Search results for: feature learning

Authors: Zenaide Carvalho da Silva, Leandro Rodrigues Ferreira, Andrey Ricardo Pimentel

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Learning styles (LS) refer to the ways and forms that the student prefers to learn in the teaching and learning process. Each student has their own way of receiving and processing information throughout the learning process. Therefore, knowing their LS is important to better understand their individual learning preferences, and also, understand why the use of some teaching methods and techniques give better results with some students, while others it does not. We believe that knowledge of these styles enables the possibility of making propositions for teaching; thus, reorganizing teaching methods and techniques in order to allow learning that is adapted to the individual needs of the student. Adapting learning would be possible through the creation of online educational resources adapted to the style of the student. In this context, this article presents the structure of a learning object interface adaptation based on the LS. The structure created should enable the creation of the adapted learning object according to the student's LS and contributes to the increase of student’s motivation in the use of a learning object as an educational resource.

Keywords: adaptation, interface, learning object, learning style

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Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Maha Saliba Foster

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Speech perception has been recognized as a bi-sensory process involving the auditory and visual channels. Compared to the auditory modality, the contribution of the visual signal to speech perception is not very well understood. Studying how the visual modality affects speech recognition can have pedagogical implications in second language learning, as well as clinical application in speech therapy. The current investigation explores the potential effect of speech visual cues on the perception of Arabic emphatics (AEs). The corpus consists of 36 minimal pairs each containing two contrasting consonants, an AE versus a non-emphatic (NE). Movies of four Lebanese speakers were edited to allow perceivers to have partial view of facial regions: lips only, lips-cheeks, lips-chin, lips-cheeks-chin, lips-cheeks-chin-neck. In the absence of any auditory information and relying solely on visual speech, perceivers were above chance at correctly identifying AEs or NEs across vowel contexts; moreover, the models were able to predict the probability of perceivers’ accuracy in identifying some of the COIs produced by certain speakers; additionally, results showed an overlap between the measurements selected by the computer and those selected by human perceivers. The lack of significant face effect on the perception of AEs seems to point to the lips, present in all of the videos, as the most important and often sufficient facial feature for emphasis recognition. Future investigations will aim at refining the analyses of visual cues used by perceivers by using Principal Component Analysis and including time evolution of facial feature measurements.

Keywords: Arabic emphatics, machine learning, speech perception, visual speech perception

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Authors: Hao-Hsi Tseng, Hsin-Yun Lee, Yu-Cheng Kuo

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In order to understand whether there is a better than the learning function of learning methods and improve the CAD Courses for enterprise’s design human resource development, this research is applied in learning practical learning computer graphics software. In this study, Revit building information model for learning content, design of two different modes of learning curriculum to learning, learning functions, respectively, and project learning. Via a post-test, questionnaires and student interviews, etc., to study the effectiveness of a comparative analysis of two different modes of learning. Students participate in a period of three weeks after a total of nine-hour course, and finally written and hands-on test. In addition, fill in the questionnaire response by the student learning, a total of fifteen questionnaire title, problem type into the base operating software, application software and software-based concept features three directions. In addition to the questionnaire, and participants were invited to two different learning methods to conduct interviews to learn more about learning students the idea of two different modes. The study found that the ad hoc short-term courses in learning, better learning outcomes. On the other hand, functional style for the whole course students are more satisfied, and the ad hoc style student is difficult to accept the ad hoc style of learning.

Keywords: development, education, human resource, learning

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Authors: Nasser Al-Rawahi

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The study aims at determining self-regulated learning strategies used by Omani physical education candidates to learn sport skills. The data were collected by a self-regulated learning theory questionnaire. The sample of the study comprised of 145 undergraduate physical education students enrolled in the department of physical education at the College of Education, Sultan Qaboos University. The findings of the study revealed that the most commonly used strategies for learning sport skills by Omani physical education candidate are ‘the effort learning strategies, planning learning strategies and evaluation learning strategies’. However, the reflection learning strategies, self-monitoring and self-efficacy learning strategies were revealed as the least used strategies by the PE candidates in learning and acquiring sport skills. Based on these findings, suggestions and recommendations for future research were provided.

Keywords: learning strategies, physical education candidates, self-regulated learning theory, Oman

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Authors: Mustafa Alshawaqfeh, Bilal Wajidy, Echin Serpedin, Jan Suchodolski

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Inflammatory Bowel disease (IBD) is a disease of the colon with characteristic inflammation. Clinically IBD is detected using laboratory tests (blood and stool), radiology tests (imaging using CT, MRI), capsule endoscopy and endoscopy. There are two variants of IBD referred to as Ulcerative Colitis (UC) and Crohn’s disease. This study employs a hybrid feature selection method that combines a correlation-based variable ranking approach with exhaustive search wrapper methods in order to find potential biomarkers for UC. The proposed biomarkers presented accurate discriminatory power thereby identifying themselves to be possible ingredients to UC therapeutics.

Keywords: ulcerative colitis, biomarker detection, feature selection, inflammatory bowel disease (IBD)

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Authors: Azli Bin Ariffin, Noor Amy Afiza Binti Mohd Yusof

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This study aims to identify effect of social network usage on the self-learning method in teaching and learning at Sultan Idris Education University. The study involved 270 respondents consisting of students in the pre-graduate and post-graduate levels from nine fields of study offered. Assessment instrument used is questionnaire which measures respondent’s background includes level of study, years of study and field of study. Also measured the extent to which social pages used for self-learning and effect received when using social network for self-learning in learning process. The results of the study showed that students always visit Facebook more than other social sites. But, it is not for the purpose of self-learning. Analyzed data showed that 45.5% students not sure about using social sites for self-learning. But they realize the positive effect that they will received when use social sites for self-learning to improve teaching and learning process when 72.7% respondent agreed with all the statements provided.

Keywords: facebook, self-learning, social network, teaching, learning

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Authors: Chloe L. Southern

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If play is deemed to fulfill children’s social, emotional, and physical domains, as well as satisfy their natural curiosity and promote self-reflexivity, it is difficult to understand why play is not prioritized to the same extent for older children. This paper explores and discusses the importance of play-based learning as well as the preliminary implications beyond the realm of kindergarten. To further extend the inquiry, discussions pertaining to play-based learning are looked at through the lens of relevant methodologies and theories. Different education systems are looked at in certain areas of the world that lead to curiosities not only towards their play-based practices and curriculum but what ideologies they have that set them apart.

Keywords: 21ˢᵗ century learning, play-based learning, student-centered learning, transformative learning

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Authors: Bunthida Chunngam, Thanyanan Worasesthaphong

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This research had objective to develop of the website learning the local wisdom in Phra Nakhon Si Ayutthaya province and studied satisfaction of system user. This research sample was multistage sample for 100 questionnaires, analyzed data to calculated reliability value with Cronbach’s alpha coefficient method α=0.82. This system had 3 functions which were system using, system feather evaluation and system accuracy evaluation which the statistics used for data analysis was descriptive statistics to explain sample feature so these statistics were frequency, percentage, mean and standard deviation. This data analysis result found that the system using performance quality had good level satisfaction (4.44 mean), system feather function analysis had good level satisfaction (4.11 mean) and system accuracy had good level satisfaction (3.74 mean).

Keywords: website, learning, local wisdom, Phra Nakhon Si Ayutthaya province

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Authors: Jonathan H. Westover, Jacque P. Westover, Maureen S. Andrade

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Though learning theories vary in complexity and usefulness, a thorough understanding of foundational learning theories is a necessity in today’s educational environment. Additionally, learning theories lead to approaches in instruction that can affect student motivation and learning. The combination of a learning theory and elements to enhance student motivation can create a learning context where the student can thrive in their educational pursuits. This paper will provide an overview of three main learning theories: (1) Behavioral Theory, (2) Cognitive Theory, and (3) Constructivist Theory and explore their connection to elements of student learning motivation. Finally, we apply these learning theories and elements of student motivation to the following two context: (1) The FastStart Program at the Community College of Denver, and (2) An Online Academic English Language Course. We discussed potential of the program and course to have success in increasing student success outcomes.

Keywords: learning theory, student motivation, inclusive pedagogy, developmental education

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: H. van Rensburg, B. Adcock, B. Al Mansouri

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This paper discusses the impact of pedagogical and learning differences on Arabic international students’ (AIS) learning when they come to study in Australia. It describes the difference in teaching and learning methods between the students’ home countries in the Arabic world and Australia. There are many research papers that discuss the general experiences of international students in the western learning systems, including Australia. However, there is little research conducted specifically about AIS learning in Australia. Therefore, the data was collected through in-depth, semi-structured interviews with AIS who are learning at an Australian regional university in Queensland. For that reason, this paper contributes to fill a gap by reporting on the learning experiences of AIS in Australia and, more specifically, on the AIS’ pedagogical experiences. Not only discussing the learning experiences of AIS, but also discussing the cultural adaptation using the Oberg’s cultural adaptation model. This paper suggests some learning strategies that may benefit AIS and academic lecturers when teaching students from a completely different culture and language.

Keywords: arabic international students, cultural adaption, learning differences, learning systems

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Authors: Çiğdem Bakır, Mecit Yüzkat

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With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower, and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software we used in our study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: preprocessing of the data used, feature detection, and classification. We tried to determine the success of our study with different accuracy metrics and the results. We presented it comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: decision tree, water quality, water pollution, machine learning

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

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In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: M. Sarrab, M. Elbasir

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Most of mobile learning applications currently available are developed for the formal education and learning environment. Those applications are characterized by the improvement of the interaction process between instructors and learners to provide more collaboration and flexibility in the learning process. Despite the long history and large amount of research on Instruction design model and mobile learning there is no complete and well defined set of steps to follow in designing mobile learning applications. Based on this scenario, this paper focuses on identifying instruction design phases considerations and influencing factors in developing mobile learning application. This set of instruction design steps includes analysis, design, development, implementation, evaluation and continuous has been built from a literature study with focus on standards for learning and mobile application software quality and guidelines. The effort is part of an Omani-funded research project investigating the development, adoption and dissemination of mobile learning in Oman.

Keywords: instruction design, mobile learning, mobile application

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

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This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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Authors: Melody Yin

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Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

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Authors: Silmy A. Humaira'

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There is an issue about Asian students including Indonesian students that tend to behave passively in the classroom and depend on the teachers’ instruction. Regarding this statement, this study attempts to address the Indonesian high school students’ attitudes on whether they have initiative and be responsible for their learning out of the classroom and if so, why. Therefore, 30 high school students were asked to fill out the questionnaires and interviewed in order to figure out their attitudes towards self-directed learning. The descriptive qualitative research analysis adapted Knowles’s theory (1975) about Self-directed learning (SDL) to analyze the data. The findings show that the students have a potential to possess self-directed learning through ICT, but they have difficulties in choosing appropriate learning strategy, doing self-assessment and conducting self-reflection. Therefore, this study supports the teacher to promote self-directed learning instruction for successful learning by assisting students in dealing with those aforementioned problems. Furthermore, it is expected to be a beneficial reference which gives new insights on the self-directed learning practice in specific context.

Keywords: ICT, learning autonomy, students’ attitudes, self-directed learning

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Authors: Ibraheem Alzahrani

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This paper seeks to discover the relationship between both the social constructivist learning theory and the collaborative learning environment. This relationship can be identified through given an example of the learning environment. Due to wiki characteristics, wiki can be used to understand the relationship between constructivist learning theory and collaborative learning environment. However, several evidences will come in this paper to support the idea of why wiki is the suitable method to explore the relationship between social constructivist theory and the collaborative learning and their role in learning. Moreover, learning activities in wiki classroom will be discussed in this paper to find out the result of the learners' interaction in the classroom groups, which will be through two types of communication; synchronous and asynchronous.

Keywords: social constructivist, collaborative, environment, wiki, activities

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Authors: Wade Ghribi, Abdelmoty M. Ahmed, Ahmed Said Badawy, Belgacem Bouallegue

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In higher education institutions, the most pressing priority is to improve student performance and retention. Large volumes of student data are used in Educational Data Mining techniques to find new hidden information from students' learning behavior, particularly to uncover the early symptom of at-risk pupils. On the other hand, data with noise, outliers, and irrelevant information may provide incorrect conclusions. By identifying features of students' data that have the potential to improve performance prediction results, comparing and identifying the most appropriate ensemble learning technique after preprocessing the data, and optimizing the hyperparameters, this paper aims to develop a reliable students' performance prediction model for Higher Education Institutions. Data was gathered from two different systems: a student information system and an e-learning system for undergraduate students in the College of Computer Science of a Saudi Arabian State University. The cases of 4413 students were used in this article. The process includes data collection, data integration, data preprocessing (such as cleaning, normalization, and transformation), feature selection, pattern extraction, and, finally, model optimization and assessment. Random Forest, Bagging, Stacking, Majority Vote, and two types of Boosting techniques, AdaBoost and XGBoost, are ensemble learning approaches, whereas Decision Tree, Support Vector Machine, and Artificial Neural Network are supervised learning techniques. Hyperparameters for ensemble learning systems will be fine-tuned to provide enhanced performance and optimal output. The findings imply that combining features of students' behavior from e-learning and students' information systems using Majority Vote produced better outcomes than the other ensemble techniques.

Keywords: educational data mining, student performance prediction, e-learning, classification, ensemble learning, higher education

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Xiuying Wang, Changliang Li, Bo Xu

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A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

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Authors: Muhamamd Shahid Farooq, Maazan Zafar, Rizawana Akhtar

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E-learning technologies are the blessings of advancements in science and technology. These facilitate the learners to get information at any place and any time by improving their self-confidence, self-efficacy and effectiveness in teaching learning process. E-learning provides an individualized learning experience for learners and remove barriers faced by students during new and creative ways of gaining information. It provides a wide range of facilities to enable the teachers and students for effective and purposeful learning. This study was conducted to explore the attitudes of university students and teachers towards e-learning working in a metropolitan university of Pakistan. The personal, institutional and technological characteristics of the teachers and students of higher education institution effect the adoption of e-learning. For this descriptive study 449 students and 35 university teachers were surveyed by using a Likert scale type questionnaire consisting of 52 statements relating to six factors "perceived usefulness, intention to adopt e-learning, ease of e-learning use, availability resources, e-learning stressors, and pressure to use e-learning". Data were analyzed by making comparisons on the basis of different demographic factors. The findings of the study show that both type of respondents have positive attitude towards e-learning. However, the male and female respondents differ in their opinion for e-learning implementation.

Keywords: e-learning, ICT, e-sources of learning, questionnaire

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Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Amin Ranjbar, Amir Arsalan Jalili Zolfaghari, Amir Abolfazl Suratgar, Mehrdad Khajavi

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This paper describes two stages of learning-based fault detection procedure in alternators. The procedure consists of three states of machine condition namely shortened brush, high impedance relay and maintaining a healthy condition in the alternator. The fault detection algorithm uses Wigner-Ville distribution as a feature extractor and also appropriate feature classifier. In this work, ANN (Artificial Neural Network) and also SVM (support vector machine) were compared to determine more suitable performance evaluated by the mean squared of errors criteria. Modules work together to detect possible faulty conditions of machines working. To test the method performance, a signal database is prepared by making different conditions on a laboratory setup. Therefore, it seems by implementing this method, satisfactory results are achieved.

Keywords: alternator, artificial neural network, support vector machine, time-frequency analysis, Wigner-Ville distribution

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Authors: O. Bourkoukou, Essaid El Bachari

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The group composing is one of the key issue in collaborative learning to achieve a positive educational experience. The goal of this work is to propose for teachers and tutors a method to create effective collaborative learning groups in e-learning environment based on the learner profile. For this purpose, a new function was defined to rate implicitly learning objects used by the learner during his learning experience. This paper describes the proposed algorithm to build an adequate collaborative learning group. In order to verify the performance of the proposed algorithm, several experiments were conducted in real data set in virtual environment. Results show the effectiveness of the method for which it appears that the proposed approach may be promising to produce better outcomes.

Keywords: building groups, collaborative learning, e-learning, learning objects

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Authors: Sulaiman Almutairy, Trevor Davies, Yota Dimitriadi

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This paper presents study investigating how to understand better the psychological readiness for mobile learning (m-learning) among Saudi students, while also evaluating m-learning in Saudi Arabia-a topic that has not yet received adequate attention from researchers. Data was acquired through a questionnaire administered to 131 Saudi students at UK universities, in July 2013. The study confirmed that students are confident using mobile devices in their daily lives and that they would welcome more opportunities for mobile learning. The findings indicated that Saudi higher education students are highly familiar with, and are psychologically ready for, m-learning.

Keywords: m-learning, mobile technologies, psychological readiness, higher education

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Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Chryssi Vitsilaki, Apostolos Kostas, Ilias Efthymiou

Abstract:

This paper presents selected best practices on online learning and teaching derived from a novel and innovating Lifelong Learning program through e-Learning, which has during the last five years been set up at the University of the Aegean in Greece. The university, capitalizing on an award-winning, decade-long experience in e-learning and blended learning in undergraduate and postgraduate studies, recently expanded into continuous education and vocational training programs in various cutting-edge fields. So, in this article we present: (a) the academic structure/infrastructure which has been developed for the administrative, organizational and educational support of the e-Learning process, including training the trainers, (b) the mode of design and implementation based on a sound pedagogical framework of open and distance education, and (c) the key results of the assessment of the e-learning process by the participants, as they are used to feedback on continuous organizational and teaching improvement and quality control.

Keywords: distance education, e-learning, life-long programs, synchronous/asynchronous learning

Procedia PDF Downloads 334

Authors: Hao Li, Guy Y. Cornejo Maceda, Yiqing Li, Jianguo Tan, Marek Morzynski, Bernd R. Noack

Abstract:

We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich database of machine learning control (MLC) optimizing a feedback law for a cost function in the plant. The proposed methodology provides (1) insights into the control landscape, which maps control laws to performance, including extrema and ridge-lines, (2) a catalogue of representative flow states and their contribution to cost function for investigated control laws and (3) visualization of the dynamics. Key enablers are classification and feature extraction methods of machine learning. The analysis is successfully applied to the stabilization of a mixing layer with sensor-based feedback driving an upstream actuator. The fluctuation energy is reduced by 26%. The control replaces unforced Kelvin-Helmholtz vortices with subsequent vortex pairing by higher-frequency Kelvin-Helmholtz structures of lower energy. These efforts target a human interpretable, fully automated analysis of MLC identifying qualitatively different actuation regimes, distilling corresponding coherent structures, and developing a digital twin of the plant.

Keywords: machine learning control, mixing layer, feedback control, model-free control

Procedia PDF Downloads 225