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

共 43693 条记录，以下是1671-1680 订阅

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Enabling Seamless Healthcare Services with RC-Based Geographical Peer-to-Peer Fog Architecture in the Healthcare 4.0 37th

Enabling Seamless Healthcare Services with RC-Based Geograph...

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37th International Conference on computer Applications in Industry and Engineering

作者： Vinaganti, Hanumanthu Tannedi, Nikhitha Chowdary Nannuri, Pooja Roy, Indranil Mitra, Reshmi Rahimi, Nick Gupta, Bidyut Southeast Missouri State Univ Cape Girardeau MO 63701 USA Univ Southern Mississippi Hattiesburg MS 39406 USA Southern Illinois Univ Carbondale Carbondale IL 62901 USA

ISBN: (纸本)9783031762727;9783031762734

This paper introduces a novel peer-to-peer (P2P) architecture tailored for resource discovery in sensor-based health-monitoring devices. Departing from traditional publish/subscribe methods, this architecture employs location-, interest-, and resource-based mechanisms to enhance efficiency and security without relying on a centralized communication model. The proposed architecture comprises a two-level overlay network: a transit ring housing group-heads representing specific resource types, and a fully connected group of peers. Theoretical analysis demonstrates that search latency remains independent of the number of peers, with constant complexity for intra-group data lookup and O(n) complexity for inter-group data lookup, where n signifies the total number of resource types. This architecture enables efficient, cost-effective, and secure management of large data throughput for medical IoT systems, offering an alternative to the centralized communication model.

关键词： Electronic health record

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Lane Detection in Autonomous Vehicles Using Unsupervised Machine Learning and Light Detection and Ranging 4th

Lane Detection in Autonomous Vehicles Using Unsupervised Mac...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Singh, Siddharth Sharma, Shruti Singh, Adarsh Dwivedi, Rajendra Kumar Madan Mohan Malaviya Univ Technol Dept ITCA Gorakhpur Uttar Pradesh India

ISBN: (纸本)9783031837890;9783031837906

The high growth in the number of automobiles has raised the demand of enhanced driver and passenger safety measures. The goal being effective and efficient application of evolving technology to address the concerns arising with partially or fully autonomous vehicles. Accurate lane positioning and lane change detection is an integral part of Advanced Driver Assistance System which helps in safer trajectory planning. The engineering marvel of Light Detection and Ranging sensors corrects global localization error and provides centimeter-level accuracy but finds its use in restricted domain due to its computational complexity and cost implied. Lane detection is a technique for automatically road markers to ensure that cars stay in their assigned lane and do not collide with vehicles in other lanes. As an alternative, machine learning enabled vision-based lane change detection is highly regarded to provide lane-level localization maintaining high level accuracy. The algorithm design provides polynomial fitting with LiDAR data that helps model perform well in bad lighting conditions and help detect curved lane marking. The technology is well adopted based on the real-world testing scenarios which affirms its real time robustness with average of 89% lane detection accuracy.

关键词： Advanced Driver Assistance System (ADAS) Dedicated Road Infrastructure for Vehicle Safety in Europe (DRIVE) Light Detection and Ranging (LiDAR) Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

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Unleashing the Potential of Agility, Resilience and Business Continuity: A Systematic Literature Review 5th

Unleashing the Potential of Agility, Resilience and Business...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Meechang, Kunruthai Pero, Margherita Medini, Khaled Ecole Mines St Etienne Henri Fayol Inst LIMOS UMR 6158 F-42000 Saint Etienne France Politecn Milan Dept Management Econ & Ind Engn Via Lambruschini 4-B I-20156 Milan Italy

ISBN: (纸本)9783031807596;9783031807602

Challenges in the recent business environment have forced manufacturers to adapt production processes while sustaining operational continuity in response to changes in consumer behaviors and the working environment post-pandemic. Even an agile company struggled to survive during COVID-19 due to restriction measures causing supply chain disruption. Thus, there is a need to extend capabilities that allow companies to be fast and flexible despite a crisis. This paper aims to investigate the potential of three manufacturing paradigms, namely agility, business continuity, and resilience, for adverse situations. A systematic literature review has been conducted to analyze existing studies and build a holistic understanding of how they are complemented manufacturing. The results indicate the relevance of the paradigms that strive to survive through changes from different perspectives: agility focuses on quick adaptation, business continuity keeps products or services delivered, and resilience recovers operations from disruptions. Integration of these paradigms is a promising approach to surviving through crisis, adapting manufacturing, and growing competitiveness for future industries. In addition, the enablers for paradigms are identified, including process, strategy, technology, and people. The knowledge gathered from this study enables future researchers to explore an explicit integration from both conceptual and operational points of view.

关键词： Agility Business continuity Resilience Crisis Manufacturing

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Histopathology Image Augmentation Through StyleGAN2-ADA 11th

Histopathology Image Augmentation Through StyleGAN2-ADA

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11th Latin American Conference on High Performance Computing

作者： Munoz, Branndon Pezoa, Raquel Gutierrez, Helen Univ Tecn Federico Santa Maria Dept Informat Valparaiso Chile Univ Tecn Federico Santa Maria Centro Cientifico Tecnol Valpara Valparaiso Chile Pontificia Univ Catolica Valpara Chile Facultad Ciencias Escuela Tecnol Med Valparaiso Chile

ISBN: (纸本)9783031800832;9783031800849

The precise classification of histopathological images is crucial for diagnosing and treating cancer, yet the scarcity of labeled data often limits it. This study investigates the efficacy of using StyleGAN2ADA data augmentation in histopathological image classification. In this work, we (i) evaluate the capability of StyleGAN2-ADA to generate realistic synthetic histopathological images, (ii) implement a data augmentation strategy using these images, and (iii) compare the performance of a binary classifier trained with and without the proposed augmentation. We trained a StyleGAN2-ADA model in a high-performance computing environment to generate high-quality synthetic images to augment histopathological datasets. We then trained binary classifiers using the augmented datasets for the PCam and IDC datasets and compared their performance with classifiers trained only with original data. Results showed a significant improvement in classifier accuracy, with a 5.9% increase in ROC (AUC) for the PCam dataset at 3% data availability and an 11.3% increase at 20% data availability. For the IDC dataset, the ROC (AUC) improved by 3.4% at 3% data availability and by 2.4% at 20% data availability. Notable enhancements were observed in cancer class metrics, particularly in low-data scenarios, demonstrating the effectiveness of StyleGAN2-ADA in improving classifier robustness and generalization. We conclude that StyleGAN2-ADA is an effective tool for generating high-quality synthetic histopathological images and that the proposed data augmentation strategy substantially enhances classifier performance. Thus, we improved the robustness and generalization of classification models in this critical medical field. Furthermore, it highlights the importance of HPC in accelerating deep learning research applied to complex medical problems.

关键词： Medical problems

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Preliminary Study About the Water Quality Prediction Models: A Textile Case Study

Preliminary Study About the Water Quality Prediction Models:...

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

作者： Silva, Ana Raquel Miranda, Rita Portela, Filipe Univ Minho Algoritmi Guimaraes Portugal IOTech Innovat Technol Trofa Portugal

ISBN: (纸本)9783031834318;9783031834325

The textile industry is a significant sector in the global economy, generating 1000 billion dollars in revenue. However, it has also caused ecological issues, particularly regarding water usage. With approximately 93 billion m(3) of water consumed per year, the industry contributes to the depletion and contamination of this natural resource due to a lack of proper reusing and quality control practices. This study focuses on examining data from 12 Portuguese textile companies to determine if it is possible to predict water quality for reuse. Two automated machine learning tools were used to evaluate over 650 data models, aiming to obtain the best results from the available data. The analysis revealed that focusing on individual companies yielded the best outcomes, with a maximum R-2 Score of 0.67, a mean absolute error of 0.32, and a root mean squared error of 0.36. These findings could have implications for improving water management practices in the textile industry.

关键词： Textile Industry Water Quality Prediction Sustainability

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Prediction of Monthly Precipitation in Guangxi Based on EVO-CNN-LSTM-Attention Model 5th

Prediction of Monthly Precipitation in Guangxi Based on EVO-...

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

作者： Zhang, Xing Wu, Jiansheng Shi, Yeqiong Li, Tiejin Guangxi Normal Univ Sci & Technol Laibin 546199 Peoples R China Guangxi Univ Sci & Technol Liuzhou 545000 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

The magnitude of precipitation is closely related to people's lives. To enhance the precision of precipitation prediction, a monthly precipitation prediction model for Guangxi based on EVO-CNN-LSTM-Attention is proposed in this paper. Firstly, the model utilizes convolutional neural network to capture spatial features of the input sequence, followed by capturing temporal correlations through long short term memory neural networks. Secondly, Attention is used to better understand internal correlations of the sequence. Finally, an energy valley optimization algorithm is introduced to optimize parameters such as learning rate, convolutional kernel size, and number of neurons that are difficult to determine in the model, making the structure of the model more reasonable, optimize model structure and training parameters. Using the historical precipitation data of Liuzhou city, Guangxi province, EVO-CNN-LSTM-Attention is compared with CNN, LSTM, CNN-LSTM and CNN-LSTM-Attention. The findings reveal that EVO-CNN-LSTM-Attention model surpasses other comparative models, exhibiting both high precision and stability.

关键词： Convolutional Neural Network Long Short Term Memory Neural Network Attention Mechanism Energy Valley Optimization Algorithm

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An Offer You Cannot Refuse? Trends in the Coercive Impact of Amazon Book Recommendations 5th

An Offer You Cannot Refuse? Trends in the Coercive Impact of...

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5th International Workshop on Algorithmic Bias in Search and Recommendation

作者： Rystrom, Jonathan H. Univ Oxford Oxford Internet Inst Oxford OX1 England

ISBN: (纸本)9783031719745;9783031719752

Recommender systems can be a helpful tool for recommending content but they can also influence users' preferences. One sociological theory for this influence is that companies are incentivised to influence preferences to make users easier to predict and thus more profitable by making it harder to change preferences. This paper seeks to test that theory empirically. We use Barrier-to-Exit, a metric for how difficult it is for users to change preferences, to analyse a large dataset of Amazon Book Ratings from 1998 to 2023. We focus the analysis on users who have changed preferences according to Barrier-to-Exit. To assess the growth of Barrier-to-Exit over time, we developed a linear mixed-effects model with crossed random effects for users and categories. Our findings indicate a highly significant growth of Barrier-to-Exit over time, suggesting that it has become more difficult for the analysed subset of users to change their preferences. However, it should be noted that these findings come with several statistical and methodological caveats including sample bias and construct validity issues related to Barrier-to-Exit. We discuss the strengths and limitations of our approach and its implications. Additionally, we highlight the challenges of creating context-sensitive and generalisable measures for complex socio-technical concepts such as "difficulty to change preferences". We conclude with a call for further research: to curb the potential threats of preference manipulation, we need more measures that allow us to compare commercial as well as non-commercial systems.

关键词： Recommender Systems Socio-technical evaluations Fairness Search Engines Audit Study

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Evaluating Trends and Identifying Research Gaps: A Thematic and Evolutionary Bibliometric Analysis of Deposit Refund Systems (1938-2024) 5th

Evaluating Trends and Identifying Research Gaps: A Thematic ...

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5th 5th Olympus - International Conference on Supply Chain

作者： Rotsios, Konstantinos Mavidis, Aristotelis Folinas, Dimitris Fotiadis, Thomas Perrotis Coll Amer Farm Sch Dept Int Business Thessaloniki 55102 Greece Democritus Univ Thrace Dept Prod Engn & Management Xanthi 67100 Greece Int Hellen Univ Dept Supply Chain Management Katerini 60100 Greece

ISBN: (纸本)9783031693434;9783031693441

This study presents a novel bibliometric analysis of Deposit Refund Systems (DRS) from 1938 to 2024, aiming to describe the thematic evolution and identify key research trends within the field. By utilizing data extracted from Scopus and applying a refined search strategy encompassing terms such as "deposit refund system," "reverse vending machine," and "container deposit legislation", among others, we gather relevant publications for analysis. The study employs a series of bibliometric tools, including co-word analysis, thematic maps, trend topic analysis, and Treemaps, to dissect the complex landscape ofDRS research. Through thesemethodologies, the research elucidates how dominant themes have shifted over the decades, highlights the emergence of new research areas, and underscores technology integration in DRS practices. Key findings reveal a significant focus on 'recycling' and 'waste management' as central themes, with emerging interests in 'plastic waste' and 'technological advancements'. The analysis also identifies gaps in current research, particularly in the socio-economic impacts of DRS and its effectiveness across different geopolitical contexts. This paper significantly contributes to understanding DRS dynamics, offering valuable insights for policymakers, practitioners, and researchers. It suggests areas for future research and underscores the need for a multidisciplinary approach to enhance DRS's effectiveness in promoting sustainable waste management practices globally, thereby providing practical guidance for implementing and improving DRS systems.

关键词： Deposit Refund Systems Bibliometric Analysis Recycling Trends Waste Management Thematic Evolution

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An Automated Technique for Detecting Sinkholes from Lidar DEMs in Railway Environments 4th

An Automated Technique for Detecting Sinkholes from Lidar DE...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Maryem, Bouali Ali, Sammuneh Muhammad Rani, El Mehouche Fakhreddine, Ababsa Bahar, Salavati Flavien, Viguier Ecole Speciale Travaux Publ ESTP Inst Rech IR Cachan France HESAM Univ Arts & Metiers ParisTech CNRS PIMMCNAM Paris France SNCF Reseau Direct Tech Reseau DGII DTR IP3M DM Matrice St Denis France

ISBN: (纸本)9783031821493;9783031821509

High-resolution LiDAR technology has revolutionized sinkhole detection, offering a more efficient and accurate alternative to traditional, labor-intensive visual inspections. The aim of this paper is to develop an automated approach to detect sinkholes in the railway environment by extracting relevant spatial features. Our methodology starts by the precise extraction of ground points from LiDAR point clouds. Efficiently isolating ground points enables the detection and analysis of ground deformations and subsidence patterns near railway tracks. The Voxel Based Ground Filtering algorithm (VBGF) is compared to the robust Cloth Simulation Filter (CSF). High-resolution Digital Elevation Models (DEMs) are subsequently generated from the LiDAR ground points, and sinkholes are detected using a segmentation-based approach. This study presents a comprehensive method for sinkhole detection, facilitating the early identification of potential sinkhole formations and offering a proactive strategy for maintaining the safety and integrity of railway infrastructur.

关键词： Sinkhole Detection Ground Filter Lidar DEMs Point Cloud

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Retention Induced Biases in a Recommendation System with Heterogeneous Users 5th

Retention Induced Biases in a Recommendation System with Het...

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5th International Workshop on Algorithmic Bias in Search and Recommendation

作者： Ma, Shichao Yelp Inc San Francisco CA 94105 USA

ISBN: (纸本)9783031719745;9783031719752

I examine a conceptual model of a recommendation system (RS) with user inflow and churn dynamics. When inflow and churn balance out, the user distribution reaches a steady state. Changing the recommendation algorithm alters the steady state and creates a transition period. During this period, the RS behaves differently from its new steady state. In particular, A/B experiment metrics obtained in transition periods are biased indicators of the RS's long-term performance. Scholars and practitioners, however, often conduct A/B tests shortly after introducing new algorithms to validate their effectiveness. This A/B experiment paradigm, widely regarded as the gold standard for assessing RS improvements, may consequently yield false conclusions. I also briefly touch on the data bias caused by the user retention dynamics.

关键词： Recommendation system A/B experimentation Bias User retention and churn System convergence

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