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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 ...

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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Enhancing Underwater Image Segmentation: A Semantic Approach to Segment Objects in Challenging Aquatic Environment 2

Enhancing Underwater Image Segmentation: A Semantic Approach...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： George, Geomol Anusuya, S. Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai602105 India

Aquatic image segmentation is complex due to the inherent complexity of light attenuation, color shifts, and vision limits in underwater settings. In order to improve underwater image segmentation and the delineation of objects in such conditions, this research study suggests a novel semantic technique. The suggested approach uses cutting-edge deep learning methods and a U-Net architecture with DenseNet-201 at its core. The dense connectivity patterns of DenseNet-201 make it possible to extract useful features, increasing gradient flow and feature reuse across the network. The model uses a combination of Dice Loss and Categorical Focal Loss is the loss function to enhance segmentation performance. It utilizes evaluation metrics like the IoU Score and F-Score, which give detailed insights into the model's performance on underwater picture segmentation tasks. According to the evaluation metrics, the "DenseNet201" architecture is the most effective one for underwater picture segmentation. On the test dataset, it received the highest Mean IoU Score (79.77/%) and F1 Score (84.85)%. Furthermore, it obtained competitive results for each class-wise IoU score, demonstrating strong segmentation performance for different object categories. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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An Improved Zeta-Based DC-DC Converter 50

An Improved Zeta-Based DC-DC Converter

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ghasri, Mohammad Gholizadeh, Hossein Hamzeh, Mohsen Sadeghi, Erfan Saif, Mehrdad School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Windsor WindsorON Canada

ISBN: (纸本)9781665464543

This paper proposes a non-isolated high-gain DC-DC converter capable of delivering a lOx voltage gain with a 50 percent duty cycle. The topology represents an enhanced version of the Zeta converter and aims to address issues such as discontinuous input current and low voltage gain of the conventional Zeta converters, making it suitable for renewable applications. Moreover, it offers a common ground between input source and load. Semiconductor stress is also kept below unity in terms of normalized voltage/current. The proposed converter is analyzed in both ideal and non-ideal modes, with a particular focus on sensitivity analysis regarding voltage gain and efficiency in the latter. Simulation results are provided to validate the theoretical relations expressed. © 2024 IEEE.

关键词： Voltage-source converters

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Biomedical signal processing for health monitoring applications: a review

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International Journal of Applied Systemic Studies 2023年第1期10卷 44-69页

作者： Sonawani, Shilpa Patil, Kailas Natarajan, Prabhu School of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University India Department of Computer Engineering Vishwakarma University India Department of Computer Science DigiPen Institute of Technology Singapore

Biomedical health monitoring systems are evolving rapidly and using non-invasive and cost effective sensors. These systems can monitor physiological parameters of the body to monitor health conditions and provide feedback. These new generation systems can use advance technologies to build intelligent systems for health monitoring and timely diseases detection and diagnosis. Biomedical signal processing and analysis of the patterns in the signals plays important role in building efficient systems, life sciences and research and is rapidly expanding in this domain. This review paper is significant as no existing review paper is giving complete information on biomedical signal processing phases as a whole. It makes four contributions;first, it gives activity flow for developing biomedical signal processing systems. Second, reviews various recent applications researched and kinds of low cost, non-invasive sensors used for biomedical health monitoring. Third, categorisation and enlisting of signal processing techniques like signal sampling, segmentation, filtering, feature extraction, dimensionality reduction and machine learning techniques in healthcare domain. Fourth, it reviews challenges in using signal processing techniques and gives future perspectives which can be helpful for the researchers to use advance techniques to build intelligent systems for reliable decision making. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Cost effectiveness

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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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A robust visual information hiding framework based on HVS pixel adaptive alpha blending (HPAAB) technique

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Multimedia Tools and Applications 2025年第14期84卷 13959-13982页

作者： Panda, Bishwabara Nayak, Manas Ranjan Mallick, Pradeep Kumar Basu, Abhishek School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of ECE RCC Institute of Information Technology Kolkata India

The popularity of the Internet and digital consumer gadgets has fundamentally changed our society and daily lives by making digital data collection, transmission, and storage exceedingly easy and convenient. However, protecting these data and preventing unauthorized changes is an important task. This challenge has arisen in several domains, including copyright protection, content authentication, information concealment, and protected communications. Several academics have developed digital watermarking algorithms to solve this issue, aiming to implant hidden data (a watermark) in digital material to affix a mark on or seal the digital data content. The proposed approach builds a spatial domain image watermarking approach using entropy, a spectral residual pixel-based saliency map, and alpha blending. This method states that the value of entropy improves the concealing capacity. To increase imperceptibility, a spectral residual-based saliency map is used to discover less visible portions of the input images and progressively incorporate more information based on entropy data. Any modifications made other than those detectable regions will be less noticeable to viewers since they result from saliency, which decides which elements of an image are the most visible from a visual perspective. Lastly, utilizing the quality criteria, the mechanism's capability is analyzed for numerous existing frameworks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Entropy

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Automatic Extraction Model of Lake Remote Sensing Images in Cold-Dry Regions Using Improved U-net Fusion CRFs

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1803-1819页

作者： Li, Honghui Zhao, Tianming Fu, Jiangyin Fu, Xueliang Department of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China School of Electronics and Informaiton Engineering Harbin Institute of Technology Harbin150006 China

Lakes in cold and arid regions play an important role in the daily lives of people. However, the health of the lake ecosystem is seriously affected by climate change, especially human activities. Remote sensing satellites provide effective means for lake ecological health monitoring. How to extract lakes in cold and arid regions from remote sensing images has become a research hotspot. There exist some issues in most of the traditional methods regarding extraction accuracy as well as susceptibility to interfer-ence. Therefore, this paper proposes a novel method named AU-net, which is based on the improved U-net model and Fully Connected Conditional Random Field (FCCRF). Firstly, semantic segmentation datasets are established using Landsat 8 OLI remote-sensing images for the intended research objects, i.e., Wulliangsuhai Lake and Hulunhu Lake. Then, the U-net model is constructed. To improve the model’s adaptive ability, the Convolutional Block Attention Module is added after the feature map is extracted from the model. Finally, post-processing is conducted based on the FCCRF. The experimental results depict that the AU-net extraction accuracy is superior to the Normalized Difference Water Index, Deeplab v3+ model, and the initial U-net model. Moreover, AU-net can eliminate the interference of complex backgrounds and small holes. Further-more, AU-net has achieved good extraction results for Wuliangsuhai Lake and Hulunhu Lake in different seasons. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Semantic Segmentation

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On the Misspecified Cramér-Rao Bound and Elliptically Symmetric Distributions 59

On the Misspecified Cramér-Rao Bound and Elliptically Symme...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Richmond, Christ D. Duke University Pratt School of Engineering Department of Electrical and Computer Engineering DurhamNC United States

ISBN: (纸本)9798331513269

Parameter estimation is among the many goals of engineering systems and scientific applications, and parameter bounds have provided significant insights into achievable system performance. These bounds are based on an assumed distribution for the data that may differ from the true data distribution, i.e. often the impacts of model misspecification are inevitable. Thus, researchers have derived Slepian-Bangs-type formulas for elliptically symmetric distributions under model misspecification to explore these effects. The present work continues this exploration by establishing a simple version of the Slepian formula applicable to circular complex compound Gaussian distributed data (a special class of non-Gaussian distributions, also sometimes called Gaussian mixtures), when the data distribution assumed for algorithm development is complex Gaussian. © 2025 IEEE.

关键词： Cramer-Rao bounds

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Adaptive Two-stage Unscented Kalman Filter for Dynamic State Estimation of Synchronous Generator Under Cyber Attacks Against Measurements

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Journal of Modern Power Systems and Clean Energy 2024年第5期12卷 1408-1418页

作者： Dongchen Hou Yonghui Sun Venkata Dinavahi Yi Wang the School of Electrical and Power Engineering Hohai UniversityNanjing 210098China the Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canada the School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China the Henan Engineering Research Center of Power Electronics and Energy Systems Zhengzhou 450001China

This paper develops an adaptive two-stage unscented Kalman filter(ATSUKF)to accurately track operation states of the synchronous generator(SG)under cyber *** achieve high fidelity,considering the excitation system of SGs,a detailed 9~(th)-order SG model for dynamic state estimation is ***,for several common cyber attacks against measurements,a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in ***,to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions,a multi-dimensional adaptive factor matrix is derived to modify the noise covariance ***,a large number of simulation experiments are carried out on the IEEE 39-bus system,which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.

关键词： Cyber attack dynamic state estimation Kalman filtering synchronous generator(SG) unscented transformation

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Lightweight Deep Learning for Human Activity Recognition in Ambient Assisted Living

Lightweight Deep Learning for Human Activity Recognition in ...

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2024 Multimedia University engineering Conference, MECON 2024

作者： Bari, Ahsanul Farid, Fahmid Al Sarker, Md. Tanjil Mansor, Sarina Karim, Hezerul Abdul Bhuiyan, Md Roman Bannah, Hasanul Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia Berlin School of Business and Informatics Faculty of Computer Science and Informatics Berlin12043 Germany

ISBN: (纸本)9798331530747

This Lightweight Deep Learning (LDL) for Multi-View Human Activity Recognition in Ambient Assisted Living Systems can significantly improve the conditions of daily activities for people living with the elderly, disabled, or simply needing assistance. Human Activity Recognition (HAR) is essential for these systems;currently, the task of identifying the wide array of human activities remains complicated. This study introduces an improved deep learning solution for recognizing human activity from multiple viewpoints in AAL environments. By utilizing skeleton data from various perspectives, such as the RHM-HAR-SK dataset, we demonstrate useful improvements in accuracy for activity recognition when compared with single-view methods. Our LeNet and Vision Transformer models have been carefully designed to excel with skeletal data, delivering outstanding performance while keeping computational requirements to a minimum. In this research study also highlights the benefits of multi-view human activity recognition in ambient assisted living and emphasizes the effectiveness of efficient deep learning models in enabling assistive technologies that are real-time and resource friendly. These advancements support more independence and safety by supporting the development of AAL systems that are more sensitive and intelligent. © 2024 IEEE.

关键词： Active learning

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