Journal articles on the topic 'Adapted learning methods'
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Özöğür Akyüz, Süreyya, Gürkan Üstünkar, and Gerhard Wilhelm Weber. "Adapted Infinite Kernel Learning by Multi-Local Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 04 (April 12, 2016): 1651004. http://dx.doi.org/10.1142/s0218001416510046.
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The interplay of machine learning (ML) and optimization methods is an emerging field of artificial intelligence. Both ML and optimization are concerned with modeling of systems related to real-world problems. Parameter selection for classification models is an important task for ML algorithms. In statistical learning theory, cross-validation (CV) which is the most well-known model selection method can be very time consuming for large data sets. One of the recent model selection techniques developed for support vector machines (SVMs) is based on the observed test point margins. In this study, observed margin strategy is integrated into our novel infinite kernel learning (IKL) algorithm together with multi-local procedure (MLP) which is an optimization technique to find global solution. The experimental results show improvements in accuracy and speed when comparing with multiple kernel learning (MKL) and semi-infinite linear programming (SILP) with CV.
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Gazzola, Mattia, Babak Hejazialhosseini, and Petros Koumoutsakos. "Reinforcement Learning and Wavelet Adapted Vortex Methods for Simulations of Self-propelled Swimmers." SIAM Journal on Scientific Computing 36, no. 3 (January 2014): B622—B639. http://dx.doi.org/10.1137/130943078.
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Khalifa, Amal, and Anthony Guzman. "Imperceptible Image Steganography Using Symmetry-Adapted Deep Learning Techniques." Symmetry 14, no. 7 (June 27, 2022): 1325. http://dx.doi.org/10.3390/sym14071325.
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Digital image steganography is the process of embedding information within a cover image in a secure, imperceptible, and recoverable way. This research extends a symmetry-adapted deep-learning approach to identify hidden patterns of images using two-dimensional convolutional neural networks (CNN). The proposed method (SteGuz) uses three CNNs to implement the different phases of the steganography process on digital image covers. SteGuz introduced a gain function, based on several image similarity metrics, to maximize the imperceptibility of the hiding process. Using 10 different pairs of cover-secret images, the performance of the proposed method was measured in terms of standard metrics such as peak signal to noise ratio (PSNR) and structured similarity index measurement (SSIM). The results showed that the proposed methodology outperformed the original method in terms of both imperceptibility and recoverability. In addition, when compared with some existing methods, SteGuz proved the outstanding performance of achieving a very high PSNR value while maintaining high accuracy of the extracted image.
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Albashish, Dheeb. "Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images." PeerJ Computer Science 8 (July 5, 2022): e1031. http://dx.doi.org/10.7717/peerj-cs.1031.
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Deep convolutional neural networks (CNN) manifest the potential for computer-aided diagnosis systems (CADs) by learning features directly from images rather than using traditional feature extraction methods. Nevertheless, due to the limited sample sizes and heterogeneity in tumor presentation in medical images, CNN models suffer from training issues, including training from scratch, which leads to overfitting. Alternatively, a pre-trained neural network’s transfer learning (TL) is used to derive tumor knowledge from medical image datasets using CNN that were designed for non-medical activations, alleviating the need for large datasets. This study proposes two ensemble learning techniques: E-CNN (product rule) and E-CNN (majority voting). These techniques are based on the adaptation of the pretrained CNN models to classify colon cancer histopathology images into various classes. In these ensembles, the individuals are, initially, constructed by adapting pretrained DenseNet121, MobileNetV2, InceptionV3, and VGG16 models. The adaptation of these models is based on a block-wise fine-tuning policy, in which a set of dense and dropout layers of these pretrained models is joined to explore the variation in the histology images. Then, the models’ decisions are fused via product rule and majority voting aggregation methods. The proposed model was validated against the standard pretrained models and the most recent works on two publicly available benchmark colon histopathological image datasets: Stoean (357 images) and Kather colorectal histology (5,000 images). The results were 97.20% and 91.28% accurate, respectively. The achieved results outperformed the state-of-the-art studies and confirmed that the proposed E-CNNs could be extended to be used in various medical image applications.
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Ding, Alison, Jiacheng Wang, and Yingjie Hang. "Efficient Sensor Calibration Via Machine Learning-Based Resampling Methods." ECS Transactions 109, no. 15 (September 30, 2022): 3–15. http://dx.doi.org/10.1149/10915.0003ecst.
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A sensor’s performance relies on the calibration model functionally relating the sensor’s response (denoted as y) to the target analyte concentration (denoted as x). However, classic statistical methods are not able to adequately quantify the uncertainty of the x concentration estimates. In this work, a machine learning (ML)-based computational resampling method was adapted to address the uncertainty quantification issues, based on which a two-stage procedure was developed for efficient sampling and modeling of the calibration data obtained via laboratory experiments. The outputs of the procedure allow for not only a single-value estimate, but also a high-likelihood range for the underlying analyte concentration in an unknown sample being tested. The calibration procedure was applied on a simulated biosensor, and evaluated through large amount of validation data. The sensor simulator was developed from a paper-based lateral flow strip (PLFS) designed for traumatic brain injury (TBI) diagnosis.
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Wang, Yu, Mengru Sun, and Yifan Duan. "Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data." Computational and Mathematical Methods in Medicine 2021 (November 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/8008731.
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The human health status can be assessed by the means of research and analysis of the human microbiome. Acne is a common skin disease whose morbidity increases year by year. The lipids which influence acne to a large extent are studied by metagenomic methods in recent years. In this paper, machine learning methods are used to analyze metagenomic sequencing data of acne, i.e., all kinds of lipids in the face skin. Firstly, lipids data of the diseased skin (DS) samples and the healthy skin (HS) samples of acne patients and the normal control (NC) samples of healthy person are, respectively, analyzed by using principal component analysis (PCA) and kernel principal component analysis (KPCA). Then, the lipids which have main influence on each kind of sample are obtained. In addition, a multiset canonical correlation analysis (MCCA) is utilized to get lipids which can differentiate the face skins of the above three samples. The experimental results show the machine learning methods can effectively analyze metagenomic sequencing data of acne. According to the results, lipids which only influence one of the three samples or the lipids which simultaneously have different degree of influence on these three samples can be used as indicators to judge skin statuses.
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Kesuma, Dytchia Septi, Zakirman Zakirman, Ade Usra Berli, Mira Meilisa, and Chichi Rahayu. "Funscience Methods to Improve Student Activeness in Science Learning." EDUKATIF : JURNAL ILMU PENDIDIKAN 4, no. 1 (December 27, 2021): 530–38. http://dx.doi.org/10.31004/edukatif.v4i1.1759.
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The new challenge faced in carrying out IPA learning in elementary school, especially in SDN 01 Kubang Siamang Bunyi is to increase student activeness and involvement. The aim of this research is to improve student liveliness and engagement through Funscience methods in IPA learning. Fun science is a method by using simple experiments that are interestingly packaged and cause fun effects for students in IPA learning. This type of research is descriptive, involving student respondents in SDN 01 Kubang Siamang Bunyi Regency 50 cities of West Sumatra Province. Data collection instruments in the form of observation sheets and interviews. Data is analyzed descriptively. The results of the data analysis showed that the application of Funscience methods improved the activeness of students in IPA learning in SDN 01 Kubang Siamang Bunyi Kenagarian Kubang Regency 50 City. Funscience can be one of the alternative methods that can be applied in IPA learning to improve and improve the quality of learning. Funscience is a method of learning that has been adapted to the characteristics of students in elementary school.
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Mularsih, Heni, and Hartini. "The Effectiveness of Cyber Learning Methods to Improve Learning Motivation in Interior Design Courses." Journal of Computational and Theoretical Nanoscience 17, no. 2 (February 1, 2020): 1147–55. http://dx.doi.org/10.1166/jctn.2020.8779.
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The Interior Design courses emphasizing accuracy requires high learning motivation. One stimulus to grow motivation is by applying teaching methods that are in accordance with the characteristics of the course, namely cyber learning methods that also facilitate accuracy and efficiency. The purpose of this research is to improve learning motivation in interior design courses through cyber learning methods employing steps of project-based learning (PjBL). The study included quasiexperiments using two-group post test design. The instrument of learning motivation was adapted from McClelland’s motivational theory. The number of participants was 60 students divided in two groups. The first students group learned by using conventional methods (control group), while the second students group learned by using cyber methods (experimental group). The number of learning was 10 meetings. After completing the meetings, the two groups were measured based on the learning motivation. The results of the study indicate that cyber learning methods can increase student’s learning motivation in interior design courses. Result shows that the experimental group of students has higher learning motivation than students in control group who were leaved uninterfered with the conventional method—the lectures.
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Krissandi, Apri Damai Sagita. "THE DEVELOPMENT OF BOOK: APPROACHES, METHODS, AND LEARNING MODELS FOR LEARNING INDONESIAN IN PRIMARY SCHOOL." NATURALISTIC : Jurnal Kajian Penelitian Pendidikan dan Pembelajaran 2, no. 1 (October 26, 2017): 62–76. http://dx.doi.org/10.35568/naturalistic.v2i1.101.
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This study aims to develop book approach, method, and model of learning Indonesian. This type of research includes research development (Research and Development). This study aims to develop and produce a specific product and test the validity of the resulting product. The development model used is a development model adapted from Borg and Gall development research steps (1983: 775). The development procedure is through the following stages: (1) determining the study of competency standards and learning materials, (2) needs analysis, (3) producing books, (4) product validation, and (5) revision of the final product. From the results of the recapitulation score, obtained the following details: 1) the score of the media expert is 4.7 with very good criteria; 2) score of Indonesian lecturers 4.8 with very good criteria; and 3) the mean of student validation score is 4.9 with very good criteria. Therefore, it can be concluded that the book approach, method, and model of learning Indonesian in primary school get an average score of 4.8 with the category of "Very Good".
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Satrya, Wahyu Fadli, and Ji-Hoon Yun. "Combining Model-Agnostic Meta-Learning and Transfer Learning for Regression." Sensors 23, no. 2 (January 4, 2023): 583. http://dx.doi.org/10.3390/s23020583.
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For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning. In this paper, we investigate how the differences in the data distributions between the old tasks and the new target task impact performance in regression problems. By performing experiments, we discover that these differences greatly affect the relative performance of different adaptation methods. Based on this observation, we develop ensemble schemes combining multiple adaptation methods that can handle a wide range of data distribution differences between the old and new tasks, thus offering more stable performance for a wide range of tasks. For evaluation, we consider three regression problems of sinusoidal fitting, virtual reality motion prediction, and temperature forecasting. The evaluation results demonstrate that the proposed ensemble schemes achieve the best performance among the considered methods in most cases.
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Mutizwa, Melissa Rutendo, Fezile Ozdamli, and Damla Karagozlu. "Smart Learning Environments during Pandemic." Trends in Higher Education 2, no. 1 (January 10, 2023): 16–28. http://dx.doi.org/10.3390/higheredu2010002.
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Education was one of the many day-to-day activities affected by the novel coronavirus pandemic (COVID-19). When countries began to shut down in April 2020, nationwide lockdowns, self-isolation, or quarantine became the new normal for everyone. The education sector was kept alive by smart learning environments. Now, more than ever, online learning and tools were implemented. This study aims to systematically review the literature on the impact of the pandemic on smart learning environments. The method adopted in this paper is a systematic literature review, and it will use the PRISMA technique. A qualitative approach was applied in the data collection process to achieve the aim. The essential advantage was that smart learning environments were convenient and easily adapted by students during the pandemic. The main challenge was connectivity issues and failure to adapt to non-traditional methods. The paper concluded a rise in the usage of smart learning environments, and educators and students adapted quickly to the shift.
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Et. al., M. Revathi,. "Effectiveness of Adapted Science Instruction for Teaching Learning of Science Temperature Concept among Students with Visual Impairment." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 27, 2021): 565–66. http://dx.doi.org/10.17762/itii.v9i2.383.
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The areas of science and mathematics have traditionally been inaccessible to students with Visual Impairment. Hence it needs adapted materials and instructional methods to understand and perform science experiments by visually impaired. This study aims to study the Effectiveness of Adapted Science Instruction on Leaning of Science Temperature Concept among students with Visual Impairment. The Investigator adapted the Science Temperature concept Activities as per the needs of Students with Visual Impairment. Visually Impaired sample from Grade VI to VIII were trained and effectivess of the Adapted Instruction was found.
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N, Nurohmat. "Effect of Online Learning on Students’ Learning Achievement." Jurnal Ilmu Pendidikan (JIP) STKIP Kusuma Negara 12, no. 2 (January 15, 2021): 165–71. http://dx.doi.org/10.37640/jip.v12i2.865.
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The Covid-19 pandemic has brought big changes in teaching and learning activities in the country. Learning activities have changed from face-to-face to online teaching and learning. Studying online changes the learning experience for many students. Rather than sitting in school and taking notes, students are taking advantage of online classes and other forms of online learning. Material and methods: Survey and interview. Interviews were conducted with the homeroom teacher. Survey of student scores in the semester before the Covid-19 pandemic and during the pandemic. Results: Based on the results obtained, on-line learning has little effect on learning achievement. Online learning has several advantages and disadvantages over learning face-to-face. Online learning offers a potentially better alternative if it is adapted to the situation and conditions of the students.
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Gareyev, A. M., A. B. Prokofiev, Yu Ryzhkova, and Dmitry Stadnik. "USING MACHINE LEARNING METHODS TO PREDICT HYDRAULIC PUMP REMAINING USEFUL LIFE." Journal of Dynamics and Vibroacoustics 7, no. 3 (September 3, 2021): 13–21. http://dx.doi.org/10.18287/2409-4579-2021-7-3-13-21.
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This article discusses a sampling algorithm for machine learning in order to capture the trend of the cumulative deterioration of the characteristics of a hydraulic pump (cumulative degradation), which affects the efficiency of its operation and manifests itself in the form of a drop in volumetric efficiency. To generate data, a simulation model of a typical station for the supply of working fluid in technological complexes, developed in the SimulationX program, is used. The transient processes of pressure change in the system are described, from the analysis of which a tendency of a decrease in the average component of the pressure signal is traced, which is used as a diagnostic feature - an indicator of the state of the system. An example is also considered that describes the possibility of assessing the residual life of the system based on data characterizing the past state of the system, and can be adapted when forming a more complex base, taking into account the use of artificial neural networks.
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García-Cuesta, Esteban, Daniel Gómez-Vergel, Luis Gracia-Expósito, Jose M. López-López, and María Vela-Pérez. "Prediction of Opinion Keywords and Their Sentiment Strength Score Using Latent Space Learning Methods." Applied Sciences 10, no. 12 (June 18, 2020): 4196. http://dx.doi.org/10.3390/app10124196.
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Most item-shopping websites give people the opportunity to express their thoughts and opinions on items available for purchasing. This information often includes both ratings and text reviews expressing somehow their tastes and can be used to predict their future opinions on items not yet reviewed. Whereas most recommendation systems have focused exclusively on ranking the items based on rating predictions or user-modeling approaches, we propose an adapted recommendation system based on the prediction of opinion keywords assigned to different item characteristics and their sentiment strength scores. This proposal makes use of natural language processing (NLP) tools for analyzing the text reviews and is based on the assumption that there exist common user tastes which can be represented by latent review topics models. This approach has two main advantages: is able to predict interpretable textual keywords and its associated sentiment (positive/negative) which will help to elaborate a more precise recommendation and justify it, and allows the use of different dictionary sizes to balance performance and user opinion interpretability. To prove the feasibility of the adapted recommendation system, we have tested the capabilities of our method to predict the sentiment strength score of item characteristics not previously reviewed. The experimental results have been performed with real datasets and the obtained F1 score ranges from 66% to 77% depending on the dataset used. Moreover, the results show that the method can generalize well and can be applied to combined domain independent datasets.
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Jones, William, Kaur Alasoo, Dmytro Fishman, and Leopold Parts. "Computational biology: deep learning." Emerging Topics in Life Sciences 1, no. 3 (November 14, 2017): 257–74. http://dx.doi.org/10.1042/etls20160025.
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Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.
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Bahamyirou, Asma, Lucie Blais, Amélie Forget, and Mireille E. Schnitzer. "Understanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimators." Statistical Methods in Medical Research 28, no. 6 (May 2, 2018): 1637–50. http://dx.doi.org/10.1177/0962280218772065.
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Data-adaptive methods have been proposed to estimate nuisance parameters when using doubly robust semiparametric methods for estimating marginal causal effects. However, in the presence of near practical positivity violations, these methods can produce a separation of the two exposure groups in terms of propensity score densities which can lead to biased estimates of the treatment effect. To motivate the problem, we evaluated the Targeted Minimum Loss-based Estimation procedure using a simulation scenario to estimate the average treatment effect. We highlight the divergence in estimates obtained when using parametric and data-adaptive methods to estimate the propensity score. We then adapted an existing diagnostic tool based on a bootstrap resampling of the subjects and simulation of the outcome data in order to show that the estimation using data-adaptive methods for the propensity score in this study may lead to large bias and poor coverage. The adapted bootstrap procedure is able to identify this instability and can be used as a diagnostic tool.
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Vukić, Tijana, Marijana Zelenik, and Tatjana Welzer. "Developing Intercultural Communication Competencies Using Various Learning Methods at a Media Communications Study Programme." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 4, no. 6 (2019): 15–28. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.46.3002.
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Various previous researchers of journalism and media communication education demonstrated the necessity and importance for media workers to be competent in intercultural communication (IC). Slovenian researchers do not discuss these topics, and IC themes are withal mostly part of a non- obligatory courses where usual teaching and learning methods are applied. To examine the effectiveness and usefulness of different teaching/learning methods in developing students’ intercultural communication competencies, an educational intervention research was suitable, as the method is being useful mostly to solve pragmatic issues. Additionally, the study aimed at determining outgoing students’ intercultural communication competencies (ICC) – knowledge, skills, attitudes, and awareness through their cognitive, affective and behavioural dimensions. Two theoretical approaches were used – one stating that ICC are developed by learning on the ground of our innate predispositions and character and the behavioural perspective. This qualitative-evaluative research was conducted within the International and Intercultural Communication course at the first year of Graduate Study of Media Communications at the University of Maribor. It was a trilingual intercultural situation where learning activities such as role-playing, pantomime, case studies, simulations, individual exploration, and reflection were implemented, since students were not used to them. Participatory observation, semi-structured interviews, qualitative questionnaires, evaluation of class activities and a final exam were used as main research methods. Even though the group adapted very soon, the nonverbal activities were most demanding, and individual activities at home the least accomplished. Although they had theoretical knowledge, experience in public speaking, proactivity and self-reflection skills at basic level, their abstract thinking skills, self-awareness and adaptation were medium. However, they showed a great deal of persistence and creativity as a part of a collaborative and cooperative activities. Therefore, focusing on the learning activities stimulating students’ ICC as a part of a real intercultural situation in journalism and mass communication (JMC) education is recommended.
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Moca, Mihaela, and Alina Badulescu. "E-LEARNING AND M-LEARNING CONTENT GENERATION AS LEARNING SUPPORT IN ECONOMIC EDUCATION." Annals of the University of Oradea. Economic Sciences 30, no. 30 (1) (July 2021): 121–29. http://dx.doi.org/10.47535/1991auoes30(1)011.
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The knowledge-based economy and the digital world are steadily transforming the teaching of younger generations but also the role and involvement of the teacher in the educational act. Accepting the essential role of economic education in the development of students’ mentalities and abilities, we implicitly recognize the central role played by teachers and teaching methods, as a considerable challenge to transform education, focus on active learning, providing students with new experiences inside and outside the classroom, involving modern methods of learning and experimentation. Generating digital educational content, as well as designing instructional tools that allow easy access to students of this content requires an effort of adaptation by educators for new educational realities. Digital literacy of teachers is necessary, as the first condition, and then it is necessary to form skills that allow the generation of content with characteristics adapted to educational needs, respecting quality criteria and ethical criteria. In this paper, the author also aims to present the ASQ platform as a space where teachers can create digital content, which can be shared nationally with other educators and which can be used successfully in specific economics classrooms.
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Antipova, Tatiana, Ioana Riurean, and Simona Riurean. "Is Distance Teaching-Learning-Evaluation Triad a Form of Digital Art?" Journal of Digital Art & Humanities 1, no. 2 (December 30, 2020): 3–19. http://dx.doi.org/10.33847/2712-8148.1.2_1.
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The pandemic situation at the beginning of March 2020 forced teachers to develop alternative teaching methods, and most important to find the best ways to keep teaching for every student no matter the situation, as for example, the lack of computer knowledge or hardware/software support. Teachers worldwide struggled to support, encourage, find the best ways not only to help students to keep learning but support them emotionally. At the end of the academic year, teachers made efforts to develop fair, appropriate evaluation procedures adapted to distance education. This paper summarizes the Distance Teaching-Learning-Evaluation (DTLE) evolution in Russia and Romania and some methods developed from March to December 2020 to support the educational activity. Some benefits, challenges and difficulties are identified during the same period of time in different DTLE scenarios, from the point of view of teachers and students, as well. Examples of new adapted methods, dedicated to the DTLE scenarios are al-so addressed in this work.
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Malyugin, Boris, Sergej Sakhnov, Svetlana Izmailova, Ernest Boiko, Nadezhda Pozdeyeva, Lyubov Axenova, Kirill Axenov, et al. "Keratoconus Diagnostic and Treatment Algorithms Based on Machine-Learning Methods." Diagnostics 11, no. 10 (October 19, 2021): 1933. http://dx.doi.org/10.3390/diagnostics11101933.
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The accurate diagnosis of keratoconus, especially in its early stages of development, allows one to utilise timely and proper treatment strategies for slowing the progression of the disease and provide visual rehabilitation. Various keratometry indices and classifications for quantifying the severity of keratoconus have been developed. Today, many of them involve the use of the latest methods of computer processing and data analysis. The main purpose of this work was to develop a machine-learning-based algorithm to precisely determine the stage of keratoconus, allowing optimal management of patients with this disease. A multicentre retrospective study was carried out to obtain a database of patients with keratoconus and to use machine-learning techniques such as principal component analysis and clustering. The created program allows for us to distinguish between a normal state; preclinical keratoconus; and stages 1, 2, 3 and 4 of the disease, with an accuracy in terms of the AUC of 0.95 to 1.00 based on keratotopographer readings, relative to the adapted Amsler–Krumeich algorithm. The predicted stage and additional diagnostic criteria were then used to create a standardised keratoconus management algorithm. We also developed a web-based interface for the algorithm, providing us the opportunity to use the software in a clinical environment.
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Brablc, Martin, Jan Žegklitz, Robert Grepl, and Robert Babuška. "Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models." Complexity 2021 (December 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/6617309.
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Reinforcement learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on techniques that have not been used much so far: symbolic regression (SR), based on genetic programming and local modelling. Using measured data, symbolic regression yields a nonlinear, continuous-time analytic model. We benchmark two state-of-the-art methods, SNGP (single-node genetic programming) and MGGP (multigene genetic programming), against a standard incremental local regression method called RFWR (receptive field weighted regression). We have introduced modifications to the RFWR algorithm to better suit the low-dimensional continuous-time systems we are mostly dealing with. The benchmark is a nonlinear, dynamic magnetic manipulation system. The results show that using the RL framework and a suitable approximation method, it is possible to design a stable controller of such a complex system without the necessity of any haphazard learning. While all of the approximation methods were successful, MGGP achieved the best results at the cost of higher computational complexity. Index Terms–AI-based methods, local linear regression, nonlinear systems, magnetic manipulation, model learning for control, optimal control, reinforcement learning, symbolic regression.
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Platonov, E. N., and K. V. Prosvirin. "PREDICTION OF TRACK STRUCTURE DEFECTS BY MACHINE LEARNING METHODS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 212 (February 2022): 8–18. http://dx.doi.org/10.14489/vkit.2022.02.pp.008-018.
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This paper proposes methods of defect prediction for railroad track superstructure objects using machine learning methods. In the railroad sector, most maintenance work is performed on a schedule, which can lead to system failure between two adjacent checks. In recent years, much attention has been paid to new technologies and “smart” approaches based on machine learning techniques, to build a predictive maintenance system. The problem of defect detection from a machine learning perspective is a classification problem with two classes. The initial observation data for the state of the superstructure of the railway track of the problem are unbalanced. This is due to the fact that one of the classes, on the objects of which a track structure defect has been registered, occurs much less frequently. Therefore, when solving the problem, an important parameter is the binarization threshold for machine learning algorithm responses. Modern methods for solving classification problems for tabular data were used to solve the problem. In addition to classical machine learning methods, such as gradient boosting, recurrent neural networks of different architectures were used. The results suggest that a practical threshold has been reached for the accuracy of model predictions, taking into account the noisiness of the input data. The practical significance of this work is that the proposed set of methods can be considered as part of a track maintenance decision-making system. It can be easily adapted for online operation and integrated with an automated measuring system based on a track geometry “recording” car.
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Momen, Moojan. "Learning from History." Journal of Baha’i Studies 2, no. 2 (1989): 55–68. http://dx.doi.org/10.31581/jbs-2.2.5(1989).
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This is an adapted form of a paper read as the Hasan Balyuzi Memorial Lecture at the Thirteenth Annual Conference of the Association of Bahá’í Studies in 1988. This paper considers the challenges caused by the influx of Third World villagers into the Bahá’í world community. The author examines what light a study of the history of the Bahá’í Faith can shed upon this phenomenon. In particular, he examines the way in which a study of Bahá’í history can assist with the problems of how to adapt our presentations of the Bahá’í Faith to the context of different cultures; how to adapt our methods of presenting the Bahá’í teachings; and how to accelerate the process of realizing these teachings in the lives of the villagers.
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Anjani, Ayu, Gita Harnum Syapitri, and Rifka Izatul Lutfia. "Analisis Metode Pembelajaran di Sekolah Dasar." FONDATIA 4, no. 1 (March 30, 2020): 67–85. http://dx.doi.org/10.36088/fondatia.v4i1.442.
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This study aims to determine the learning methods used in elementary schools. The learning method used must be adapted to the learning material and learning media that will be used in the teaching and learning process. With the learning method, it is expected that the teaching and learning process can be carried out well so that it can improve the quality and quantity of students in the future. Effective learning methods can facilitate the implementation of the learning process and facilitate students in absorbing the material conveyed by the instructor.
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Nurohmat, Nurohmat. "The Effect of Online Learning on Students’ English Learning Achievement." Scripta : English Department Journal 7, no. 2 (September 30, 2020): 58–65. http://dx.doi.org/10.37729/scripta.v7i2.831.
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The Covid-19 pandemic has brought big changes in teaching and learning activities in the country. Learning activities have changed from face-toface to online teaching and learning. Studying online changes the learningexperience for many students. Rather than sitting in school and taking notes, students are taking advantage of online classes and other forms of online learning. Materials and Methods: Survey and interview. Interviews were conducted with the homeroom teacher. Survey of student scores in the semester before the Covid-19 pandemic and during the pandemic. Results: Based on the results obtained, on-line learning has little effect on learning achievement. Online learning has several advantages and disadvantages over learning face-toface. Online learning offers a potentially better alternative if it is adapted to the situation and conditions of the students.
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Zidny, Robby, and Ingo Eilks. "Learning about Pesticide Use Adapted from Ethnoscience as a Contribution to Green and Sustainable Chemistry Education." Education Sciences 12, no. 4 (March 22, 2022): 227. http://dx.doi.org/10.3390/educsci12040227.
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This study shows how students can learn about green and sustainable chemistry by using the knowledge perspective of a local indigenous culture as a starting point. This learning approach encourages students to learn chemistry by connecting culture with learning about green chemistry. The cultural context selects the use of a phytochemical agent (d-limonene) from a local plant (Citrus grandis), which is a traditional botanical pesticide that is used by the Baduy tribe in Western Java, Indonesia. A simple low-cost extraction method that uses a kitchen microwave was chosen to introduce modern green extraction methods to learners. This facilitates the students’ ability to evaluate the “greenness” of this method, as compared to more conventional extraction methods, such as Soxhlet and steam distillation. The learning activities were conducted during the COVID-19 pandemic with the help of an online learning management system (SPADA) and Google Meet. The students’ feedback and selected results are discussed below.
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Fehaima, Amaria. "The leading methodology for researching ideal methods in an ESP classroom." Global Journal of Foreign Language Teaching 12, no. 3 (August 31, 2022): 124–31. http://dx.doi.org/10.18844/gjflt.v12i3.6383.
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Studying ESP involves more than simply learning a collection of technical linguistic words; it is about learning how to use English in a particular situation while also including the necessary skills and language learning objectives. The current study aimed to examine the rationale for employing the translation method to teach ESP to third-year computer science students at the University of Tlemcen. This study employed a case study approach. A questionnaire was administered to ESP the teachers in the department of Science and Technology, and classroom observations on third-year students of computer science were conducted. The study's findings indicated that students pursuing third-year computer science courses at Tlemcen University experience a range of difficulties while interacting with professional resources. The proposed translation method in ESP may be incorporated and adapted as an effective technique for ESP teachers and students to improve learners’ language skills related to their academic field. Keywords: Content-based approach; ESP Context; ESP courses; ESP Students.
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Cherif, Youssouf Ismail, and Abdelhakim Dahimene. "IMPROVED VOICE-BASED BIOMETRICS USING MULTI-CHANNEL TRANSFER LEARNING." IADIS INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS 15, no. 1 (October 7, 2020): 99–113. http://dx.doi.org/10.33965/ijcsis_2020150108.
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Identifying the speaker has become more of an imperative thing to do in the modern age. Especially since most personal and professional appliances rely on voice commands or speech in general terms to operate. These systems need to discern the identity of the speaker rather than just the words that have been said to be both smart and safe. Especially if we consider the numerous advanced methods that have been developed to generate fake speech segments. The objective of this paper is to improve upon the existing voice-based biometrics to keep up with these synthesizers. The proposed method focuses on defining a novel and more speaker adapted features by implying artificial neural networks and transfer learning. The approach uses pre-trained networks to define a mapping from two complementary acoustic features to a speaker adapted phonetic features. The complementary acoustics features are paired to provide both information about how the speech segments are perceived (type 1 feature) and produced (type 2 feature). The approach was evaluated using both a small and large closed-speaker data set. Primary results are encouraging and confirm the usefulness of such an approach to extract speaker adapted features whether for classical machine learning algorithms or advanced neural structures such as LSTM or CNN.
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Beck, Daniel, Trevor Cohn, Christian Hardmeier, and Lucia Specia. "Learning Structural Kernels for Natural Language Processing." Transactions of the Association for Computational Linguistics 3 (December 2015): 461–73. http://dx.doi.org/10.1162/tacl_a_00151.
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Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.
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Tumbel, Noldin Jerry, Deisye Supit, and Citra Anjeli Suoth. "Hubungan antara Metode Pembelajaran Diskusi dan Keaktifan Belajar Siswa Mata Pelajaran IPS." DIKDAS MATAPPA: Jurnal Ilmu Pendidikan Dasar 5, no. 2 (July 17, 2022): 368. http://dx.doi.org/10.31100/dikdas.v5i2.2027.
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The purpose of this study was to determine the relationship between discussion learning methods and student learning activities in social studies subjects. The researcher adapted the questionnaire from Khasanah (2021) and Diendrich (1992) and then distributed it to 125 students of class VIII A, B, C, and D at Adventist Junior High School Airmadidi Unklab to find out whether the discussion learning method was often used for student learning activities in social studies subjects. This research is a quantitative research that is descriptive and correlative, used to answer the formulation of the problem. The purpose of this study was to determine the relationship between discussion learning methods and student learning activities in social studies subjects. The researcher adapted the questionnaire from Khasanah (2021) and Diendrich (1992) and then distributed it to 125 students of class VIII A, B, C, and D at Adventist Junior High School Airmadidi Unklab to find out whether the discussion learning method was often used for student learning activities in social studies subjects. This research is a quantitative research that is descriptive and correlative, used to answer the formulation of the problem.
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Lin, Jianxin, Yijun Wang, Zhibo Chen, and Tianyu He. "Learning to Transfer: Unsupervised Domain Translation via Meta-Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11507–14. http://dx.doi.org/10.1609/aaai.v34i07.6816.
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Unsupervised domain translation has recently achieved impressive performance with Generative Adversarial Network (GAN) and sufficient (unpaired) training data. However, existing domain translation frameworks form in a disposable way where the learning experiences are ignored and the obtained model cannot be adapted to a new coming domain. In this work, we take on unsupervised domain translation problems from a meta-learning perspective. We propose a model called Meta-Translation GAN (MT-GAN) to find good initialization of translation models. In the meta-training procedure, MT-GAN is explicitly trained with a primary translation task and a synthesized dual translation task. A cycle-consistency meta-optimization objective is designed to ensure the generalization ability. We demonstrate effectiveness of our model on ten diverse two-domain translation tasks and multiple face identity translation tasks. We show that our proposed approach significantly outperforms the existing domain translation methods when each domain contains no more than ten training samples.
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Zakrepina, Alla V., Tatyana Yu Butusova, Elena A. Kinash, and Alyona I. Kamelkova. "Analysis of Learning Activities Dynamics among First-Graders with Intellectual Disabilities." Integration of Education 26, no. 2 (June 30, 2022): 266–96. http://dx.doi.org/10.15507/1991-9468.107.026.202202.266-296.
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Introduction. The article is devoted to the issue of learning activity of first-graders with intellectual disabilities, the relevance of which is dictated by the search for factors of effectiveness of the educational process. In the theory and practice of remedial education of learners of the studied category much attention is paid to their learning skills in the learning process, but for the first time the most significant in the dynamics of the ratio of opportunities and achievements in mastering the content of the adapted educational program is considered. The purpose of the study is to analyze the learning opportunities and the results of the performance of academic tasks in the subject areas of adapted programs by first graders with intellectual disabilities. Materials and Methods. The sample included 95 first-graders with intellectual disabilities. The following methods were used to process research results: standardized observation to explore learning opportunities and learning outcomes; pedagogical testing to study the results of the implementation of educational tasks in accordance with the subject areas of the adapted training programs (Russian, mathematics, etc.); Wilcoxon T-test, Spearman correlation analysis, principal component factor analysis followed by varimax-normalized matrix rotation, Statistica version 10. Results. We identified statistically significant variance in the structure of correlations between learning opportunities and the results of completing educational tasks in the subject areas of adapted programs at the current and intermediate cut in subjects studying for options 1 and 2 of ABEP. We have noted the variability of the factor structure of learning opportunities and the results of the implementation of educational tasks in the subject areas of ABEP at the current and intermediate cut in both groups. We found obvious advantages of practice-oriented skills in assignments in subjects such as visual activity, music, manual labor, etc. and the ways of their implementation (by joint actions, by showing and imitating) over verbal assignments (in subjects – Russian language, mathematics, reading, etc.) and methods of their implementation (orally, according to verbal instructions), which allows us to consider the importance of the practice-oriented skills in teaching first-graders with intellectual disabilities. Discussion and Conclusion. The results of empirical research make a significant contribution to special pedagogy, to its methodological resource, as they expand not only the understanding of the possibilities and results of learning of modern learners with intellectual disabilities, but specify the determinants of the dynamics of their learning activities.
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Marks, Diane B. "Flipping The Classroom: Turning An Instructional Methods Course Upside Down." Journal of College Teaching & Learning (TLC) 12, no. 4 (October 6, 2015): 241–48. http://dx.doi.org/10.19030/tlc.v12i4.9461.
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Higher education and teacher education in particular are entering a time of transformation. With major forces like shifting demographics, new technologies, and the move from an industrial to an information society, teacher educators need not only to prepare teachers for new ways of teaching, but also must adopt and model best practices for these new teaching methods. This study examines how several key strategies from Flipped Classroom theory can be adapted to an instructional methods course. Findings show that with careful curriculum design, both content and methods learning objectives can be taught and mastered with Flipped Classroom methods.
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Rivaldy Hermawan, Muhammad. "Teaching Method As Capital In Information Exchange Activities." Devotion Journal of Community Service 3, no. 8 (June 14, 2022): 783–89. http://dx.doi.org/10.36418/dev.v3i08.181.
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Teaching methods can be adapted to the type of activity carried out and also adapted to the subjects being taught. Basically deciding on a teaching method in a teaching activity is very difficult to determine and it is impossible to apply only one method, it is possible that there will be collaboration between teaching methods so that they can complement each other. With the hope that the learning and teaching process goes well and can be accepted by students. This study aims to determine and analyze the use of learning methods in an effort to increase student learning outcomes to the maximum in teaching and learning activities. This study uses both quantitative and qualitative research data obtained from various journals. The research method in this article uses a systematic literature review approach adopted from Keathley and Van Aken (2013). The stages of literature review are carried out through several stages, namely article selection, article review, and analysis of findings. The results of the study explain that the methods that can be used in learning activities that can be understood by students must be based on the strengths and weaknesses of students. Teachers are required to meet these deficiencies from each student such as developing learning programs, using assistive media that can stimulate students to actively participate in teaching and learning activities. From this research it can be found that the importance of implementing appropriate and effective methods is an obligation. With the hope that learning activities will run as planned and not deadlocked. Therefore, some methods of teaching and learning activities that are deemed appropriate and effective may be applied in the actual learning process.
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Brehar, Raluca, Delia-Alexandrina Mitrea, Flaviu Vancea, Tiberiu Marita, Sergiu Nedevschi, Monica Lupsor-Platon, Magda Rotaru, and Radu Ioan Badea. "Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images." Sensors 20, no. 11 (May 29, 2020): 3085. http://dx.doi.org/10.3390/s20113085.
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The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.
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Abdullah, Moch Asmawi, and Samsudin. "INCREASING LEARNING OUTCOMES RHYTMIC GYMNASTICS ACTIVITIES THROUGH ACTIVE LEARNING APPROACHES." International Journal of Engineering Technologies and Management Research 6, no. 10 (April 21, 2020): 65–70. http://dx.doi.org/10.29121/ijetmr.v6.i10.2019.581.
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The application of the rhythmic gymnastics learning approach must be in accordance with thecharacteristics of students, for that it requires an innovative and active learning approachaccording to all the existing characters. The method used is action research using Kemmis andMc. Taggart with the design of planning, observation, action and reflection. The results showedan increase in rhythmic gymnastics learning outcomes. This is evidenced by the average in thepre cycle which is 43.2 with a percentage of 42.29%, in the first cycle increased to 63.9 with apercentage of 55.26% and in the second cycle increased to 87.3 with a percentage of 100%. So,it can be concluded that the active learning approach can improve the learning outcomes ofrhythmic gymnastics. Because the application of an active learning approach is adapted to thecharacteristics of students who want to learn and facilitate the learning process inunderstanding the rhythmic gymnastics methods.
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Moşteanu, Narcisa. "Teaching techniques adapted for online delivery to achieve course learning outcomes in a virtual environment." Journal of Digital Art & Humanities 2, no. 2 (December 29, 2021): 33–50. http://dx.doi.org/10.33847/2712-8148.2.2_3.
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Today we are moving from traditional learning to e-learning via digital means. The entire humanity learned how to adapt overnight to digital life and leave the traditional doing things behind. Digitization has been around for several years, but its use has become a necessity with the Covid-19 pandemic. The blockade forced us to work remotely overnight, to use digital networks to communicate, make payments, learn, all sectors of activity had to adapt to the digital age in one night. This paper shows how the teaching and learning approaches need to adapt to new communication requirements and students' needs to achieve course learning outcomes in a virtual environment. This paper uses both a quantitative and qualitative method to analyze the professors and students’ perspectives on the techniques of online teaching-learning, during the isolation period and after, and what are the best methods recommended to be used for online learning taking into consideration how students can maintain their class’s attention and how can get actively involved in a learning process. The value of this study is to develop a holistic image of online teaching-learning-assessment activities, to ensure the efficiency and quality of the educational process in the university environment.
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Parwita Setya Wardhani, Rika Yuliastanti, Annisa Silfia, M Fahrudin Ali Fikri, Agus Hendrawan, Novita Sari, and Erick Fajar Subhekti. "Evaluation of online learning implementation as learning culture change in pandemic time." World Journal of Advanced Research and Reviews 14, no. 2 (May 30, 2022): 680–85. http://dx.doi.org/10.30574/wjarr.2022.14.2.0487.
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The Covid-19 pandemic has changed the learning culture from offline learning to online. Various online learning methods are carried out and adapted to conditions in the existing environment. From the implementation of this change in learning culture, it is necessary to evaluate its sustainability, especially the problems faced during the implementation of online learning. This research was conducted to see how the implementation of online learning in the Postgraduate Program at Lambung Mangkurat University. By apply a qualitative method in this study, we involve the competent informants being the Head of the Study Program, Lecturer and students in two study programs, namely the Biology Education and Basic Principles and Environmental Ethics (PSDAL). The main data is obtained from 6 informants and available secondary data. The results of the evaluation can be seen from the readiness of human resources, in this case lecturers and students are ready and have adapted well. E-Learning is re-integrated with the academic system so that data from the learning process in E-learning is reported directly to the campus academic system. Blended learning method one of the learning models that can be applied through the use of technology-based media used in the two study programs. This online learning has a positive impact on students with a higher level of independence, as well as for lecturers with a higher level of creativity in delivering material. Meanwhile, the obstacles to online learning are signal interference and quota fees (although the campus has provided a subsidy of IDR 200,000/month, which is still not enough).
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Miloserdova, Elena M. "Methods for teaching professional terminology in the context of distance learning." Tambov University Review. Series: Humanities, no. 193 (2021): 120–25. http://dx.doi.org/10.20310/1810-0201-2021-26-193-120-125.
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The features of the methods for teaching professional terminology in a distance format are considered. The main components, means and principles on which the lessons are based are indicated. The most popular and functional online educational platforms are presented and de-scribed. In addition, we aim to present and structure the components of an online lesson that are essential for the successful and productive assimilation of new language material. The stages of work with professionally adapted text are considered in detail. Examples of assignments for all types of pre-text, during-text and post-text work are given. A generalized structure and characteristics of the main components of an online lesson when working with professional nomenclature are given. It is concluded that each section and aspect of the methods of foreign language teaching has its own characteristics of work associated with the stages and means used in the classroom, passing through the same stages as the classroom; that teaching the language of a specialty in the conditions of distance education can fully replace offline learning, provided that the material of the lesson is adequately selected, the correct step-by-step course of the lesson. The work is addressed to teachers of Russian as a foreign language, teachers of Russian as a non-native language in secondary school.
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Zhou, Yi, Lulu Liu, Haocheng Zhao, Miguel López-Benítez, Limin Yu, and Yutao Yue. "Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges." Sensors 22, no. 11 (May 31, 2022): 4208. http://dx.doi.org/10.3390/s22114208.
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With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.
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Anggraeni, Dewi, and Saiful Bahri. "Development of Blended Learning in Flipped Learning-Based in Islamic Education Learning During the Covid-19 Pandemic." Al-Insyiroh: Jurnal Studi Keislaman 8, no. 1 (March 22, 2022): 90–109. http://dx.doi.org/10.35309/alinsyiroh.v8i1.4451.
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This study aims to develop a learning model for Islamic Education based on Flipped Learning. The method used in this research is the method of research and development (Research & Development). The R&D method is a research method used to produce products and test their effectiveness. The model used by the researcher adapted the ADDIE development model. Based on the needs analysis stage, student analysis, curriculum analysis, material analysis, and analytical analysis and learning methods are carried out. There is a need for the development of PAI learning media with a local wisdom approach. Media development based on the validity test carried out by media experts and material experts obtained the average validation result by media experts, namely 87.133%. In comparison, the impact of the validity test by material experts was 86.75%. The results of field trials on users are excellent. The average score of students was 86.5%, and educators were 86.5%.
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Ding, Alison, Jiacheng Wang, and Yingjie Hang. "Efficient Sensor Calibration Via Machine Learning-Based Resampling Methods." ECS Meeting Abstracts MA2022-02, no. 61 (October 9, 2022): 2223. http://dx.doi.org/10.1149/ma2022-02612223mtgabs.
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It remains a challenge to accurately and precisely calibrate a sensor in an experimentally-efficient manner. The calibration task at hand is to quantify the functional relationship between the sensor’s response (denoted as y) and the target analyte concentration (denoted as x) based on laboratory experiments. In practical use, the sensor’s mathematical calibration model needs to be coupled with the device to make inference regarding the concentration of the analyte in an environment based on the observed sensor response. It is critical to provide a single-value estimate for the analyte concentration, as well as to quantify the uncertainty of the target estimate (i.e., to provide the possible range of the underlying concentration level). However, classic statistical methods are not able to provide valid inference on the target quantity of interest due to the following difficulties in sensor calibration. First, not only the typical forward modeling but also the inverse estimation is involved. The former refers to the generation of a calibration model functionally relating the analyte concentration to the sensor response (x→y) based on experimental data; The latter concerns the use of the calibration model in practical setting to obtain from the observed response the estimation of the unknown concentration (y→x). Second, the variability of the sensor responses tends to be heterogeneous as opposed to homogeneous, which is an assumption that classic statistical methods rely on. In light of the above, a computational resampling method was adapted in this work to address the machine learning (ML)-based inference issues (i.e., the uncertainty quantification of the estimated analyte concentrations). Built on the inference capability, a two-stage experimental design procedure was developed to guide the efficient sampling of calibration data via laboratory experiments. The ML-based inference and experimental design methods are integrated in the chemometric calibration procedure, which was applied on a simulated biosensor to demonstrate its efficiency over traditional methods. The sensor simulator is developed from the real experimental data for a paper-based lateral flow strip (PLFS) designed to measure in blood the diagnosis biomarkers for traumatic brain injury. PLFS is finding increasing applications in detection of various diseases in point-of-care testing settings. Figure 1
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Seo, Sambu, and Klaus Obermayer. "Soft Learning Vector Quantization." Neural Computation 15, no. 7 (July 1, 2003): 1589–604. http://dx.doi.org/10.1162/089976603321891819.
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Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function based on a likelihood ratio and derive a learning rule using gradient descent. The new approach provides a way to extend the algorithms of the LVQ family to different distance measure and allows for the design of “soft” LVQ algorithms. Benchmark results show that the new methods lead to better classification performance than LVQ 2.1. An additional benefit of the new method is that model assumptions are made explicit, so that the method can be adapted more easily to different kinds of problems.
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Nazelliana, Dian. "Aplikasi Pembelajaran Anatomi Tubuh Manusia berbasis Android untuk Siswa Sekolah Dasar." Jurnal Informatika Universitas Pamulang 5, no. 4 (December 31, 2021): 612. http://dx.doi.org/10.32493/informatika.v5i4.7932.
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SDN Beji Timur 3 is an elementary school in the Depok area. The delivery of cognitive learning material in this school using conventional methods has an effect on the effectiveness of student learning at school. Learning activities have two important elements in the form of teaching methods and learning media and the two are interrelated. The process of selecting teaching methods is also adapted to the learning media. Several aspects need to be considered in relation to the objectives of learning activities, types of tasks. Another aspect is the response of students during learning activities including student characteristics. Learning media has the main function as a tool for educators to teach that is influenced by climate, conditions, and the environment. Efforts to improve effective learning methods to accelerate the learning process so that learning materials are delivered interestingly at the right time. This study answers this learning problem by creating an Android-based learning media application to explain material about the introduction of human anatomy.
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Latha, R., and Ramakrishnan. "EFFECTIVENESS OF BLENDED LEARNING CLASSROOM IN COMPUTER SCIENCE TEACHING AND LEARNING." International Journal of Research -GRANTHAALAYAH 7, no. 10 (June 13, 2020): 174–79. http://dx.doi.org/10.29121/granthaalayah.v7.i10.2019.383.
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In recent trends teaching and learning become very interesting due to the innovative methods of teaching using many technologies and practical tools. This study is dealing with new trends in education that can help one to become a modern teacher. Students like the teaching of modern teachers because they can actively participate in the learning process. E-content, Video Lessons and Online teaching and many other tools are used to present the subject innovatively and productively. Blended learning classroom is the latest educational technique that is being adapted in modern classrooms to enhance the learning experience of the students. The researcher is a Computer Science educator and she prepared a video lesson on "Working with Linux - Ubuntu" which is in Volume-I, Unit-I, Chapter 5 of XI Standard Computer Science text book in Tamil Nadu State Board Samacheer Kalvi new syllabus. The conclusion of her study reveals that blended learning classroom has positive impact on XI Std pupil's academic fulfilment of Computer Science.
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Hou, Zejiang, Julian Salazar, and George Polovets. "Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation." Transactions of the Association for Computational Linguistics 10 (2022): 1249–65. http://dx.doi.org/10.1162/tacl_a_00517.
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Abstract Large pretrained language models (PLMs) are often domain- or task-adapted via finetuning or prompting. Finetuning requires modifying all of the parameters and having enough data to avoid overfitting while prompting requires no training and few examples but limits performance. Instead, we prepare PLMs for data- and parameter-efficient adaptation by learning to learn the difference between general and adapted PLMs. This difference is expressed in terms of model weights and sublayer structure through our proposed dynamic low-rank reparameterization and learned architecture controller. Experiments on few-shot dialogue completion, low-resource abstractive summarization, and multi-domain language modeling show improvements in adaptation time and performance over direct finetuning or preparation via domain-adaptive pretraining. Ablations show our task-adaptive reparameterization (TARP) and model search (TAMS) components individually improve on other parameter-efficient transfer like adapters and structure-learning methods like learned sparsification.
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Richardson, Pamela. "Participatory Video (remote, online): Participatory research methods for sustainability ‐ toolkit #2." GAIA - Ecological Perspectives for Science and Society 31, no. 2 (July 15, 2022): 82–84. http://dx.doi.org/10.14512/gaia.31.2.4.
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Participatory Video (PV) is geared towards fostering dialogue around a shared issue. Videos developed with smartphones and similar devices are created by participants as a tool for communication and reflection, which can support many different steps along a participatory research journey. Videos communicate the participants’ perspectives, while the integral group-based learning process creates a space for critical reflection, solidarity-building, skills development and the inclusion of less literate participants. In the context of the COVID-19 pandemic, the method has recently been adapted for online, remote facilitation, which entails ethical challenges and time commitments, but can effectively support communication across social and physical distances.
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Vilceanu, M. Olguta. "Using Insights from Learning Journals to Rewrite Teaching and Learning Goals in Student-Friendly Language." International Journal of Sociotechnology and Knowledge Development 7, no. 1 (January 2015): 33–44. http://dx.doi.org/10.4018/ijskd.2015010103.
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Understanding the course mission and learning goals is an essential step for students in any learning environment. Reflective writing assignments may assist instructors in their quest to reformulate teaching and learning goals in a language accessible to students, while maintaining the mission and intent of the class relevant to their professional field and requirements. This study proposes using software-driven content analysis methods borrowed from media and communication research in an attempt to first identify the degree and manner in which students internalize the language of instruction; and second, rewrite course/training materials in a language that reflects student discourse. Because it can use either free-form or formal writing, this strategy can be adapted for a variety of interdisciplinary contexts.
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Amerstorfer, Carmen M. "Past its expiry date? The SILL in modern mixed-methods strategy research." Studies in Second Language Learning and Teaching 8, no. 2 (July 24, 2018): 497–523. http://dx.doi.org/10.14746/ssllt.2018.8.2.14.
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Has the Strategy Inventory for Language Learning (SILL) passed its expiry date? The SILL (Oxford, 1990) was designed as a self-evaluation tool to measure the frequency of language learning strategies used by foreign and second language (L2) learners. With simple mathematics, learners can analyze their strategy preferences overall and in six categories (i.e., memory, cognitive, compensation, metacognitive, affective, and social strategies). Diverging from its original purpose, the SILL became the most popular instrument in LLS research, which brought widespread acclaim but also criticism. This article explains what makes the SILL an extraordinary tool for learners, teachers, and researchers and how it can be adapted to suit specific contexts and the demands of a modern world. An example of how the SILL can be integrated into mixed-methods research demonstrates how the instrument can fulfil additional purposes to those originally intended. Despite its naturally quantitative orientation, the SILL contributed to the acquisition of rich qualitative information, which enabled a holistic view of five individual L2 learners. In addition to new insights about strategic L2 learning, the study attests that the SILL has not expired yet, but perhaps needs a modern touch, for instance, in the form of adaptation or combination with other research methods and the inclusion of strategies for learning language with technology.