Types Of Learning Methods Research Articles

Prediction of Heavy Metals Concentration in the Groundwater Resources in Razan Plain: Extreme Learning Machine vs. Artificial Neural Network and Multivariate Adaptive Regression Spline

Background: The groundwater is known as a major water source for domestic, industrial and agricultural purposes in the Razan Plain. Therefore, the prediction of toxic and essential elements (arsenic, lead, and zinc) content in groundwater resources of this area is important. Objectives: The main aim of this study was to investigate the extreme learning machine model as a novel model for the prediction of heavy metals concentration at Razan Plain, Hamedan province, Iran. Methods: In this descriptive study, a total of 60 groundwater specimens were collected from 20 semi-deep and deep wells across the studied area. After preparing the specimens in the laboratory, the elements’ content was detected using inductively coupled plasma-optical emission spectrometry (ICP-OES) in three replicates. In the next step, three types of machine learning methods, including, extreme learning machine (ELM), artificial neural network (ANN) and multivariate adaptive regression spline (MARS) were used to predict the heavy metals concentration in groundwater resources in Razan Plain, Hamedan, Iran. The models were trained using training data (the first 80% of the data) to find optimum values for weights and biases followed by testing using testing data (the first 20% of the data) collected from the study area. The used data were representative of the concentration of the As, Zn and Pb in Razan Plain. Three evaluation measures, correlation coefficient (r), coefficient of determination (R2) and root mean square error (RMSE) were applied to investigate the accuracy of models in estimation of heavy metals concentration. Results: The results showed that the mean content (µg/L) of the analyzed elements in groundwater samples of Razan Plain was 6.35 for As, 5.24 for Pb, and 32.4 for Zn. In addition, based on the findings the superiority of ELM was confirmed compared with the ANN and MARS models. ELM model decreased RMSE for ANN and MARS by 39.8% and 47.8% for As, 38.5% and 59.8% for Zn, and 64.4% and 75.5% for Pb, respectively. The results indicated that the ELM model can be successfully utilized for predicting heavy metals concentration in groundwater resources. Conclusions: The developed ELM approach can be successfully applied to estimate the concentration of As, Pb, and Zn in Razan Palin.

Predicting Convergence Rate of Namaklan Twin Tunnels Using Machine Learning Methods

Convergence prediction of tunnels has always been one of the important issues of geotechnical projects. Developing prediction models is a good approach to predict convergence, when there is no knowledge of the possibility of convergence occurrence in the future. In this study, convergence rates of two tunnels from the Namaklan twin tunnel were predicted by different types of machine learning methods. Artificial neural networks (ANNs), multivariate linear regression (MLR), multivariate nonlinear regression (MNR), support vector regression (SVR), Gaussian process regression (GPR), regression trees and ensembles of trees (ET) were applied to predict the convergence rate (CR). The six parameters of cohesion (c), internal friction angle (Φ), uniaxial compressive strength of rock mass (σc), rock mass rating, overburden height (H) and the number of installed rock bolts (NB) were selected as predictor parameters. The dataset was collected via field investigations and laboratory experiments. The results showed that the MLP–ANN model can successfully predict the CR with the determination coefficient (R2) of 0.93. The RBF–ANN model is also successful in predicting the CR with R2 of 0.81. The SVR, MNR and MLR models were also constructed to obtain an empirical formula for predicting the CR. Comparing among the three models showed that the SVR model is more successful (R2 = 0.66) than the MNR and MLR models with R2 of 0.65 and 0.61, respectively. However, the SVR model is placed in the next rank of the ANN models. Among the rest models, except the ET model (R2 = 0.66), the RT and GPR models have no good capability for the prediction of the CR. In total, assessing the statistical indices indicated that the ANNs are superior to the other models in predicting the CR. However, the SVR model could be considered to be a reliable predictive model for convergence rate estimation.

Efektivitas Metode Cooperatif Learning dan Jig Saw Learning Terhadap Belajar Siswa

Learning outcomes are the culmination of all teaching and learning activities, because they are part of the benchmarks for the assessment of learners and teachers through various learning patterns and methods within a certain period of time. This is to become a new and further framework for teaching and learning activities (KBM). 
 Learning outcomes are also inspired by methods from the teacher to students and their feedback. Various types of learning methods have been used as a learning formulation, one of which is Jig Saw Learning, this method is an interesting strategy to use if the material to be studied can be divided into several parts and the material is not in the order. The advantages of this strategy are all students learn and teach others. 
 This method is can be implemented in various subjects with supporting capacities and tools such as syllabus, implementation of learning (RPP), student activity sheets (LKS), formative tests, and test validity. In this study the authors conducted a study to what extent the effectiveness of the Jig Saw Learning method was applied in Indonesian language lesson by comparing the theory with the application of the method, not only on learning outcomes, but also on students' understanding and mastery of the subject matter.

Factors promoting informal and formal learning strategies among school leaders

ABSTRACT The professional development of principals is a key element in improving the quality of educational systems. Although it is common for the training of educational leaders to be associated to formal pre- and in-service principal development programmes, informal learning opportunities are increasingly common. However, research analysing informal learning processes among this professional group is scarce. The purpose of this paper is to identify the factors promoting informal learning strategies, online and face-to-face, used by principals as part of their professional development. The methodology involved implementing an online self-administered questionnaire to a sample of 715 school leaders. The study begins with a univariate and bivariate descriptive analysis. Three multiple regression models are then developed, which allow to check the independent effects among the variable criteria used and the three types of learning methods under consideration. The results show that the development of formal and informal learning activities among principals clearly requires a ‘learning-friendly’ organisational culture, a clear motivation for the profession, a strong professional determination and a positive perception of professional competencies as principals. Schools should start promoting the transition from job-based to competence-based staff systems and structures, in which informal learning strategies become relevant as a basis for promoting innovation.

Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption

This paper presents a comparison of the impact of various unsupervised ensemble learning methods on electricity load forecasting. The electricity load from consumers is simply aggregated or optimally clustered to more predictable groups by cluster analysis. The clustering approach consists of efficient preprocessing of data obtained from smart meters by a model-based representation and the K-means method. We have implemented two types of unsupervised ensemble learning methods to investigate the performance of forecasting on clustered or simply aggregated load: bootstrap aggregating based and the newly proposed density-clustering based. Three new bootstrapping methods for time series analysis methods were newly proposed in order to handle the noisy behaviour of time series. The smart meter datasets used in our experiments come from Australia, London, and Ireland, where data from residential consumers were available. The achieved results suggest that for extremely fluctuating and noisy time series the forecasting accuracy improvement through the bagging can be a challenging task. However, our experimental evaluation shows that in most of the cases the density-based unsupervised ensemble learning methods are significantly improving forecasting accuracy of aggregated or clustered electricity load.

Deep learning-based methods for person re-identification: A comprehensive review

In recent years, person re-identification (ReID) has received much attention since it is a fundamental task in intelligent surveillance systems and has widespread application prospects in numerous fields. Given an image of a pedestrian captured from one camera, the task is to identify this pedestrian from the gallery set captured by other multiple cameras. It is a challenging issue since the appearance of a pedestrian may suffer great changes across different cameras. The task has been greatly boosted by deep learning technology. There are mainly six types of deep learning-based methods designed for this issue, i.e. identification deep model, verification deep model, distance metric-based deep model, part-based deep model, video-based deep model and data augmentation-based deep model. In this paper, we first give a comprehensive review of current six types of deep learning methods. Second, we present the detailed descriptions of existing person ReID datasets. Then, some state-of-the-art performances of methods over recent years on several representative ReID datasets are summarized. Finally, we conclude this paper and discuss the future directions of the person ReID.

Metode Problem Base Learning (PBL) dalam Peningkatan Pemahaman Fikih Kontemporer

method Problem Based Learning is one type of learning method based on cognitive psychology that assumes that learning is a process of changing behavior thanks to experience. Judging from the philosophical aspects of the function of the school as an arena or container to prepare students to be able to live in society, then this method is a possible and very important strategy to develop. Problem solving is seen as a process to find a combination of a number of rules that can be applied in an effort to overcome a new situation. So that this learning model as a way of presenting lesson material by making the problem as the starting point of the discussion to be analyzed and synthesized in an effort to find a solution to the problem being studied. Moreover, the use of the right method and approach will produce an understanding that can be tested to be used as a benchmark in conducting the next learning process, especially if using a problem-based approach in conducting a religious study by raising problems. At the same time, deliberation together in order to obtain answers to the problem with the classic books or the latest fatwas as a reference of the opinions put forward. In addition, the use of problem-based methods also has advantages over other types of learning methods, such as lecture and question and answer methods. Especially in solving various problems in the field of Masailul Fiqh or Bahtsul Masail.

A Comparative Study of State-of-the-Art Deep Learning Algorithms for Vehicle Detection

In recent years, the deep learning object detection algorithms using 2D images have become the powerful tools for road object detection in autonomous driving. In fact, the deep learning methods for road vehicle detection have achieved the remarkable results. Although there have been a large number of studies that thoroughly explored various types of deep learning methods for vehicle detection, there are a few studies that compare and evaluate the detection time and detection accuracy of the mainstream deep learning object detection algorithms for vehicle detection. Here, this article compares five mainstream deep learning object detection algorithms in vehicle detection, namely the faster RCNN, R-FCN, SSD, RetinaNet, and YOLOv3 on the KITTI data and analyze the obtained results. The detection time and detection accuracy of the five object-detection algorithms on the KITTI test set are compared and analyzed; the PR curve and AP value are used to evaluate the detection accuracy.

Comparison of medical image classification accuracy among three machine learning methods.

Low-quality medical images may influence the accuracy of the machine learning process. This study was undertaken to compare accuracy of medical image classification among machine learning methods, as classification is a basic aspect of clinical image inspection. Three types of machine learning methods were used, which include Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolution Neural Network (CNN). To investigate changes in accuracy related to image quality, we constructed a single dataset using two different file formats of DICOM (Digital Imaging and Communications in Medicine) and JPEG (Joint Photographic Experts Group). The JPEG format contains less color information and data capacity than the DICOM format. CNN classification was accurate for both datasets, whereas SVM and ANN accuracy decreased with the loss of data from DICOM to JPEG formats. CNN is more accurate than conventional machine learning methods that utilize the manual feature extraction.

Bringing active learning into the accounting classroom

PurposeThe purpose of this study is to investigate the effect of an accounting education pedagogy incorporating active learning approaches designed to engage first-year undergraduate business students and to aspire them to continue accounting as their academic major and entry into the accounting profession.Design/methodology/approachData were collected from a questionnaire with a pre-/post-test design of 24 undergraduate business students enrolled in a course titled Accounting Active Learning Seminar (AALS) (test group) and 33 students who did not participate in the AALS (control group). The AALS incorporates various types of active learning methods designed by the authors to inspire students to continue with accounting as a career choice.FindingsThe findings show that participation in the AALS improved student’s motivation in accounting education and the likelihood of choosing accounting as their academic major. The active learning methods implemented in the AALS were effective in improving students’ confidence, of which degree contributed to students’ stronger works aspiration towards accounting professions. Further it was found that students who did not participate in the AALS tended to have lower attention dimensions of motivation, which was also significantly associated with lower percentage of students’ choice of academic major in accounting.Originality/valueThis is one of the few studies to empirically examine active learning on student engagement and performance with a focus on accounting. While the evidence shows that active learning has pedagogical benefits, the full potential of active learning is more likely to be realized when accounting educators design active learning carefully to address the “attention” and “confidence” attributes.

Evaluating active learning methods for annotating semantic predications.

ObjectivesThis study evaluated and compared a variety of active learning strategies, including a novel strategy we proposed, as applied to the task of filtering incorrect semantic predications in SemMedDB.Materials and methodsWe evaluated 8 active learning strategies covering 3 types—uncertainty, representative, and combined—on 2 datasets of 6,000 total semantic predications from SemMedDB covering the domains of substance interactions and clinical medicine, respectively. We also designed a novel combined strategy called dynamic β that does not use hand-tuned hyperparameters. Each strategy was assessed by the Area under the Learning Curve (ALC) and the number of training examples required to achieve a target Area Under the ROC curve. We also visualized and compared the query patterns of the query strategies.ResultsAll types of active learning (AL) methods beat the baseline on both datasets. Combined strategies outperformed all other methods in terms of ALC, outperforming the baseline by over 0.05 ALC for both datasets and reducing 58% annotation efforts in the best case. While representative strategies performed well, their performance was matched or outperformed by the combined methods. Our proposed AL method dynamic β shows promising ability to achieve near-optimal performance across 2 datasets.DiscussionOur visual analysis of query patterns indicates that strategies which efficiently obtain a representative subsample perform better on this task.ConclusionActive learning is shown to be effective at reducing annotation costs for filtering incorrect semantic predications from SemMedDB. Our proposed AL method demonstrated promising performance.

Pharmacists' learning needs in the era of expanding scopes of practice: Evolving practices and changing needs.

Continuing professional development fosters advancement in knowledge and skills and can support changes in practice. Research suggests that pharmacists perceive the need for additional training to take on expanded roles. However, the types of professional development and preferred learning methods are unclear. To identify pharmacists' professional learning needs in order to support expanded roles in practice. A mixed-methods approach was used. In focus groups made up of pharmacists and pharmacy students, learning needs associated with their perceptions of pharmacists' roles were examined. A web-based survey of Albertan pharmacists determined the knowledge, experience, and confidence required to engage in professional activities, preferred learning methods, and professional development needed to support pharmacists currently and in future. Analysis of data obtained from 5 focus groups (n = 42) revealed learning needs related to changes in pharmacists' roles. Pharmacists valued the social aspect of learning as well as the role of certification in building confidence. Survey data (n = 416) indicated that a high proportion of pharmacists reported training needs in the areas of physical assessment, interpreting laboratory tests, and making decisions about complex drug therapy. Community pharmacists and those with bachelor degrees were more likely to indicate a need for additional training in various patient care skills. Pharmacists reported a preference for learning with peers and learning at work within teams. Meeting various learning needs will foster pharmacists in their current and future professional roles, including situated learning in the workplace. These findings will be of interest to employers and educators in supporting pharmacists' evolving roles in practice.

Language/Dialect Recognition Based on Unsupervised Deep Learning

Over the past decade, bottleneck features within an i-Vector framework have been used for state-of-the-art language/dialect identification (LID/DID). However, traditional bottleneck feature extraction requires additional transcribed speech information. Alternatively, two types of unsupervised deep learning methods are introduced in this study. To address this limitation, an unsupervised bottleneck feature extraction approach is proposed, which is derived from the traditional bottleneck structure but trained with estimated phonetic labels. In addition, based on a generative modeling autoencoder, two types of latent variable learning algorithms are introduced for speech feature processing, which have been previous considered for image processing/reconstruction. Specifically, a variational autoencoder and adversarial autoencoder are utilized on alternative phase of speech processing. To demonstrate the effectiveness of the proposed methods, three corpora are evaluated: 1) a four Chinese dialect dataset, 2) a five Arabic dialect corpus, and 3) multigenre broadcast challenge corpus (MGB-3) for arabic DID. The proposed features are shown to outperform traditional acoustic feature MFCCs consistently across three corpora. Taken collectively, the proposed features achieve up to a relative +58% improvement in $C_{\text{avg}}$ for LID/DID without the need of any secondary speech corpora.

РУССКИЙ ЯЗЫК КАК ИНОСТРАННЫЙ В КОНТЕКСТЕ МЕЖКАФЕДРАЛЬНОЙ ИНТЕГРАЦИИ В МЕДИЦИНСКОМ ВУЗЕ

The article studies the problem of training of foreign specialists for foreign public health service and the role of the Russian language in the development of future specialty. It is shown that a necessary condition for the acquisition of the future profession students in medical school is interdepartmental integration. The authors studied the necessary conditions for training of foreign specialists at the Medical Higher School, among them there is a system of factors, types of learning and teaching methods and independent work. The authors found that successful inclusion in the educational process of the University is hindered with the lack of training and skills that Russian students have, and also with the inability to work with primary sources, the low level of General training received at school, at home, etc. It is noted that the acquisition of future professional roles for foreign students relates to linguistic and psycho-pedagogical problems that are not relevant for Russian students. The efficiency of t teaching foreign students in medical higher school depends on many factors, among them there is the conscious attitude of foreign students to the choice of profession, the degree of assimilation of educational material, the quality of teaching theoretical disciplines and subjects. The studies confirm that throughout training in medical school poor knowledge of the Russian language in one way or another complicates the acquisition of special knowledge and forms the stable stereotype of communicative competence complexity. The authors conclude that the successful development of a medical specialty in a foreign environment requires interdepartmental integration of the Russian language Department and the relevant departments as a necessary condition for the acquisition of high quality knowledge and skills to get future profession.

The Effect of Scaffolded Think-Group-Share Learning on Indonesian Elementary Schooler Satisfaction and Learning Achievement in English Classes

The purpose of this study was to examine whether or not “Scaffolded Think-Group-Share” learning can have a positive effect on student satisfaction and learning achievement in English classes of an Indonesian elementary school. To achieve this purpose, this study compared the findings from the two dependent variables (i.e., student satisfaction and English learning achievement) in “Scaffolded Think-Group-Share” learning with those in “Group Investigation” and “Learning Together” learning, which are other types of cooperative learning methods. According to the findings, there were statistically significant differences in student satisfaction and English learning achievement between the Scaffolded Think-Group-Share group, the Group Investigation group, and the Learning Together group. This study implies that “Scaffolded Think-Group-Share” learning has the potential to enhance student satisfaction and comprehension in English as a foreign language (EFL) classes. Keywords: Cooperative learning, Scaffolding, Satisfaction, English learning achievement

Using machine learning techniques for occupancy-prediction-based cooling control in office buildings

Heating, ventilation, and air-conditioning (HVAC) are among the major energy demand in the buildings sector globally. Improving the energy efficiency of such systems is a critical objective for mitigating greenhouse gas emissions and transitioning towards renewable sources of energy supply. The interest of this paper is to explore means to increase the efficiency of HVAC systems in accommodating occupants’ behavior in real time. For instance, rooms in office buildings are not always occupied by occupants during scheduled HVAC service periods. This offers an opportunity to reduce unnecessary energy demands of HVAC systems associated with occupants’ behavior. An in-depth analysis of occupants’ stochastic behavior within an office building is conducted in this paper. A demand-driven control strategy is proposed that automatically responds to occupants’ energy-related behavior for reducing energy consumption and maintains room temperature for occupants with similar performances as a static cooling. In this control strategy, two types of machine learning methods – unsupervised and supervised learning – are applied to learn occupants’ behavior in two learning processes. The occupancy-related information learned by the algorithms is used by a set of specified rules to infer real-time room setpoints for controlling the office's space cooling system. This learning-based approach intends to reduce the need for human intervention in the cooling system’s control. The proposed strategy was applied to control the cooling system of the office building under real-world conditions. Eleven case study office spaces were selected, representing three typical office uses: single person offices, multi-person offices, and meeting rooms. The experimental results report between 7% and 52% energy savings as compared to the conventionally-scheduled cooling systems.

Extreme Learning Machine for Joint Embedding and Clustering

Abstract Clustering generic data, i.e., data not specific to a particular field, is a challenging problem due to their diverse complex structures in the original feature space. Traditional approaches address this problem by complementing clustering with feature learning methods, which either capture the intrinsic structure of the data or represent the data such that clusters are better revealed. In this paper, we propose an approach referred to as Extreme Learning Machine for Joint Embedding and Clustering (ELM-JEC), which incorporates desirable properties of both types of feature learning methods at the same time, specifically by (1) preserving the manifold structure of the data in the original space; (2) maximizing the class separability of the data in the embedded space. Since either type of method has improved clustering performance in some cases, our motivation is to integrate the two desirable properties to further improve the accuracy and robustness of clustering. Additional notable features of ELM-JEC are that it provides nonlinear feature mappings and achieves feature learning and clustering in the same formulation. The proposed approach can be implemented using alternating optimization, and its clustering performance compares favorably with several state-of-the-art methods on the real-world benchmark datasets.

Polynomial dictionary learning algorithms in sparse representations

Dictionary learning has been extensively studied in sparse representations. However, existing dictionary learning algorithms are developed mainly for standard matrices (i.e. matrices with scalar elements), and little attention has been paid to polynomial matrices, despite their wide use for describing convolutive signals or for modeling acoustic channels in room and underwater acoustics. In this paper, we propose a polynomial dictionary learning technique to deal with signals with time delays. We present two types of polynomial dictionary learning methods based on the fact that a polynomial matrix can be represented either as a polynomial of matrices (i.e. the coefficient in the polynomial corresponding to each time lag is a scalar matrix) or equally as a matrix of polynomial elements (i.e. each element of the matrix is a polynomial). The first method allows one to extend any state-of-the-art dictionary learning method to the polynomial case; and the second method allows one to directly process the polynomial matrix without having to access its coefficient matrices. A sparse coding method is also presented for reconstructing convolutive signals based on a polynomial dictionary. Simulations are provided to demonstrate the performance of the proposed algorithms, e.g. for polynomial signal reconstruction from noisy measurements.

Stellar Atmospheric Parameterization Based on Deep Learning

Deep learning is a typical learning method widely studied in the fields of machine learning, pattern recognition, and artificial intelligence. This work investigates the problem of stellar atmospheric parameterization by constructing a deep neural network with five layers, and the node number in each layer of the network is respectively 3821-500-100-50-1. The proposed scheme is verified on both the real spectra measured by the Sloan Digital Sky Survey (SDSS) and the theoretic spectra computed with the Kurucz's New Opacity Distribution Function (NEWODF) model, to make an automatic estimation for three physical parameters: the effective temperature (Teff), surface gravitational acceleration (lg g), and metallic abundance (Fe/H). The results show that the stacked autoencoder deep neural network has a better accuracy for the estimation. On the SDSS spectra, the mean absolute errors (MAEs) are 79.95 for Teff/K, 0.0058 for (lg Teff/K), 0.1706 for lg (g/(cm·s−2)), and 0.1294 dex for the [Fe/H], respectively; On the theoretic spectra, the MAEs are 15.34 for Teff/K, 0.0011 for lg (Teff/K), 0.0214 for lg(g/(cm · s−2)), and 0.0121 dex for [Fe/H], respectively.

Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis

It is a challenging problem to design excellent dictionaries to sparsely represent diverse fault information and simultaneously discriminate different fault sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple fault diagnosis of motor bearings. Compared with the state-of-the-art fault detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple fault feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of fault features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus weak feature leakage problem is avoided compared to typical learning methods.