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2024
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EAI Endorsed Transactions on e-Learning is an open access, peer-reviewed scholarly journal focused on topics belonging to the variegated and engaging e-Learning landscape, ranging from various types of distance learning (e.g., online, mobile, cloud, hybrid) to virtual laboratory environments supported by sound pedagogies, cutting-edge technologies and much more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a triannual frequency. Authors are not charged for article submission and processing.
Editor(s)-in-Chief:
Yudong Zhang
,
Emeritus Editors-in-Chief
and
Marco Zappatore
Aims & Scope
Indexing
Editorial Board
Special Issues
EAI Endorsed Transactions on e-Learning is open access, a peer-reviewed scholarly journal focused on topics belonging to the variegated and engaging e-Learning landscape, ranging from various types of
...
distance learning (e.g., online, mobile, cloud, hybrid) to virtual laboratory environments supported by sound pedagogies, cutting-edge technologies and much more. The journal publishes research, review, commentaries, editorials, technical articles, and short communications with a triannual frequency. Authors are not charged for article submission and processing. INDEXING: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions Accessibility and usability of online learning Assessment of e-learning strategies and outcomes Augmented reality and associated pedagogies Best Practices of e-Learning around the world Big Data in global e-Learning Blended learning Collaborative learning in in various learning environments High-impact practices in e-Learning Immersive learning and mixed reality Innovative solutions for e-Learning Intelligent learning and intelligent learning management systems Learning analytics Massive open online courses Mobile learning and its large-scale implementation Security and privacy in education and e-learning systems Social learning and social networks Social and organizational perspectives Standards and infrastructures Student engagement in various learning settings Educational Models and Frameworks Virtual and Augmented Learning Environments
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DOAJ DBLP CrossRef EBSCO Discovery Service OCLC Discovery Services EuroPub Publons MIAR Microsoft Academic Search UlrichsWEB Hellenic Academic Libraries Link Ingenta Connect Publicly Available Content
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Database (ProQuest) Advanced Technologies & Aerospace Database (ProQuest) Education Collection (ProQuest) Education Database (ProQuest) SciTech Premium Collection (ProQuest) Social Science Premium Collection (ProQuest) ProQuest Central Student™ Google Scholar
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Call for Papers: Special issue on: Intelligent Application in Special Education (IASE) (Manuscript submission deadline: 2021-06-30; Notification of acceptance: 2021-07-30; Submission of final revised
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paper: 2021-08-30; Publication of special issue (tentative): 2021-09-17) Lead Guest Editor: Xianwei Jiang (Nanjing Normal University of Special Education, China)
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Alberto Bucciero (Università del Salento, Italy) Amruth Kumar (Ramapo College of New Jersey, USA) Anasol Pena Rios (University of Essex and British Telecom) Andrea Pandurino (Università del Salento, I
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taly) Andrey Lyamin (ITMO University, Russia) Athanassios Jimoyiannis (University of Peloponnese, Greece) Dagmar Caganova (Slovak University of Technology) Debbie Holley (Bournemouth University, UK) Ed Currie (Middlesex University, UK) Edward Currie (Middlesex University) Eusebio Scornavacca (University of Baltimore, USA) Evgeniy Efimchik (ITMO University, Russia) Francisco Bellido (Universidad de Córdoba, Spain) George Evangelinos (Anglia Ruskin University, UK) Goran Trajkovski (Western Governors University, USA) Gordon Hunter (Kingston University, UK) Jaime Meza (Universitat Politècnica de Catalunya, Spain) Jamshid Beheshti (McGill University, Canada) Jia Chen (Shanghai International Studies University) John Carfora (Loyola Marymount University, USA) John O'Connor (Dublin Institute of Technology, Ireland) Jonathon Richter (Director of iLRN) Luca Ferrari (Università di Bologna, Italy) Mark J. W. Lee (School of Education Charles Sturt University, Australia) Markus Helfert (Dublin City University, Ireland) Matthias Glowatz (University College Dublin, Ireland) Melody Buckner (University of Arizona, USA) Michelle Crosby-Nagy (International Business School, Hungary) Nian-Shing Chen (Editor of Educational Technology and Society) Nicoletta Adamo-Villani (Purdue University, USA) Nurul Badrul (Centre for Instructor and Advanced Skill Training, Malaysia) Radhika Pai (Manipal Institute of Technology, India) Regina Kaplan-Rakowski (Southern Illinois University, USA) Richard Boateng (University of Ghana, Ghana) Sabrina Leone (Università Politecnica delle Marche, Italy) Shuai Liu (Hunan Normal University, China) Stefano Za (LUISS University, Italy) Sujan Shrestha (University of Baltimore, USA) Victor Zamudio (Instituto Tecnologico de Leon, Mexico) Vladimír Bradáč (Ostravská Univerzita, Czech Republic) Vladimir Uskov (Bradley University, USA) Xuesong Zhai (Science and Technology University of China) Xianwei Jiang (Nanjing Normal University of Special Education, China) S. Suman Rajest George (Vels Institute of Science, Technology & Advanced Studies, India) Emeritus Editors-in-Chief Marco Zappatore, University of Salento, Italy
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Recently Published
Most Popular
Transformer-Guided Video Inpainting Algorithm Based on Local Spatial-Temporal joint
Appears in:
el 24(4): e2
Authors:
Jing Wang, ZongJu Yang
Published:
10th Dec 2024
Abstract:
INTRODUCTION: Video inpainting is a very important task in computer vision, and it’s a key component of various practical applications. It also plays an important role in video occlusion removal, traf
...
fic monitoring and old movie restoration technology. Video inpainting is to obtain reasonable content from the video sequence to fill the missing region, and maintain time continuity and spatial consistency. OBJECTIVES: In previous studies, due to the complexity of the scene of video inpainting, there are often cases of fast motion of objects in the video or motion of background objects, which will lead to optical flow failure. So the current video inpainting algorithm hasn’t met the requirements of practical applications. In order to avoid the problem of optical flow failure, this paper proposes a transformer-guided video inpainting model based on local Spatial-temporal joint. METHODS: First, considering the rich Spatial-temporal relationship between local flows, a Local Spatial-Temporal Joint Network (LSTN) including encoder, decoder and transformer module is designed to roughly inpaint the local corrupted frames, and the Deep Flow Network is used to calculate the local bidirectional corrupted flows. Then, the local corrupted optical flow map is input into the Local Flow Completion Network (LFCN) with pseudo 3D convolution and attention mechanism to obtain a complete set of bidirectional local optical flow maps. Finally, the roughly inpainted local frame and the complete bidirectional local optical flow map are sent to the Spatial-temporal transformer and the inpainted video frame is output. RESULTS: Experiments show that the algorithm achieves high quality results in the video target removal task, and has a certain improvement in indicators compared with advanced technologies. CONCLUSION: Transformer-Guided Video Inpainting Algorithm Based on Local Spatial-Temporal joint can obtain high-quality optical flow information and inpainted result video.
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Deep learning-based lung nodule detection: a review
Appears in:
el 24(1):
Authors:
Feifei Zhou
Published:
29th Feb 2024
Abstract:
CT scan acquisition is fast and cost-effective and has become the main lung imaging tool. However, the increase in large numbers of CT scans has placed a heavy burden on radiologists; therefore, autom
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ated lung nodule detection techniques are needed to reduce the workload of radiologists and computer-aided detection systems are proposed for further accurate diagnosis of the condition. This review provides a comprehensive overview of recent automated lung nodule detection techniques and challenges, etc., as well as a detailed overview and discussion of current research gaps, future developments, and research trends. Relevant articles published in databases such as IEEE Xplore, Science Direct, PubMed, and Web of Science cover research algorithms published from 2014 to 2023, mainly discussing deep learning-based techniques. The schemes presented in these articles, the databases used, the experimental results, and the performance of the algorithms are compared and discussed. This work aims to introduce researchers and readers to the latest techniques and their advances in the detection of lung nodules in the last decade, which will help researchers and radiologists to further understand the latest techniques in this field.
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Ethic wars: student and educator attitudes in the context of ChatGPT
Appears in:
el 24(1):
Authors:
Süleyman Eken
Published:
29th Jan 2024
Abstract:
Technologists and educators have been both fascinated and frightened since the publication of ChatGPT. ChatGPT has both supporters and detractors, but it is informative for individuals in the educatio
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n community to look at the educational research on AI in education in order to gain understanding and establish objective judgments about the importance of ChatGPT in education. In this paper, we first present the journey of OpenAI GPT models, then give the implications of ChatGPT for education. Then, we list works for detection ChatGPT based texts and other precautions. Finally, an example of an exam with ChatGPT answers is given.
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Empowering Young Athletes: Elevating Anti-Doping Education with Virtual Reality
Appears in:
el 24(1):
Authors:
Vasileios Barkoukis, Despoina Ourda, George Palamas, Panagiota Pouliou
Published:
18th Jan 2024
Abstract:
In recent times, doping's prevalence in sports has gained substantial recognition, sparking a concerted effort from researchers, policymakers, and sports bodies to underscore the critical role of impa
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ctful anti-doping education initiatives. An exhaustive examination of current literature underscores a critical requirement for advanced educational interventions that can effectively combat the multifaceted challenges presented by doping across the spectrum of competitive and recreational athletes. In response to this exigency, this paper introduces an innovative paradigm to redefine anti-doping education through the fusion of virtual reality (VR) technology. This proposed approach seeks to leverage VR's immersive potential, offering dynamic and interactive learning experiences that authentically mirror the complexities surrounding doping decisions. By immersing athletes within lifelike scenarios, VR education aims to provide a nuanced understanding of the psychological and emotional facets associated with doping, all within a secure and controlled environment. However, while the potential of VR in anti-doping education is promising, it also necessitates addressing technical, ethical, and usability considerations, an aspect that this paper further explores.
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A review of research and development of semi-supervised learning strategies for medical image processing
Appears in:
el 24(1):
Authors:
Shengke Yang
Published:
16th Jan 2024
Abstract:
Accurate and robust segmentation of organs or lesions from medical images plays a vital role in many clinical applications such as diagnosis and treatment planning. With the massive increase in labele
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d data, deep learning has achieved great success in image segmentation. However, for medical images, the acquisition of labeled data is usually expensive because generating accurate annotations requires expertise and time, especially in 3D images. To reduce the cost of labeling, many approaches have been proposed in recent years to develop a high-performance medical image segmentation model to reduce the labeling data. For example, combining user interaction with deep neural networks to interactively perform image segmentation can reduce the labeling effort. Self-supervised learning methods utilize unlabeled data to train the model in a supervised manner, learn the basics and then perform knowledge transfer. Semi-supervised learning frameworks learn directly from a limited amount of labeled data and a large amount of unlabeled data to get high quality segmentation results. Weakly supervised learning approaches learn image segmentation from borders, graffiti, or image-level labels instead of using pixel-level labeling, which reduces the burden of labeling. However, the performance of weakly supervised learning and self-supervised learning is still limited on medical image segmentation tasks, especially on 3D medical images. In addition to this, a small amount of labeled data and a large amount of unlabeled data are more in line with actual clinical scenarios. Therefore, semi-supervised learning strategies become very important in the field of medical image processing.
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A Review of Deep Learning Approaches for Early Diagnosis of Alzheimer's Disease
Appears in:
el 24(1):
Authors:
MengBo Xi
Published:
16th Jan 2024
Abstract:
Alzheimer's disease (AD), one of the major neurodegenerative diseases, has become the most common cause of dementia problems. Up to now, there is a lack of effective targeted therapeutic drugs and eff
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ective treatment modalities to stop the progression of the disease. With the continuous development of computer technology, the use of computer-aided diagnostic technology tools for AD early classification studies will provide clinicians with important assistance. Deep learning-based Alzheimer's disease (AD) imaging classification has become a current research hotspot. In this paper, we first describe the commonly used publicly available datasets in the AD imaging classification task; then introduce the commonly used deep learning classification models for AD diagnosis; secondly, we compare the studies that target different biomarkers of the subjects and the use of unimodal or a combination of different modalities for the early classification of AD; and finally, The challenges of AD classification are summarized and future research directions are proposed.
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Key performance indicators-based monitoring, measuring progress and ranking of Aspirational Districts
Appears in:
el 24(1):
Authors:
Yogesh Kumar Yadav, Amit Kumar Gautam
Published:
9th Jan 2024
Abstract:
The Aspirational District Program (ADP) is an effort to transform those districts which are lacking in development. It focuses on each district's strengths, identifies areas for quick development, and
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tracks advancement by rating districts regularly. By competing with and learning from others in the spirit of competitive & cooperative federalism, districts are pushed and encouraged to first catch up with the best district within their state, and then aim to become one of the best in the nation. The government's "Sabka Saath Sabka Vikas aur Sabka Vishwas" initiative aims to improve residents' quality of life while promoting inclusive growth. The ADP fundamentally aims to localise the Sustainable Development Goals, resulting in national advancement. In this paper we have described the delta rankings, Key performance Indicators (KPI) and the effects on development of aspirational districts. The delta ranking of the Aspirational Districts combines the innovative use of data with pragmatic administration, keeping the district at the locus of inclusive development.
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Mobile Learning for COVID-19 Prevention
Appears in:
el 24(1):
Authors:
Zhiyi Wang
Published:
8th Jan 2024
Abstract:
In recent years, due to the explosion of COVID-19, people's expectation for accessing personalized learning resources anytime and anywhere has become stronger. The features of m-learning such as acces
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sibility and personalization greatly satisfy people's needs and are therefore widely used. In this paper, a study was conducted to investigate and analyze how m-learning can help prevent COVID-19. The study shows that m-learning can help disseminate outbreak-related messages and provide people with personalized knowledge, so that it can enhance public health and community safety. While there are still many challenges, m-learning remains a valuable tool for preventing and mitigating the spread of COVID-19 globally, and provides a solid reference for deepening m-learning development in the future.
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Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model
Appears in:
el 23(1):
Authors:
A Govardhan, A Madhavi, A Nagesh
Published:
20th Dec 2023
Abstract:
The exponential development of online learning resources has led to an information overload problem. Therefore, recommender systems play a crucial role in E-learning to provide learners with personali
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sed course recommendations by automatically identifying their preferences. In addition, e-Learning platforms such as MOOCs and LMS have been criticised for their low course completion rates, and one of the primary reasons is that they do not provide personalised course recommendations for users with varying interests. Rapidly locating the courses that users are interested in on enormous e-Learning platforms can have a significant impact on the quality of learning and the dissemination of knowledge to the learner. This paper examines the most prevalent recommendation techniques utilised in E-learning. We examined how to apply Deep Transformer based Ensembled Attention Model (DTEAM) on e-Learning system in order to achieve personalized course recommendations. The proposed recommendation model uses BERT as its foundation integrated MLM and Transformers. Predicted course recommendations are more aligned with the interests of users. Our experimental results proved that traditional recommendation algorithms, such as collaborative filtering and item-based filtering are incapable of producing superior results. The consequence of the research can assist students in selecting courses according to their preferences and improve their learning caliber
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Review of AlexNet for Medical Image Classification
Appears in:
el 23(1):
Authors:
Yudong Zhang, Wenhao Tang, Shuihua Wang, Junding Sun
Published:
20th Dec 2023
Abstract:
In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification. The variants of neural network models with ever-increasing
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performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and exploding, etc. AlexNet first utilizes the dropout technique to mitigate overfitting and the ReLU activation function to avoid gradient vanishing. Therefore, we focus our discussion on AlexNet, which has contributed greatly to the development of CNNs in 2012. After reviewing over 40 papers, including journal papers and conference papers, we give a narrative on the technical details, advantages, and application areas of AlexNet.
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Design of UML Diagrams for WEBMED - Healthcare Service System Services
Appears in:
el 23(1): e5
Authors:
Nivetha S., Dr.S. Suriya
Downloads:
2723
Abstract:
Healthcare service has huge demand these days as it really helps in managing a hospital or a medical office. The scope of Healthcare service systems is increasing by each day and it is true for the en
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tire world. Some of these solutions include improved awareness about Healthcare services and health policies. The objective of this system is to provide medical assistance to people instantly with the help of technology. This system eradicates the cultural sensitivity that prevails in many hospitals and improvises the quality of medical assistance. The captivating features of this system are online doctor, medicines at doorstep, bulletin of awareness. The users can also navigate and choose among various insurance schemes that are displayed.Unified Modeling language (UML) is a standardized modeling language enabling developers to specify, visualize, construct and document artifacts of a software system. It uses graphic notation to create visual models of software systems. This paper contains the UML diagrams for better understanding of the system with the help of Star UML tool.Usecase diagrams are used during the analysis phase of a project to identify system functionalities. Class diagram represents the static view of an application.The class diagrams are the only UML diagrams, which can be mapped directly with object-oriented languages.Activity diagram is an important behavioral diagram in UML diagram to describe dynamic aspects of the system. Activity diagram is essentially an advanced version of flow chart that modeling the flow from one activity to another activity.The state machine diagram shows the different states of an entity and focuses more on how it responds to various events by changing from one state to another. Statechart diagram is used to capture the dynamic aspect of a system. State machine diagrams are used to represent the behavior of an application. The sequence diagram focuses on the messages that are passed during an interaction in a time based perspective.A Communication diagram models the interactions between objects or parts in terms of sequenced messages. It describes both the static structure and dynamic behavior of a system. Component diagrams are used to model the physical aspects of a system. It does not describe the functionality of the system but it describes the components used to make those functionalities. Deployment Diagram is a type of diagram that specifies the physical hardware on which the software system will execute. It also determines how the software is deployed on the underlying hardware. UML is a modeling language used by software developers.UML can be used to develop diagrams and provide users with ready-to-use, expressive modeling examples. Some UML tools generate program language code from UML.UML can be used for modeling a system independent of a platform language. UML is a graphical language for visualizing, specifying, constructing, and documenting information about software-intensive systems.UML gives a standard way to write a system model, covering conceptual ideas.
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The Power of AI-Assisted Diagnosis
Appears in:
el 23(4): e3
Authors:
None
Downloads:
889
Abstract:
The rapid advancements in artificial intelligence (AI) have unleashed a wave of transformative technologies, and one area that has witnessed significant progress is AI-assisted diagnosis in healthcare
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. With the ability to analyze vast amounts of medical data, learn from patterns, and make accurate predictions, AI systems hold immense potential to revolutionize the diagnostic process, enabling earlier detection, improved accuracy, and personalized treatment recommendations. This review aims to explore the impact of AI in healthcare, specifically focusing on its role in assisting physicians with diagnosis, highlighting the benefits, challenges, and ethical considerations associated with the integration of AI systems into clinical practice. Through the utilization of AI's capabilities, the enhancement of patient outcomes, optimization of resource allocation, and the reshaping of medical professionals' approaches to diagnosis and treatment can be achieved.
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A Concept Map based Teaching of Compiler Design for Undergraduate Students
Appears in:
el 22(1): e4
Authors:
Venkatesan Subramanian, Pallapa Venkataram, Kalaivany Karthikeyan
Downloads:
795
Abstract:
In undergraduate engineering, most of the subjects do not have the open visibility of the Industry and Research requirements. Students are interested mostly on subjects which are useful for Industry p
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lacement. They do not show interest in non-open visibility subjects if an instructor teaches by simply following the textbook. Considering this, we presented a concept map based teaching methodology with Research and Industry assignments and problems. The proposed methodology focus on improving the teaching quality and students’ understanding level. In this paper, we have taken the Compiler Design subject and presented the concept map. To understand the eectiveness of the proposed methodology, the students feedback was collected and evaluated using the sign-test and the students’ submitted problems and assignments were evaluated to understand their level. The analysis results show that most of students studied Compiler Design with interest as a result of proposed teaching methodology.
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The Twenty First Century E- Learning Education Management & Implication for Media Technology Adoption in the Period of Pandemic
Appears in:
el 22(1): e1
Authors:
Ado Saleh Kazaure, Ugochukwu O. Matthew, Ogechukwu N. Onyedibe, Jazuli S. Kazaure, Abraham N. Okafor
Downloads:
635
Abstract:
INTRODUCTION: The relevance of multimedia electronic learning(e-learning) education in the ongoing COVID-19 pandemic in the developing nations are justifiable on the pedagogical connections between t
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he twenty first century digital automation and education itself. Multimedia is a creative combination of computer hardware , software and lifeware that allows for integration of video, animation, audio, graphical information and text resources in an interactive engagement , in which information are accessed interactively with any information processing devices. OBJECTIVES: To enable personalizable and autonomous learning accomplishments when multimedia educational tools are merged , which allows for diversity in curriculum presentation. METHODS: The current research investigated 400 postgraduate students of faculty of computer science and information technology who adopted the multimedia e-learning education approach to ensure that the expected date of graduation was not extended during the recent institution lock . RESULTS :The research observed that out of six multimedia e-learning education tools used, e-mail functionalities, chat apps, audio/video computing application and discussion forum were mostly used to provide meaningful interactive engagement while blogs and webcast were less utilized. CONCLUSION: The research proposed an enhanced level electronic participation, electronic readiness and e-learning education framework that matched the standards for the smartest educational reform that will enable regular and consistent educational accomplishment without disruptions of academic workflow in the global educational business ,notwithstanding the severity of any future pandemic similar to ongoing COVID-19.
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Covid-19 Diagnosis by Gray-level Cooccurrence Matrix and Genetic Algorithm
Appears in:
el 22(1): e2
Authors:
Hei-Ran Cheong, Mackenzie Brown, Zuojin Hu, Xiaoyan Jiang
Downloads:
434
Abstract:
Currently, improving the identification of COVID-19 with the help of computer vision and artificial intelligence has received great attention from researchers. This paper proposes a novel method for a
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utomatic detection of COVID-19 based on chest CT to help radiologists improve the speed and reliability of tests for diagnosing COVID-19. Our algorithm is a hybrid approach based on the Gray-level Cooccurrence Matrix and Genetic Algorithm. The Gray-level Cooccurrence Matrix (GLCM) was used to extract CT scan image features, GA algorithm was used as an optimizer, and a feedforward neural network was used as a classifier. Finally, we use 296 chest CT scan images to evaluate the detection performance of our proposed method. To more accurately evaluate the accuracy of the algorithm, 10-run 10-fold cross-validation was introduced. Experimental results show that our proposed method outperforms state-of-the-art methods in terms of Sensitivity, Accuracy, F1, MCC, and FMI.
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COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine
Appears in:
el 22(1): e3
Authors:
Zuojin Hu, William Wang, Xue Han
Downloads:
430
Abstract:
In recent years, COVID-19 has spread rapidly among humans. Chest CT is an effective means of diagnosing COVID-19. However, the diagnosis of CT images still depends on the doctor's visual judgment and
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medical experience. This takes a certain amount of time and may lead to misjudgment. In this paper, a new algorithm for automatic diagnosis of COVID-19 based on chest CT image data was proposed. The algorithm comprehensively uses WE to extract image features, uses ELM for training, and finally passes k-fold CV validation. After evaluating and detecting performance on 296 chest CT images, our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, F1, MCC and FMI.
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Analyzing the Effects of Eco-Spirituality on Organizational Commitment and Employee Engagement Among Female Academics in Higher Education
Appears in:
el 23(2): e2
Authors:
Anshu Singh, Shaan Gulhar, Priyanka Agarwal
Downloads:
413
Abstract:
This study investigated the connections between eco-spirituality, organizational commitment, and employee engagement by female academics within higher education institutions. The results of this study
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indicate that eco-spirituality has an effect on organizational commitment, and organizational commitment has an effect on employee engagement. Both of these relationships were found to be significant. In addition, this research's findings indicate a direct and indirect relationship between employee engagement and eco-spirituality. Even though this relationship has never been investigated in any of the previous studies, the findings of this research show that there is such a relationship. An employee engagement study, an organizational commitment study, and an employee spirituality study were all conducted using regression analysis. We also considered the correlation between the two data sets when analyzing the connection between the dependent and the independent variables. An examination of the construct item's dependability was carried out.
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Teaching Physical Exercise with Music – Pedometric Evaluation
Appears in:
el 23(2): e3
Authors:
G. Vinod Kumar , S. Sivachandiran , W.Vinu W
Downloads:
377
Abstract:
In everyday life and culture, music can be encountered and experienced in a variety of forms, and it plays a role in mood swings. Numerous studies have shown that listening to music while exercising i
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ncreases both the amount of time spent exercising as well as the interest level in the activity. It is hypothesised that instructing pupils in physical activities through the medium of music would have a beneficial effect on them. Fifty-five students from the Faculty of Physical Education were chosen to serve as study subjects in order to investigate the impact that music has on the process of learning and doing the activity. This study was carried out over the course of two days, and the data was gathered by counting the number of footsteps that participants made throughout a period of 20 minutes of instruction with or without music. The exercises were demonstrated to the participants over the course of two days; on the first day, they were demonstrated with music, and on the second day, they were demonstrated without music. According to the findings of this study, there is a discernible contrast between instructing activities with and without the use of music. The topic revealed a tremendous amount of interest and vitality when it was practised with music. The pedometric measure improved with musical training, and males did much better than girls in this regard.
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Effective Tamil Character Recognition Using Supervised Machine Learning Algorithms
Appears in:
el 23(2): e1
Authors:
Dr. S. Suriya, Sashwath K. G., S. Nivetha, Elakkiya G., Ajay Venkat S., P. Pavithran
Downloads:
374
Abstract:
Computational linguistics is the branch of linguistics in which the techniques of computer science are applied to the analysis and synthesis of language and speech. The main goals of computational lin
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guistics include: Text-to- speech conversion, Speech-to-text conversion and Translating from one language to another. A part of Computational Linguistics is the Character recognition. Character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. Character recognition methodology mainly focuses on recognizing the characters irrespective of the difficulties that arises due to the variations in writing style. The aim of this project is to perform character recognition for of one of the complex structures of south Indian language ‘Tamil’ using a supervised algorithm that increases the accuracy of recognition. The novelty of this system is that it recognizes the characters of the Predominant Tamil Language. The proposed approach is capable of recognizing text where the traditional character recognition systems fails, notably in the presence of blur, low contrast, low resolution, high image noise, and other distortions. This system uses Convolutional Neural Network Algorithm that are able to exact the local features more accurately as they restrict the receptive fields of the hidden layers to be local. Convolutional Neural Networks are a great kind of multi-layer neural networks that uses back-propagation algorithm. Convolutional Neural Networks are used to recognize visual patterns directly from pixel images with minimal preprocessing. This trained network is used for recognition and classification. The results show that the proposed system yields good recognition rates.
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Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis
Appears in:
el 23(2): e4
Authors:
Wenhao Tang
Downloads:
355
Abstract:
As the widespread transmission of COVID-19 has continued to influence human health since late 2019, more intersections between artificial intelligence and the medical field have arisen. For CT images,
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manual differentiation between COVID-19-infected and healthy control images is not as effective and fast as AI. This study performed experiments on a dataset containing 640 samples, 320 of which were COVID-19-infected, and the rest were healthy controls. This experiment combines the gray-level co-occurrence matrix (GLCM) and random vector function link (RVFL). The role of GLCM and RVFL is to extract image features and classify images, respectively. The experimental results of my proposed GLCM-RVFL model are validated using K-fold cross-validation, and the indicators are 78.81±1.75%, 77.08±0.68%, 77.46±0.73%, 54.22±1.35%, and 77.48±0.74% for sensitivity, accuracy, F1-score, MCC, and FMI, respectively, which also confirms that the proposed model performs well on the COVID-19 detection task. After comparing with six state-of-the-art COVID-19 detection, I ensured that my model achieved higher performance.
more >>
Publisher
EAI
ISSN
2032-9253
Number of Volumes
9
Last Published
2024-04-18
