Type Of Learning Method Research Articles

Can “ Zero waste city” policy promote green technology? Evidence from econometrics and machine learning

The promotion of green technology innovation (GTI) is regarded as an effective way to protect the environment and achieve sustainable development. The “Zero waste city” construction pilot policy (ZWCP), is an important policy for the promotion of waste management, and the achievement of sustainable development and circular economy. The paper explores the effect of ZWCP on GTI using the difference-in-differences model and machine learning. The result shows that ZWCP significantly promotes GTI. In addition, causal forest modeling, a type of machine learning method, similarly validates this result. The mechanism effect suggests that ZWCP promotes GTI by increasing research expenditure and enhancing informational development. The heterogeneity effect suggests that ZWCP is more powerful in promoting GTI when it is implemented in western, low administrative level, and resource-based cities. The paper provides a reference for waste management policies and development of GTI in other countries.

Evaluation of Effectiveness of Self-Supervised Learning in Chest X-Ray Imaging to Reduce Annotated Images.

A significant challenge in machine learning-based medical image analysis is the scarcity of medical images. Obtaining a large number of labeled medical images is difficult because annotating medical images is a time-consuming process that requires specialized knowledge. In addition, inappropriate annotation processes can increase model bias. Self-supervised learning (SSL) is a type of unsupervised learning method that extracts image representations. Thus, SSL can be an effective method to reduce the number of labeled images. In this study, we investigated the feasibility of reducing the number of labeled images in a limited set of unlabeled medical images. The unlabeled chest X-ray (CXR) images were pretrained using the SimCLR framework, and then the representations were fine-tuned as supervised learning for the target task. A total of 2000 task-specific CXR images were used to perform binary classification of coronavirus disease 2019 (COVID-19) and normal cases. The results demonstrate that the performance of pretraining on task-specific unlabeled CXR images can be maintained when the number of labeled CXR images is reduced by approximately 40%. In addition, the performance was significantly better than that obtained without pretraining. In contrast, a large number of pretrained unlabeled images are required to maintain performance regardless of task specificity among a small number of labeled CXR images. In summary, to reduce the number of labeled images using SimCLR, we must consider both the number of images and the task-specific characteristics of the target images.

The Effect of Motivation and Interaction Effects on Improving Mathematical Comprehension Skills through Problem and Inquiry-Based Learning

This study investigates the influence of motivation levels and interaction effects on students' Mathematical Comprehension Ability (KPM) while utilizing Problem-Based Learning (PBL) and Inquiry-Based Learning (IBL). Employing a quasi-experimental with the 3x2 factorial design, the research was conducted with fourth-grade students in Ciamis Regency, West Java. The findings indicated that the average KPM increase for students using PBL was 53%, which is considered moderate, whereas for IBL, it was 29%, which is categorized as low. Significantly, there were variations in learning effects between PBL and IBL (p 0.001), with PBL demonstrating a more pronounced impact on KPM improvement. However, the influence of motivation level on KPM improvement was deemed insignificant (p = 0.192), as was the interaction effect between learning methods and motivation level on KPM improvement (p = 0.057). In conclusion, the study suggests that PBL is more effective than IBL in enhancing students' KPM. At the same time, motivation level does not significantly influence KPM improvement, and there is no interaction effect between the type of learning method and motivation level on KPM improvement.

대학 실시간 온라인 수업유형에 따른 수업만족도와 학습실재감에 대한 상호작용 분석

Objectives The purpose of this study was to examine interaction between learning method and couse division on learning satisfaction and learning presence in the synchronous universities online class.
 Methods Based on such research theories, the study conducted an online survey of 309 Korean university stu-dents who took synchronous online class before September 2020.
 The questionnaire was developed through a prior study analysis, and 308 respondents, excluding one unfaithful case, were targeted for analysis. The data was analyzed by using the Microsoft Excel spread sheet and SPSS.22 program to verify the research hypothesis. The reliability test was conducted to verity the internal consistency by calculating Cronbach' α coefficient. In addition, a Two-way ANOVA was conducted to analyze the differences in learning satisfaction between he learning method and course division, and a multivariate analysis(MANOVA) was conducted to analyze the differences in the learning presence according to he learning method and course division. Results First, in synchronous online classes at universities, the learning satisfaction level according to the type of course division was found to be no significant difference, and there were significant differences only in the type of learning method. Second, the presence of learning about the course division did not reveal any significant difference. Third, in synchronous classes, the interaction of learning satisfaction with the interaction of course di-vision and learning method was found to be ineffective. Fourth, the interaction effect of the course division and learning method on the learning presence was shown in synchronous online classes.
 Conclusions The synchronous online lectures were difficult to completely replace existing face-to-face lectures. Therefore, based on the characteristics of the platform on which synchronous online lectures are conducted and the learner's needs, a continuous study of the model of synchronous online classes should be processed.

Should we really use graph neural networks for transcriptomic prediction?

The recent development of deep learning methods have undoubtedly led to great improvement in various machine learning tasks, especially in prediction tasks. This type of methods have also been adapted to answer various problems in bioinformatics, including automatic genome annotation, artificial genome generation or phenotype prediction. In particular, a specific type of deep learning method, called graph neural network (GNN) has repeatedly been reported as a good candidate to predict phenotypes from gene expression because its ability to embed information on gene regulation or co-expression through the use of a gene network. However, up to date, no complete and reproducible benchmark has ever been performed to analyze the trade-off between cost and benefit of this approach compared to more standard (and simpler) machine learning methods. In this article, we provide such a benchmark, based on clear and comparable policies to evaluate the different methods on several datasets. Our conclusion is that GNN rarely provides a real improvement in prediction performance, especially when compared to the computation effort required by the methods. Our findings on a limited but controlled simulated dataset shows that this could be explained by the limited quality or predictive power of the input biological gene network itself.

Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring.

Flow cytometry (FC) is routinely used for hematological disease diagnosis and monitoring. Advancement in this technology allows us to measure an increasing number of markers simultaneously, generating complex high-dimensional datasets. However, current analytic software and methods rely on experienced analysts to perform labor-intensive manual inspection and interpretation on a series of 2-dimensional plots via a complex, sequential gating process. With an aggravating shortage of professionals and growing demands, it is very challenging to provide the FC analysis results in a fast, accurate, and reproducible way. Artificial intelligence has been widely used in many sectors to develop automated detection or classification tools. Here we describe a type of machine learning method for developing automated disease classification and residual disease monitoring on clinical flow datasets.

Capacitated spatial clustering with multiple constraints and attributes

Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing data, classification, logistic optimization and infrastructure optimization. Depending on the application at hand, a multitude of extensions to the clustering problem may be necessary. In this article, we propose a number of novel extensions to PACK, a recent capacitated partitional spatial clustering method which uses an optimization algorithm that is based on linear programming tasks. These extensions relate to the relocation and location preference of cluster centers, outliers, and non-spatial attributes, and they can be considered jointly. In the context of edge server placement, these improve the spatial location of servers while considering, for example, application placement on the servers in response to spatial application usage patterns. We demonstrate the usefulness of an extended version of PACK with an example with simulated data, as well as a real world example in edge server placement for a city region with various different setups. These setups are evaluated with summary statistics about spatial proximity and attribute similarity. As a result, the similarity of the clusters was improved by 53% at best while simultaneously the proximity degraded only by 18%. The extensions provide valuable means for including non-spatial information in the cluster analysis, and to attain better overall proximity and similarity.

An efficient framework for solving forward and inverse problems of nonlinear partial differential equations via enhanced physics-informed neural network based on adaptive learning

In recent years, the advancement of deep learning has led to the utilization of related technologies to enhance the efficiency and accuracy of scientific computing. Physics-Informed Neural Networks (PINNs) are a type of deep learning method applied to scientific computing, widely used to solve various partial differential equations (PDEs), demonstrating tremendous potential. This study improved upon original PINNs and applied them to forward and inverse problems in the nonlinear science field. In addition to incorporating the constraints of PDEs, the improved PINNs added constraints on gradient information, which further enhanced the physical constraints. Moreover, an adaptive learning method was used to update the weight coefficients of the loss function and dynamically adjust the weight proportion of each constraint term. In the experiment, the improved PINNs were used to numerically simulate localized waves and two-dimensional lid-driven cavity flow described by partial differential equations. Meanwhile, we critically evaluate the accuracy of the prediction results. Furthermore, the improved PINNs were utilized to solve the inverse problems of nonlinear PDEs, where the results showed that even with noisy data, the unknown parameters could be discovered satisfactorily. The study results indicated that the improved PINNs were significantly superior to original PINNs, with shorter training time, increased accuracy in prediction results, and greater potential for application.

Active Learning the High-Dimensional Transferable Hubbard U and V Parameters in the DFT + U + V Scheme.

Density functional theory (DFT) is a powerful quantum mechanical computational tool to perform electronic structure calculations for materials. Few DFT methods can ensure accuracy and efficiency simultaneously. DFT + U + V is an alternative effective approach to overcome this drawback. However, the accuracy sensitively depends on the self-consistent estimation of the high-dimensional onsite and intersite Hubbard interaction U and V terms. We propose Bayesian optimization using a dropout (BOD) algorithm, one type of active learning method, to optimize U and V terms. The DFT + U + V with U/V obtained by BOD can produce improved electronic properties for diverse bulk materials of comparable quality to the hybrid functionals with lower computational cost compared to the linear response approach. Note that the band gaps calculated by BOD are somewhat different from that of hybrid functionals by simply applying the same U/V parameters as in the case of surface slabs and interfaces, which suggests that the transferability of U/V from the bulk models to slabs and interfaces is not as well as expected. BOD is extended to calculate the U/V parameters for slabs and interfaces and reach similar results as bulk solids. Moreover, we find that the U/V are reasonably transferable between surface slabs and interfaces with different thicknesses under various effects of quantum confinement, which contributes to fast access to the electronic properties of large-scale systems with higher accuracy.

Automatic first break picking with structured random forests

AbstractIn the seismic wave exploration field, first break picking is an important underlying task. With the emergence of massive seismic data, it is urgent to develop automatic and accurate picking algorithms to relieve the huge workload of manual picking. Although many traditional and machine learning based techniques have been developed, it is still a challenging task to obtain satisfactory picking results in practice because of complex subsurface structures and low signal‐to‐noise ratio. Ensemble learning has been demonstrated to be a powerful tool to produce good prediction results in many fields. Its core idea is to create multiple models with some special techniques and then to combine the prediction results of each model with a fusion strategy. Structured random forests , a type of ensemble learning method, have been proven to be quite effective in implementing structured learning tasks. Based on the observation that adjacent traces can provide useful information to pick the first arrival time for a specific trace, we propose in this paper a novel structured random forest based first break picking framework. In particular, a multi‐scale normalization technique is presented to make full use of the amplitude information at different scales. To capture local features of first breaks, an enhanced feature map based on the short‐term/long‐term average ratio method is also computed. By extracting patches from two channel feature maps, we construct a structured random forest to predict the locations of the first breaks. On the basis of the probability score map produced by the structured random forest, an effective post‐processing strategy is proposed to further deal with the detected results. By conducting experiments with synthetic and field data, the proposed method is shown to be effective in identifying first breaks. It is significantly superior to the short‐term/long‐term average ratio method and support vector machine in terms of picking accuracy. With the advantage of parallel computing, the computational cost of structured random forest is acceptable, that is, it is much lower than the support vector machine while being higher than the short‐term/long‐term average ratio method. In addition, the experiments also confirm that the multi‐scale normalization plays an important role in improving the picking performance of the structured random forest.

AN ANALYSIS OF STUDENTS’ PERCEPTION USING KAHOOT GAME IN VOCABULARY LEARNING PROCESS

In general, teaching and learning activities are mostly supported by books, the presentation of the materials by the teacher, and written or oral exercises. This type of learning method is monotonous. With technological developments and the internet as an educator, it is reasonable to utilize technology-based online learning applications, such as Kahoot. This research aims to find out the students' perceptions using Kahoot game in the vocabulary learning process. The participants of this research are XI Bahasa class students at MAN 1 BONE who have experienced using Kahoot. This qualitative research used questionnaire, interview, and documentation as instruments, purposive sampling technique was used to select participants to get data through interview. The results showed that Kahoot provides many positive impacts on students' learning. It was found that students perceive Kahoot as an online learning platform that increases interest, improves their vocabulary knowledge, and increases their motivation and enthusiasm which leads to active learning through flexible learning process. In addition, this research found difficulties that students experienced when using Kahoot, such as poor internet connection.

The Effect of the Student Teams Achievement Division (STAD) Learning Method on Student Learning Outcomes in Indonesian Language Lessons at School

This study aims to determine the effect of the STAD type of cooperative learning method. The method used is descriptive quantitative. Data were obtained from tests and questionnaires. The source of the data from this study was a sample of 31 from the control class and 31 from the experimental class. The results of this study are that learning before using the Student Teams Achievement Division (STAD) learning method is still monotonous learning and lack of activity in class it affects student learning outcomes. but after applying the Student Teams Achievement Division (STAD) learning method it can be stated that it has increased. This is evidenced by Based on the calculations that have been carried out, the average value of the control class is 85.07 and the average value of the experimental class is 90.48. Based on these average values, it can be seen that the average experimental class = 90.48 > average control class = 85.07.

Stimulus-specific random effects inflate false-positive classification accuracy in multivariate-voxel-pattern-analysis: A solution with generalized mixed-effects modelling

When conducting multivariate-voxel pattern analysis (MVPA), researchers typically compute the average accuracy for each subject and statistically test if the average accuracy is different from the chance level across subjects (by-subject analysis). We argue that this traditional by-subject analysis leads to inflated Type-1 error rates, regardless of the type of machine learning method used (e.g., support vector machine). This is because by-subject analysis does not consider the variance attributed to the idiosyncratic features of the stimuli that have a common influence on all subjects (i.e., the random stimulus effect). As a solution, we proposed the use of generalized linear mixed-effects modelling to evaluate average accuracy. This method only requires post-classification data (i.e., it does not consider the type of classification methods used) and is easily implemented in the analysis pipeline with common statistical software (SPSS, R, Python, etc.). Using both statistical simulation and real fMRI data analysis, we demonstrated that the traditional by-subject method indeed increases Type-1 error rates to a considerable degree, while generalized mixed-effects modelling that incorporates random stimulus effects can indeed maintain the nominal Type-1 error rates.

The Use of Cooperative Learning Method Type of Listening Team to Increase Students’ Learning Outcomes in Social Studies at 4th Grade of Elementary School

Cooperative learning (CL) has a pivotal role in the teaching and learning process to achieve better academic performance using an attractive teaching trait. This study aims at investigating the use of the Cooperative Learning method type listening team, knowing the students’ response and their learning outcomes in a social science subject. The study is conducted at one of the Public Elementary schools in Rangkasbitung. Classroom Action Research was employed in this study. The Cooperative Learning method type of listening team was conducted within 2 cycles. There are 22 students as the subjects of the current study. The data of the study were collected using two research instruments, they are observation and written test. The results of the study revealed that the activities of both teachers and students in the learning process using a Cooperative Learning type listening team improve students’ learning outcomes. The level of students’ learning mastery in the pre-cycle was 24%, in cycle 1 was 48% and in cycle 2 the passing grade reached 79%. While, the activities of teachers and students increased by 75% in the use of cooperative learning methods of the previous team listening and learning outcomes of students also increased, and 75% of the number of students achieved the standard of minimum completeness of mastery learning predetermined. Based on the data findings, it can be highlighted that the CL method type listening team is an effective method to improve both students’ learning outcomes and their activities during the class.

Penggunaan Metode Kooperatif Model Student Teams Achievement Divisions (STAD) Untuk Meningkatkan Hasil Belajar Pada Mata Pelajaran IPS Kelas IX SMPN 2 Kapuas Kuala Semester 2 Tahun Pelajaran 2019/2020

This study uses a class action research design. This research consists of two cycles. Each cycle consists of planning, action, observation, and reflection. The research data were obtained from test and non-test instruments. The test instrument was in the form of quiz questions at the end of cycle I and cycle I, while the non-test instruments were in the form of observation sheets, interviews and questionnaires. Nontest data analysis using qualitative techniques. The results of the successful process in this classroom action research were student activities which increased from cycle I by 61.05% to 94.73% in cycle I. Student skills in cycle I showed a value of 3 or sufficient category, while in cycle I obtained a value of 5 or very good. Recognition of the existence of diversity among students also experienced an increase from a value of 3 in cycle I to 5 in cycle I. The average student learning outcomes in cycle I was 69.82 to 81.25 in cycle I, while the average learning completeness in cycle I by 57.15% increased in cycle I to 85.71%. Based on the results of the study, it can be concludedthat the use of the cooperative learning method type STAD (Student Teams in Achievement Divisions) basic competencies Potential and Distribution of Indonesian Natural Resourcescan increase the activity and learning outcomes of Class 9.2 students of SMP Negeri 2 Kapuas Kuala.

Recent Landscape of Deep Learning Intervention and Consecutive Clustering on Biomedical Diagnosis

Background: Consecutive Clustering is one type of learning method that is built on neural network. It is frequently used in different domains including biomedical research. It is very useful for consecutive clustering (adjacent clustering). Adjacent clustering is highly used where there are various specific locations or addresses denoting each individual features in the data that need to be grouped consecutively. One of the useful consecutive clustering in the field of biomedical research is differentially methylated region (DMR) finding analysis on various CpG sites (features). Method: So far, many researches have been carried out on deep learn- ing and consecutive clustering in biomedical domain. But for epigenetics study, very limited survey papers have been published till now where con- secutive clustering has been demonstrated together. Hence, in this study, we contributed a comprehensive survey on several fundamental categories of consecutive clustering, e.g., Convolutional Neural Network(CNN) Auto- Encoder (AE), Restricted Boltzmann Machines (RBM) and Deep Belief Net- work (DBN), Recurrent Neural Network (RNN), Deep Stacking Networks (DSN), Long Short Term Memory (LSTM) / Gated Recurrent Unit (GRU) Network etc., along with their applications, advantages and disadvantages. Different forms of consecutive clustering algorithms are covered in the second section (viz., supervised and unsupervised DMR finding methods) used for DNA methylation data have been described here along with their advantages, shortcomings and overall performance estimation (power, time). Conclusion: Our survey paper provides a latest research work that have been done for consecutive clustering algorithms for healthcare purposes. All the usages, benefits and shortcomings along with their performance evaluation of each algorithm has been elaborated in our manuscript by which new biomedical researchers can understand and use those tools and algorithms for their research prospective.

Implementasi Model Pembelajaran Kooperatif Team Accelerated Instruction untuk Meningkatkan Hasil Belajar Akuntansi Siswa SMA

This study aims to improve student’s of class XII IPS1 SMA Negeri 1 Tirtayasa academic year of 2021/2022 with implementation Cooperative Learning Method Type Team Accelerated Instruction (TAI). The data collection techniques used in this research were test and documentation. The including instrument used in this research are test and field note. The data analysis technique in this research was quantitative descriptive data analysis. Based on the research result, on the cycle I, the average score of accounting learning outcome in the pretest was 44,33 while in the post test, the average score was 82,11 or increased 37,78 or 85,22%. On the cycle II, the average score of accounting learning outcome in the pretest was 48,89, while the post test, the average score was 91,11 or increased 42,22 or 86,36%. On the cycle I, the student’s accounting learning outcome completeness increased from 5,56% on the pretest to 63,89% on the posttest or increased 58,33%. On the cycle II, the student’s accounting learning outcome completeness increased from 11,11% of pretest to 86,11% on the posttest or increased 75,00% based on the result, hypothesis of action on this research is accepted.

A Comparison between Three Tuning Strategies for Gaussian Kernels in the Context of Univariate Genomic Prediction.

Genomic prediction is revolutionizing plant breeding since candidate genotypes can be selected without the need to measure their trait in the field. When a reference population contains both phenotypic and genotypic information, it is trained by a statistical machine learning method that is subsequently used for making predictions of breeding or phenotypic values of candidate genotypes that were only genotyped. Nevertheless, the successful implementation of the genomic selection (GS) methodology depends on many factors. One key factor is the type of statistical machine learning method used since some are unable to capture nonlinear patterns available in the data. While kernel methods are powerful statistical machine learning algorithms that capture complex nonlinear patterns in the data, their successful implementation strongly depends on the careful tuning process of the involved hyperparameters. As such, in this paper we compare three methods of tuning (manual tuning, grid search, and Bayesian optimization) for the Gaussian kernel under a Bayesian best linear unbiased predictor model. We used six real datasets of wheat (Triticum aestivum L.) to compare the three strategies of tuning. We found that if we want to obtain the major benefits of using Gaussian kernels, it is very important to perform a careful tuning process. The best prediction performance was observed when the tuning process was performed with grid search and Bayesian optimization. However, we did not observe relevant differences between the grid search and Bayesian optimization approach. The observed gains in terms of prediction performance were between 2.1% and 27.8% across the six datasets under study.

Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit.

Locomotion mode recognition provides the prosthesis control with the information on when to switch between different walking modes, whereas the gait phase detection indicates where we are in the gait cycle. But powered prostheses often implement a different control strategy for each locomotion mode to improve the functionality of the prosthesis. Existing studies employed several classical machine learning methods for locomotion mode recognition. However, these methods were less effective for data with complex decision boundaries and resulted in misclassifications of motion recognition. Deep learning-based methods potentially resolve these limitations as it is a special type of machine learning method with more sophistication. Therefore, this study evaluated three deep learning-based models for locomotion mode recognition, namely recurrent neural network (RNN), long short-term memory (LSTM) neural network, and convolutional neural network (CNN), and compared the recognition performance of deep learning models to the machine learning model with random forest classifier (RFC). The models are trained from data of one inertial measurement unit (IMU) placed on the lower shanks of four able-bodied subjects to perform four walking modes, including level ground walking (LW), standing (ST), and stair ascent/stair descent (SA/SD). The results indicated that CNN and LSTM models outperformed other models, and these models were promising for applying locomotion mode recognition in real-time for robotic prostheses.

Penerapan metode pembelajaran kooperatif tipe make a match dalam meningkatkan hasil belajar peserta didik

the aim of this study was to determine the effect of applying the Make A Match cooperative learning method in improving the learning outcomes of class X students at SMA Budhi Warman 1, East Jakarta. The research method used is the experimental method with the design used is the true experimental design for the posttest only control design. The population of this study were all students of class X SMA Budhi Warman 1 East Jakarta and the samples were class X IPS 1 and X IPS 2. The instrument used was a multiple choice test with 13 validated questions. The research results obtained: There is an effect of applying the make a match type of cooperative learning method in improving the learning outcomes of class X students at SMA Budhi Warman 1 East Jakarta. This is shown by the t test, where the value t-count = 25.72 is greater than the value t-Table = 2.015 for α = 0.05.