Application Of Learning Methods Research Articles

A novel robotic-assisted deep learning-enabled computer vision approach for nondestructive diagnosis of railway bolt faults

Abstract Railways play a vital role in the inland transportation system worldwide, and abnormal bolt components at the track joints are the main cause of train accidents. The detection and identification of faults in rail bolt components are of considerable research importance. To address this problem, numerous researchers have opted for computer vision-based methods to accomplish the detection and identification of the target, but the existing methods have poor detection performance diminished detection capabilities when the target position changes or some feature information is occluded, and the detection speed and accuracy are far from meeting the requirements of practical applications. Therefore, based on the construction of a dedicated dataset for bolt components, this paper uses the K-means dimensional clustering algorithm to re-cluster the dataset according to the target size characteristics, with the aim of reduce the bounding box regression error. At the same time, a novel loss function iteration method is proposed by incorporating an adaptive optimization algorithm, in order to improve the detection speed and ensure good convergence, and the model complexity is reduced based on deep model pruning. Finally, the optimized detection model is implemented on the robotic-assisted platform for testing, and the experimental results verify that the algorithm can quickly and accurately complete various fault diagnosis tasks of bolt components in practical applications. The main achievements of this study include the construction of a large-scale image dataset for novel rail bolt components and propelled the application of deep learning methods in vision-based rail bolt fault diagnosis.

Data-efficient software defect prediction: A comparative analysis of active learning-enhanced models and voting ensembles

As software systems undergo escalating complexity, the identification of bugs and defects becomes pivotal for ensuring seamless user experiences and averting potentially costly post-release issues. This study addresses this critical need by concentrating on the application of active learning methods in code defect prediction. The investigation focuses on the efficacy of active learning combined with ensemble methods, leveraging the dynamic selection and labeling of training instances to increase model performance, reduce the demand for exhaustive labeling efforts, and enhance the effectiveness of code defect prediction systems. Various traditional and ensemble methods are deployed, employing diverse query strategies (uncertainty, margin, and entropy sampling) to assess if active variants can rival original approaches while significantly downsizing the training set. Evaluation encompasses classical classification metrics (AUC, Kappa, and MCC), supplemented by a proposed easy-to-interpret performance index that takes into consideration not only the traditional metric outcomes but also the percentage of the initial dataset utilized, aligned with the dual nature of the problem. The results, elucidated through graphical representations and statistical tests, unveil the advantage of active methods, showcasing reductions of the initial training set by at least 75% in approximately 64% of cases.

Pore Water Pressure Prediction Based on Machine Learning Methods—Application to an Earth Dam Case

Pore Water Pressure Prediction Based on Machine Learning Methods—Application to an Earth Dam Case

INOVASI PEMBELAJARAN SEJARAH KEBUDAYAAN ISLAM DALAM MENINGKATKAN MINAT BELAJAR SISWA DI MADRASAH TSANAWIYAH NEGERI 1 PROBOLINGGO

SKI learning using the mnemonic learning method and audiovisual media provides new experiences for the success of education in Madrasahs, based on the results of observations conducted by researchers at Madrasah Tsanawiyah Negeri 1 Probolinggo, it shows researchers that they apply interesting learning technology in learning the history of Islamic culture, thus, the feeling Being bored and not enthusiastic about learning becomes positive energy for students to be enthusiastic about returning to learning. This is the reason for researchers to research and explore this phenomenon by carrying out research activities. The focus of this research is the implementation of innovative mnemonic learning methods and audiovisual media in SKI learning. Thus, the research aims to explore innovative mnemonic learning methods and the use of audiovisual media in SKI learning. The research method used is qualitative with a phenomenological type, interview activities, observation and documentation data analysis are the data collection methods used by researchers. The results of the research show that using these methods and media has succeeded in increasing students' interest in learning, meaning that the application of mnemonic learning methods and audiovisual media creates interesting, dynamic and interactive learning, which makes students more enthusiastic and enthusiastic in participating in SKI learning, of course with full support. from teachers and students is the key to the success of this innovation. Meanwhile, the implications of this research include: 1) Increasing interest in learning, 2) Developing learning methods, 3) Enriching the curriculum, 4) Developing learning materials, 5) Strengthening Islamic identity.

Pengaruh Metode Pembelajaran, Gaya Belajar dan Motivasi Belajar terhadap Hasil Belajar Siswa MI Roudlotul Mustashlihin Masangankulon Sukodono Sidoarjo

The learning method at MI. Roudlotul Mustashlihin Masangankulon has been using more expository models or approaches", or traditional such as lectures without a touch of creation and motivation, so that it cannot make students become active and rise to jump to find the potential that is within them and then develop it and this monotonous learning method certainly makes students passive, depressed, and as if he wanted to run away from his class. This study aims to explore the influence of learning methods, learning styles, and student motivation on student learning outcomes in MI. Roudlotul Mustashlihin Masangankulon. This study uses a quantitative research approach. According to Sugiyono (2016), the quantitative research approach is a research based on the philosophy of positivism, which is used to research certain populations and samples. while according to Arikunto (2013). The results showed that learning methods have a significant influence on student learning outcomes, showing that the application of innovative and interactive learning methods can increase student academic achievement. Second, students' learning styles also have a strong impact on learning outcomes, emphasizing the importance of understanding students' learning preferences and adapting learning strategies to suit their learning styles. Finally, learning motivation has a significant influence on student learning outcomes, suggesting that high levels of motivation can be a key factor in achieving academic success.

Control of Qubit Dynamics Using Reinforcement Learning

The progress in machine learning during the last decade has had a considerable impact on many areas of science and technology, including quantum technology. This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in a single qubit. The goal is to create an RL agent that learns an optimal policy and thus discovers optimal pulses to control the qubit. The most crucial step is to mathematically formulate the problem of interest as a Markov decision process (MDP). This enables the use of RL algorithms to solve the quantum control problem. Deep learning and the use of deep neural networks provide the freedom to employ continuous action and state spaces, offering the expressivity and generalization of the process. This flexibility helps to formulate the quantum state transfer problem as an MDP in several different ways. All the developed methodologies are applied to the fundamental problem of population inversion in a qubit. In most cases, the derived optimal pulses achieve fidelity equal to or higher than 0.9999, as required by quantum computing applications. The present methods can be easily extended to quantum systems with more energy levels and may be used for the efficient control of collections of qubits and to counteract the effect of noise, which are important topics for quantum sensing applications.

Utilizing Profound Learning Methods for Alzheimer's Infection Discovery: An Exhaustive Survey

Alzheimer’s disease (AD) is a major public health issue around the world. It is a progressive disease that causes memory loss and cognitive decline. Early detection is essential for early intervention and treatment of AD. In recent years, there has been a surge in the use of deep learning techniques to analyze complex medical data. This includes neuroimaging data as well as genetic data. This has led to promising advances in the detection of AD. In this paper, we will review the application of various deep learning methods in the field of Alzheimer’s Disease. We will look at the challenges that traditional diagnostic methods face and how these challenges can be overcome by using deep learning approaches. We will also look at different deep learning architectures and data modalities and techniques used in the field of AD detection. Finally, we will look at limitations and future direction of the field to help researchers and practitioners to develop more accurate and effective AD detection methods. Keywords: Alzheimer's Disease, deep learning, neuroimaging, biomarkers, early detection, diagnosis.

Optimizing the model-prediction of date palm fronds-derived producer gas and third generation biodiesel powered dual-fuel engine by employing Bayesian-optimized Boosted Regression Trees for enhanced prognostics

The global issue concerning the dual problem of economic energy shortage as well as greenhouse gas emission needs urgent attention. Due to its abundance and carbon neutrality, waste biomass is a good energy conversion feedstock. This research suggests a unique method for using date palm frond waste biomass-derived producer gas and algal biodiesel as sustainable diesel engine fuels. The Bayesian-optimized neural networks and Bayesian-optimized Boosted Regression Trees were employed to optimize engine combustion and emission performance. This was further enhanced with the application of k-cross fold to prevent the model from overfitting during training. The working of dual-fuel engines is a complex phenomenon owing to the employment of two different types of fuel one is liquid (biodiesel + diesel) while the other one is gaseous (Producer gas). The present endeavor was undertaken to handle this with the application of advanced data learning methods. The engine was tested at different compression ratios, engine loads, and fuel injection pressures for brake thermal efficiency, diesel fuel substitution, and emission. Both the Bayesian-optimized neural networks and Bayesian-optimized Boosted Regression Trees could predict the engine output with good prediction efficiency with comparable performance, however, the Bayesian-optimized Boosted Regression Trees-based models were more robust with a coefficient of determination value in the range of 0.9617–0.9999 and mean squared errors as low as 0.001. The mean absolute percentage error was very low in the range of 0.0316–5.231. The Theil’s U2 values were in the range of 0.0029–0.0126.

ANALISIS PENGGUNAAN METODE PEMBELAJARAN TERHADAP MOTIVASI BELAJAR SISWA

Education is a planned effort to realize the learning process so that students develop their potential to develop the skills needed by themselves, society, nation and state. In order for learning to be conducted optimally, all learning processes must apply learning methods. The problem that occurs is the low learning motivation of children at SD Negeri Karet 2 which causes learning objectives do not be achieved. It is caused by several factors including the use of lecture learning methods and inadequate infrastructure. The research method is qualitative by applying descriptive analysis techniques. Based on the results of the analysis, it was found that aspects that influence students' learning motivation include the learning methods used. And, aspects that influence students' learning motivation include the willingness, time, obligation and perseverance.

A Survey on Theories and Application for Self Driving Cars on Deep Learning Methods

In today’s, Self-driving cars are one of the hot research topics in technology, which has a great impact on social and economic development. Self-driving cars, powered by deep learning algorithms, represent a groundbreaking fusion of artificial intelligence and transportation technology. Deep learning is also one of the current major areas in artificial intelligence research. It has been widely applied in natural language understanding, image processing, etc. In recent years, more and more deep learning methods have been introduced to the solution in the field of self-driving cars and have achieved excellent results. This survey provides information related to self-driving cars and summarizes the application of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions are analyzed based on deep learning methods, such as lane detection, scene recognition and navigation etc. Also briefly described are some representative approaches to self-driving cars using deep learning methods. Finally, future challenges in applications of deep learning for self-driving cars are described.

Analysis of Students' Mathematical Connection Ability in Solving Task of Function

This study aims to analyze the student’s mathematical connection ability in topic of function, by using a descriptive qualitative analysis. Twenty-five students of grade 10 from one of the favorite schools in Sukabumi, West Java participated in the study. Data were collected by conducting instruments mathematical connection ability test and a short interview. The result showed that students’ mathematical connections were still in the low category with an average score of 43. Recommendations for future research are suggested, especially in improving students’ ability in mathematical connection by applying learning methods that will enhance their skills. Keywords: function, mathematical connection ability, solving task

Transfer‐Learning‐Enabled 3D reconfigurable broadband solar metamaterial absorbers design

Research on metamaterials shows excellent potential in the field of solar energy harvesting. In recent years, the design of broadband solar metamaterial absorbers (SMAs) has attracted significant interest with the wide application of deep learning methods. This paper proposes a deep neural network (DNN) to realize forward prediction and inverse design of reconfigurable 3D SMAs. In the inverse design, a polarization-insensitive broadband SMA with an absorption bandwidth of 2.7 μm and an average absorption rate of 97.6% with an adjustable bandwidth range of 369 nm is successfully designed. The design of SMAs with different structures is also realized by a transfer learning method to improve the training speed and performance further. Using the transfer learning approach, the training speed of the neural network target model can be accelerated, and its training performance can be improved on small datasets by utilizing the trained neural network source model. Meanwhile, using the trained inverse design target model, a polarization-insensitive broadband SMA was designed with an absorption bandwidth of 2.7 μm, an average absorption of 97.9%, and an adjustable bandwidth range of 141 nm. Finally, we verified the solar energy harvesting capability of the designed broadband SMAs under real-world conditions using air mass (AM) 1.5, and they were calculated to be capable of harvesting the vast majority of the energy. The method is instructive in the design process of SMAs and can be effectively used to explore multifunctional complex nanophotonic devices.

Reconstruction of elasticity modulus distribution base on semi-supervised neural network

Accurate reconstruction of tissue elasticity modulus distribution has always been an important challenge in ultrasound elastography. Considering that existing deep learning-based supervised reconstruction methods only use simulated displacement data with random noise in training, which cannot fully provide the complexity and diversity brought by in-vivo ultrasound data, this study introduces the use of displacement data obtained by tracking in-vivo ultrasound radio frequency signals (i.e., real displacement data) during training, employing a semi-supervised approach to enhance the prediction accuracy of the model. Experimental results indicate that in phantom experiments, the semi-supervised model augmented with real displacement data provides more accurate predictions, with mean absolute errors and mean relative errors both around 3%, while the corresponding data for the fully supervised model are around 5%. When processing real displacement data, the area of prediction error of semi-supervised model was less than that of fully supervised model. The findings of this study confirm the effectiveness and practicality of the proposed approach, providing new insights for the application of deep learning methods in the reconstruction of elastic distribution from in-vivo ultrasound data.

Application of deep learning method for time reduction and precision improvement in displacement measurement during in-situ SEM tensile test

In this study, a novel material deformation measurement method is suggested to evaluate the mechanical properties of thin films. It is important to identify the mechanical properties of thin films in order to do a reliable design of these products. One of the methods to get the mechanical properties of thin films is to acquire images during an in-situ SEM (Scanning Electron Microscope) tensile test and analyze them. However, SEM image acquisition is mostly affected by the time varying motion of pixel positions in the consecutive images, a phenomenon called drift and distortion. In this study, some images of low resolution during in-situ SEM tensile test of the thin film are acquired in short time and restored to high resolution images using the deep learning model called Super Resolution Residual Network (SRResNet) to calculate the deformation of thin films. The proposed method increased the precision of measurement by 5 times, improved the accuracy of measurement by 20%, and reduced the measurement time by 19 times.

Fast SPECT/CT planar bone imaging enabled by deep learning enhancement.

The application of deep learning methods in rapid bone scintigraphy is increasingly promising for minimizing the duration of SPECT examinations. Recent works showed several deep learning models based on simulated data for the synthesis of high-count bone scintigraphy images from low-count counterparts. Few studies have been conducted and validated on real clinical pairs due to the misalignment inherent in multiple scan procedures. To generate high quality whole-body bone images from 2× and 3× fast scans using deep learning based enhancement method. Seventy-six cases who underwent whole-body bone scans were enrolled in this prospective study. All patients went through a standard scan at a speed of 20cm/min, which followed by fast scans consisting of 2× and 3× accelerations at speeds of 40 and 60cm/min. A content-attention image restoration approach based on Residual-in-Residual Dense Block (RRDB)is introduced to effectively recover high-quality images from fast scans with fine-details and less noise. Our approach is robust with misalignment introduced from patient's metabolism, and shows valid count-level consistency. Learned Perceptual Image Patch Similarity (LPIPS) and Fréchet Inception Distance (FID) are employed in evaluating the similarity to the standard bone images. To further prove our method practical in clinical settings, image quality of the anonymous images was evaluated by two experienced nuclear physicians on a 5-point Likert scale (5= excellent). The proposed method reaches the state-of-the-art performance on FID and LPIPS with 0.583 and 0.176 for 2× fast scans and 0.583 and 0.185 for 3× fast scans. Clinic evaluation further demonstrated the restored images had a significant improvement compared to fast scan in image quality, technetium 99m-methyl diphosphonate (Tc-99m MDP) distribution, artifacts, and diagnostic confidence. Our method was validated for accelerating whole-body bone scans by introducing real clinical data. Confirmed by nuclear medicine physicians, the proposed method can effectively enhance image diagnostic value, demonstrating potential for efficient high-quality fast bone imaging in practical settings.

Analysis of Islamic Religious Education (PAI) Problems and Solutions in Facing the Development of the 21st Century

In the development of the 21st century, Islamic religious education faces a number of challenges that need to be overcome. It can be seen that the lack of adequate facilities, such as reading books, causes students' disinterest in reading, the accuracy of using learning methods, and the like. The purpose of the study is (1) to find and describe the problems of Islamic religious education (2) analyze these problems, especially in Islamic religious education in the 21st century. Then, the type used in this study is using a qualitative approach with library research methods. The gains in this study from the forms of problems and solutions in Islamic religious education (PAI) in the development of the 21st Century are: First, internal problems (1) mastery of the material, (2) love the teaching profession, (3) skills in teaching, (4) evaluate student learning. Second, external problems (1) managing classes, (2) applying learning methods and models, (3) lack of teacher interaction with students, (4) utilization of learning media. So, to overcome these problems, a comprehensive and holistic approach is needed. Then it also takes strong collaboration between educational institutions, government, and other parties to be able to form a relevant curriculum in accordance with the times. Furthermore, the researcher provides suggestions for the next author to study more deeply and comprehensively about the problems of Islamic religious education in the 21st century. Especially on the causal factors and implications in learning through the use of technology.

Holistic Curriculum Development in Islamic Religious Education Institutions in Primary Schools or Madrasah Ibtidaiyah

The era has experienced developments and changes in various fields. These developments and changes can affect changes in the education system. Nowadays, education has become an important part of social life, because education can change the quality of one's life. It can be proven that the higher the level of education, the more knowledge and experience a person has so that it is easier to solve life problems. One of the cornerstones of education is the curriculum, so when there are developments in various fields that can affect the education system, the curriculum is the same. Problems that arise in society also encourage changes in the education system. The curriculum is here to solve problems and meet the demands of society. The curriculum is designed to facilitate existing learning. This research uses qualitative research methods with the type of library study (library research), which is a method of collecting data by understanding and studying theories from various literature related to research, collecting data by searching for sources and reconstructing from various sources such as books, journals, and existing research. The indicators of success include understanding the concept of Islam, applying the values of religious teachings in daily life, improving student behavior and attitudes, and improving the quality of individual character Developing a holistic curriculum for Islamic religious education in elementary schools or madrasah ibtidaiyah is an important step in developing balanced and moral individuals. This curriculum covers the development of all aspects of a person, including physical, mental, emotional and spiritual. The implementation of a holistic curriculum requires the active role of teachers and the application of creative and interactive learning methods. The success of a holistic curriculum can be seen from the achievements of students in understanding religious concepts, applying religious education values, changing behavior and improving the quality of individual character. By developing a holistic curriculum, it is hoped that Islamic religious education can contribute to the formation of a pious national character.

Reliable fabric defect detection via Bayesian uncertainty modeling

Despite the demonstrated capability of deep learning models in detecting anomalies in textile images, their predictions in real-world applications tend to be overly confident, especially when faced with defect types not previously encountered in the training set or when dealing with low-quality annotations. This excessive confidence in predictions limits the practical application of deep learning methods in textile defect detection, as it fails to provide inspectors with reliable guidance on when to trust the model's predictions and when manual verification is necessary. To address this issue, this paper introduces a Bayesian fabric anomaly detection model that utilizes Variational Inference (VI) to apply Bayesian inference to the widely used U-Net architecture. During the inference phase, the model employs Monte Carlo sampling to perform multiple forward passes, generating three types of uncertainty estimations and per-pixel uncertainty maps, thus providing comprehensive evidence for decision-making. This method not only estimates the uncertainty of model predictions but also improves the F1 score by 2-4% over the baseline U-Net model in the frequency domain. This study proves the Bayesian approach boosts fabric anomaly detection and decision-making by optimizing model performance and reducing reliance on inaccurate predictions.

En-WBF: A Novel Ensemble Learning Approach to Wastewater Quality Prediction Based on Weighted BoostForest

With the development of urbanization, the accurate prediction of effluent quality has become increasingly critical for the real-time control of wastewater treatment processes. The conventional method for measuring effluent biochemical oxygen demand (BOD) suffers from significant time delays and high equipment costs, making it less feasible for timely effluent quality assessment. To tackle this problem, we propose a novel approach called En-WBF (ensemble learning based on weighted BoostForest) to predict effluent BOD in a soft-sensing manner. Specifically, we sampled several independent subsets from the original training set by weighted bootstrap aggregation to train a series of gradient BoostTrees as the base models. Then, the predicted effluent BOD was derived by weighting the base models to produce the final prediction. Experiments on real datasets demonstrated that on the UCI dataset, the proposed En-WBF approach achieved a series of improvements, including by 28.4% in the MAE, 40.9% in the MAPE, 29.8% in the MSE, 18.2% in the RMSE, and 2.3% in the R2. On the Fangzhuang dataset, the proposed En-WBF approach achieved a series of improvements, including by 8.8% in the MAE, 9.0% in the MAPE, 12.8% in the MSE, 6.6% in the RMSE, and 1.5% in the R2. This paper contributes a cost-effective and timely solution for wastewater treatment management in real practice with a more accurate effluent BOD prediction, validating the research in the application of ensemble learning methods for environmental monitoring and management.

Sequence Modeling with Recurrent Neural Networks (RNNs) for Student Learning Behavior Pattern Recognition in a Flipped Classroom

The flipped classroom model has become increasingly popular in education, altering the traditional methods of teaching. It is important to understand and acknowledge how students learn within this framework in order to optimize instructional strategies and promote personalized learning. This study investigates the use of Sequence Modeling with Recurrent Neural Networks (RNNs) to identify patterns in student learning behavior within a flipped classroom setting. The proposed deep learning architecture utilizes RNNs to analyze sequential patterns in students' interactions with the flipped classroom materials, while also incorporating attention mechanisms to better detect important patterns and temporal dynamics in the learning process. Multimodal learning techniques are also employed, combining data from various sources to gain a comprehensive understanding of student behavior. Additionally, clustering techniques using autoencoders are explored to group students with similar learning behaviors. Predictive models, such as RNN or LSTM networks, are developed to forecast future learning behaviors and provide insights into potential challenges or successes for individual students. The effectiveness of this framework is evaluated using real-world data from flipped classroom implementations, with performance metrics like recall, precision, and accuracy used to assess the success of the sequence modeling approach in recognizing and predicting student behavior patterns. Overall, the application of deep learning methods, specifically sequence modeling with RNNs, demonstrates potential for improving personalized learning experiences and facilitating proactive interventions to support diverse student needs.