Application Of Learning Model Research Articles

Leveraging infrared spectroscopy for automated structure elucidation.

The application of machine learning models in chemistry has made remarkable strides in recent years. While analytical chemistry has received considerable interest from machine learning practitioners, its adoption into everyday use remains limited. Among the available analytical methods, Infrared (IR) spectroscopy stands out in terms of affordability, simplicity, and accessibility. However, its use has been limited to the identification of a selected few functional groups, as most peaks lie beyond human interpretation. We present a transformer model that enables chemists to leverage the complete information contained within an IR spectrum to directly predict the molecular structure. To cover a large chemical space, we pretrain the model using 634,585 simulated IR spectra and fine-tune it on 3,453 experimental spectra. Our approach achieves a top-1 accuracy of 44.4% and top-10 accuracy of 69.8% on compounds containing 6 to 13 heavy atoms. When solely predicting scaffolds, the model accurately predicts the top-1 scaffold in 84.5% and among the top-10 in 93.0% of cases.

Exploring the role of machine learning models in risk assessment models for developed organizations’ management decision policies

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R2 from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R2 is compared to the experiments. No changes in Test R2’s were observed on GBM and XGBoost, which retained same Test R2 across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R2 of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R2; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics?

Nonadiabatic dynamics is key for understanding solar energy conversion and photochemical processes in condensed phases. This often involves the non-Markovian dynamics of the reduced density matrix in open quantum systems, where knowledge of the system's prior states is necessary to predict its future behavior. In this study, we explore time-series machine learning methods for predicting long-time nonadiabatic dynamics based on short-time input data, comparing these methods with the physics-based transfer tensor method (TTM). To understand the impact of memory time on these approaches, we demonstrate that non-Markovian dynamics can be represented as a linear map within the Nakajima-Zwanzig generalized quantum master equation framework. We further propose a practical method to estimate the effective memory time, within a given tolerance, for reduced density matrix propagation. Our predictive models are applied to various physical systems, including spin-boson models, multistate harmonic (MSH) models with Ohmic spectral densities and for a realistic organic photovoltaic system composed of a carotenoid-porphyrin-fullerene triad dissolved in tetrahydrofuran. Results indicate that the simple linear-mapping fully connected neural network (FCN) outperforms the more complicated nonlinear-mapping networks including the gated recurrent unit (GRU) and the convolutional neural network/long short-term memory (CNN-LSTM) in systems with short memory times, such as spin-boson and MSH models. Conversely, the nonlinear CNN-LSTM and GRU models yield higher accuracy in the triad MSH systems characterized by long memory times. These findings offer valuable insights into the role of effective memory time in non-Markovian quantum dynamics, providing practical guidance for the application of time-series machine learning models to complex chemical systems.

Dynamic inferences of crash risks in freeway merging zones: a spatio-temporal deep learning model

Freeway merging zones are critical for freeway operations and management due to potential crashes arising from complex vehicle merging behaviours. This paper investigates the application of a spatio-temporal deep learning model to infer crash risks in the zones. We first introduce a crash risk index based on Time-To-Collision and vehicle merging patterns. An innovation is that the developed spatio-temporal transformer model can analyze the evolving risk index. This model effectively captures dynamic risk features through a multi-head attention mechanism within its spatio-temporal learning components. Numerical experiments on nine inference tasks with varying spatial resolutions show improved performance of the model with lower resolution. Moreover, the ST-Transformer model is benchmarked against three advanced deep learning models, which consistently demonstrates its superiority in capturing spatio-temporal dependence in risk sequences. This investigation significantly contributes to a richer understanding of proactive traffic safety, providing valuable insights for advanced freeway management and driver assistance systems.

Pengaruh Model Game Based Learning Terhadap Aktivitas dan Hasil Belajar Komputer Siswa Kelas V di SD Plus Marhamah

The variation of the game-based learning model and aims to determine its influence on the activities and learning outcomes of grade V students at SD Plus Marhamah. The research method applied in this study is quantitative with a quasy experiment design. The sampling technique uses purposive sampling. The sample was represented by class V.B as the control group and class V.C as the experimental group, each totaling 25 students. Data was collected through questionnaires for learning activities and tests for learning outcomes. Data analysis included normality test, homogeneity test, and t-test at a significance level of 0.05 to see the difference between the control group and the experiment. The results of the study showed that there was a significant influence of the application of the game-based learning model on student activities and learning outcomes. Based on this parametric statistical test (one tailed test), significant differences can be seen between the experimental class and the control class, both in activities and learning outcomes. Thus, it can be concluded that the game-based learning model has a positive and significant impact on the activities and learning outcomes of grade V students at SD Plus Marhamah

Pengaruh Model Pembelajaran Discovery Learning dan Keterampilan Berpikir Kreatif Terhadap Pemahaman Konsep Biologi Siswa SMA Bunda Hati Kudus

In the era of globalization marked by the presence of Society 5.0, education faces new challenges. One of the essential skills that students need to have is critical thinking. The discovery learning learning model is considered effective in fostering students' critical thinking skills, so that it can improve their understanding of concepts. This study aims to determine the influence of the discovery learning model and creative thinking skills on the understanding of biology concepts of SMA Bunda Hati Kudus students. The research method used is a quantitative method. The population of this study used 160 students in class XI science of Bunda Hati Kudus High School for the 2022/2023 school year. The sample used was 64 people. Hypothesis testing using MANOVA and ANOVA techniques with a significance level of 0.05. The results of the study show that the application of the discovery learning learning model significantly improves the understanding of concepts, critical thinking skills, and creative thinking skills of SMA Bunda Hati Kudus students. Statistical analysis with the F test showed a very significant difference (p < 0.05) between the group of students who used the discovery learning model and the group that used the direct learning model. These findings indicate that the discovery learning model can be an effective alternative to improve the quality of student learning

THE EFFECT OF PROBLEM-BASED LEARNING ASSISTED WITH PHET SIMULATION ON STUDENTS’ LEARNING OUTCOMES OF LIGHT AND OPTICAL DEVICES OPTICS TOPIC

This research is based on the preliminary study conducted at SMPN 16 Padang which found that students' learning outcomes in science teaching are still considered suboptimal. This is indicated by several students' scores that have not yet met the Learning Outcome Completion Criteria of 80. This research aims to determine the effect of Problem-Based Learning (PBL) model assisted by PhET simulations on the learning outcomes of eighth-grade students at SMPN 16 Padang. The type of research is a quasi-experiment with a non-equivalent control group design. The population in this study is all the eighth-grade students of SMPN 16 Padang. Sample collection was conducted using purposive sampling techniques. Thus, the research samples were obtained from students in class VIII 2 and VIII 4. The research samples consisted of experimental class applying the Problem-Based Learning model assisted by PhET simulations and control class using the conventional learning model. Data collection was conducted through tests administered before (pretest) and after the treatment (posttest) on the experimental and control classes in the form of multiple-choice questions. The obtained data were analyzed using inferential statistical analysis, and the results of the two-sample t-test for the pre-test and N-gain were 0.002 for the pre-test significance and 0.000 for the N-Gain significance, which means was rejected or there was a significant difference between the two groups because the significance value (p) was less than 0.05. Thus, it can be concluded that there is a significant influence of the application of the PBL learning model assisted by PhET simulations on the learning outcomes of eighth-grade students in the subject of light and optical instruments.

Optimizing Volleyball Bottom Passing Skills: Unveiling the Impact of the STAD Learning Model on Learning Outcomes

The study focused on improving the lower passing skills of 8th-grade female students at SMP Negeri 6 Sumedang in volleyball. Recognizing the significance of passing in enhancing team attacks and overall match performance, the researcher employed the student teams achievement division (STAD) learning model to address existing skill deficiencies. The research identified common issues in lower passing execution, including imperfect movement stages, improper ball contact in the hands, ball deviation from the intended target, and an inability to control the ball’s direction. The results indicated a positive impact, with a significant increase in the lower passing skills observed during the volleyball underpassing tests. The t-test results, with T_count = 16.4 > T_table = 1.725, demonstrated a statistically significant improvement, confirming that the STAD learning model contributed to enhanced learning outcomes. In summary, the research concluded that the application of the STAD learning model positively influenced the learning outcomes related to lower passing skills in volleyball among 8th-grade female students at SMP Negeri 6 Sumedang. The findings emphasized the efficacy of employing cooperative learning strategies, such as STAD, to address specific skill challenges in sports education. Keywords: STAD, bottom passing, volleyball

IMPLEMENTASI MODEL PEMBELAJARAN PROJECT BASED LEARINING (PJBL) BERBASIS VIDEO PEMBELAJARAN INTERAKTIF TERHADAP MINAT BELAJAR SISWA

The rapid advancement of technology forces every individual to face increasingly complex challenges. This also has an impact on the world of education, where the ability of learners is developed along with the times. This study aims to determine whether there is an influence and difference in student learning interest using a project-based learning model (PJBL) with the help of interactive learning videos in video editing subjects. The research method used is a pseudo-experimental design with a quantitative approach, and data collection is done through a questionnaire given to 66 grade X students at SMK Darus Syifa in Cilegon city. The results showed that the application of the project-based learning model influenced student interest in learning, and there were differences in the increase in the average percentage of questionnaire scores, where the control class had an average score of 53.26% while the experimental class reached 60.69%. This research is expected to be a learning model in basic video editing subjects and provide benefits for teachers, students, and future researchers.

Enhancing investment strategies through machine learning: A comprehensive analysis across market sectors

Abstract. This study presents a detailed exploration of the application of machine learning (ML) models to optimize investment strategies across various market sectors, including equity markets, fixed income and derivatives, and the volatile cryptocurrency markets. We evaluate three primary ML models: linear regression, decision trees, and neural networks, based on their predictive accuracy, computational efficiency, and robustness to market volatility. A rigorous process involving backtesting and cross-validation assesses each model's performance. Our framework encompasses data preprocessing, feature engineering, model implementation, and a nuanced approach to risk assessment integrating Value at Risk (VaR) and the Sharpe Ratio. We demonstrate the models' effectiveness in predicting stock prices, interest rates, and cryptocurrency price movements. The application of our ML framework led to the development of dynamic portfolio optimization strategies that significantly outperform traditional methods. This study contributes to the understanding of ML's potential to revolutionize investment strategies, providing a foundation for future research and practical applications in financial markets.

Utilizing a combination of experimental and machine learning methods to predict and correlate between accelerated and natural aging of CFRP/AL adhesive joints under hygrothermal conditions

AbstractThis study investigates how carbon fiber reinforced polymer (CFRP)‐to‐aluminum adhesive joints behave under accelerated aging conditions with hygrothermal exposure and compares these findings against naturally aged samples to evaluate material reliability in challenging environments. The CFRP‐to‐aluminum adhesive joints were manufactured and subjected to natural aging for durations ranging from 1 to 3 years with 6‐month intervals, as well as accelerated aging (hygrothermal) for periods ranging from 100 to 1200 h, with intervals of 50 h. Subsequently, the mechanical properties of the joints were evaluated using a three‐point bending test. To forecast natural aging times from accelerated aging data, five machine learning models were utilized: artificial neural network, support vector regression, linear regression, polynomial regression, and random forest regression. Hygrothermal aging significantly degraded the matrix, causing a shift in failure modes from cohesive to mixed types (cohesive, adhesive, and fiber tear failures), leading to a notable decline in bending strength. The study observed a 23.13% strength reduction in samples aged naturally for 3 years and a 24.33% decrease in those subjected to 1000 h of accelerated aging. The random forest regressor demonstrated superior accuracy in predicting natural aging times across different accelerated aging periods. Through the application of machine learning models, this study introduces a novel approach to forecast natural aging durations using data from accelerated aging experiments. This method shows potential for optimizing joints and composite structures, ultimately improving their durability and minimizing the likelihood of failures during operational use.Highlights Studied hygrothermal effects on accelerated aging of carbon fiber reinforced polymer/Aluminum (AL) adhesive joints. Noted strength reduction from hygrothermal aging. Used five machine learning models; random forest regression had the highest accuracy. Analyzed correlation between natural and accelerated aging of dissimilar adhesive joints.

Improving the Computational Thinking Abilities of Junior High School Students Through Problem-Based Learning

Computational Thinking is a crucial skill that students must master in the 21st century. Computational Thinking encompasses a systematic process that enables individuals to formulate problems, develop strategies, and find effective, efficient, and reusable solutions. This research aims to enhance students' Computational Thinking abilities through the application of the Problem-Based Learning (PBL) model, which is effective in facilitating active learning and problem-solving. The methodology used in this study is Classroom Action Research (CAR) with a spiral approach developed by Stephen Kemmis and Robin McTaggart. Data collection is conducted through post-tests administered at the end of each cycle phase. The obtained data are analyzed using a designed assessment rubric, wherein students are categorized based on their Computational Thinking abilities and foundational Computational Thinking skills. The results indicate that by the end of Cycle II, of students achieved a moderate to high level of Computational Thinking ability. These findings affirm that the implementation of the Problem-Based Learning learning model can significantly improve students' Computational Thinking skills.

Comparative analysis of X-ray image classification of pneumonia based on deep learning algorithm

Abstract. In this paper, we compare and analyse the effectiveness of deep learning models such as AlexNet, Vgg, GoogleNet and MobileNet in the task of pneumonia image classification. During the training process, GoogleNet showed the fastest convergence speed, reaching convergence at the 3rd epoch; subsequently, the other three models gradually stabilised. In the end, the loss of AlexNet is 0.426, the loss of Vgg is 0.566, the loss of GoogleNet is 0.936, and the loss of MobileNet is 0.626. By selecting the weights of the round with the best training effect of each model, the results of classification accuracy are obtained: 88.9% for AlexNet, 92.6% for Vgg, and 92.6% for GoogleNet. 92.6%, GoogleNet is 85.2%, and MobileNet is 96.3%. MobileNet demonstrated the best prediction performance on the test set. These results provide a useful reference for the application of deep learning models in medical imaging..

Spatial patterns of hydroecological health in the semi-arid yellow river basin: Revelations from machine learning models

The ecosystem of semi-arid watersheds is influenced by a combination of natural climate factors, rainfall, and habitat destruction, resulting in complex mechanisms of spatial differentiation and evolution of water ecological health. Indicator selection in mainstream water ecological health assessment methods, such as the Index of Biotic Integrity (IBI), often relies on subjective reference point choices. This approach tends to overlook the comprehensive impacts and interactions among various environmental stressors. For watersheds significantly influenced by natural climatic factors, considerable uncertainties arise, leading to a lack of scientific justification for establishing water ecological health protection goals. In this study, the nonlinear capabilities of the random forest (RF) model were applied to reduce subjectivity in traditional water ecological health assessments and to more accurately reveal the emerging spatial differentiation patterns and underlying causes of water ecological health in the Wei River Basin (WRB), the largest typical semi-arid watershed of the Yellow River in China. Our findings indicate: (1) Traditional evaluation indices indicate that the overall water ecological health of the WRB is classified as sub-healthy (60 %). The core indicators include dominant species, total algal density, and the percentage of diatom density, with no significant spatial differentiation observed. (2) An improved water ecological health assessment method for semi-arid watersheds, based on the RF model, has been developed to replace traditional subjective judgment steps. This method establishes a complex multi-input–output response relationship (R2 > 0.85) between environmental stress indicators and the biological integrity index for the WRB. (3) The model results identify key driving factors affecting changes in water ecological health in semi-arid watersheds, with the sensitivity of the new model increasing nearly 11-fold compared to traditional IBI methods. (4) Following improvements, the water ecological health characteristics of the WRB exhibit significant spatial heterogeneity, with a higher dispersion coefficient (1.21), and demonstrate enhanced nonlinear response trends to climatic factors. The application of machine learning models indicates that traditional methods may underestimate the extent of ecological health degradation in watersheds and tend to oversimplify spatial heterogeneity characteristics.

Deep learning for prediction and classifying the dynamical behaviour of piecewise-smooth maps

This paper explores novel ways of predicting and classifying the dynamics of piecewise smooth maps using various deep learning models. Moreover, we have used machine learning models such as Decision Tree Classifier, Logistic Regression, K-Nearest Neighbor, Random Forest, and Support Vector Machine for predicting the border collision bifurcation in the 1D normal form map and the 1D tent map. The decision tree classifier best predicts the border collision bifurcation for the 1D normal form map, the random forest, and the 1D tent map. This study introduces a novel application of deep learning models to cobweb diagrams and phase portraits, which provides a new perspective for classifying regular and chaotic behaviour. Further, we classified the regular and chaotic behaviour of the 1D tent map and the 2D Lozi map using deep learning models like Convolutional Neural Network (CNN), ResNet50, and ConvLSTM via cobweb diagram and phase portraits, where CNN exhibits better performance than other models. We also classified the chaotic and hyperchaotic behaviour of the 3D piecewise smooth map using deep learning models such as the Feed Forward Neural Network (FNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). We have shown that LSTM performs best for classifying chaotic and hyperchaotic behaviour. Additionally, LSTM outperforms other models in accuracy and computational efficiency, making it highly effective for real-time analysis. Finally, deep learning models such as Long Short Term Memory (LSTM) and Recurrent Neural Network (RNN) are used for reconstructing the two-parameter bifurcation charts of 2D normal form map, in which LSTM is more precise than RNN in reconstructing the two-parametric charts.

UNVEILING THE DECISION MAKING PROCESS IN ALZHEIMER’S DISEASE DIAGNOSIS: A CASE-BASED COUNTERFACTUAL METHODOLOGY FOR EXPLAINABLE DEEP LEARNING

BackgroundThe field of Alzheimer's disease (AD) diagnosis is undergoing significant transformation due to the application of deep learning (DL) models. While DL surpasses traditional machine learning in disease prediction from structural magnetic resonance imaging (sMRI), the lack of explainability limits clinical adoption. Counterfactual inference offers a way to integrate causal explanations into these models, enhancing their robustness and transparency. New methodThis study develops a novel methodology combining U-Net and generative adversarial network (GAN) models to create comprehensive counterfactual diagnostic maps for AD. The proposed methodology uses case-based counterfactual reasoning for robust decision classification for counterfactual maps to understand how changes in specific features affect the model's predictions. Comparison with existing methodsThe proposed methodology is compared with state-of-the-art visual explanation techniques across the ADNI dataset. The proposed methodology is also benchmarked against other gradient-based and generative models for its ability to generate comprehensive counterfactual maps. ResultsThe results demonstrate that the proposed methodology significantly outperforms existing methods in accuracy, sensitivity, and specificity while providing detailed counterfactual maps that visualize how slight changes in brain morphology could lead to different diagnostic outcomes. The proposed methodology achieves an accuracy of 95% and an AUC of 0.97, illustrating its superiority in capturing subtle yet crucial anatomical features. ConclusionsBy generating intuitive visual explanations, the proposed methodology improves the interpretability and robustness of AD diagnostic models, making them more reliable and accountable. The use of counterfactual inference enhances clinicians' understanding of disease progression and the impact of different interventions, fostering precision medicine in AD care.

The Effect Of The Application Of Problem-Based Learning Model On Learning Outcomes In Ppkn Subjects For Class XI Students At MAN 2 Mataram

The Effect Of The Application Of Problem-Based Learning Model On Learning Outcomes In Ppkn Subjects For Class XI Students At MAN 2 Mataram

Application of the asicc learning model in human physiological system to improve students' critical thinking and creative thinking skills

Critical thinking and creative thinking skills are needed to improve the quality and competitive human resources. Therefore, the application of appropriate learning models is expected to stimulate students' critical thinking and creative thinking skills. This research aims to improve critical thinking and creative thinking skill scores using the ASICC learning model. This type of research is classroom action research which is carried out in 2 cycles, with 3 meetings in each cycle. Data on students' critical thinking and creative thinking skills were collected using essay tests on science learning materials. Data analysis was carried out by comparing the N-gain scores in cycle 1 and cycle 2 for students' critical thinking and creative thinking skills. The results of the research revealed that there was an increase that occurred in cycle 2, namely 5.6% in the aspects of moderate critical thinking skills and high creative thinking, as well as in the aspects of critical thinking skills and high creative thinking by 5.6%. Meanwhile, in the previous cycle, these two aspects did not have a high category. Another result found from this research is that students who have low critical thinking skills do not necessarily have low creative thinking skills. This is because students who have low critical thinking skills may have moderate creative thinking skills or vice versa. Thus, the application of the ASICC learning model is effective in improving the scores of critical thinking skills and creative thinking skills of students at Junior High School 5 Kertosono.

Application of the Inquiry-Based Learning Model with Education for Sustainable Development to Enhance Critical Thinking Skills and Sustainable Awareness

To improve the quality of education and develop students' 21st-century competencies, particularly critical thinking, the inquiry-based learning model presents itself as a viable option to enhance students' cognitive abilities and awareness of sustainability. Incorporating Education for sustainable development into the inquiry-based learning model introduces a relevant approach to addressing contemporary global challenges, especially in sustainable development education. This research seeks to assess the enhancement of students' critical thinking skills and sustainable awareness through the application of the inquiry-based learning model infused with ESD content. The research method used in this study is a quasi-experiment with a non-equivalent control group design. This research was conducted on class X students at one of the MAN in Bandar Lampung, with each class consisting of 35 and 36 students. Data were obtained from the results of pre-tests and post-tests of critical thinking skills, sustainable awareness questionnaires, and student response surveys. The results of the study indicate that there was an improvement in students' critical thinking skills between the experimental and control classes. There was also a significant difference in sustainable awareness between the experimental and control classes. The inquiry-based learning model integrated with ESD content showed a significant and fairly strong positive correlation between students' critical thinking skills and sustainable awareness

Exploring machine learning models for predicting meson mass and width

Abstract This paper explores the application of various machine learning models to predict the mass and width of mesons based on their quark content and quantum numbers. Using deep neural networks (DNNs), ensemble methods, and traditional regression models, we demonstrate the effectiveness of these approaches in providing accurate predictions. By utilizing the relationship between isospin, charge conjugation, G-parity, we achieve more precise predictions and reveal the unique differences in meson properties. Additionally, we investigate the effects of incorporating quantum numbers into different ML algorithms, highlighting how these features impact the predictive performance of each model.