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Augmented reality for area measurement reasoning of elementary students

Educational Technology Research and Development 2025年 1-35页

作者： Flavin, Eunhye Chung, Moon Hwang, Sunghwan Flavin, Matthew T. Center for Education Integrating Science Mathematics and Computing Georgia Institute of Technology 505 10th St NW Atlanta 30332 GA United States Department of Education Studies Stonehill College 320 Washington Street North Easton 02322 MA United States Department of Mathematics Education Chuncheon National University of Education 126 Gongji-ro Gangwon-do Chuncheon-si South Korea School of Electrical and Computer Engineering Georgia Institute of Technology 777 Atlantic Dr NW Atlanta 30309 GA United States

Area measurement is a foundational component across STEM fields. The area formula offers limited insight for children trying to develop their area measurement reasoning skills, particularly when dealing with composite shapes. Spatial structuring, where children explore structural units of a 2D space, is an alternative approach. However, poor manipulation of physical units can reinforce misconceptions about area measurement. To address this issue, we developed MeoGeo, an augmented reality (AR) smartphone application. MeoGeo allows children to readily create virtual structural arrays with units of varying sizes, superimposing them on their surroundings in real-time. We conducted an exploratory multiple-case study over a 7-week program to examine the effect of AR activities. Data included written responses from three elementary students on paper-and-pencil tests, along with in situ videos recordings. Our findings indicate that engagement in AR activities facilitated reasoning skills in area measurement for both basic and composite shapes. Furthermore, students maintained their reasoning skills beyond the intervention period. Our study underscores the importance of developing AR systems that align explicitly with the developmental progression of each child, and it highlights the critical role of the instructor in effective execution of AR activities. © The Author(s) 2025.

关键词： Area measurement Augmented reality Elementary education Intelligent system Mathematics education Mathematics reasoning

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Investigating the computation of smoothed deceptive routes in topographic terrains for agent-based simulation systems

Investigating the computation of smoothed deceptive routes i...

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Computational Intelligence in Security, Defence and Biometrics (CISDB), IEEE Symposium on

作者： Luigi P. Souza Thiago R. S. Leão Crhistopher Lenhard Edison P. Freitas Luis A. L. Silva Graduate Program in Computer Science Federal Univ. of Santa Maria Santa Maria Brazil Undergraduate Course in Computer Science Federal Univ. of Santa Maria Santa Maria Brazil Institute of Informatics Federal Univ. of Rio Grande do Sul Porto Alegre Brazil Applied Computing Department Federal Univ. of Santa Maria Santa Maria Brazil

ISBN: (数字)9798331508296

ISBN: (纸本)9798331508302

The capacity to deceptively move in hilly terrains is fundamental to agents in simulation systems for tactical and strategic military training. Such an ability to deceive the adversary can ensure a relevant advantage by hiding the real goals of a mission. Using pairs of real and deceptive mission goals, this paper investigates the planning of realistic deceptive routes for simulated agents. With real-world terrain elevation maps, it shows how to explore pathfinding algorithms with relevant path-smoothing characteristics (Theta* and WJPS*, contrasting with the standard $A^{*}$ algorithm) in terrains with pronounced relief features. The study analyzes the effects of terrain elevation costs and the representation of relief contour lines on the determination of more realistic deceptive paths. This work also investigates how users can adjust a Last Topographic Deceptive Point ($L D P_{T}$) calculation to enhance the pathfinding algorithm’s ability to produce more deceptively dense and topographically aware routes. Experimental results for different deceptive topographic path planning strategies are evaluated according to statistical models showing that Theta*, despite being slower than the base $A^{*}$ method in most cases, generated smoothed paths while maintaining a similar deception density for the proposed strategies. On the other hand, WJPS outperformed both in execution time for certain strategies while maintaining the smoothed path characteristic and resulting in a path with lower deceptive capacity.

关键词： Training Hands Biometrics Costs Path planning Planning Security Standards Computational intelligence

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Towards Precise Peach Disease Detection: Leveraging PCNN with Enhanced Activation and AFHOA Fine-Tuning

Towards Precise Peach Disease Detection: Leveraging PCNN wit...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Cuddapah Anitha R Mahaveerakannan Sakthi Govindaraju Tamil Thendral M Haseena Sunkesula Department of Computer Science and Engineering School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India School of Computer Science and Engineering Galgotias University Greater Noida Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of MCA Annamacharya Institute of Technology and Sciences (Autonomous) Annamayya Rajampet Andhra Pradesh

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Many innovations in farming and manufacturing have helped the world’s seven billion inhabitants get the food they need. Diseases and pests provide the greatest risk to food products because they reduce both the number besides quality of food. Various methodical methods have been devised to enhance food production while also reducing the risk of pests and illnesses affecting plants and fruits. The diagnosis of diseases is still dependent on human skill and manual labor when using these techniques. These past ten years have seen widespread usage of AI for the automation of various tasks, including harvesting in agriculture. Processing photos and identifying things using Machine Learning procedures is gaining popularity. We can automate harvesting by identifying plant diseases using Machine Learning algorithms; this will allow us to grow more food while cutting down on crop losses. Using the parallel convolution neural network (PCNN) suggested in this paper to detect peach disease traits—using the improved elu activation function rather than the traditional ReLu activation function—we first create an image denoising procedure to mitigate the impact of the complicated background on the images. Next, the classification accuracy is enhanced by fine-tuning the hyper-parameters of PCNN using the Archerfish hunting optimizer (AFHOA) model. This method proved effective for peach disease diagnosis, as evidenced by the results of 25513 photos. The greatest detection accuracy for black spot, peach was almost 92% to 96% accordingly.

关键词： Technological innovation Plant diseases Accuracy Machine learning algorithms Convolution Neural networks Manuals Machine learning Manufacturing Medical diagnosis

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NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization

arXiv

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arXiv 2023年

作者： Lu, Junru Li, Jiazheng Wallace, Byron C. He, Yulan Pergola, Gabriele Department of Computer Science University of Warwick United Kingdom Department of Informatics King's College London United Kingdom Northeastern University United States The Alan Turing Institute United Kingdom

Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts. These summaries are then used to train an extractive summarizer, learning the most relevant content to be simplified. Then, to ensure the narrative consistency of the simplified text, we synthesize auxiliary narrative prompts combining key phrases derived from the syntactical analyses of the original text. Our model achieves results significantly better than the seq2seq baseline on an English medical corpus, yielding 3%∼4% absolute improvements in terms of lexical similarity, and providing a further 1.1% improvement of SARI score when combined with the baseline. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach. Codes and models are released here: https://***/LuJunru/NapSS. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Estimation of Clinical Parameters for Glaucoma Detection Using Image Processing and Deep Learning Techniques from the Biomedical Human Eye Images

Estimation of Clinical Parameters for Glaucoma Detection Usi...

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Emerging Innovations and Advanced computing (INNOCOMP), International Conference on

作者： Guru Prasad M S Chandrappa S Gulshan Dhasmana Naveen Kumar H N Shilpa Mathpati Ninad Gangodkar Dept. of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Dept. of Computer Science and Engneering Jain (Deemed-to-be University) Bengaluru India Department of ECE Vidyavardhaka College of Engineering Mysuru India School of Computing and Information Technology REVA University Bengaluru India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

The technique for distinguishing the essential glaucoma is being exhibited in this segment utilizing a changed neural system with the back spread calculation ideas. The morphological administrators ideas are being utilized for the handling of the cup and disc, in the sense, the cup and the disc zones are discovered from which the proportion is registered, from where the illness can be recognition seeing the proportion. Utilizing an updated highlight extraction process, the highlights of the disc portion and the cup can be discovered, which could be utilized in the idea of cup to plate apportion (cup to disc ratio) recognition, regardless of whether the given test image is glaucomatic or not. Classification is also done using artificial neural networks (ANN) along with the training process. Simulations have been done in the Matlab environment by developing. m codes. Once the developed program is run, the simulation results are observed. From the simulation results, it can be observed that using the computed values of the cup to disc ratio, the severity of the disease can be computed (whether it is normal or moderate or severe glaucoma). Databases have been taken from the hospitals and on-line standard websites. The quantitative results are tabulated in a table. Finally, it was found that after taking a survey of 50 images, 16 were affected with normal, 16 were affected with moderate and the rest were affected with severe case of glaucoma. The Matlab results of the simulation done using coding demonstrated the profoundness with respect to the strategy proposed or developed in this exploration write-up using the concept of neural networks.

关键词： Glaucoma Surveys Training Technological innovation Simulation Artificial neural networks Standards

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Use of Faceted Search: The Effect on Researchers

Use of Faceted Search: The Effect on Researchers

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7th International Conference on Advances in Visual informatics, IVIC 2021

作者： Mahdi, Mohammed Najah Ahmad, Abdul Rahim Qassim, Qais Saif Subhi, Mohammed Ahmed Informatics and Computing in Energy Universiti Tenaga Nasional Kajang Selangor Malaysia Universiti Tenaga Nasional Kajang Selangor Malaysia Ibri College of Technology Ibri Oman Al Hikma University College Department of Computer Engineering Baghdad Iraq

ISBN: (纸本)9783030902346

The extensive amount of results obtained from any Web search operation and loads of related and/or irrelevant hits presented on the user’s screen are still poses challenges in the information retrieval field of study;especially if the user is an academic researcher and is looking for reliable and focused results. Therefore, improving the performance of Web search engines continues to be an active research topic. One of the biggest challenges to search engine optimization is when a user submits incomplete query statements or fragmented keywords. Using broken or fragmented keywords the semantic correlation will fail to result in inconsistent and outsized search results. This oversized (or overloaded) problem can be mitigated by utilizing the Exploratory Search technique with a faceted search refining mechanism. This study’s main goal is to present a short review of the existing Exploratory Search techniques and faceted search implementations and shed light on the main limitations and shortcomings. © 2021, Springer Nature Switzerland AG.

关键词： Search engines

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Lightweight Deep Learning for Resource-Constrained Environments: A Survey

arXiv

引用

arXiv 2024年

作者： Liu, Hou-I Galindo, Marco Xie, Hongxia Wong, Lai-Kuan Shuai, Hong-Han Li, Yung-Yui Cheng, Wen-Huang Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130000 China Faculty of Computing and Informatics Multimedia University Cyberjaya63100 Malaysia Hon Hai Research Institute Taipei114 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas. Copyright © 2024, The Authors. All rights reserved.

关键词： computer hardware

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Fused Fuzzy Deep Learning and Information Steganography for Privacy Preservation in Medical Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Jingxue Zhao, Pengbiao Deng, Erqiang Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Xiong, Hu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China the School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China the KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India the Division of Research and Development Lovely Professional University Phagwara India the University Centre of Research and Development Chandigarh University Mohali India the Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia the Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

—Medical Consumer Electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be processed using segmentation, diagnosis, recognition to obtain useful information. Deep learning plays an important role in medical image segmentation and recognition. However, other personal information about the patient, such as previous cases, is often required to determine the final diagnosis. However, sending personal information and medical images generated through MCE to the clinic directly exposes patient privacy. Therefore, in this paper, we propose a digital medical image privacy protection framework based on information steganography with fuzzy deep learning. The proposed framework transforms patient privacy information into invisible noise and hides it in the deep covert space of the image. This method plays a crucial role in ensuring patient privacy and data security. Furthermore, this method can ensure a high level of image segmentation accuracy that supports the development of personalized medical services. The experimental results show that the proposed framework can embed patient information in medical images without affecting the segmentation task, and effectively protect privacy. Finally, the proposed framework provides new possibilities and impetus for the popularization of MCE. © 2025 IEEE.

关键词： Medical imaging

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Multi-Tiered Self-Contrastive Learning for Medical Microwave Radiometry (Mwr) Breast Cancer Detection

SSRN

引用

SSRN 2024年

作者： Galazis, Christoforos Wu, Huiyi Goryanin, Igor Department of Computing Imperial College London London United Kingdom National Heart & Lung Institute Imperial College London London United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom Okinawa Institute Science and Technology Okinawa Japan MMWR LTD United Kingdom

The pursuit of enhanced breast cancer detection and monitoring techniques is a paramount healthcare objective, driving the need for innovative imaging technologies and diagnostic approaches. This study introduces a novel multi-tiered self-contrastive model tailored for the application of microwave radiometry (MWR) breast cancer detection. Our approach encompasses three distinct models: Local-MWR (L-MWR), Regional-MWR (R-MWR), and Global-MWR (G-MWR), each engineered to analyze varying sub-regional comparisons within the breasts. These models are cohesively integrated through the Joint-MWR (J-MWR) network, which leverages the self-contrastive data generated at each analytical level to enhance detection capabilities. Employing a dataset comprising 4,932 cases of female patients, our research showcases the effectiveness of our proposed models. Notably, the J-MWR model distinguishes itself by achieving a Matthews correlation coefficient of 0.74 ± 0.018, surpassing existing MWR neural networks and contrastive methods. These results highlight the significant potential of self-contrastive learning techniques in improving both the diagnostic accuracy and generalizability of MWR-based breast cancer detection processes. Such advancements hold considerable promise for further investigative and clinical endeavors. The source code will be made publicly available. © 2024, The Authors. All rights reserved.

关键词： Diagnosis

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Energy Generation from Plastic Composites: A Systematic Review of Sustainable Practices and Technologies

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Circular Economy and Sustainability 2025年第2期5卷 1307-1343页

作者： Ametefe, Divine Senanu Ametefe, George Dzorgbenya John, Dah Aliu, Abdulmalik Adozuka Owen, Macaulay M. Shuib, Solehuddin Hamid, Aisha Wireless Communication Technology Group (WiCOT) College of Engineering School of Electrical Engineering Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia Department of Biotechnology College of Science Engineering and Technology Osun State University Osogbo Nigeria College of Computing Informatics and Mathematics Universiti Teknologi MARA (UiTM) Selangor Puncak Perdana 40150 Malaysia College of Built Environment Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia Institute of Energy Infrastructure IEI Universiti Tenaga Nasional UNITEN Jalan IKRAM-UNITEN Selangor Kajang 43000 Malaysia School of Mechanical Engineering College of Engineering Universiti Teknologi MARA UiTM Shah Alam 40450 Malaysia School of Chemistry and Environment Faculty of Applied Sciences Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia

Energy generation from plastic composites offers a viable solution to the dual challenges of plastic waste management and renewable energy production. This study explores the potential of plastic composites for energy generation through key waste-to-energy technologies, including incineration, pyrolysis, gasification, and anaerobic digestion. The study employs a Systematic Literature Review (SLR) methodology, following the PRISMA guidelines, and draws from two primary databases: scienceDirect and Web of science. Each method is assessed in terms of energy efficiency, environmental impact, economic feasibility, and scalability, based on studies published between 2020 and the second quarter of 2024. Pyrolysis and gasification emerge as particularly promising technologies due to their flexibility and higher energy recovery potential. Incineration, although widely used and well-established, presents challenges related to emissions, despite improvements in emissions control technologies. Anaerobic digestion, though limited in its application to conventional plastics, provides sustainability benefits when used with biodegradable materials. The analysis reveals regional disparities in research focus, the influence of policy frameworks, and the need for technological innovation to optimize these processes. Ultimately, the findings highlight the importance of integrating energy recovery from plastic waste within circular economy models to achieve sustainable waste management and renewable energy goals. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Circular Economy Plastic Recycling Renewable Energy Production Sustainable Waste Management Waste-to-Energy

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