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检索条件"丛书名=Communications in Computer and Information Science"

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Depth Completion Enhancement with Morphological Filtering and a Variation of the Infinity Laplacian

Depth Completion Enhancement with Morphological Filtering an...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Lazcano, Vanel Calderero, Felipe Univ Mayor Fac Ciencias Ingn & Tecnol Santiago Chile Nuclio Digital Sch Madrid 28002 Spain

ISBN: (纸本)9783031834349;9783031834356

A depth map is fundamental for many computer vision applications, such as autonomous vehicles. A sensor like RealSense or LiDAR can acquire a depth map. However, depth data are incomplete due to sensor errors or occlusions between the objects in the scene. Completing this data in depth is a fundamental task for those applications. This manuscript shows an improvement of a depth interpolation model by adding morphological filters to the raw depth data to eliminate outliers and noise. This paper uses a model to interpolate data given by a variation of the infinity Laplacian. We have trained our model using three pictures of the publicly available KITTI database. The results show that our model plus morphological filter outperforms contemporary models. We empirically demonstrated that this kind of filter eliminates outliers and artifacts of the depth acquisition process.

关键词： Depth Completion Anisotropic diffusion Infinity Laplacian

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From Static Graph Attention Generation to Dynamic Graph Attention Coefficient 8th

From Static Graph Attention Generation to Dynamic Graph Atte...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Das Jui, Tonni Benton, Mary Lauren Baker, Erich Baylor Univ Waco TX 76798 USA Belmont Univ Nashville TN USA

ISBN: (纸本)9783031856273;9783031856280

Graph attention networks have demonstrated competitive performance in node classification tasks. Nonetheless, recent studies have highlighted their limitation in generating static attention scores. Static attention coefficients imply that attention scores for node pairs remain fixed regardless of variational query keys or structural information. Consequently, researchers have explored diverse strategies to devise a dynamic attention mechanism suitable for graph data. In this vein, we introduce a dynamic attention-based graph attention network (GAT) grounded in structural information learning and engaged in enhancing its efficacy.

关键词： dynamic attention network information variation node classification graph embedding node features

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User-Centered-Development: A Hackathon for Bridging Engineering and Design Disciplines 8th

User-Centered-Development: A Hackathon for Bridging Engineer...

引用

8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Katz, Adi Chassidim, Hadas Shamoon Coll Engn Dept Ind Engn & Management Ashdod Israel Shamoon Coll Engn Dept Software Engn Beer Sheva Israel

ISBN: (纸本)9783031838446;9783031838453

This paper presents a collaborative hackathon initiative between the Software Engineering (SE) and Visual Communication (VC) departments at an academic engineering college. The hackathon brought together students from "Software Project Management" and "User Experience and Cognition" courses, working in agile development teams to bridge the gap between technical and design focuses. By fostering active learning and simulating real-world digital system development, the hackathon allowed SE students to apply their technical expertise while learning design thinking and usability principles. VC students gained experience translating technical requirements into visually compelling designs. This collaboration facilitated a deeper understanding of user-centered design and the software agile development process, promoting critical thinking and creativity aligned with Bloom's highest learning levels: analyzing, evaluating, and creating. Student feedback reflected a mix of positive insights and constructive criticism, emphasizing the value of interdisciplinary collaboration in enhancing user experience while identifying areas for improvement, such as feedback clarity and the need for better time management during the hackathon. The study's approach mirrors key aspects of professional environments, preparing students to tackle the real-world challenges they will encounter after graduation.

关键词： Software development lifecycle User-centered design Human computer interactions User experience Requirement specification

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The Impact of Robotic Process Automation in Higher Education 4th

The Impact of Robotic Process Automation in Higher Education

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Guarda, Teresa Sanchez, Jose Fernanda Augusto, Maria Torres, Washington Univ Estatal Peninsula Santa Elena La Libertad Ecuador Minho Univ Algoritmi Res Ctr Guimaraes Portugal BiTrum Res Grp Leon Spain

ISBN: (纸本)9783031832093;9783031832109

The education sector is faced with many challenges in a world that is developing at a very rapid pace, such as the increasing burden of administrative tasks on educators. This study looks into the potential solution of Robotic Process Automation to these challenges and its impacts on higher education. We analyze the existing literature on RPA adoption by higher education using a systematic review and draw attention to the current state of applications and potential benefits. Our findings show that RPA would significantly enhance administrative operations, reduce errors, and free educators for teaching and student engagement. RPA could be a significant opportunity to establish efficiency and effectiveness in the learning environment, although not free from challenges, such as the cost of initial investment and job displacement. The paper concludes with recommendations for institutions to successfully integrate RPA while ensuring a balanced approach is taken to maximize benefits and mitigate associated risks.

关键词： Robotic Process Automation Potential Challenges Impact in Education

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Data Augmentation for Improved Melanoma Classification in Convolutional Neural Networks 4th

Data Augmentation for Improved Melanoma Classification in Co...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Hermosilla, Pamela Soto, Ricardo Ponce, Jefte Suazo Jara, Cristian Crawford, Broderick Contreras, Sebastian Pontificia Univ Catolica Valparaiso Valparaiso Chile

ISBN: (纸本)9783031832093;9783031832109

Convolutional Neural Networks (CNNs) are highly effective in various computer vision tasks. However, their performance is heavily dependent on the quality and quantity of the training data. This study aims to optimise CNN performance using pattern augmentation techniques to address the problem of limited training data in medical image analysis. Using the DenseNet architecture and the HAM10000 dataset of melanoma and non-melanoma images, the researchers applied a range of augmentation methods, including random rotation, brightness adjustment, random zoom, horizontal flip, height and width shift, and random cropping. The methodology involved evaluating the impact of these augmentation techniques on CNN training. The results showed that random crop and horizontal flip significantly improved classification accuracy by 1-2% over 30 epochs. Extending training to 50 epochs further enhanced performance, though not all methods showed significant improvements;random crop and horizontal flip remained the most effective. These findings highlight the crucial role of selecting appropriate augmentation techniques and training epochs in optimizing CNN performance. The study underscores the importance of dataset diversity and augmentation strategies, particularly in medical image analysis where early and accurate diagnosis is critical. Future research should explore different network architectures and datasets to validate and generalize these results.

关键词： Convolutional Neural Networks Data augmentation Skin cancer

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Differential Evolution Algorithm Based on Staged Adaptive Mutation Strategy Selection 5th

Differential Evolution Algorithm Based on Staged Adaptive Mu...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Chong, Yunyun Han, Mingzhang Zhao, Xinchao Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Differential evolution Algorithm has been widely used due to its efficient performance. However, different mutation strategies and parameter settings can greatly influence the performance of DE. In order to weaken the impact of a specific mutation strategy on DE algorithm and to fully leverage the advantages of existing mutation strategies, this article designs a new mechanism, which can adaptively choose mutation strategy by using selection probability memory(SPM) in the process of evolution. By combining this mechanism with adaptive parameters' setting, a new adaptive DE algorithm is proposed(AMDE). In AMDE, the process of evolution is divided into three stages, various mutation strategies are available for choice at each stage. AMDE improves the performance by combining the advantages of multiple mutation operators. The performance of AMDE is evaluated on 29 functions. Compared to differential evolution variants and other state-of-the-art algorithms, the experimental results show that AMDE is superior.

关键词： Differential evolution (DE) adaptive mutation strategy selection probability memory multiple stages

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Application of the Linear Model on the Optimal Fleet Calculation for Internal Transportation of a Peruvian University 1

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Agurto-Macazana, Maximo Enrique Cerquiera-Hoyos, Brenda Huamani-Nahui, Cristian Gerardo Paucar-Martinez, Antonio Pedro Ninaquispe-Soto, Mario Riega-Viru, Yasmina Univ Nacl Mayor San Marcos Fac Geol Min Met & Geog Engn Lima Peru Univ Nacl Mayor San Marcos Fac Math Sci Lima Peru Univ Privada Norte Virtual Campus Lima Peru Univ Privada Norte Directorate Res Innovat & Social Responsibil Lima Peru

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

The present study focuses on optimizing the internal transportation fleet of a Peruvian university using a linear programming model to adjust the size of the fleet. Through a quantitative and descriptive approach, user data was collected at 10 stops for 30 days, where a linear programming mathematical model was applied to optimize the size of the internal transport fleet, the solution of which was achieved through the use of the Solver add-on Excel. The results indicated that the optimal fleet is 2 units, which improves transportation planning and management, providing a more effective and satisfactory service. The research highlights the need for formal optimization methods to address university-level mobility problems. It demonstrates that route optimization can significantly reduce congestion and improve operational efficiency, contributing to a more sustainable and efficient transport service.

关键词： Route optimization internal transportation linear programming fleet management Operational efficiency

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Feature Selection Based on Membrane Clustering 7th

Feature Selection Based on Membrane Clustering

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7th International Conference on Optimization and Learning

作者： Sheuly, Sharmin Sultana Deivard, Johannes Seceleanu, Tiberiu Xiong, Ning Malardalen Univ Vasteras Sweden Dev Solut AB Eskilstuna Sweden

ISBN: (纸本)9783031779404;9783031779411

Given increased complex data with high dimensions, feature selection aims to select a subset of features to increase the efficiency of machine learning. This paper proposes a new feature selection method based on membrane computing. The proposed method has two main advantages. First, it provides a new solution to search for feature combinations while requiring no model construction (which is time-consuming) to evaluate a feature subset. Second, feature selection is embedded in a membrane clustering algorithm, which is designed to enable searching for the best feature subset and finding active cluster centres at the same time. The designed clustering algorithm mimics the behavior of multiple cells and it has stronger global search ability than existing evolutionary algorithms. The efficacy of the proposed method has been shown by the evaluation of a set of benchmark data sets.

关键词： feature selection membrane computing clustering

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Heart Disease Analysis and Prediction with Machine Learning Techniques Using Cleveland Dataset 5th

Heart Disease Analysis and Prediction with Machine Learning ...

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5th International Symposium on Intelligent Computing Systems

作者： Govindaraju, Kishore Kalimuthu, Govindaraju Vellore Inst Technol Chennai Tamil Nadu India Higher Colleges Technol Fujairah U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

For the past two decades, heart diseases have been the major cause of deaths worldwide. Various risk factors along with aging population, and evolving lifestyles have been major contributors for their prevalence. The World Health Organization has made various efforts to emphasize the need to address the ongoing challenge of cardiovascular diseases on a global scale. Adopting Machine Learning (ML) algorithms can be utilized by health care officials to study and analyze the risk of various medical conditions and furthermore, come up with better diagnostic tools to treat the same. The Machine Learning tools study the datasets provided to them, analyse them for patterns and scan the factors for relationships among them gaining knowledge on how every factor affects the final outcome. The primary focus of this research is based on the implementation of advanced machine learning techniques to predict and prevent heart diseases using the Cleveland dataset. Careful preprocessing which includes outlier removal, missing data imputation and feature scaling has been applied on the dataset. Exploratory data analysis has been conducted to understand the relationships between various factors contributing to heart diseases. Amongst the various ML techniques used, XGBoost has shown impressive accuracies of up to 95%. In addition to that, the ROC curve analysis exhibits an exceptional under the curve area of 0.99 emphasizing on the model's ability to differentiate between positive and negative outcomes efficiently. Findings from this study underscore the efficacy of employing ML techniques. The results obtained help contribute to the ongoing efforts of leveraging data driven methods to enhance the prediction and prevention of cardiovascular diseases.

关键词： Heart disease Cleveland Data set Machine Learning Techniques XG Boost's ROC Curve Analysis

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Predicting and Discovering Weather Patterns in South Africa Using Spatial-Temporal Graph Neural Networks 5th

Predicting and Discovering Weather Patterns in South Africa ...

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5th Southern African Conference for Artificial Intelligence Research

作者： Gaibie, Adeeb Amir, Hamza Nandutu, Irene Moodley, Deshendran Univ Cape Town Artificial Intelligence Res Unit & Neurosci Inst Cape Town South Africa Ctr Artificial Intelligence Res Cape Town South Africa Busitema Univ POB 236 Tororo Uganda

ISBN: (纸本)9783031782541;9783031782558

This study explores Spatial-Temporal Graph Neural Networks (STGNNs) for predicting and discovering weather patterns in South Africa. We compared the predictive performance of three recent STGNNs, i.e. the Adaptive Graph Convolutional Recurrent Network (AGCRN), Conditional Local Convolution Recurrent Network (CLCRN) and Graph WaveNet (GWN) to a Temporal Convolution Network (TCN) to predict temperature, humidity, pressure and wind speed at 45 weather stations in South Africa. We also analysed the quality and usability of the spatial-temporal dependency graph learnt by the different STGNNs. Using the TCN as a baseline we analyse the increase in performance by adding spatial information and identify the stations most likely to be influenced by changes in neighbouring stations. While AGCRN achieved the best overall predictive performance, the dependency graph learned by CLCRN captured the most plausible spatial-temporal dependencies. The findings highlight the strengths and limitations of AGCRN and CLCRN for weather prediction and for automatic discovery of prominent weather patterns in South Africa.

关键词： Spatial Temporal Graph Neural Networks Weather prediction Spatial Temporal Data Mining

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