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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

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

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 paper: 2021-08-30; Publication of special issue (tentative): 2021-09-17)

Lead Guest Editor: Xianwei Jiang (Nanjing Normal University of Special Education, China)

  • 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, Italy)
  • 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

  • Infusing Aboriginal Perspectives in Cyber Education

    Appears in:
    el 25(1):

    Authors:
    Mohiuddin Ahmed, John Shannahan

    Published:
    30th Apr 2025

    Abstract:
    While human factors are important in cyber security, the discipline has largely not explored incorporating indigenous perspectives—or, more specifically, in an Australian context, Aboriginal perspectives—in its curricula. In this paper, we introduce a promising approach for aligning Aboriginal perspectives with the needs of cyber security graduates and incorporating diverse perspectives into cyber degrees. The approach advocates for the centrality of good curriculum design fundamentals: backward design, constructive alignment, and student outcomes. The paper ends by reflecting on challenges and lessons from the first implementation and review of the material. It provides recommendations for other cyber practitioners exploring ways of incorporating indigenous perspectives in their teaching. 

  • A Review of Deep Learning Methods for Brain Tumor Detection

    Appears in:
    el 25(1):

    Authors:
    Shuaichao Wen

    Published:
    11th Apr 2025

    Abstract:
    A brain tumor is a serious neurological condition caused by the growth of abnormal cells in various regions of the brain, leading to a variety of health issues. Although the specific causes of brain tumors are not yet fully understood, known risk factors include genetic predisposition, ionizing radiation, viral infections, and exposure to certain chemicals. With the advancement of deep learning technology, computer-aided diagnosis systems can offer crucial support for the early diagnosis of brain tumors. Brain tumor image classification using deep learning has emerged as a prominent area of research. This article begins by summarizing the publicly available datasets frequently utilized in brain tumor classification tasks. It then provides an overview of the models commonly applied for diagnosing brain tumors. Following this, the paper reviews the advancements made in the field of brain tumor classification research to date. Finally, it highlights the future trends and challenges in brain tumor classification.

  • Improvements in Brain Tumor Segmentation Methods Based on Convolutional Neural Networks

    Appears in:
    el 25(1):

    Authors:
    Tong Liu, Yuzhuo Li, Lihong Zhang, Chongxin Xu, Yingbo Liang

    Published:
    11th Apr 2025

    Abstract:
    Convolutional Neural Networks (CNNs) have emerged as a prominent research area in deep learning in recent years. U-Net, an essential model within CNNs, has gradually become a research focus in the field of medical image segmentation due to its remarkable segmentation performance. This paper presents a comprehensive overview of brain tumor segmentation methods based on CNNs. Firstly, it introduces common medical image datasets in the field of brain tumor segmentation. Secondly, it offers detailed reviews on the common improvements to 2D U-Net, 3D U-Net, and improvements based on other CNNs for brain tumor segmentation. Finally, it discusses the future development directions of CNNs for brain tumor segmentation.

  • Applications of Image Segmentation Techniques in Medical Images

    Appears in:
    el 25(1):

    Authors:
    Yang-yang Hou

    Published:
    11th Apr 2025

    Abstract:
    Image segmentation is an important research direction in medical image processing tasks, and it is also a challenging task in the field of computer vision. At present, there have been many image segmentation methods, including traditional segmentation methods and deep learning-based segmentation methods. Through the understanding and learning of the current situation in the field of medical image segmentation, this paper systematically combs it. Firstly, it briefly introduces the traditional image segmentation methods such as threshold method, region method and graph cut method, and focuses on the commonly used network architectures based on deep learning such as CNN, FCN, U-Net, SegNet, PSPNet, Mask R-CNN. At the same time, the application in medical image segmentation is expounded. Finally, the challenges and development opportunities of medical image segmentation technology based on deep learning are discussed.

  • ARM-Net: Improved MRI brain tumor segmentation method based on attentional mechanism and residual module

    Appears in:
    el 25(1):

    Authors:
    MingHu

    Published:
    11th Apr 2025

    Abstract:
    INTRODUCTION: Accurate tumor segmentation is a prerequisite for reliable diagnosis and treatment of brain cancer. Gliomas, a highly prevalent and life-threatening type of brain tumor, pose a challenge for segmentation due to the intricate nature of brain structures and unpredictable appearances on brain MRI images. OBJECTIVES: Current methods for brain tumor segmentation mostly rely on deep convolutional neural networks, which suffer from significant loss of feature information during encoding and decoding and the inability to capture tumor contours in detail. METHODS: To address these challenges, this study rethinks the network architecture for MRI brain tumor segmentation. It proposes ARM-Net: an improved method for MRI brain tumor segmentation based on attention mechanisms and residual modules. Firstly, inverted external attention and dilated gated attention are employed in the last two layers of the encoder to enable the network to interact with both lesion areas and global information, facilitating better interaction among the four modalities. Secondly, different numbers of Res-Paths are added in the encoder's first two layers and the decoder's last two layers to effectively mitigate the semantic gap issues caused by traditional skip connections. RESULTS: Experiments on the BraTS 2019 dataset demonstrate that ARM-Net outperforms other similar models in terms of segmentation performance. CONCLUSION: The experiment showed that the ARM-Net model could segment the contour structure of the tumor better than other methods.  

  • A Review of Real-Time Semantic Segmentation Methods for 2D Data in the Context of Deep Learning

    Appears in:
    el 25(1):

    Authors:
    Meng Gao, Haifeng Sima

    Published:
    11th Apr 2025

    Abstract:
    Semantic segmentation is a key research topic in the field of computer vision, aiming to assign each pixel to the corresponding category based on the semantic information in the image. This technology has significant application value in fields such as virtual reality and autonomous driving.With the rapid development of deep learning, particularly with the advent of FCN, image semantic segmentation has made substantial progress. Fully supervised learning, which trains deep learning models using labeled data, has demonstrated excellent performance in semantic segmentation tasks. This paper provides a comprehensive discussion and analysis of fully supervised semantic segmentation algorithms for 2D data in deep learning. First, it introduces the concept of semantic segmentation, its development, and its application scenarios. Next, it systematically reviews and categorizes current real-time semantic segmentation algorithms, analyzing the characteristics and limitations of each. Additionally, this paper presents a complete evaluation framework for real-time semantic segmentation, including relevant datasets and evaluation metrics. Based on this foundation, it identifies several challenges currently facing the field and suggests potential directions for future research. Through this summary and analysis, the paper aims to provide valuable insights for researchers conducting studies on image semantic segmentation.

  • A Review of Hypergraph Neural Networks

    Appears in:
    el 25(1):

    Authors:
    Xinke Zhi

    Published:
    11th Apr 2025

    Abstract:
    In recent years, Graph Neural Networks (GNNs) have seen notable success in fields such as recommendation systems and natural language processing, largely due to the availability of vast amounts of data and powerful computational resources. GNNs are primarily designed to work with graph data that involve pairwise relationships. However, in many real-world networks, the relationships between entities are complex and go beyond simple pairwise connections, as seen in scientific collaboration networks, protein networks, and similar domains. If these complex relationships are directly represented as pairwise relationships using graph structures, it can lead to information loss. A hypergraph, as a special kind of graph-structured data, can represent higher-order relationships that cannot be fully captured by graphs, thereby addressing the limitations of graphs. In light of this, researchers have begun to focus on how to design neural networks on hypergraphs, leading to the proposal of hypergraph neural network (HGNN) models for downstream tasks. Therefore, this paper reviews the existing hypergraph neural network models. The review is conducted from two perspectives: spectral analysis methods and neural network methods on hypergraphs, discussing both unfolded and non-unfolded methods, and further subdividing them based on their algorithm characteristics and application scenarios. Subsequently, the design concepts of various algorithms are analyzed and compared, and the advantages and disadvantages of each type of algorithm are summarized based on experimental results. Finally, potential future research directions in hypergraph learning are discussed.

  • 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, traffic 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.

  • A Community Detection Algorithm Based on Balanced Label Propagation

    Appears in:
    el 24(1):

    Authors:
    Ting Liu, Xiaohong Zhang, Huijuan Jia

    Published:
    16th Jul 2024

    Abstract:
    OBJECTIVES: In conventional label propagation algorithms, the randomness inherent in the selection order of nodes and subsequent label propagation frequently leads to instability and reduces the accuracy of community detection outcomes. METHODS: First, select the initial node according to the node importance and assign different labels to each initial node, aiming to reduce the number of iterations of the algorithm and improve the efficiency and stability of the algorithm; second, identify the neighbor node with the largest connection to each initial node for the pre-propagation of the labels; then, the algorithm traverses the nodes in descending order of the node importance for the propagation of labels to reduce the randomness of the label propagation process; finally, the final community is formed through the rapid merging of small communities. RESULTS: The experimental results on multiple real datasets and artificially generated networks show that the stability and accuracy are all improved. CONCLUSION: The proposed community detection algorithm based on balanced label propagation is better than the other four advanced algorithms on Q and NMI values of community division results.

  • Artificial Intelligence in Mathematical Modeling of Complex Systems

    Appears in:
    el 24(1):

    Authors:
    Ting Zhao

    Published:
    26th Mar 2024

    Abstract:
    This article introduces artificial intelligence techniques in mathematical modelling of complex systems and their applications. Mathematical modelling of complex systems is a method of studying the structure and behaviour of complex systems, aiming to understand interactions and nonlinear effects in the system. Commonly used modelling methods include system dynamics, network theory, and algebraic methods. Artificial intelligence technologies include machine learning and deep learning, which can be used for tasks such as prediction and classification, anomaly detection, optimization and decision-making. In mathematical modelling of complex systems, artificial intelligence technology can learn system patterns and laws from large amounts of data, and can be applied to image and speech recognition, time series analysis and other fields. Deep learning and machine learning are important branches of artificial intelligence. They realize the modelling and analysis of complex systems by building neural network models. Data-driven modelling is a modelling method based on actual data that, combined with traditional theoretical modelling, can better describe and predict the behaviour of complex systems. Self-control of complex systems means that the system realizes its own optimization and adjustment through adaptive control algorithms and feedback mechanisms. In summary, artificial intelligence technology has broad application prospects in mathematical modelling of complex systems and will provide new tools and methods for in-depth understanding and solving problems in complex systems.

  • Design of UML Diagrams for WEBMED - Healthcare Service System Services

    Appears in:
    el 23(1): e5

    Authors:
    Nivetha S., Dr.S. Suriya

    Downloads:
    2960

    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 entire 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.

  • The Power of AI-Assisted Diagnosis

    Appears in:
    el 23(4): e3

    Authors:
    None

    Downloads:
    1043

    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. 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.

  • 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:
    1017

    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 placement. 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.

  • 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:
    766

    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 the 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.

  • COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

    Appears in:
    el 22(1): e3

    Authors:
    Zuojin Hu, William Wang, Xue Han

    Downloads:
    561

    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 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. 

  • 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:
    558

    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 automatic 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.

  • 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:
    533

    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 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.

  • 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:
    512

    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 linguistics 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.

  • Teaching Physical Exercise with Music – Pedometric Evaluation

    Appears in:
    el 23(2): e3

    Authors:
    G. Vinod Kumar , S. Sivachandiran , W.Vinu W

    Downloads:
    503

    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 increases 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.

  • Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis

    Appears in:
    el 23(2): e4

    Authors:
    Wenhao Tang

    Downloads:
    482

    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, 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.
Publisher
EAI
ISSN
2032-9253
Number of Volumes
11
Last Published
2024-07-16
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