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Recognizing students emotions in game-based learning environment

International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Hamrouni, Abir Bendella, Fatima SIMPA Laboratory Department of Computer Science L’université Des Sciences Et de La Technologie d’Oran-Mohamed Boudiaf (USTO-MB) Oran Algeria

Serious Games as video games whose main goal is education rather than entertainment must adapt to the cognitive and emotional aptitudes of the learner-player to optimize his interaction experience and adapt intervention strategies to keep the learner-player engaged and motivated during the game session. In this context, great importance is given to emotions since they influence cognitive skills such as perception and decision-making of learner-players. This study presents an emotion self-reporting tool based on the “Experience Sampling Method” approach. This tool is designed not only to collect and analyze the emotions of learner-players but also to better understand the correlation between learning and entertainment. We present the results of this study and their implications for the design of adaptive game-based learning. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Correlation Emotions Experience sampling method Game-based learning Reporting Serious games

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Improving plant disease classification using realistic data augmentation

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Multimedia Tools and Applications 2024年第38期83卷 86141-86160页

作者： Benabbas, Wassim Brahimi, Mohammed Akhrouf, Samir Fortas, Bilal Department of Computer Science Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj Algeria Algiers Algeria Laboratory of Physical Chemistry and Biological Materials Skikda Algeria Laboratory of Informatics and its Applications Mohamed Boudiaf University M’Sila Algeria Department of Computer Science Mohamed Boudiaf University M’Sila Algeria Department of agronomics sciences Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj Algeria Sétif Algeria

Recently, several studies have used deep convolutional neural networks (DCNN) for plant disease classification based on leaf symptoms images. Most of these studies use the PlantVillage dataset, which contains simple images with simple backgrounds. This has led to classifiers achieving high accuracy in academic settings but underperforming in practical scenarios where images taken by farmers are much more complex. In this paper, we propose a data augmentation (DA) method that transforms simple images from the PlantVillage dataset into more complex images, thereby bridging the gap between academic results and practical performance. Our technique focuses on making the images more realistic by generating images containing multiple leaves and complex backgrounds, such as soil. To evaluate the impact of our proposed method, we trained DCNN models using various augmentation methods and tested them on a dataset containing highly complex images comparable to those taken by farmers, posing a substantial challenge. The experimental results showed that models trained on images generated by our method outperformed traditional data augmentation methods, such as geometric transformations. Moreover, our method is competitive with Generative Adversarial Networks (GANs) without requiring any training phase. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Plant diseases

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ECO-BAT: A New Routing Protocol for Energy Consumption Optimization Based on BAT Algorithm in WSN

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computers, Materials & Continua 2021年第2期66卷 1497-1510页

作者： Mohammed Kaddi Abdallah Banana Mohammed Omari LDDI Laboratory Mathematics and Computer Science DepartmentUniversity of AdrarAdrar01000Algeria Mathematics and Computer Science Department University of AdrarAdrar01000Algeria

Wireless sensor network (WSN) has been widely used due to its vastrange of applications. The energy problem is one of the important problems influencingthe complete application. Sensor nodes use very small batteries as a powersource and replacing them is not an easy task. With this restriction, the sensornodes must conserve their energy and extend the network lifetime as long as ***, these limits motivate much of the research to suggest solutions in alllayers of the protocol stack to save energy. So, energy management efficiencybecomes a key requirement in WSN design. The efficiency of these networks ishighly dependent on routing protocols directly affecting the network *** is one of the most popular techniques preferred in routing *** this work we propose a novel energy-efficient protocol for WSN based on a batalgorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithmfor WSN) to prolong the network lifetime. We use an objective function thatgenerates an optimal number of sensor clusters with cluster heads (CH) to minimizeenergy consumption. The performance of the proposed approach is comparedwith Low-Energy Adaptive Clustering Hierarchy (LEACH) and EnergyEfficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interestingin terms of energy-saving and prolongation of the network lifetime.

关键词： WSNs network lifetime routing protocols ECO-BAT bat algorithm CH energy consumption LEACH EEMOB

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Advancements in Photoaligned Azo-Dye Thin-Film Polarizer with Exceptional Dichroic Ratio

Advancements in Photoaligned Azo-Dye Thin-Film Polarizer wit...

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International Symposium, Seminar, and Exhibition, Display Week 2024

作者： Cheng, Yue-Chu Tseng, Man-Chun Ho, Yeuk-Lung Vashchenko, Olena Vashchenko, Valerii Tang, Shu-Tuen Yeung, Sze-Yan Kwok, Hoi-Sing State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

This study presents the development of ultra-thin azo-dyepolarizers, notable for their minimal thickness (100-200 nm) and superior optical performance, with polarization efficiency exceeding 99.9%, extinction ratios above 4000, transmittance over45.3%, and dichoric ratio over 100. Utilizing cost-effective solution-processing methods, we fabricate these high-performance polarizers and determine optimal experimental conditions for peak efficacy. Our research includes comprehensive Scanning Electron Microscopy analyses to understand the microstructural properties of these azo-dye thin film polarizers, contributing significantly to the field of advanced display technologies. © 2024, John Wiley and Sons Inc. All rights reserved.

关键词： Liquid crystal displays

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IoT Systems and Battery-Based Energy Sources

IoT Systems and Battery-Based Energy Sources

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Sajja, Komal Sai Bhowmik, Biswajit Brics Laboratory National Institute of Technology Karnataka Surathkal Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798350322309

The Internet of Things (IoT) has rapidly expanded over the past few years and has begun taking over many aspects of our lives, including hospitals, houses, roadways, and much more. Due to its expansion, IoT may become the future technology in the following years. IoT is a collection of real-world items equipped with electronics, software, networks, and sensors to collect and exchange data. To power IoT devices, there must be a proper understanding of the energy consumption of thesensors and the kind of energy sources to be used. This paper provides state-of-the-art battery-powered IoT systems. First, the study discusses IoT's architecture and various applications. The study then extends IoT energy resources. Next, the study examines the use of batteries in IoT systems, their advantages and disadvantages, followed by unravelling the ambient energy harvesting mechanism. Finally, we present related works on battery-based IoT systems. © 2023 IEEE.

关键词： Internet of things

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Int-BEB: Enhanced Binary Exponential Backoff using Q-learning 5

Int-BEB: Enhanced Binary Exponential Backoff using Q-learnin...

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5th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Zerguine, N. Ferhat Abbas Setif 1 University Networks and Distributed Systems Laboratory Department of Computer Science Setif Algeria

ISBN: (纸本)9798350381450

Because of their development and success, MANETs are now preferred alternatives in many fields of study and application. The IEEE 802.11 standard is the basis of most wireless technologies. To treat collisions in wireless networks, IEEE 802.11 Distributed Coordination Function (DCF) MAC layer employs Binary Exponential Backoff (BEB) algorithm, that is helpful in decreasing the potential of collision, but comes at the expense of several network performance metrics, such as medium access delay, bandwidth management, power use, and fairness. A Deep Learning technique called Deep Reinforcement Learning (DRL) allows an agent to communicate with the environment in order to accomplish a goal. In the present paper, we suggest Q-learning (QL), one of the DRL approaches to improve the Binary Exponential Backoff (BEB) in MANETs. The intelligently designed Int-BEB aims for better CW selection;this selection strikes to reduce collisions as much as possible and to give more chances to nodes that make collisions to access the channel by taking into consideration their residual energy. In-depth simulations are performed to evaluate the performance of the proposed mechanism. The performance of Int-BEB is evaluated in diverse networks with several criteria such as packet successful transmission, packet loss and energy. © 2023 IEEE.

关键词： Reinforcement learning

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LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT Image Segmentation

LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, QiSong Wu, JiaRou Wang, YingZhuo Qu, HuiLi Nie, Shanling Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Australia

ISBN: (纸本)9798350337488

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet's strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images. © 2023 IEEE.

关键词： COVID-19 Downsample LDE-UNet Semantic segmentation

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On Improving the Energy Efficiency in Wireless Sensor Network Using Hybrid Firefly and Simulated Annealing Algorithms 1

On Improving the Energy Efficiency in Wireless Sensor Networ...

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1st International Conference on Big Data, IoT, Web Intelligence and Applications, BIWA 2022

作者： Demri, Mohammed Rahmoun, Abdellatif Barmaki, Fatiha Oubadi, Sihem Ahmed Draia University of Adrar Lddi Laboratory Department of Mathematics and Computer Science Algeria High School of Computer Sciences Sidi Bel-Abbès LabRI Laboratory Sidi Bel-Abbès Algeria Ahmed Draia University of Adrar Department of Mathematics and Computer Science Algeria 2 Laboratory Department of Computer Science Algeria

ISBN: (纸本)9798350334494

In this paper, we propose a Hybrid Routing Protocol based on Firefly and Simulated Annealing algorithms (HRP-FASA) as an effective protocol that optimizes the energy consumption of nodes, thus prolonging the overall network lifetime. The idea behind this hybridization is twofold, improving the global search behavior (exploration) and accepting worst solutions (exploitation) with some probability (avoid getting trapped in local minima). Thus, finding the best cluster head's positioning (which is the ideal choice for the cluster head). With regard to energy consumption, the stability period and the amount of data packets delivered to the base station, our proposed protocol outperforms two other famous clustering based routing protocols involved in this study namely, LEACH and LEACH-C. © 2022 IEEE.

关键词： Simulated annealing

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Automated Segmentation of COVID-19 Infected Lungs via Modified U-Net Model 15

Automated Segmentation of COVID-19 Infected Lungs via Modifi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Maharshi Patanjali Cps Lab Brics Laboratory Department of Computer Science and Engineering Mangalore Karnataka Surathkal575025 India

ISBN: (纸本)9798350370249

The COVID-19 pandemic has led to significant outbreaks in more than 220 countries worldwide, profoundly impacting the public health and lives. As of February 2024, over 774 million cases have been reported, with more than 7,035,337 deaths recorded. Therefore, there is a significant need for automated image segmentation to serve as clinical decision support. This paper presents a novel automated segmentation framework that dynamically generates distinct and randomized image patches for training using preprocessing techniques and extensive data augmentation. The proposed architecture employs a semantic segmentation approach, ensuring accuracy despite limited data availability. Experimental assessment comprises a visual inspection of the predicted segmentation outcomes. Quantitative evaluation of segmentation includes standards performance metrics such as precision, recall, Dice score, and Intersection over Union (IoU). The results exhibit a remarkable Dice coefficient score of 98.3% and an IoU rate surpassing 96.8%, demonstrating the model's robustness in identifying COVID-19-infected lung regions. © 2024 IEEE.

关键词： Computed Tomography COVID-19 Deep Learning Lung Segmentation U-Net

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Improving the Interpretability Through Maximizing Mutual Information for EEG Emotion Recognition

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IEEE Transactions on Affective Computing 2025年第2期16卷 744-757页

作者： Yang, Hua Chen, C. L. Philip Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science RaleighNC27695 United States South China University of Technology Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou510335 China

Trustworthy Graph Neural Networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling emotional connections between EEG channels. However, these GNNs lack interpretability due to the absence of explicit rationale behind their predictions. This paper conducts a comprehensive identification of important EEG channels to enhance the interpretability of EEG emotion recognition from the perspective of mutual information. Specifically, an Adjacency-Explainable Graph Neural Network (AEG) for ante-hoc interpretability is proposed to capture genuine EEG emotional connections, which gives a theoretical guarantee to remove spurious connections. Moreover, a Channel-wise Adaptive Class Activation Mapping Explainer (CACA) for post-hoc interpretability is developed to locate the EEG channels that contribute most to predictions. Experimental results on three datasets, i.e., SEED, SEED-IV, and DREAMER, prove that imbuing training processes with enhanced interpretability ensures significant performance improvements in emotion recognition. Quantitative comparisons of post-hoc interpretability also demonstrate the superiority of CACA. Furthermore, this paper illustrates two potential applications of the proposed methodologies, showing their broader utility and significance. © 2010-2012 IEEE.

关键词： Graph neural networks

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