggregate (PWA) in different confining pressure and pore pressure. Based on the test results, a database was established. On this basis, a unique machine learning method was developed via combining the Logic Development Algorithm (LDA) with the ensemble technique of Adaptive Boosting Algorithm (BA) and Artificial Neural Network (ANN), which is the first attempt to utilize this model for estimating the permeability of PWA cement mortar. The research outcomes indicate, the corrected permeability and porosity of PWA cement mortar rises with the increase of PWA content, and the changing magnitude is more significant in high confining pressure; The corrected permeability and porosity of cement mortar with a higher content of PWA is more sensitive to the variation of confining pressure; The BA-ANN model optimised by LDA outperforms than the conventional machine learning models in terms of predictive accuracy and efficiency when assessing the permeability of PWA cement mortar; The sensitivity analysis by using the novel model reveals that confining pressure, PWA content and porosity have a relatively high impact on the permeability of PWA cement mortar, while the impact of pore pressure is relatively small. The study findings can provide guidance for the permeability and porosity design of relevant PWA concrete engineering facilities.

Machine Learning Models in the large-scale prediction of parking space availability for sustainable cities

The search for effective solutions to address traffic congestion presents a significant challenge for large urban cities. Analysis of urban traffic congestion has revealed that more than 70% of it can be attributed to prolonged searches for parking spaces. Consequently, accurate prediction of parking space availability in advance can play a vital role in assisting drivers to find vacant parking spaces quickly. Such solutions hold the potential to reduce traffic congestion and mitigate its detrimental impacts on the environment, economy, and public health. Machine learning algorithms have emerged as promising approaches for predicting parking space availability. However, comparative studies on those machine learning models to evaluate the best suited for a large-scale prediction and within a given prediction time period are missing.In this study, we compared nine machine learning algorithms to assess their efficiency in predicting long-term, large-scale parking space availability. Our comparison was based on two approaches: using on-street parking data alone and 2) incorporating data from external sources (such as weather data). We used automatic machine learning models to compare the performance of different algorithms according to the prediction efficiency and execution time. Our results indicated that the automated machine learning models implemented were well fitted to our data. Notably, the Extra Tree and Random Forest algorithms demonstrated the highest efficiency among the models tested. Moreover, we observed that the Random Forest algorithm exhibited less computational demand than the Extra Tree algorithm, making it particularly advantageous in terms of execution time. Therefore, this work suggests that the Random Forest algorithm is the most suitable machine learning model in terms of efficiency and execution time for accurately predicting large-scale, long-term parking space availability.

Math Learning Outcomes with Cooperative Learning Model (STAD) Reviewed from Students’ Self Efficacy

This study aims to determine the effectiveness of the STAD cooperative learning model in terms of students' self-efficacy towards mathematics learning outcomes, particularly in the topics of Least Common Multiple (KPK) and Greatest Common Divisor (FPB), for fourth-grade students at SD Negeri Manyaran 02, divided into two learning groups: Class IV.A with 25 students and Class IV.B with 24 students. The research design employed in this study is quasi-experimental. Quasi-experimental research is conducted under conditions that do not allow for full control. Data collection utilized questionnaire and test techniques, followed by data analysis using a two-way ANOVA and further post hoc tests (multiple comparisons). Based on the tests, the test instrument was declared valid, with difficulty index reaching 𝑝 < 0.30 for difficult criteria and 𝑝 > 0.70 for easy criteria, as well as a reliability index of 𝑟11 > 0.70. The study findings indicate that there is no significant effect of the STAD cooperative learning model on mathematics learning outcomes when viewed from the self-efficacy of fourth-grade students. However, the relationship between the STAD model and self-efficacy in mathematics learning outcomes shows positive results. Students with higher self-efficacy indices are proven to have better learning outcomes compared to those with lower self-efficacy levels.

Pembelajaran Connecting, Organizing, Reflecting, Extending (CORE) Untuk Meningkatkan Kemampuan Berpikir Kritis Matematis Ditinjau dari Self-Efficacy

This research aims (1) to see the increase in students' mathematical critical thinking abilities using the connecting, organizing, reflecting, and extending (CORE) learning model in terms of the level of self-efficacy, (2) to find out whether there are differences in the increase in mathematical critical thinking abilities between students who have high and low levels of self-efficacy, and (3) to determine the interaction between the CORE learning model and self-efficacy in improving critical thinking skills. This research used a sample of 57 students from class VII SMP Taruna Terpadu Bogor. The sampling technique for this research uses a cluster random sampling technique. The instruments used are test instruments in the form of critical thinking ability questions and non-test instruments in the form of self-efficacy questionnaires. The results of this research show that (1) there is no difference in the increase in mathematical critical thinking abilities between students who study the CORE learning model and the conventional learning model, (2) there is a difference in the increase in students' mathematical critical thinking abilities between students who have a high level of self-efficacy and low, (3) there is no interaction between the CORE learning model and self-efficacy on mathematical critical thinking skills

A model for multi-attack classification to improve intrusion detection performance using deep learning approaches

This proposed model introduces novel deep learning methodologies. The objective here is to create a reliable intrusion detection mechanism to help identify malicious attacks. Deep learning based solution framework is developed consisting of three approaches. The first approach is Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) with seven optimizer functions such as adamax, SGD, adagrad, adam, RMSprop, nadam and adadelta. The model is evaluated on NSL-KDD dataset and classified multi attack classification. The model has outperformed with adamax optimizer in terms of accuracy, detection rate and low false alarm rate. The results of LSTM-RNN with adamax optimizer is compared with existing shallow machine and deep learning models in terms of accuracy, detection rate and low false alarm rate. The multi model methodology consisting of Recurrent Neural Network (RNN), Long-Short Term Memory Recurrent Neural Network (LSTM-RNN), and Deep Neural Network (DNN). The multi models are evaluated on bench mark datasets such as KDD’99, NSL-KDD, and UNSWNB15 datasets. The models self-learnt the features and classifies the attack classes as multi-attack classification. The models RNN, and LSTM-RNN provide considerable performance compared to other existing methods on KDD’99 and NSL-KDD dataset.

A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU

The state-of-health (SOH) of lithium-ion batteries has a significant impact on the safety and reliability of electric vehicles. However, existing research on battery SOH estimation mainly relies on laboratory battery data and does not take into account the multi-faceted nature of battery aging, which limits the comprehensive and effective evaluation and prediction of battery health in real-world applications. To address these limitations, this study utilizes real electric vehicle operational data to propose a comprehensive battery health evaluation indicator and a deep learning predictive model. In this study, the battery capacity, ohmic resistance, and maximum output power were initially extracted as individual health indicators from actual vehicle operation data. Subsequently, a methodology that combines the improved criteria importance through inter-criteria correlation (CRITIC) weighting method with the grey relational analysis (GRA) method is employed to construct the comprehensive battery health evaluation indicator. Finally, a prediction model based on the attention mechanism and the bidirectional gated recurrent unit (Att-BiGRU) is proposed to forecast the comprehensive evaluation indicator. Experimental results using real-world vehicle data demonstrate that the proposed comprehensive health indicator can provide a thorough representation of the battery health state. Furthermore, the Att-BiGRU prediction model outperforms traditional machine learning models in terms of prediction accuracy.

Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration.

The prognosis of diffuse midline glioma (DMG) patients with H3K27M (H3K27M-DMG) alterations is poor; however, a model that encourages accurate prediction of prognosis for such lesions on an individual basis remains elusive. We aimed to construct an H3K27M-DMG survival model based on DeepSurv to predict patient prognosis. Patients recruited from a single center were used for model training, and patients recruited from another center were used for external validation. Univariate and multivariate Cox regression analyses were used to select features. Four machine learning models were constructed, and the consistency index (C-index) and integrated Brier score (IBS) were calculated. We used the receiver operating characteristic curve (ROC) and area under the receiver operating characteristic (AUC) curve to assess the accuracy of predicting 6-month, 12-month, 18-month and 24-month survival rates. A heatmap of feature importance was used to explain the results of the four models. We recruited 113 patients in the training set and 23 patients in the test set. We included tumor size, tumor location, Karnofsky Performance Scale (KPS) score, enhancement, radiotherapy, and chemotherapy for model training. The accuracy of DeepSurv prediction is highest among the four models, with C-indexes of 0.862 and 0.811 in the training and external test sets, respectively. The DeepSurv model had the highest AUC values at 6 months, 12 months, 18 months and 24 months, which were 0.970 (0.919-1), 0.950 (0.877-1), 0.939 (0.845-1), and 0.875 (0.690-1), respectively. We designed an interactive interface to more intuitively display the survival probability prediction results provided by the DeepSurv model. The DeepSurv model outperforms traditional machine learning models in terms of prediction accuracy and robustness, and it can also provide personalized treatment recommendations for patients. The DeepSurv model may provide decision-making assistance for patients in formulating treatment plans in the future.

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.

Concept drift and machine learning model for detecting fraudulent transactions in streaming environment

<span lang="EN-US">In a streaming environment, data is continuously generated and processed in an ongoing manner, and it is necessary to detect fraudulent transactions quickly to prevent significant financial losses. Hence, this paper proposes a machine learning-based approach for detecting fraudulent transactions in a streaming environment, with a focus on addressing concept drift. The approach utilizes the extreme gradient boosting (XGBoost) algorithm. Additionally, the approach employs four algorithms for detecting continuous stream drift. To evaluate the effectiveness of the approach, two datasets are used: a credit card dataset and a Twitter dataset containing financial fraud-related social media data. The approach is evaluated using cross-validation and the results demonstrate that it outperforms traditional machine learning models in terms of accuracy, precision, and recall, and is more robust to concept drift. The proposed approach can be utilized as a real-time fraud detection system in various industries, including finance, insurance, and <br /> e-commerce.</span>

The Aesthetics of Disharmony: Harnessing Sounds and Images for Dynamic Soundscapes Generation

This work presents an autonomous approach that explores the dynamic generation of relaxing soundscapes for games and artistic installations. Differently from past works, this system can generate music and images simultaneously, preserving human intent and coherency. We present our algorithm for the generation of audiovisual instances and also a system based on this approach, verifying the quality of the outcomes it can produce in light of current approaches for the generation of images and music. We also instigate the discussion around the new paradigm in arts, where the creative process is delegated to autonomous systems, with limited human participation. Our user study (N=74) shows that our approach overcomes current deep learning models in terms of quality, being recognized as human production, as if the outcome were being generated out of an endless musical improvisation performance.

Mineral Prospectivity Mapping of Tungsten Polymetallic Deposits Using Machine Learning Algorithms and Comparison of Their Performance in the Gannan Region, China

AbstractThe current study aims at assessing the capabilities of five machine learning models in terms of mapping tungsten polymetallic prospectivity in the Gannan region, China. The five models include logistic regression (LR), support vector machine (SVM), random forest, convolutional neural network (CNN), and light gradient boosting machine (LGBM) models. Three types of data sets including geochemical data, lithostratigraphic contacts, and faults were employed to generate 16 evidential maps that were used to build the machine learning models. Tungsten polymetallic deposits were randomly separated into two parts: 80% for training and 20% for validating. Performance of the models was evaluated through receiver operating characteristic and K‐fold cross validation, with an emphasis on the variable influence within different machine learning methods. The results show that the models are especially sensitive to the chemical elements: Be, Bi, Pb, and Cd, implying that they are closely related to tungsten polymetallic mineralization. Compared to other models, the LGBM and CNN models performed best, while the LR model was the most stable. The results also indicated that the CNN model can predict the maximum number of tungsten polymetallic deposits within the minimum area based on the prediction‐area plot analysis, while the SVM model can capture the least amount of tungsten polymetallic deposits within the largest area. Finally, 18 prospective areas were delineated according to the prediction results of the machine learning models, which provided important guidance for tungsten polymetallic exploration in the study area.