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A Conceptual Framework for a Critical Approach to the Digital World: Integrating Digital Humanities and Informal Learning into Educational Design 1

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3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Georgopoulou, Maria-Sofia Troussas, Christos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Aigaleo Greece

ISBN: (数字)9783031441462

ISBN: (纸本)9783031441455

Technology has opened up new possibilities in the way we live, communicate, and learn. The daily engagement with activities of the digital world and the re-conceptualization of citizenship highlights the need to connect school to the wider socio-cultural context, in order to prepare tomorrow’s citizens to thrive in the local and global community. To this end, this paper introduces a conceptual framework, which consists of three main pillars: technology as a subject of critical analysis by the humanities, intentional formal and informal learning, and learning theories. The novelty of this study lies in the nature of the educational design, which is oriented towards shaping active, literate, and autonomous identities in the digital world through authentic student-centered learning practices that reflect current pedagogical approaches. It addresses secondary education, as little work has been undertaken in this area, regarding Digital Humanities and connection of formal and informal learning spaces. By providing a basis for transforming educational practice into an active, engaging, critical, and autonomous learning experience, the proposed framework has the potential to revolutionize the way we approach the digital world in education. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： E-learning

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Deep Learning Quantitative Susceptibility Mapping With Reduced Training Time Based on Local Field Transformation

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第2期9卷 1527-1538页

作者： Wu, Ming-Long Huang, De-Rong Cheng, Jhih-Shan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan National Cheng Kung University Institute of Medical Informatics Tainan701 Taiwan

A quantitative susceptibility mapping (QSM) approach using single-orientation imaging data is proposed in this study. The proposed method generates local field maps at five predefined orientations via deep learning from single-orientation local field maps, followed by physical dipole inversion using the analytical Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS) algorithm. We termed the method pseudo COSMOS (pCOSMOS). By isolating the physical dipole inversion process from deep learning, training of the neural network model becomes greatly accelerated. Brain imaging data from 10 healthy subjects (7 training and 3 testing) acquired at 3T with five different orientations relative to the main magnetic field were included to evaluate the reconstruction quality. Results compared against two iterative (Morphology Enabled Dipole Inversion or MEDI, and Structural Feature-based Collaborative Reconstruction or SFCR) and two deep learning QSM approaches (QSMnet and Learned Proximal Convolutional Neural Network or LPCNN) using COSMOS as the reference standard revealed that the susceptibility maps obtained from pCOSMOS yielded global quantitative performance comparable to QSMnet and LPCNN, while being prominently superior to MEDI and SFCR. Estimation errors in magnetic susceptibility were between 0.01 and 0.02 ppm voxel-wise, and 0.006 ± 0.004 ppm for five selected gray matter areas. Visual inspection further suggested better preserved tissue boundaries in pCOSMOS than QSMnet and LPCNN. Training time was reduced from 2 days for LPCNN and 12 hours for QSMnet to 3.2 hours for pCOSMOS. It is concluded that the proposed pCOSMOS method is an alternative deep learning QSM approach when computational efficiency is essential. © 2017 IEEE.

关键词： Brain mapping

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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Optimizing task scheduling in cloud environments: a hybrid golden search whale optimization algorithm approach

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Neural Computing and Applications 2025年 1-23页

作者： Acharya, Biswaranjan Panda, Sucheta Das, Satyabrata Majhi, Santosh Kumar Gerogiannis, Vassilis C. Kanavos, Andreas Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla India Bilaspur India Department of Digital Systems University of Thessaly Larissa Greece Department of Informatics Ionian University Corfu Greece

Managing fluctuating workloads and optimizing resource utilization in cloud environments pose significant challenges, particularly in fields requiring real-time data processing, such as healthcare. This paper introduces a novel hybrid metaheuristic technique, the Golden Search Whale Optimization Algorithm (GSWOA), specifically designed for scheduling independent dynamic biomedical data. GSWOA merges the strengths of Golden Search Optimization (GSO) and Whale Optimization Algorithm (WOA), optimizing numerical function optimization and achieving a balance between exploration and exploitation. The algorithm’s effectiveness was assessed using MATLAB by applying standard benchmark functions and further evaluated on a real-world biomedical dataset within the CloudSim environment. The performance evaluations demonstrate that GSWOA significantly outperforms existing metaheuristic and traditional scheduling techniques, achieving a 42.71% increase in resource utilization and a 14.17% reduction in makespan compared to conventional methods. These results highlight GSWOA’s potential to enhance scheduling efficiency substantially in cloud computing infrastructures, suggesting it is a powerful tool for complex task allocations. Future research will explore the scalability of GSWOA and its applicability across other data-intensive sectors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Scheduling algorithms

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Deep Learning Analysis of Histopathology Images for Breast Cancer Detection: A Comparative Study of ResNet and VGG Architectures 18

Deep Learning Analysis of Histopathology Images for Breast C...

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18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023

作者： Kanavos, Andreas Mylonas, Phivos Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350327717

Medical image analysis has undergone significant advancements with the emergence of deep learning techniques, offering great promise in improving diagnostic precision and expediting patient care. This research investigates the effectiveness of ResNet and VGG architectures in detecting breast cancer through the analysis of histopathology images. By meticulously fine-Tuning hyperparameters and optimizers, we establish robust and accurate deep learning models. Our findings reveal that the ResNet model with the SGD optimizer excels, surpassing the performance of VGG in terms of accuracy and F1-score. However, employing transfer learning with pre-Trained VGG16 and ResNet50 networks does not yield competitive results, potentially due to disparities in input image size and data distribution. The primary focus of this study is to address the critical challenge of early breast cancer detection, ultimately leading to enhanced patient outcomes. By exploring state-of-The-Art deep learning architectures and methodologies, we contribute to the growing body of research aimed at leveraging artificial intelligence for medical diagnosis. © 2023 IEEE.

关键词： Diagnosis

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Detection and Counting Bacilli Tuberculosis Images using Classical Image Processing and Deep Learning 24

Detection and Counting Bacilli Tuberculosis Images using Cla...

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2nd International Conference on Communications, Computing and Artificial Intelligence, CCCAI 2024

作者： Yuniarti, Heny Fatichah, Chastine Sigit, Riyanto Department of Informatics Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Indonesia Department of Computer Engineering Politeknik Elektronika Negeri Surabaya Indonesia

ISBN: (纸本)9798400716898

Tuberculosis (TB) has been known as the top two murdering disease worldwide after HIV/AIDS. Indonesia is the second country with the highest number of tuberculosis. One of the reasons for the high number of cases is that the microscopic sputum smear image analysis process takes a long time. This study compares the detection and counting of bacilli images using classical image processing and deep learning to ascertain the performance of both methods. Steps in classical image processing consist of RGB to HSV converting, HSV thresholding, morphological operation and bacilli counting. YOLOv5 is used as deep learning method in this research to detect and count bacilli. While classical image processing accuracy is 59.59%, YOLOv5 can achieve 86.09% which is better than the previous research. By comparing classical image processing methods and deep learning, it is evident that deep learning techniques can detect and count overlapping bacilli. This result can be further improved by increasing the learning dataset especially the severe TB patient. © 2024 ACM.

关键词： Image analysis

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Optimizing Player Engagement in an Educational Virtual Game through Fuzzy Logic-based Challenge Adaptation 8

Optimizing Player Engagement in an Educational Virtual Game ...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Krouska, Akrivi Troussas, Christos Voutos, Yorghos Mylonas, Phivos Sgouropoulou, Cleo University of West Attica Department of Informatics and Computer Engineering Egaleo Greece

ISBN: (纸本)9798350383676

Adaptive virtual learning environments provide an ideal foundation for enhancing personalized learning experience. Moreover, the incorporation of game elements enhances motivation levels, further enhancing the potential for improving learning outcomes. This paper explores the integration of fuzzy logic as a dynamic adaptivity tool in virtual educational games. By employing user-specific characteristics as input, including student progress, gaming time, and knowledge, the output of the fuzzy logic system represents the challenging level presented to the player in the educational game. Then, a set of rules is applied to tailor the complexity and pacing of challenges presented to learners. This adaptation extends to virtual character behavior, the learning path, and task complexity, aligning with learning objectives and proficiency levels. The study presents a practical implementation of this fuzzy logic-driven adaptivity mechanism in a virtual game environment designed for C++ programming education. The virtual game has been evaluated and the results of the evaluation showcase the potential of this approach in enhancing the learning process by tailoring the environment to learners' specific needs and preferences. © 2023 IEEE.

关键词： Fuzzy logic

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ShipQuest: A Bottom-Up Approach for Building a Modern Greek Maritime Ontology 18

ShipQuest: A Bottom-Up Approach for Building a Modern Greek ...

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18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023

作者： Mathe, Eirini Mouratidis, Despoina Voutos, Yorghos Kermanidis, Katia Mylonas, Phivos Kanavos, Andreas Ionian University Department of Informatics Corfu Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350327717

The ShipQuest Ontology is a pioneering legal maritime ontology designed for modeling and integrating legal information in the Greek language. This standardized representation of legal concepts within the maritime domain offers a structured approach to managing legal data. By formalizing legal information in Greek, the ShipQuest Ontology ensures semantic interoperability and seamless integration of diverse legal data sources. It encompasses essential classes, subclasses, properties, and relationships pertaining to legal aspects of maritime activities, including legal entities, contracts, regulations, liabilities, and more. The ShipQuest Ontology plays a critical role in stream-lining legal operations within the maritime industry, offering a unified and comprehensive knowledge base for legal practitioners, researchers, and decision-makers. Its adoption fosters collaboration and knowledge exchange within the maritime community, promoting standardized legal terminology and practices. This paper delves into the construction and utilization of the ShipQuest Ontology, detailing its architecture, development process, and implementation. Furthermore, we showcase its practical benefits by illustrating its application in real-world scenarios. By addressing the challenges of legal data heterogeneity and facilitating semantic consistency, the ShipQuest Ontology sets a precedent for enhanced efficiency and effectiveness in the legal domain of maritime activities. © 2023 IEEE.

关键词： Ontology

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Enhancing Users' Interactions in Mobile Augmented Reality Systems Through Fuzzy Logic-Based Modelling of computer Skills 19

Enhancing Users' Interactions in Mobile Augmented Reality Sy...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Troussas, Christos Papakostas, Christos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9789897586729

Mobile augmented reality (AR) systems offer exciting opportunities for blending digital content with the real world. However, engagement in mobile AR environments mainly relies on users' computer skills, which vary among users and impact their ability to utilize this technology. This research addresses this gap in understanding the influence of users' computer skills on interactions in mobile AR. In view of the above, this paper presents a fuzzy logic-based model to assess and refine users' computer skills in the context of mobile AR systems. Modelling the users' computer skills, the system is responsible for providing personalized assistive messages and feedback. These messages are designed to align with the fuzzy weights that have been established, enhancing users' interactions within the mobile AR environment. The presented approach is integrated in a personalized mobile AR system for spatial ability training. The evaluation results demonstrate a highly positive outlook. A major conclusion of this work is that fuzzy logic modelling has significant potential to enhance user experiences and drive advancements in mobile augmented reality technology. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Fuzzy logic

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Implementation of Multi Attribute Utility Theory Method for Government Assistance Recipient Recommendation System in Dadapayu Village, Semanu District, Gunung Kidul Regency 15

Implementation of Multi Attribute Utility Theory Method for ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Yumarlin, M.Z. Rahayu, Sri Bororing, Jemmy Edwin Kurniawan, Safar Dwi Fitriastuti, Fatsyahrina Rozak, Muhamad Department of Informatics Faculty of Engineering Universitas Janabadra Indonesia Diploma Program in Computer Engineering Politeknik Harapan Bersama Indonesia

ISBN: (纸本)9798331300579

The Indonesian government is furnishing help in the form of" Bantuan Langsung Tunai(BLT)" or the Direct Cash backing program in order to make up for the recent increase in the price of energy oil painting(BBM), which has easily had an impact on the lives of the destitute and the general public. The BLT program is enforced throughcross-sectoral collaboration, where institutions unite grounded on their separate functions and duties. Aid distribution opinions are grounded on multiple applicable rudiments and aren't primarily determined by one criterion when the MAUT fashion is applied. A system's recommended feasibility score of 78 is attained from the evaluation findings of system testing, which were carried out using a questionnaire exercising the System Usability Scale(SUS) approach. With a score in the "Acceptable" range, the system is supposed good since it's adequately satisfactory.. © Grenze Scientific Society, 2024.

关键词： Electric grounding

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