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AI-Driven Wearables for Driver Health and Safety 1

引用

16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Diaz-Santos, Sonia Caballero-Gil, Pino Caballero-Gil, Candido Univ La Laguna Dept Comp Engn & Syst Camino San Francisco Paula 38200Canary Islands San Cristobal la Laguna Spain

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

This preliminary research presents a novel driver health monitoring system that aims to prevent accidents by utilizing wearable devices and artificial intelligence. The system incorporates advanced sensors in smartwatches to continuously monitor vital signs. It identifies any abnormalities that could potentially compromise driving safety. Real-time analysis of these metrics by artificial intelligence algorithms provides alerts and actionable insights. The system alerts drivers to take necessary precautions or seek medical advice if they have hypertension or irregular heartbeats. In addition, it monitors driver behavior to identify signs of fatigue and stress and provides suggestions to reduce risks. By offering timely recommendations, the system helps to mitigate immediate dangers and supports long-term health management. This preliminary system has the potential to enhance road safety by addressing health-related factors that contribute to accidents. This research contributes to ambient intelligence by integrating wearable health monitoring with artificial intelligence, offering a comprehensive solution for safer transportation. The integration of these technologies paves the way for smarter, health-conscious driving environments, promoting overall well-being and reducing accident rates.

关键词： Driver Health Monitoring System Wearable Devices Artificial Intelligence Sensors Vital Signs Smartwatches Driving Safety Road Safety Ambient Intelligence

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Comparative Analysis of U-Net with Transfer Learning and Attention Mechanism for Enhanced Medical Image Segmentation 4th

Comparative Analysis of U-Net with Transfer Learning and Att...

引用

4th International Conference on Digital Technologies and Applications (ICDTA)

作者： El Abassi, Fouzia Darouichi, Aziz Ouaarab, Aziz Cadi Ayyad Univ FSTG Comp & Syst Engn Lab L2IS Marrakech 40000 Morocco

ISBN: (纸本)9783031686528;9783031686535

Medical image segmentation is an important component in computeraided diagnosis and treatment. In this work, we study the application of the U-Net architecture to dermoscopy image segmentation, the former being a crucial stage in any algorithm attempting to detect and classify skin lesions. To enhance the segmentation task, we address two different approaches: we propose a novel method by integrating Attention gates inside the U-Net architecture (Attention U-Net), and we perform Transfer Learning for the encoder part of the U-Net using an array of pre-trained models leveraging ISIC-2017 dataset. The experimental configuration consists in a comparison between the common U-Net, the Attention U-Net and five Transfer Learning adaptations that each one uses one pre-trained model (VGG16, VGG19, MobileNetV2, ResNet50 and EfficientNetB0) in the encoder. The importance of Transfer Learning is highlighted through a thorough evaluation based on three key metrics: Loss, Intersection over Union (IoU), and Dice Similarity Coefficient (DSC). The DSC scores demonstrate superior segmentation performance of the enhanced architectures across benchmark analyses compared to the baseline U-Net model. Compared to other Transfer Learning variants, U-Net+MobileNetV2 is the overall best performer with a high DSC of 89.75%. It outperforms the respect DSC values of U-Net+ResNet50 (85.15%), U-Net+VGG19 (84.56%), U-Net+VGG16 (83.71%), and U-Net+EfficientNetB0 (50.44%). Additionally, due to the AttentionMechanism added to U-Net architecture, theDSCincreases to 52.46%. These results collectively underline the efficacy of Transfer Learning, particularly with MobileNetV2, in substantially improving segmentation accuracy, as measured by the DSC metric.

关键词： Semantic Segmentation Deep Learning Dermoscopy Transfer Learning Attention mechanism Encoder-Decoder architectures

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Optimizing Solar Energy Harvesting: A Comprehensive Study on Photovoltaic Tracking systems and Their Impact on Renewable Energy Efficiency 2nd

Optimizing Solar Energy Harvesting: A Comprehensive Study on...

引用

2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Ahmed, Saadaldeen Rashid Hussain, Abadal-Salam T. Tawfeq, Jamal Fadhil Sulaiman, Sazan Kamal Sekhar, Ravi Solke, Nitin Taha, Taha A. Ahmed, Omer K. Ahmed, Shouket A. Alayen Univ Coll Engn Artificial Intelligence Engn Dept Nasiriyah Iraq Bayan Univ Comp Sci Dept Erbil Kurdistan Iraq Al Kitab Univ Tech Engn Coll Dept Med Instrumentat Tech Engn Kirkuk Iraq Al Farahidi Univ Med Tech Coll Dept Med Instrumentat Tech Engn Baghdad Iraq Knowledge Univ Coll Engn Dept Comp Engn Erbil Iraq Symbiosis Int Deemed Univ SIU Symbiosis Inst Technol SIT Pune Campus Pune 412115 Maharashtra India Northern Tech Univ Unit Renewable Energy Kirkuk Iraq

ISBN: (纸本)9783031628801;9783031628818

This study explores the role of solar tracking systems in enhancing energy capture from photovoltaic modules. The objective is to understand renewable energy fundamentals and analyze the efficiency of a solar tracking system compared to a static one in terms of voltage, current, and temperature. The research emphasizes the importance of adjusting the solar tracking incidence angle for optimal energy production. While acknowledging the advantages, the study also notes potential drawbacks such as increased maintenance and disruptions in extreme sunlight conditions. Overall, the project aims to contribute insights into renewable energy concepts and the practical implications of solar energy installations.

关键词： SunSync Modules Solar Tracking systems Photovoltaic Modules Incidence Angle Optimization Renewable Energy Efficiency

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Semi-phenomenological Approach to the Description of Gold Nanoclusters 13th

Semi-phenomenological Approach to the Description of Gold Na...

引用

13th Computer Science Online Conference

作者： Nikulushkin, Alexander Bobrovs, Vjaceslavs Moscow Inst Phys & Technol Ctr Photon & 2D Mat Dolgoprudnyi 141700 Russia Riga Tech Univ Inst Photon Elect & Telecommun LV-1048 Riga Latvia

ISBN: (纸本)9783031705946;9783031705953

Plasmonics attracts great attention nowadays due to the possibility to induce and tune near and far fields of a system in various wavelength ranges. One of the interesting problems is to correctly identify and predict optical properties of plasmonic nanoparticles from the first principles. In this paper we consider a semi-phenomenological approach to calculate optical responce of gold nanospheres and dimers of such particles via quantum polarisabilities of their electrons. We show, that our calculations are in good agreement with experimental data. #CSOC1120.

关键词： nanoparticle nanophotonics plasmonics golden nanocluster dimer

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Measuring Implicit Bias Using SHAP Feature Importance and Fuzzy Cognitive Maps

Measuring Implicit Bias Using SHAP Feature Importance and Fu...

引用

Intelligent systems Conference (IntelliSys)

作者： Grau, Isel Napoles, Gonzalo Hoitsma, Fabian Koumeri, Lisa Koutsoviti Vanhoof, Koen Eindhoven Univ Technol Informat Syst Grp Eindhoven Netherlands Eindhoven Univ Technol Eindhoven Artificial Intelligence Syst Inst Eindhoven Netherlands Tilburg Univ Dept Cognit Sci & Artificial Intelligence Tilburg Netherlands Hasselt Univ Business Informat Res Grp Hasselt Belgium

ISBN: (纸本)9783031477201;9783031477218

In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii) building a Fuzzy Cognitive Map model able to quantify implicit bias, and (iii) using the SHAP feature importance to active the neural concepts when performing simulations. The results using a real case study concerning fairness research support our two-fold hypothesis. On the one hand, it is illustrated the risks of using a feature importance method as an absolute tool to measure implicit bias. On the other hand, it is concluded that the amount of bias towards protected features might differ depending on whether the features are numerically or categorically encoded.

关键词： Fairness Implicit Bias Explainable artificial intelligence Feature importance Fuzzy cognitive maps

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Actions to Promote Knowledge about Accessibility and Inclusion in Higher Education in Brazil 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： da Silva Spigel, Cibele Cesario de la Higuera Amato, Cibelle Albuquerque Martins, Valeria Farinazzo Programa Posgrad Disturbios Desenvolvimento Sao Paulo Brazil Programa Posgrad Computacao Aplicada Sao Paulo Brazil

ISBN: (数字)9783031456510

ISBN: (纸本)9783031456503;9783031456510

In the Brazilian context, public policies focus on inclusive education for elementary and high school students. Fortunately, these students are already starting to reach universities, but most teachers are not prepared to meet the demands of these students efficiently. This paper presents some strategies to promote knowledge to higher education teachers about accessibility and inclusion in a classroom context. For that, two modules of an online course were prepared, besides an ebook with contents in dialogue with each other. Two data collection instruments were used to understand the profile of the target audience and their perception of the course.

关键词： Accessibility Higher Education Accessible digital teaching material

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Integrating Temporal Context into Streaming Data for Human Activity Recognition in Smart Home 1

引用

16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Vicini, Marina Rudorfer, Martin Dai, Zhuangzhuang Manso, Luis J. Aston Univ Aston St Birmingham B4 7ET England Legrand Care Innovat HouseNorthumberland Business Pk5 Silver Cramlington NE23 7RY England

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

With the global population ageing, it is crucial to enable individuals to live independently and safely in their homes. Using ubiquitous sensors such as Passive InfraRed sensors (PIR) and door sensors is drawing increasing interest for monitoring daily activities and facilitating preventative healthcare interventions for the elderly. Human Activity Recognition (HAR) from passive sensors mostly relies on traditional machine learning and includes data segmentation, feature extraction, and classification. While techniques like Sensor Weighting Mutual Information (SWMI) capture spatial context in a feature vector, effectively leveraging temporal information remains a challenge. We tackle this by clustering activities into morning, afternoon, and night, and encoding them into the feature weighting method calculating distinct mutual information matrices. We further propose to extend the feature vector by incorporating time of day and day of week as cyclical temporal features, as well as adding a feature to track the user's location. The experiments show improved accuracy and F1-score over existing state-of-the-art methods in three out of four real-world datasets, with highest gains in a low-data regime. These results highlight the potential of our approach for developing effective smart home solutions to support ageing in place.

关键词： Activity Recognition Data Segmentation Temporal Context Mutual Information Sliding Window Smart Home

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Using the Metadata-Based App-PI Ecosystem to Assess the Privacy Impact of Health Apps 1

引用

16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Mercedes Martinez-Gonzalez, M. Perez-Fuente, Alejandro Aparicio, Amador Criado-Lozano, Pablo A. Univ Valladolid Privacy Engn Res Grp Valladolid Spain Univ Europea Miguel Cervantes Valladolid Spain

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

Mobile applications (apps) facilitate the management of devices and sensors from mobile devices in IoE environments. However, their use carries risks for the privacy of their users: many of them manage personal data. The App-PI (App Privacy Impact) ecosystem analyzes the impact of apps on privacy, addressing the challenge of knowing, understanding and mitigating these risks. In App-PI, a metadata warehouse, a set of analysis tools that calculate indicators, a visualization platform, and verification processes, collaborate. Data flows between these components to provide persons using the visualization platform with accurate, reliable, and understandable information. The warehouse hosts metadata related to the privacy and security of mobile apps. The data flow starts with the collection and integration of data hosted in the warehouse. The analysis tools use these data to calculate indicators that provide objective measures of the risk associated with each app. These values are the input for a verification process based on static analysis, which provides confidence. To make it easier for end users to understand these indicators, they are displayed on the visualization platform with easy-to-understand charts. The flows and usefulness of this ecosystem are shown for health and wellness apps, characteristic of IoE environments.

关键词： Privacy Metadata Mobile Apps Health Apps IoE

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Analysis of Deepfake Technology Development Trends Based on Patent Landscape 13th

Analysis of Deepfake Technology Development Trends Based on ...

引用

13th Computer Science Online Conference

作者： Nikolaev, A. S. Kokorina, O. A. Kirillov, N. N. Antipov, A. A. ITMO Univ Fac Technol Management & Innovat 11 Tchaikovsky St St Petersburg Russia

ISBN: (纸本)9783031702990;9783031703003

This article presents the features of the development of technological trends and examines existing market processes in the global economy related to Deepfake technology, by referring to the institution of intellectual property based on information about registered patent applications and issued patents, which published by patent offices of all countries of the world. Because of a patent search using Deepfake technology, analytical data generated using a professional information retrieval analytical system. The authors of the article identified current trends for patenting the results of intellectual activity, one way or another related to Deepfake. The work identifies promising markets for product launches, also formulates basic recommendations on Deepfake technology from the point of view of the institution of intellectual property. The authors' recommendations will be useful in shaping organizational strategies and making operational decisions in terms of responding to certain trends associated with Deepfake. The results of the study contribute to the development of the patent strategy of innovative organizations working in the field of information technology.

关键词： Deepfake technology technology assessment patinformatics intellectual property analysis

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Intuitive Human-Swarm Interaction with Gesture Recognition and Machine Learning 9th

Intuitive Human-Swarm Interaction with Gesture Recognition a...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Khen, Golan Zhao, Detim Baca, Jose Univ Maryland College Pk MD 20742 USA Arizona State Univ Polytech Campus Mesa AZ 85212 USA Texas A&M Univ Corpus Christi TX 78412 USA

ISBN: (纸本)9789819735556;9789819735563

Unmanned Aerial systems (UAS), commonly known as drones, have revolutionized various industries with their diverse applications. As the demand for seamless and intuitive drone control grows, researchers are exploring innovative approaches to improve human-swarm interaction. This paper presents a novel method for operating a swarm of drones in real time using wearable technology and machine learning. Through the integration of motion capture data and classification algorithms, we strive to achieve an intuitive level of control that is accessible to users with varying skill levels. While the full realization of this approach remains a work in progress, our research lays the groundwork for future endeavors in this domain. In this paper, we discuss the limitations of existing control methods and present our methodology for data preprocessing, model training and testing, and result analysis. Our findings indicate the potential of this approach and open avenues for refining the interaction between humans and drone swarms.

关键词： Unmanned Aerial systems (UAS) Drones Human-swarm interaction Wearable technology Machine learning Motion capture Classification algorithms Intuitive control Real-time control Gesture recognition

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