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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Moritani, Miu Kurihara, Kazutaka Tsuda University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350355079

When experiencing dry eyes or fatigue, using eye drops can provide relief. However, some people are not good at applying eye drops. This study proposes a system that automatically applies eye drops while playing a game. The system consists of an obniz Board, two DC motor-driven pumps controlled by the board, a button, and a web application used on a smartphone. The proposed system takes advantage of the characteristics of games that attract user's attention. It aims to reduce the psychological burden on the user by detecting the user's eyes opening from camera images during game play and applying eye drops. Three types of game designs are prototyped, and the effectiveness of each design is investigated. © 2024 IEEE.

关键词： Game design

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Human Action Recognition Based on Embedded HMM 5

Human Action Recognition Based on Embedded HMM

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Bukht, Tanvir Fatima Naik Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

One of the most active subfields of computer vision and pattern recognition, human action recognition seeks to identify specific actions in still photos. The wide range of possible uses in this area has attracted a lot of interest from researchers in fields like human-computer interaction, video analysis, and surveillance. Researchers have developed a variety of algorithms and methods to accurately identify and classify the various actions that individuals performed while they were captured in photographs. An embedded HMM is going to be utilised in the process of developing an action recognition technique that will be presented in this article. It is recommended that the HSV colour transformation be carried out first in order to enhance the clarity of the video frames. The MOT and Vibe methods are used to get the silhouette. Feature extraction is accomplished through the utilisation of Texton Map and ORB. The fuzzy optimisation process is then utilised for the purpose of feature discrimination. After that, the features are input into an embedded HMM, where they are categorised into specific human actions based on the final qualities that they possess. The UT interaction data that was used in the experiment was recognised 93.8%. © 2024 IEEE.

关键词： computer vision

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Feature Extraction Techniques and Their Role in Alzheimer's Disease Diagnosis: A Comprehensive Review 3

Feature Extraction Techniques and Their Role in Alzheimer's ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rai, Preeti Ambooj Kukkar, Sania Dept. of Computer Science and Applications New Delhi India

ISBN: (纸本)9798331518592

Feature extraction is essential for breaking down complicated datasets into more digestible and useful formats, particularly in medical imaging. Accurate and timely diagnosis is critical in the context of Alzheimer's disease (AD), and MRI scan feature extraction algorithms have demonstrated significant potential. This study looks at a number of feature extraction methods, their applications across several fields, and their specific use in Alzheimer's disease diagnosis. By examining MRI-based research, we highlight important advancements and contributions in the field, emphasizing the techniques' potential to improve diagnostic accuracy. Despite these developments, there are still things that can be done to optimize feature extraction processes, such as reducing computational costs, enhancing model generalization, and refining feature selection for improved clinical outcomes. The paper continues by discussing the challenges of applying these methods in real-world clinical settings, including differences in patient demographics and imaging procedures. In conclusion, advanced feature extraction methods-particularly those that enable early detection, lower computing requirements, and provide more precise interpretations of MRI data-that employ deep learning and machine learning algorithms-have the potential to revolutionize Alzheimer's disease detection. The paper also makes recommendations for future research directions to get beyond current limitations and maximize the usage of feature extraction techniques for Alzheimer's disease diagnosis. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Smart Garden Watering and Lighting System Integrated with Machine Learning Model to Maintain Urban Gardens

Smart Garden Watering and Lighting System Integrated with Ma...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shahapure, Arsheyee Siddha, Rishika Tewary, Tanmay Shahapure, Aniruddha Mantri, Shamla Kamble, Aparna Dept. Computer Science and Engineering MIT-WPU Pune India

ISBN: (纸本)9798331512965

Garden maintenance may be a difficult task, particularly in urban settings. Many people are not skilled enough to determine their plants' precise requirements for lighting and watering. Conventional gardening techniques frequently result in underwatering or overwatering, wasting resources, and producing unhealthy plants. Our completely automated smart garden watering and lighting system offers a fully automated solution to these problems. With its sophisticated sensors, this system keeps an eye on the quantity of moisture in the soil and the amount of sunshine it receives, making sure plants get the correct amount of both for healthy growth. Traditional gardening involves guesswork and physical effort, which our solution avoids by automating the irrigation and lighting operations. You may have the advantages of a flourishing garden without the trouble of ongoing upkeep with our intelligent gardening system. Our method offers a dependable and effective solution to care for your plants and improve your urban green space, regardless of experience level. © 2025 IEEE.

关键词： Support vector machines

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Dynamic Adoptive Gaussian Mixture Model for Multi-Object Detection Over Natural Scenes 5

Dynamic Adoptive Gaussian Mixture Model for Multi-Object Det...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Ahmed, Muhammad Waqas Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Recent strides in visionary technologies have profoundly impacted the domain of multi-object recognition, serving as a linchpin in transformative applications such as augmented reality integration, robotic navigation, and autonomous driving. This research introduces an innovative paradigm for multi-object detection, marked by substantial advancements in accuracy and efficiency. The proposed methodology combines Dynamic Adoptive Gaussian Mixture Model (DAGMM) with advanced feature extraction and fusion techniques. with cutting-edge feature extraction and fusion techniques. Specifically, we leverage the capabilities of HOG, Akaze and Brisk feature extraction methods to capture intricate object descriptors with a rich data profile. Subsequently, we introduce a nuanced approach involving weighted variance thresholds for feature fusion, enhancing the discriminative prowess of the extracted features. Finally, for classification, we incorporate Convolutional Neural Networks (CNN). We meticulously assessed the performance of our model with two demanding datasets: UIUC sports and Caltech-101. Our DAGMM showcased exceptional proficiency, delivering impressive segmentation accuracy rates of 86.9 % and 8 1. 1 % over UIUC sports and Caltech-101, respectively, when evaluated against rigorously self-annotated ground truth. Furthermore, our approach achieved a substantial 87.2 % object classification accuracy, validating its effectiveness. © 2024 IEEE.

关键词： Object recognition

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PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier 3

PhishVQC: Optimizing Phishing URL Detection with Correlation...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shahriyar, Farhan Tanbhir, Gazi Raihan Chy, Abdullah Md Tanzin, Mohammed Abdul Al Arafat Mashrafi, Jisan World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Southern University Bangladesh Dept. of Computer Science and Engineering Chittagong Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331523893

Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive information. Traditional machine learning techniques struggle to perform well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and variational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows. © 2025 IEEE.

关键词： Qubits

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Designing for Embodied Sense-making of mathematics: Perspectives on Directed and Spontaneous Bodily Actions

Designing for Embodied Sense-making of Mathematics: Perspect...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Chatain, Julia Gashaj, Venera Muttappillil, Bibin Sumner, Robert W. Kapur, Manu ETH Zurich Dept. of Computer Science Dept. of Humanities Social and Political Sciences Switzerland Loughborough University School of Science Centre for Early Mathematics Learning United Kingdom ETH Zurich Dept. of Computer Science Switzerland ETH Zurich Dept. of Humanities Social and Political Sciences Switzerland

ISBN: (纸本)9798400705830

While mathematics is conventionally viewed as an abstract discipline, contemporary perspectives on embodied cognition underscore the signifcance of integrating students’ bodily experiences into the learning process. However, the efcacy of embodied learning activities, as compared to traditional methods, remains under scrutiny. We argue that both directed and spontaneous bodily actions should be considered when designing embodied learning activities, and explore such bodily actions through two studies. A quantitative user study involving directed bodily actions in Virtual Reality and on tablet reveals vr’s support for math-anxious and body-aware learners, and distinct movement patterns related to varying mathematical abilities. A subsequent qualitative analysis identifes key characteristics of spontaneous bodily actions, namely coarseness, muscle tension, repetitions, anchors, perspective, and metaphors. Derived from both studies, we propose design recommendations, advocating for expanded embodied interaction design, consideration of embodied metaphors, coarse gesturing for deep features identifcation, supporting of sense-making anchors, and in-vr learning assessments. © 2024 Copyright held by the owner/author(s).

关键词： Virtual reality

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The journey of computer science and software engineering in Israeli schools

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ACM Inroads 2022年第3期13卷 29-37页

作者： Cohen, Avi Dolev, Shlomi Gal-Ezer, Judith Computer Science & Information Technology Ministry of Education State of Israel Israel Dept. of Computer Science Ben-Gurion University of the Negev Beer-Sheva84105 Israel Dept. of Mathematics and Computer Science The Open University of Israel Raanana43107 Israel

We present a historical overview of how computer science (CS) was introduced in schools in Israel. We analyze the importance of the inclusion of CS in school curricula, for the individual development of pupils’ capabilities in all facets, and for Israel’s society and economy. We stress the need to expose all pupils to CS in a seamless learning gamified approach, in a spiral deepening of the two essential complementing aspects of CS—algorithms and programming. We start with perceptible physical moving robots that demonstrate an unbiased reaction of algorithms and programs, and gradually train for developing capabilities to abstract data structures, algorithms, and programs. We detail the journey of the introduction of computer science to the Israel Education system. The CS Curricula are described starting with elementary school, into middle school and then high school. Implementation issues are also briefly discussed. Finally, we discuss our vision for future developments in Israel’s CS education. © 2022 Association for Computing Machinery.

关键词： Curricula

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Development of VR Kyogi Karuta Game System 13

Development of VR Kyogi Karuta Game System

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Maruyama, Ayaka Kurihara, Kazutaka Tsuda Univercity Dept. Computer Science Tokyo Japan

ISBN: (纸本)9798350355079

We developed a VR game system for Hyakunin Isshu Karuta, a traditional Japanese card game. The game is a fastest finger first type card game, where memory and quick reaction are key to winning. In our previous research, we explored ways to make the game enjoyable for a diverse range of players by allowing them to adjust each VR environment within the system. The prototype developed from this research is limited to a casual rule set called Chirashi Karuta. This study aims to expand the system to include a more complex style of Hyakunin Isshu Karuta, known as Kyogi Karuta, and to incorporate features that assist players by helping them understand the rules and cultural background or by guiding them to act according to the rules even without full comprehension. It involves different ways of taking cards, the concept of one's own and the opponent's territory, and procedures for changing the ownership of cards during the game. Our system also implements poetic SFX and poetic landscapes features to help players to understand the world of the classical poems of Hyakunin Isshu Karuta. © 2024 IEEE.

关键词： Virtual reality

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Synergizing Insights for Precise Rice Leaf Disease Diagnosis Via Multi-Modal Fusion 10

Synergizing Insights for Precise Rice Leaf Disease Diagnosis...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Pally, Shelly Chelsy Jenefa, J. Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350384369

Rice holds a significant position in India, especially in the southern part of the country, where people tend to eat some rice at least once a day. Farmers are facing a huge loss due to diseases in leaf, which is the main problem of agriculture. By using techniques like machine learning, main problems detection can be done. This review, discusses common plant diseases that affect the leaf. Some include Leaf Spots, Rusts, Fusarium Wilt, Early Blight, Powdery Mildew and Downey Mildew. Our research found that machine learning techniques on rice plants make finding diseases on leaves easier. Finally, we concluded that the most accurate method is the Enhanced VGG16, with an accuracy of 99.60% because it is really good at spotting diseases on rice leaves because it's great at recognizing the small details and patterns in leaf pictures. This helps it to tell the diseases apart more accurately and make fewer mistakes in identifying them. © 2024 IEEE.

关键词： Plant diseases

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