Application Of Learning Methods Research Articles

A Review of Enhancement Techniques for Cone Beam Computed Tomography Images

Cone Beam Computed Tomography (CBCT) has emerged as a valuable imaging modality for various medical applications due to its ability to provide three-dimensional information with minimal radiation exposure. However, CBCT images often suffer from inherent limitations, such as increased noise, artifacts, and reduced spatial resolution. This paper presents a comprehensive review of image processing techniques employed to enhance the quality of CBCT images, addressing the challenges posed by acquisition hardware and image reconstruction algorithms. The review covers a range of preprocessing and post-processing methods, including denoising, artifact correction, and resolution improvement techniques. These methods encompass various mathematical algorithms, machine learning approaches, and hybrid models, which aim to mitigate the imperfections present in CBCT data while preserving diagnostically relevant information. Additionally, this paper discusses the application of deep learning methods, convolutional neural networks, and generative adversarial networks in CBCT image enhancement. These advanced techniques have shown promise in tackling the complex nature of CBCT data and optimizing image quality.

Lightweight Oriented Detector for Insulators in Drone Aerial Images

Due to long-term exposure to the wild, insulators are prone to various defects that affect the safe operation of the power system. In recent years, the combination of drones and deep learning has provided a more intelligent solution for insulator automatic defect inspection. Positioning insulators is an important prerequisite step for defect detection, and the accuracy of insulator positioning greatly affects defect detection. However, traditional horizontal detectors lose directional information and it is difficult to accurately locate tilted insulators. Although oriented detectors can predict detection boxes with rotation angles to solve this problem, these models are complex and difficult to apply to edge devices with limited computing power. This greatly limits the practical application of deep learning methods in insulator detection. To address these issues, we proposed a lightweight insulator oriented detector. First, we designed a lightweight insulator feature pyramid network (LIFPN). It can fuse features more efficiently while reducing the number of parameters. Second, we designed a more lightweight insulator oriented detection head (LIHead). It has less computational complexity and can predict rotated detection boxes. Third, we deployed the detector on edge devices and further improved its inference speed through TensorRT. Finally, a series of experiments demonstrated that our method could reduce the computational complexity of the detector by approximately 49 G and the number of parameters by approximately 30 M while ensuring almost no decrease in the detection accuracy. It can be easily deployed to edge devices and achieve a detection speed of 41.89 frames per second (FPS).

A multi-scale context-aware and batch-independent lightweight network for green tide extraction from SAR images

ABSTRACT The outbreaks of green tide have caused severe harm to the marine environment and human society. Synthetic Aperture Radar (SAR) plays an important role in green tide monitoring by virtue of its high resolution and cloud-free nature. The existing green tide extraction methods still face challenges in identifying multi-scale green tide patches due to noise interference, uneven greyscale and blurred boundaries in SAR images. Meanwhile, the practical application of deep learning methods with high precision is limited due to the complexity of the model and the large amount of computation. Therefore, we propose a multi-scale context-aware and batch-independent lightweight green tide extraction network called MBL-Net. A novel lightweight heterogeneous backbone is designed to extract multi-scale discriminative features and improve segmentation efficiency by using multi-scale selection kernel (MSK) modules and lightweight stages. Meanwhile, Triplet attention module is introduced to improve the internal consistency of the green tide region and suppress the effect of speckle noise. Then, the mixed pooling-based channel prior module (MCPM) is used to expand the receptive field of the network and extract the fine green tide structure by fusing multi-scale features. In addition, Filter Response Normalisation (FRN) is innovatively applied for feature normalization in the decoding stage, eliminating batch dependency. In order to verify the effectiveness of the proposed method, a dataset is built using the Sentinel-1 images of the Yellow Sea, China, from 2019 to 2021. The experimental results show that the proposed method achieves an overall accuracy of 98.59% with 0.970 G FLOPs and 3.525 M parameters, which ensures high precision and improves green tide detection efficiency. Compared with several representative networks, this method can capture more details of green tide with fewer parameters and faster calculation speed.

Application of project-based learning method in Entrepreneurship education (PKWU) subjects of Labschool UPI

Application of project-based learning method in Entrepreneurship education (PKWU) subjects of Labschool UPI

E-Learning Mata Pelajaran Informatika melalui Model Problem Based Learning berbasis Moodle

The method of delivering learning orally and providing notes in the form of photocopies or notes, is less effective in saving time and less maximally obtained by students. The purpose of this study is to analyze and design adaptive e-learning media that suits the needs of teachers and students at SMKN 6 Surakarta. The method used in this study is the R&D method using the ADDIE model with only the stages of analysis, design and implementation. Observations and questionnaires are used for the data collection process, and the data analysis techniques used are descriptive statistics. Our findings are to produce e-learning to support students in the application of problem-based learning methods in the classroom. The test results show that the entire functionality of the system is working properly. This system makes the learning process with problem-based learning in the classroom more flexible and accessible from anywhere, increases collaboration between students virtually, utilizes technology to present learning materials that are more interactive and engaging..

Explicit coordinated signal control using soft actor–critic for cycle length determination

AbstractExplicit signal coordination carries prior knowledge of traffic engineering and is widely accepted for global implementation. With the recent popularity of reinforcement learning, numerous researchers have turned to implicit signal coordination. However, these methods inevitably require learning coordination from scratch. To maximize the use of prior knowledge, this study proposes an explicit coordinated signal control (ECSC) method using a soft actor–critic for cycle length determination. This method can fundamentally solve the challenges encountered by traditional methods in determining the cycle length. Soft actor–critic was selected among various reinforcement learning methods. A single agent was administered to the arterials. An action is defined as the selection of a cycle length from among the candidates. The state is represented as a feature vector, including the cycle length and features of each leg at every intersection. The reward is defined as departures that indirectly minimize system vehicle delays. Simulation results indicate that ECSC significantly outperforms the baseline methods, as evident in system vehicle delay across nearly all demand scenarios and throughput in high demand scenarios. The ECSC revitalizes explicit signal coordination and introduces new perspectives on the application of reinforcement learning methods in signal coordination.

Pengembangan Kompetensi Mahasiswa Sosiologi Pedesaan Dalam Analisis Kehidupan Masyarakat Desa Melalui Penerapan Metode Pembelajaran Inkuiri Sosial

The aim of this research is to determine the development of rural sociology students' competence in analyzing village community life through the application of social inquiry learning methods. The research method applied in this study is the action research method. This research is included in the applied research category which integrates knowledge, research and action. This research explains the effectiveness of social inquiry learning methods in improving the analytical competence of rural sociology students. The social inquiry method, which emphasizes active student involvement through exploration and investigation of social phenomena, has proven effective in improving students' understanding, analytical skills and critical thinking abilities. Through applying this method, students not only understand rural sociology theory but are also able to apply it in empirical analysis. The implementation of this method in the form of action research shows a significant increase in student learning outcomes from the first cycle to the second cycle. The research results show that students who are involved in the social inquiry method are able to carry out a more in-depth analysis of social phenomena in rural areas, as well as showing increased cooperation skills and courage in discussions. This conclusion is supported by observation results which show an increase in the quality of learning and the results of student self-evaluation.

Analisis Keefektifan Metode Game Edukatif terhadap Keaktifan dan Kerja Sama Siswa Sekolah Dasar

The application of learning methods is still lacking so most students may feel bored and unmotivated during lessons because learning methods have not been applied properly. In addition, the limited use of media in the learning process causes students' lack of understanding of what they are learning and students' activeness is not seen during the learning process. The purpose of this article is to determine the effectiveness of educational games. Many people use media as an additional tool for learning due to technological advances that occur in the current era of globalization. The use of media in learning aims to make the educational game method effective in increasing student engagement and cooperation in elementary schools. In addition, the use of media makes it easier for teachers to convey material and make students better understand what they are learning. Therefore, teachers must ensure that the materials and media used are appropriate. This article will discuss the use of game-laden interactive multimedia in learning in elementary school. It will also analyze the participation and cooperation of students in primary schools as well as the factors that influence the application of game-laden interactive methods in the learning process.

Application of deep learning and statistical methods in predicting Taiwanese stock trends

Since its inception, the stock market has been a topic of considerable interest. Its variation and the complexity of integrating technology into the stock market have made it difficult for stock market trends to be fully understood. Various metrics and analytical approaches have been proposed in response to such changes, ranging from purely technical metrics to hardware upgrades. The widespread application of deep learning in the stock market, from basic metrics (opening price, closing price, highest price, lowest price, trading volume) to machine learning in sentiment analysis, further increases the possibility of increasing profits. Some front-end techniques, such as noise reduction through mathematical models, enhance the accuracy of deep learning models. However, few studies have centered on predicting long-term stock price changes. The traditional moving average (MA) cannot rapidly reflect drastic changes on its curve even though it can display trends; therefore, this study proposes an MA-based approach that improves the 200-day MA such that its delayed response to actual prices in real-time can be overcome. This deep learning model training was performed by combining 200-day MA data with two other types of MA data, thereby creating a new approach to metric analysis. The sample consisted of stocks of 13 Taiwanese companies with a high market cap: Taiwan Semiconductor Manufacturing Co., Ltd., MediaTek Inc., Chunghwa Telecom Co., Ltd., Fubon Financial Holding Co., Ltd., Cathay Financial Holding Co., Ltd., Nan Ya Plastics Corp., United Microelectronics Corp., Delta Electronics, Inc., CTBC Financial Holding Co., Ltd., Mega Financial Holding Co., Ltd., Formosa Chemicals & Fibre Corp., Hon Hai Precision Industry Co., Ltd., and Formosa Plastics Corp. Through multiple evaluation metrics, the experimental results revealed that the proposed model performed better in general than the traditional MA model for all stocks.

Analisis Penerapan Metode Pembelajaran Diskusi Kelompok Terhadap Pemahaman Siswa Pada Pembelajaran di SMA Negeri 21 Medan

This type of research is qualitative with a case study which aims to analyze the application of the group discussion learning method to students' understanding of integrated social studies learning material at SMA Negeri 21 Medan. The group discussion method was applied as an effort to increase students' understanding of complex integrated social studies material. The respondents in this study were class X5 and in research. This research uses primary data obtained through interviews with 10 students and 45 students XI IPS 3 direct observation of students at SMA Negeri 21 Medan who are the samples or respondents to this research. The results of the research show that the application of the group discussion learning method significantly increases students' understanding of integrated social studies learning material. Students who were involved in group discussions showed increased ability to understand integrated social studies concepts and were able to relate information between integrated subjects. The application of the group discussion learning method is effective in increasing students' understanding of integrated social studies learning material at SMA Negeri 21 Medan.

A computer vision approach to estimate the localized sea state

This research presents a novel application of computer vision (CV) and deep learning methods for real-time sea state recognition, aiming to contribute to improving the operational safety and energy efficiency of seagoing vessels, key factors in meeting the legislative carbon reduction targets. Our work focuses on utilizing sea images in operational envelopes captured by a single stationary camera mounted on the ship bridge. The collected images are used to train a deep learning model to automatically recognize the state of the sea based on the Beaufort scale. To recognize the sea state, we used 4 state-of-the-art deep neural networks with different characteristics that proved useful in various computer vision tasks: Resnet-101, NASNet, MobileNet_v2, and Transformer ViT -b32. Furthermore, we have defined a unique large-scale dataset, collected over a broad range of sea conditions from an ocean-going vessel prepared for machine learning. We used the transfer learning approach to fine-tune the models on our dataset. The obtained results demonstrate the potential for this approach to complement traditional methods, particularly where in-situ measurements are unfeasible or interpolated weather buoy data is insufficiently accurate. This study sets the groundwork for further development of sea state classification models to address recognized gaps in maritime research and enable safer and more efficient maritime operations.

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.

Application of the Problem-Solving Method to Improve Critical Thinking Abilities in Social Studies Learning for Elementary School Students

Teaching, one of which is the problem solving learning mein Social studies learning in elementary schools places more emphasis on social sciences in studying social phenomena and problems that occur in society. Problems that occur today are that there are still many arguments that social studies learning is rote learning. However, in reality from social studies learning there are phenomena that can be explored by students, so that students can find real problems in the environment to use as learning and can improve students' ability to think critically in responding to these problems. Optimal development of critical thinking skills requires interactive classes. In order for learning to be interactive, the learning design must be attractive so that students can be actively involved in the learning process. Learning that develops critical thinking skills involves students as thinkers, not as someone being taught. The teacher acts as a mediator, facilitator and motivator who helps students in learning and not tthod where through the Problem Solving method it is hoped that it can facilitate understanding of the subject matter provided and later can improve the quality of the learning process which can then improve students' critical thinking in social studies learning. This research aims to analyze the application of the problem solving method to improve critical thinking abilities in elementary school students' social studies learning. The method used in this research is the literature study method. The results of the research show that the application of the Problem Solving learning method emphasizes students' ability to solve a problem. In this way, students' critical thinking skills will continue to be trained.

INTEGRATION OF ISLAMIC RELIGIOUS EDUCATION LEARNING METHODS IN THE 21st CENTURY AT MA NAHDHOTUT THOLIBIN

In facing education challenges in the modern era, Islamic Religious Education (PAI) learning at MA Nahdlotut Tholibin seeks to integrate religious values relevant to the times' needs. The PAI curriculum, which still uses many traditional methods, is considered less effective in attracting the interest of students who grow up in the era of globalization, which further erodes moral and ethical values. Surveys conducted in several educational institutions found that although students understand religious concepts, they often have difficulty implementing them in real life. This research aims to develop a more relevant and applicable learning methodology which is theoretical and supports the practice of students' daily lives. This study uses a descriptive qualitative approach by conducting in-depth interviews, classroom observations, and document analysis to evaluate the application and suitability of 21st-century learning methods. Participants in this study include PAI teachers, students, and administrators from MA Nahdlotut Tholibin. The data was analyzed using content analysis techniques. The study results show that learning methods with 21st-century skills such as Communication, Collaboration, Critical Thinking and Problem Solving, and Creativity and Innovation can increase students' understanding and motivation to learn. This approach helps students become critical thinkers and practitioners of good religious values in modern society. This research provides insight into the importance of designing a curriculum responsive to changing times. It supports the development of students' interpersonal skills and strengthens their ability to integrate religious teachings into daily decision-making.

Implementasi Metode Pembelajaran Aktif Time Token Terhadap Motivasi Belajar Siswa SD Yapita pada Mata Pelajaran Bahasa Indonesia Kelas 6A

Indonesian is taught in grades 1-6 of primary school. Learning is divided into lower and upper grades, with a thematic approach and specialized teaching materials The uniqueness is seen in the thematic approach and teaching materials used. Indonesian language learning in the lower grades aims for students to focus on language skills, understand forms and functions, and use them creatively. This study uses qualitative methods to understand social phenomena from the views of participants. Data was collected from students who conducted teaching assistance at Yapita Surabaya Elementary School for three months. The purpose of the study was to determine the impact of the application of the time token learning method on the motivation of students in class 6A. The research sample consisted of 28 students. The learning strategy encourages changes in the motivation of Yapita Elementary School students in class 6A to learn Indonesian. The time token learning model has advantages in motivating students to participate more independently, improving students' communication skills, and helping students develop habits of listening, sharing, contributing, and accepting criticism. However, the disadvantage of this model is that it can only be used for certain subjects and is not suitable for classes with a large number of students. The steps in time token learning involve conditioning the class for discussion, giving each student a speaking coupon, and awarding points depending on the situation. Students who have used their coupons cannot speak.

A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model

A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model

A review on learning based image reflection removal algorithms

Capturing images through semi-reflective surfaces, such as glass windows and transparent enclosures, often leads to a reduction in visual quality and can adversely affect the performance of computer vision algorithms. As a result, image reflection removal has garnered significant attention among computer vision researchers. With the growing application of deep learning methods in various computer vision tasks, such as super-resolution, inpainting, and denoising, convolutional neural networks (CNNs) have become an increasingly popular choice for image reflection removal. The purpose of this paper is to provide a comprehensive review of learning-based algorithms designed for image reflection removal. Firstly, we provide an overview of the key terminology and essential background concepts in this field. Next, we examine various datasets and data synthesis methods to assist researchers in selecting the most suitable options for their specific needs and targets. We then review existing methods with qualitative and quantitative results, highlighting their contributions and significance in this field. Finally, some considerations about challenges and future scope in image reflection removal techniques are discussed.

Transformation of Christian Religious Education Learning in Jayapura: Conventional and Innovative Methods in the 21st Century Context

The demands of the 21st century in the education world require adaptive and innovative learning. The implementation of Christian Religious Education (PAK) in Jayapura is accompanied by both conventional and unconventional, or innovative methods to support the effectiveness of learning. The aim of this research is to elaborate on the methods of PAK learning in Jayapura, Papua. The method used in this study is a qualitative descriptive method by applying literature review. Data were obtained through documentation techniques, involving downloading and documenting supporting data from books, research or journal papers, and proceedings from the last 10 years. The data were analyzed by classifying the effective application of learning methods. The results of this research indicate that the PAK learning methods applied in Jayapura, Papua, in terms of effectiveness, show that both conventional and unconventional methods are equally important in their implementation. Conventional methods tend to use techniques such as lectures, discussions, question and answer sessions, and assignments. While unconventional methods based on students' needs have not been optimally implemented in Jayapura

Threshold-based earthquake early warning for high-speed railways using deep learning

Earthquakes are disasters that threaten the operational safety of high-speed railways. To obtain reliable alerts for the earthquake monitoring and early warning systems of high-speed railways, based on magnitude and peak ground acceleration (PGA) thresholds (M = 5.5 and PGA = 40 cm/s2), an earthquake early warning (EEW) method for high-speed railways using deep learning is proposed. And the application of deep learning method in EEW for high-speed railway is explored. We design a single-station deep learning network architecture (named the CT architecture) by combining convolutional neural and transformer networks, and with that architecture, we train two separate models (CT-M and CT-PGA models) using the strong motion data recorded from the Kyoshin Network in Japan, which are used to predict whether the magnitude and PGA exceed the thresholds for issuing an alert. To verify the robustness of the method, we apply it to the M7.3 earthquake and M7.4 earthquake off the coast of Fukushima in 2021–2022. Results show that within 10 s after P-wave arrival, the accuracy of the alert reaches 90 %, and the average observed lead time reaches 18 s. The proposed method displays potential application on EEW systems for high-speed railways.

Using artificial intelligence and deep learning to optimise the selection of adult congenital heart disease patients in S-ICD screening

IntroductionThe risk of complications associated with transvenous ICDs make the subcutaneous implantable cardiac defibrillator (S-ICD) a valuable alternative in patients with adult congenital heart disease (ACHD). However, higher S-ICD ineligibility and higher inappropriate shock rates-mostly caused by T wave oversensing (TWO)- are observed in this population. We report a novel application of deep learning methods to screen patients for S-ICD eligibility over a longer period than conventional screening. MethodsAdult patients with ACHD and a control group of normal subjects were fitted with a 24-h Holters to record their S-ICD vectors. Their T:R ratio was analysed utilising phase space reconstruction matrices and a deep learning-based model to provide an in-depth description of the T: R variation plot for each vector. T: R variation was compared statistically using t-test. Results13 patients (age 37.4 ± 7.89 years, 61.5 % male, 6 ACHD and 7 control subjects) were enrolled. A significant difference was observed in the mean and median T: R values between the two groups (p < 0.001). There was also a significant difference in the standard deviation of T: R between both groups (p = 0.04). ConclusionsT:R ratio, a main determinant for S-ICD eligibility, is significantly higher with more tendency to fluctuate in ACHD patients when compared to a population with normal hearts. We hypothesise that our novel model could be used to select S-ICD eligible patients by better characterisation of T:R ratio, reducing the risk of TWO and inappropriate shocks in the ACHD patient cohort.