Search results for: training needs assessment model

Authors: Alexandra Telitsyna, Galina Semya, Elvira Garifulina

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Introduction: Starting from 2012 Russia conducts deinstitutionalization policy and now the main indicator of success is the number of children living in institutions. Active family placement process has resulted in residents of the institution now mainly consists of adolescents with behavioral and emotional problems, children with disabilities and groups of siblings. Purpose of science research: The purpose of science research is to identify factors for child’s wellbeing while temporary stay in an orphanage and the subjective assessment of children's level of well-being (psychological well-being). Methods: The data used for this project was collected by the questionnaire of 72 indicators, a tool for monitoring the behavior of children and caregivers, an additional questionnaire for children; well-being assessment questionnaire containing 10 scales for three age groups from preschool to older adolescents. In 2016-2018, the research was conducted in 1873 institution in 85 regions of Russia. In each region a team of academics, specialists from Non-profits, independent experts was created. Training was conducted for team members through a series of webinars prior to undertaking the assessment. The results: To ensure the well-being of the children, the following conditions are necessary: 1- Life of children in institution is organised according to the principles of family care (including the creation of conditions for attachment to be formed); 2- Contribution to find family-based placement for children (including reintegration into the primary family); 3- Work with parents of children, who are placed in an organization at the request of parents; 4- Children attend schools according to their needs; 5- Training of staff and volunteers; 6- Special environment and services for children with special needs and children with disabilities; 7- Cooperation with NGOs; 8 - Openness and accessibility of the organization. Conclusion: A study of the psychological well-being of children showed that the most emotionally stressful for children were questions about the presence and frequency of contact with relatives, and the level of well-being is higher in the presence of a trusted adult and respect for rights. The greatest contribution to the trouble is made by the time the child is in the orphanage, the lack of contact with parents and relatives, the uncertainty of the future.

Keywords: identifying factors, orphans, Russia, wellbeing

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Authors: Jinyoung Choi, Tae-Ho Yoon, Sunmook Lee

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Degradation of standard oxygen transfer efficiency (SOTE) with time was observed for the assessment of lifetime of metal-based air-diffuser, which displaced a polymer composite-based air-diffuser in order to attain a longer lifetime in the actual field. The degradation of air-diffuser occurred due to the failure of the formation of small and uniform air bubbles since the patterns formed on the disc of air-diffuser deteriorated and/or changed from their initial shapes while they were continuously exposed to the air blowing condition during the operation in the field. Therefore, the lifetime assessment of metal-based air-diffuser was carried out through an accelerated degradation test by accelerating the air-blowing conditions in 200 L/min, 300 L/min, and 400 L/min and the lifetime of normal operating condition at 120 L/min was predicted. It was found that Weibull distribution was the most proper one for describing the lifetime distribution of metal-based air-diffuser in the present study. The shape and scale parameters indicated that the accelerated blowing conditions were all within the acceleration domain. The lifetime was predicted by adopting inverse power model for a stress-life relationship and estimated to be B10=94,004 hrs with CL=95%. Acknowledgement: This work was financially supported by the Ministry of Trade, Industry and Energy (Grant number: N0001475).

Keywords: accelerated degradation test, air-diffuser, lifetime assessment, SOTE

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Authors: Elizabeth A. Haruna, Bulus Kpame, Kankanala Venkateswarlu

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The interaction of health and athletic performance with biologic aging has been an interesting and intriguing area for research. There has been a general acknowledgement of its importance to major public health and elite performance outcomes. There are many questions unanswered about the mechanisms of effects and dose-response changes. An attempt has been made in this paper to highlight potentially positive effects of regular training on the aging process and its effects on health. Age associated decline in health and performance results from the combination of the aging process itself, inactive lifestyle and primary diseases. An attempt is made in this paper to critically review what is known and what is unknown about evidence based changes, common to disuse and aging. Mechanisms responsible for the slowing decline in muscle mass and muscle force (sarcopenia) down of age – associated, weakness and fatigability due to year round athletic training have been discussed. It is in this regard we have attempted to share our views on advances made so far in understanding the impact of aging on health. We also attempted to explain how the biological effects of aging are minimized during appropriate year round athletic training. On the basis of available research evidence it was concluded that exercise training significantly slow down the deleterious effects of aging on health.

Keywords: aging, atrophy, sarcopenia, plyometric training

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Authors: Gupta. Ashok, V. talakokula, S. bhalla

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Rebar corrosion is one of the main causes of damage and premature failure of the reinforced concrete (RC) structures worldwide, causing enormous costs for inspection, maintenance, restoration and replacement. Therefore, early detection of corrosion and timely remedial action on the affected portion can facilitate an optimum utilization of the structure, imparting longevity to it. The recent advent of the electro-mechanical impedance (EMI) technique using piezo sensors (PZT) for structural health monitoring (SHM) has provided a new paradigm to the maintenance engineers to diagnose the onset of the damage at the incipient stage itself. This paper presents a model based approach for corrosion assessment based on the equivalent parameters extracted from the impedance spectrum of concrete-rebar system using the EMI technique via the PZT sensors.

Keywords: impedance, electro-mechanical, stiffness, mass, damping, equivalent parameters

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Chien-kuo Chang, Bo-wei Wu, Yi-yun Tang, Min-chiu Wu

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This paper proposes a power equipment diagnosis system based on partial discharge (PD), which is characterized by increasing the readability of experimental data and the convenience of operation. This system integrates a variety of analysis programs of different data formats and different programming languages and then establishes a set of interfaces that can follow and expand the structure, which is also helpful for subsequent maintenance and innovation. This study shows a case of using the developed Convolutional Neural Networks (CNN) to integrate with this system, using the designed model architecture to simplify the complex training process. It is expected that the simplified training process can be used to establish an adaptive deep learning experimental structure. By selecting different test data for repeated training, the accuracy of the identification system can be enhanced. On this platform, the measurement status and partial discharge pattern of each equipment can be checked in real time, and the function of real-time identification can be set, and various training models can be used to carry out real-time partial discharge insulation defect identification and insulation state diagnosis. When the electric power equipment entering the dangerous period, replace equipment early to avoid unexpected electrical accidents.

Keywords: partial discharge, convolutional neural network, partial discharge analysis platform, adaptive deep learning

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Authors: Long-Shan Wu, Ming-Chen Ko, Chien-Chang Ho, Po-Fu Lee, Jenn-Woei Hsieh, Ching-Yu Tseng

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This study aimed to determine the effects of whole-body vibration (WBV) training on lower-extremity muscle strength and balance control performance among community-dwelling middle-aged and older adults in the United States. Twenty-nine participants without any contraindication of performing WBV exercise completed all the study procedures. Participants were randomly assigned to do body weight exercise with either an individualized vibration frequency and amplitude, a fixed vibration frequency and amplitude, or no vibration. Isokinetic knee extensor power, limits of stability, and sit-to-stand tests were performed at the baseline and after 8 weeks of training. Neither the individualized frequency-amplitude WBV training protocol nor the fixed frequency-amplitude WBV training protocol improved isokinetic knee extensor power. The limits of stability endpoint excursion score for the individualized frequency-amplitude group increased by 8.8 (12.9%; p = 0.025) after training. No significant differences were observed in fixed and control group. The maximum excursion score for the individualized frequency-amplitude group at baseline increased by 9.2 (11.5%; p = 0.006) after training. The average weight transfer time score significantly decreased by 0.21 s in the fixed group. The participants in the individualized group showed a significant increase (3.2%) in weight rising index score after 8 weeks of WBV training. These results suggest that 8 weeks of WBV training improved limit of stability and sit-to-stand performance. Future studies need to determine whether WBV training improves other factors that can influence posture control.

Keywords: whole-body vibration training, muscle strength, balance control, middle-aged and older adults

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Authors: Zahara Abdulhussan Al-Awadai

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In recent years, serious games have started to gain an increasing interest as a tool to support learning across different educational and training fields. It began to serve as a powerful educational tool for improving learning outcomes. In this research, we discuss the potential of virtual experiences and games research outside of the games industry and explore the multifaceted impact of serious games and related technologies on various aspects of our lives. We highlight the usage of serious games as a tool to improve education and other applications with a purpose beyond the entertainment industry. One of the main contributions of this research is proposing a model that facilitates the design and development of serious games in a flexible and easy-to-use way. This is achieved by exploring different requirements to develop a model that describes a serious game structure with a focus on both aspects of serious games (educational and entertainment aspects).

Keywords: game development, requirements, serious games, serious game model

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: Mustafa M. Donma, Orkide Donma

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Metabolic syndrome (MetS) is a disease associated with obesity. It is a complicated clinical problem possibly affecting body composition as well as macrominerals. These parameters gain further attention, particularly in the pediatric population. The aim of this study is to investigate the amount of discrete body composition fractions in groups that differ in the severity of obesity. Also, the possible associations with calcium (Ca), phosphorus (P), magnesium (Mg) will be examined. The study population was divided into four groups. Twenty-eight, 29, 34, and 34 children were involved in Group 1 (healthy), 2 (obese), 3 (morbid obese), and 4 (MetS), respectively. Institutional Ethical Committee approved the study protocol. Informed consent forms were obtained from the participants. The classification of obese groups was performed based upon the recommendations of the World Health Organization. Metabolic syndrome components were defined. Serum Ca, P, Mg concentrations were measured. Within the scope of body composition, fat mass, fat-free mass, protein mass, mineral mass were determined by a body composition monitor using bioelectrical impedance analysis technology. Weight, height, waist circumference, hip circumference, head circumference, and neck circumference values were recorded. Body mass index, diagnostic obesity notation model assessment index, fat mass index, and fat-free mass index values were calculated. Data were statistically evaluated and interpreted. There was no statistically significant difference among the groups in terms of Ca and P concentrations. Magnesium concentrations differed between Group 1 and Group 4. Strong negative correlations were detected between P as well as Mg and fat mass index as well as diagnostic obesity notation model assessment index in Group 4, the group, which comprised morbid obese children with MetS. This study emphasized unique associations of P and Mg minerals with diagnostic obesity notation model assessment index and fat mass index during the evaluation of morbid obese children with MetS. It was also concluded that diagnostic obesity notation model assessment index and fat mass index were more proper indices in comparison with body mass index and fat-free mass index for the purpose of defining body composition in children.

Keywords: children, fat mass, fat-free mass, macrominerals, obesity

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Authors: Rabi Mouhcine, Amrouch Mustapha, Mahani Zouhir, Mammass Driss

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In this paper, we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.

Keywords: recognition, handwriting, Arabic text, HMMs, embedded training

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Authors: Mohammed Moustafa, Khaled Ayad, Waleed Reda

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Shoulder impingement syndrome is the most common disorder of the shoulder, resulting in functional loss and disability. Objective: This study was designed to compare between the effects of scapular muscle training versus rhythmic stabilization exercises in treatment of shoulder impingement syndrome. Methods: Thirty patients participated in this study; they were assigned randomly into two experimental groups. The first experimental group (A) consisted of 15 patients with a mean age (21.87±2.72) years; they received graduated rhythmic stabilization exercises and stretching of the posterior capsule. The second experimental group (B) consisted of 15 patients with a mean age (22.27±2.94) years; they received scapular muscle training exercises in addition to stretching of the posterior capsule. Treatment was given three times per week, every other day, for four consecutive weeks. Patients have been evaluated pretreatment and post treatment for shoulder pain severity and functional disability. Results: Both groups showed highly statistical significant reduction in pain severity and functional disability measured post-treatment when compared with their corresponding values in pretreatment assessment. Conclusion: Both of rhythmic stabilization exercises and scapular muscle training are effective interventions to reduce shoulder pain severity and functional disability.

Keywords: impingement syndrome, scapular exercises, rhythmic stabilization exercises, posterior capsule stretch

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Authors: Layla Albdr

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Saudi Arabia instituted the policy of Sensitizing and Training Stakeholders for E-learning and witnessed wide adoption in many institutions. However, it is at the infancy stage and needs time to develop to mirror the US and UK. The majority of the higher education institutions in Saudi Arabia have adopted E-learning as an alternative to traditional methods to advance education. Conversely, effective implementation of the policy of sensitization and training of stakeholders for E-learning implementation has not been attained because of various challenges. The objectives included determining the challenges and opportunities of the E-learning policy of sensitization and training of stakeholders in Saudi Arabia's higher education and examining if sensitization and training of stakeholder's policy will help promote the implementation of E-learning in institutions. The study employed a descriptive research design based on qualitative analysis. The researcher recruited 295 students and 60 academic staff from four Saudi Arabian universities to participate in the study. An online questionnaire was used to collect the data. The data was then analyzed and reported both quantitatively and qualitatively. The analysis provided an in-depth understanding of the opportunities and challenges of E-learning policy in Saudi Arabian universities. The main challenges identified as internal challenges were the lack of educators’ interest in adopting the policy, and external challenges entailed lack of ICT infrastructure and Internet connectivity. The study recommends encouraging, sensitizing, and training all stakeholders to address these challenges and adopt the policy.

Keywords: e-learning, educational policy, Saudi Arabia, policy of sensitization and training

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Authors: Laidi Maamar, Hanini Salah

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The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Nancy Retzlaff, Sarah J. Berkemer, Trudie Strauss

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In the last couple of decades, the advent of digitalization of any kind of data was probably one of the major advances in all fields of study. This paves the way for also analysing these data even though they might come from disciplines where there was no initial computational necessity to do so. Especially in linguistics, one can find a rather manual tradition. Still when considering studies that involve the history of language families it is hard to overlook the striking similarities to bioinformatics (phylogenetic) approaches. Alignments of words are such a fairly well studied example of an application of bioinformatics methods to historical linguistics. In this paper we will not only consider alignments of strings, i.e., words in this case, but also alignments of syntax trees of selected Indo-European languages. Based on initial, crude alignments, a sophisticated scoring model is trained on both letters and syntactic features. The aim is to gain a better understanding on which features in two languages are related, i.e., most likely to have the same root. Initially, all words in two languages are pre-aligned with a basic scoring model that primarily selects consonants and adjusts them before fitting in the vowels. Mixture models are subsequently used to filter ‘good’ alignments depending on the alignment length and the number of inserted gaps. Using these selected word alignments it is possible to perform tree alignments of the given syntax trees and consequently find sentences that correspond rather well to each other across languages. The syntax alignments are then filtered for meaningful scores—’good’ scores contain evolutionary information and are therefore used to train the sophisticated scoring model. Further iterations of alignments and training steps are performed until the scoring model saturates, i.e., barely changes anymore. A better evaluation of the trained scoring model and its function in containing evolutionary meaningful information will be given. An assessment of sentence alignment compared to possible phrase structure will also be provided. The method described here may have its flaws because of limited prior information. This, however, may offer a good starting point to study languages where only little prior knowledge is available and a detailed, unbiased study is needed.

Keywords: alignments, bioinformatics, comparative linguistics, historical linguistics, statistical methods

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Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

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It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

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Authors: Zachary Pan

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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Luís Rocha, Adam Gąska, Enrico Savio, Michael Marxer, Christoph Battaglia

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The changes observed in the last years in the field of manufacturing and production engineering, popularly known as "Fourth Industry Revolution", utilizes the achievements in the different areas of computer sciences, introducing new solutions at almost every stage of the production process, just to mention such concepts as mass customization, cloud computing, knowledge-based engineering, virtual reality, rapid prototyping, or virtual models of measuring systems. To effectively speed up the production process and make it more flexible, it is necessary to tighten the bonds connecting individual stages of the production process and to raise the awareness and knowledge of employees of individual sectors about the nature and specificity of work in other stages. It is important to discover and develop a suitable education method adapted to the specificities of each stage of the production process, becoming an extremely crucial issue to exploit the potential of the fourth industrial revolution properly. Because of it, the project “Train4Dim” (T4D) intends to develop complex training material for digital manufacturing, including content for design, manufacturing, and quality control, with a focus on coordinate metrology and portable measuring systems. In this paper, the authors present an approach to using an active learning methodology for digital manufacturing. T4D main objective is to develop a multi-degree (apprenticeship up to master’s degree studies) and educational approach that can be adapted to different teaching levels. It’s also described the process of creating the underneath methodology. The paper will share the steps to achieve the aims of the project (training model for digital manufacturing): 1) surveying the stakeholders, 2) Defining the learning aims, 3) producing all contents and curriculum, 4) training for tutors, and 5) Pilot courses test and improvements.

Keywords: learning, Industry 4.0, active learning, digital manufacturing

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Authors: Newton Muhury, Armando A. Apan, Tek N. Marasani, Gebiaw T. Ayele

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The changing climate has degraded freshwater availability in Australia that influencing vegetation growth to a great extent. This study assessed the vegetation responses to groundwater using Terra’s moderate resolution imaging spectroradiometer (MODIS), Normalised Difference Vegetation Index (NDVI), and soil water content (SWC). A hydrological model, SWAT, has been set up in a southeast Australian river catchment for groundwater analysis. The model was calibrated and validated against monthly streamflow from 2001 to 2006 and 2007 to 2010, respectively. The SWAT simulated soil water content for 43 sub-basins and monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) were applied in the machine learning tool, Waikato Environment for Knowledge Analysis (WEKA), using two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The assessment shows that different types of vegetation response and soil water content vary in the dry and wet seasons. The WEKA model generated high positive relationships (r = 0.76, 0.73, and 0.81) between NDVI values of all vegetation in the sub-basins against soil water content (SWC), the groundwater flow (GW), and the combination of these two variables, respectively, during the dry season. However, these responses were reduced by 36.8% (r = 0.48) and 13.6% (r = 0.63) against GW and SWC, respectively, in the wet season. Although the rainfall pattern is highly variable in the study area, the summer rainfall is very effective for the growth of the grass vegetation type. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Jareeluk Ratanaphan

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The purposed of this research were; 1. To survey the teacher’s needs on knowledge about special education management for special needs student 2. Development of distance training packages for teacher on special education management for special needs student 3. to study the effects of using the packages on trainee’s achievement 4. to study the effects of using the packages on trainee’s opinion on the distance training packages. The design of the experiment was research and development. The research sample for survey were 86 teachers, and 22 teachers for study the effects of using the packages on achievement and opinion. The research instrument comprised: 1) training packages on special education management for special needs student 2) achievement test 3) questionnaire. Mean, percentage, standard deviation, t-test and content analysis were used for data analysis. The findings of the research were as follows: 1. The teacher’s needs on knowledge about teaching for a learner with learning disability, mental retardation, autism, physical and health impairment and research in special education. 2. The package composed of special education management for special needs student document and manual of distance training packages. The document consisted by the name of packages, the explanation for the educator, content’s structure, concept, objectives, content and activities. Manual of distance training packages consisted by the explanation about a document, objectives, explanation about using the package, training schedule, and evaluation. The efficiency of packages was established at 79.50/81.35. 3. The results of using the packages were the posttest average scores of trainee’s achievement were higher than the pretest. 4. The trainee’s opinion on the package was at the highest level.

Keywords: distance training package, teacher, learner with special needs

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: Nur Amalina Dayana Abd Aziz

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Assessment is an integral part of teaching and learning in any syllabus in the world. Recently, a new assessment system, School-Based Assessment (SBA) was introduced and implemented in the Malaysian education system to promote a more holistic, integrated and balanced assessment system. This effort is part of the reformation made in the Government Transformation Plan (GTP) to produce a world-class human capital as we are reaching and achieving the Vision 2020 in the near future. However, this new change has raised awareness and concerns from teachers, students, parents and non-profit organizations on how the new assessment is to be implemented and how it is affecting the students and teachers particularly. Therefore, this paper aims to investigate the issues that teachers face in implementing SBA in primary schools, the measures taken to address the issues and to propose ways of managing school-based assessment. Five national primary schools focusing in the urban areas in the Selangor state are chosen for this study to carry out. Data for the study will be gathered from interviews with teachers from each school, surveys and classrooms observation will be conducted in each school, and relevant documents are collected from the selected schools. The findings of this study will present the current issues that teachers from various types of national primary schools are facing and what actions they took to overcome the problems in carrying out SBA. Suggestions on how to better manage school-based assessment for teachers are also provided in this paper.

Keywords: community of practice, curriculum, managing change, school-based assessment

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Authors: Meenakshi Srivastava, Anke A. De Boer, Sip Jan Pij

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Teacher’s preparedness towards special educational needs (SEN) of the students in regular schools is an important factor in making education inclusive as a goal to provide education for all. The current study measured the long term effects of an in-service teacher training programme which focused on the inclusion of students with a range of SEN. The programme was on three particular aspects: teachers’ attitudes, their knowledge about SEN and knowledge about teaching methods. A refresher course was also organized for participants of the initial training programme. The long term effects were examined by teachers using a self-report questionnaire (n = 38). The wider effects of the initial training were recorded by interviewing school principals (n = 4). Repeated measures of ANOVA revealed significant effects: more positive attitudes and increased knowledge about SEN among teachers who took the refresher course (n = 18) compared to those who had not (n = 19). Principals also found a more positive attitude, sensitivity and increased awareness about SEN among the participants.

Keywords: inclusion, students with special educational needs, teacher training, follow-up, attitudes change

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Authors: Sangseung Kang, Kyekyung Kim, Suyoung Chi

Abstract:

A good posture is the most important factor in riding. In this paper, we present an effective posture correction system for a riding simulator environment to provide position error detection and customized training functions. The proposed system detects and analyzes the rider's posture using depth data and inertial sensing data. Our experiments show that including these functions will help users improve their seat for a riding.

Keywords: posture correction, posture training, riding posture, riding simulator

Procedia PDF Downloads 473

Authors: J. L. Andrade-Pineda, J.M. León-Blanco, M. Calle, P. L. González-R

Abstract:

The enrollment in current Master's degree programs usually pursues gaining the expertise required in real-life workplaces. The experience we present here concerns the learning process of "Project Management Methodology (PMM)", around a cooperative/collaborative mechanism aimed at affording students measurable learning goals and providing the teacher with the ability of focusing on the weaknesses detected. We have designed a mixed summative/formative evaluation, which assures curriculum engage while enriches the comprehension of PMM key concepts. In this experience we converted the students into active actors in the evaluation process itself and we endowed ourselves as teachers with a flexible process in which along with qualifications (score), other attitudinal feedback arises. Despite the high level of self-affirmation on their discussion within the interactive assessment sessions, they ultimately have exhibited a great ability to review and correct the wrong reasoning when that was the case.

Keywords: cooperative-collaborative learning, educational management, formative-summative assessment, leadership training

Procedia PDF Downloads 167

Authors: Jonathan Iworiso

Abstract:

Forecasting the equity premium out-of-sample is a major concern to researchers in finance and emerging markets. The quest for a superior model that can forecast the equity premium with significant economic gains has resulted in several controversies on the choice of variables and suitable techniques among scholars. This research focuses mainly on the application of Regression Training (RT) techniques to forecast monthly equity premium out-of-sample recursively with an expanding window method. A broad category of sophisticated regression models involving model complexity was employed. The RT models include Ridge, Forward-Backward (FOBA) Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Relaxed LASSO, Elastic Net, and Least Angle Regression were trained and used to forecast the equity premium out-of-sample. In this study, the empirical investigation of the RT models demonstrates significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk. Thus, the forecasting models appeared to guarantee an investor in a market setting who optimally reallocates a monthly portfolio between equities and risk-free treasury bills using equity premium forecasts at minimal risk.

Keywords: regression training, out-of-sample forecasts, expanding window, statistical predictability, economic significance, utility gains

Procedia PDF Downloads 106

Authors: Gianpaolo Ghiani, Emanuela Guerriero, Emanuele Manni, Alessandro Romano

Abstract:

In this article, we deal with a variant of the classical course timetabling problem that has a practical application in many areas of education. In particular, in this paper we are interested in high schools remedial courses. The purpose of such courses is to provide under-prepared students with the skills necessary to succeed in their studies. In particular, a student might be under prepared in an entire course, or only in a part of it. The limited availability of funds, as well as the limited amount of time and teachers at disposal, often requires schools to choose which courses and/or which teaching units to activate. Thus, schools need to model the training offer and the related timetabling, with the goal of ensuring the highest possible teaching quality, by meeting the above-mentioned financial, time and resources constraints. Moreover, there are some prerequisites between the teaching units that must be satisfied. We first present a Mixed-Integer Programming (MIP) model to solve this problem to optimality. However, the presence of many peculiar constraints contributes inevitably in increasing the complexity of the mathematical model. Thus, solving it through a general purpose solver may be performed for small instances only, while solving real-life-sized instances of such model requires specific techniques or heuristic approaches. For this purpose, we also propose a heuristic approach, in which we make use of a fast constructive procedure to obtain a feasible solution. To assess our exact and heuristic approaches we perform extensive computational results on both real-life instances (obtained from a high school in Lecce, Italy) and randomly generated instances. Our tests show that the MIP model is never solved to optimality, with an average optimality gap of 57%. On the other hand, the heuristic algorithm is much faster (in about the 50% of the considered instances it converges in approximately half of the time limit) and in many cases allows achieving an improvement on the objective function value obtained by the MIP model. Such an improvement ranges between 18% and 66%.

Keywords: heuristic, MIP model, remedial course, school, timetabling

Procedia PDF Downloads 604

Authors: Charles L. Sigmund, M. A. Aed, Lissa Graciela Rivera Picado

Abstract:

One critical component of the training development process is the evaluation of the impact and value of the program. Oftentimes, however, learning organizations bypass this phase either because they are unfamiliar with effective methods for measuring the success or effect of the training or because they believe the effort to be too time-consuming or cumbersome. As a result, most organizations that do conduct evaluation limit their scope to Kirkpatrick L1 (reaction) and L2 (learning), or at most carry through to L4 (results). In 2021 Microsoft made a strategic decision to assess the measurable and monetized impact for all training launches and designed a scalable and program-agnostic tool for providing full-scale L5 return on investment (ROI) estimates for each. In producing this measurement tool, the learning and development organization built a framework for making business prioritizations and resource allocations that is based on the projected ROI of a course. The analysis and measurement posed by this process use a combination of training data and operational metrics to calculate the effective net benefit derived from a given training effort. Business experts in the learning field generally consider a 10% ROI to be an outstanding demonstration of the value of a project. Initial findings from this work applied to a critical customer-facing program yielded an estimated ROI of more than 49%. This information directed the organization to make a more concerted and concentrated effort in this specific line of business and resulted in additional investment in the training methods and technologies being used.

Keywords: evaluation, measurement, return on investment, value

Procedia PDF Downloads 184

Authors: Anand Malik

Abstract:

The Himalayas with high tectonic activities poses a great threat to human life and property. Climate change is another reason which triggering extreme events multiple fold effect on high mountain glacial environment, rock falls, landslides, debris flows, flash flood and snow avalanches. One such extreme event of cloud burst along with breach of moraine dammed Chorabri Lake occurred from June 14 to June 17, 2013, triggered flooding of Saraswati and Mandakini rivers in the Kedarnath Valley of Rudraprayag district of Uttrakhand state of India. As a result, huge volume of water with its high velocity created a catastrophe of the century, which resulted into loss of large number of human/animals, pilgrimage, tourism, agriculture and property. Thus a comprehensive assessment of debris flow hazards requires GIS-based modeling using numerical methods. The aim of present study is to focus on analysis and mapping of debris flow movements using geospatial data with flow-r (developed by team at IGAR, University of Lausanne). The model is based on combined probabilistic and energetic algorithms for the assessment of spreading of flow with maximum run out distances. Aster Digital Elevation Model (DEM) with 30m x 30m cell size (resolution) is used as main geospatial data for preparing the run out assessment, while Landsat data is used to analyze land use land cover change in the study area. The results of the study area show that model can be applied with great accuracy as the model is very useful in determining debris flow areas. The results are compared with existing available landslides/debris flow maps. ArcGIS software is used in preparing run out susceptibility maps which can be used in debris flow mitigation and future land use planning.

Keywords: debris flow, geospatial data, GIS based modeling, flow-R

Procedia PDF Downloads 270