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6th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Kao, Chiao-Wen Wang, Shih-Tung Huang, Chi-Sheng Ming Chuan University Department of Applied Artificial Intelligence Taipei Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350396133

Global aging presents profound challenges, especially concerning the well-being and mobility of the elderly. Fortunately, technological progress offers means for those with mobility limitations. With the aging population, the significance of the safety and accessibility of passive mobility methods becomes increasingly apparent. In this context, many individuals are using wheelchairs or other assistive devices to maintain their independence for daily mobility. Outdoor environments pose numerous challenges for wheelchair users, such as curbs, uneven road surfaces, and unexpected obstacles. These challenges elevate the risk of accidents and limit freedom of movement. To address these challenges and enhance the adaptability of assistive devices outdoors, we proposed a method by combining YOLOv8 for semantic segmentation with real-time edge detection of road scenes to accurately estimate curb positions. By leveraging the proposed method, various elements of the environment are accurately identified in real-time, including curbs and road conditions. Moreover, by calculating the distance between the wheelchair and the curbs, the system provides timely alerts and guidance to users regarding potential hazards or obstacles, empowering them to swiftly respond to their surroundings and avoid collisions for safety. For the elderly with visual impairments or declining reaction capabilities, the benefits of the system are significant. By enhancing safety, accessibility, and adaptability, the system revolutionizes mobility assistance and empowers individuals to lead more fulfilling and independent lives as they age. © 2024 IEEE.

关键词： Wheelchairs

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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computer Systems science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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OFFLINE-FIRST STRATEGIES - A NOVEL CONCEPT FOR THE MIGRATION OF WORKLOADS USING VIRTUAL MACHINES OMITING LIMITATIONS OF TRADITIONAL SERVICE DEPLOYMENT CONCEPTS 21

OFFLINE-FIRST STRATEGIES - A NOVEL CONCEPT FOR THE MIGRATION...

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21st International Conference on applied Computing 2024, AC 2024 and 23rd International Conference on WWW/Internet 2024, ICWI 2024

作者： Cocos, Henry-Norbert Frankfurt University of Applied Sciences Department of Computer Science and Engineering Nibelungenplatz 1 Frankfurt am MainD-60318 Germany

ISBN: (纸本)9789898704627

The project aims to investigate the outsourcing of cloud services to clients by including heterogeneous, client-side resources and cooperation between cloud and client in the execution of services. For this purpose, an offline-first strategy shall be formulated, developed, and investigated to be used as a counterpart to the cloud-first strategy and enable the utilization of services without a permanent cloud connection. The scope of the offline-first strategy is to achieve autonomy for applications while cooperating with cloud services. To achieve the independence and simultaneous cooperation of applications in this context, methods of service migration are to be researched to enable the provision of services. In summary, this should increase resilience when using services, improve the stability of services along the cloud-to-edge supply, and allow transparent use for the end user. Furthermore, lab experiments were conducted to investigate the behavior of virtual machines during live migration in two setups, simulating a WAN link. The experiments show that migration over a WAN link takes five times longer for RAM-intensive tasks than in a LAN. This provides initial insights into selecting the technological basis for this project. © 2024 Proceedings of the International Conferences on applied Computing and WWW/Internet 2024. All rights reserved.

关键词： Cloud platforms

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High Resolution Solar Image Generation Using Generative Adversarial Networks

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Annals of Data science 2024年第5期11卷 1545-1561页

作者： Dash, Ankan Ye, Junyi Wang, Guiling Jin, Huiran Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States School of Applied Engineering and Technology New Jersey Institute of Technology NewarkNJ07102 United States

We applied Deep Learning algorithm known as Generative Adversarial Networks (GANs) to perform solar image-to-image translation. That is, from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) line of sight magnetogram images to SDO/Atmospheric Imaging Assembly (AIA) 0304-Å images. The Ultraviolet (UV)/Extreme Ultraviolet observations like the SDO/AIA 0304-Å images were only made available to scientists in the late 1990s even though the magnetic field observations like the SDO/HMI have been available since the 1970s. Therefore, by leveraging Deep Learning algorithms like GANs we can give scientists access to complete datasets for analysis. For generating high resolution solar images, we use the Pix2PixHD and Pix2Pix algorithms. The Pix2PixHD algorithm was specifically designed for high resolution image generation tasks, and the Pix2Pix algorithm is by far the most widely used image to image translation algorithm. For training and testing we used the data for the year 2012, 2013 and 2014. After model training, we evaluated the model on the test data. The results show that our deep learning models are capable of generating high resolution (1024 × 1024 pixels) SDO/AIA0304 images from SDO/HMI line of sight magnetograms. Specifically, the pixel-to-pixel Pearson Correlation Coefficient of the images generated by Pix2PixHD and original images is as high as 0.99. The number is 0.962 if Pix2Pix is used to generate images. The results we get for our Pix2PixHD model is better than the results obtained by previous works done by others to generate SDO/AIA 0304 images. Thus, we can use these models to generate AIA0304 images when the AIA0304 data is not available which can be used for understanding space weather and giving researchers the capability to predict solar events such as Solar Flares and Coronal Mass Ejections. As far as we know, our work is the first attempt to leverage Pix2PixHD algorithm for SDO/HMI to SDO/AIA0304 image-to-image translation. © Th

关键词： Generative adversarial networks

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DESIGN AND IMPLEMENTATION OF A VIRTUAL LAB PLATFORM FOR ACADEMIC TEACHING: EXPERIENCE IN THE DEVELOPMENT AND USE OF A NEW PLATFORM FOR THE DIGITIZATION OF LABORATORIES FOR computer science CLASSES 21

DESIGN AND IMPLEMENTATION OF A VIRTUAL LAB PLATFORM FOR ACAD...

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21st International Conference on applied Computing 2024, AC 2024 and 23rd International Conference on WWW/Internet 2024, ICWI 2024

作者： Baun, Christian Kappes, Martin Cocos, Henry-Norbert Koch, Malte Martin Petrozziello, Maurizio Frankfurt University of Applied Sciences Department of Computer Science and Engineering Nibelungenplatz 1 Frankfurt am MainD-60318 Germany

ISBN: (纸本)9789898704627

This paper presents a virtual platform for university teaching that simplifies the creation and use of complex IT setups, even for students with little to no experience. Setting up infrastructures for courses like computer networks or IT security has traditionally been challenging. However, the platform presented in this paper simplifies the setup by using virtualization techniques for compute, network, and storage resources, reducing the efforts in preparation for lecturers, researchers, and students. The platform gives access to complex IT setups utilizing free software components. The project employs Proxmox Virtual Environment and CEPH for virtualization and distributed storage, respectively, providing a flexible and practical solution. On top of that, a novel graphical web interface for the easy access and use of the virtual infrastructures is implemented using ReactFlow. © 2024 Proceedings of the International Conferences on applied Computing and WWW/Internet 2024. All rights reserved.

关键词： Students

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3GPP SEAL as an Edge Application: The Ultimate Enabler of Flexible and Universal Communication between Vulnerable Road Users and Autonomous Vehicles 100

3GPP SEAL as an Edge Application: The Ultimate Enabler of Fl...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Charpentier, Vincent Leyendeckers, Amaryllis Camelo, Miguel Marquez-Barja, Johann Slamnik-Kriještorac, Nina University of Antwerp-imec IDLab Faculty of Applied Engineering Belgium University of Antwerp-imec IDLab Department of Computer Science Belgium

ISBN: (纸本)9798331517786

Enhancing communication between Vulnerable Road Users (VRUs) and Unmanned Automated Vehicles (UAVs) has significant potential to improve road safety. The need for this communication is due to the fact that VRUs will no longer be able to establish physical eye contact with UAVs, given the absence of a human driver behind the steering wheel. However, a challenge in the state-of-the-art technologies for Connected, Cooperative, and Automated Mobility (CCAM), i.e. ITS-G5 (IEEE 802.11p) and Cellular Vehicle-to-Everything (C-V2X), is the lack of a unified communication stack that connects all types of users. This is because the current generation of CCAM communication technologies requires dedicated hardware devices that cannot be easily installed on devices carried by VRUs (such as phones or wearables). This paper aims to address this challenge by providing a real-life, sophisticated solution that offers the CCAM communication stack as a Network-as-a-Service (NaaS) in the 5G and Beyond ecosystem. Integration is achieved by relying on the Service Enabler Architecture Layer (SEAL) principles standardised by the 3rd Generation Partnership Project (3GPP). These architectural principles are embedded in the design of Network-Aware Edge Applications (EdgeApps), which are the building blocks of vertical services in 5G and Beyond. This way, any device or user with the capability to connect to 5G will also be able to retrieve important CCAM services from the network by using EdgeApps. In addition, no dedicated CCAM hardware is needed. Furthermore, this paper provides key lessons learned from the challenges encountered in connecting VRUs and UAVs by integrating CCAM into the 5G and Beyond ecosystem. Moreover, we have conducted real-life experiments to evaluate the system-level latency characteristics of the proposed solution and compared them with those of ITS-G5 and C-V2X. © 2024 IEEE.

关键词： 5G mobile communication systems

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Integrating AI Orchestration and Lifecycle Management in 6G Networks: A Pipeline Approach

Integrating AI Orchestration and Lifecycle Management in 6G ...

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2024 IEEE Latin-American Conference on Communications, LATINCOM 2024

作者： Jimenez, Julian Gavrielides, Andreas Slamnik-Krijestorac, Nina Latre, Steven Marquez-Barja, Johann M. Camelo, Miguel University of Antwerp - imec IDLab - Department of Computer Science Belgium University of Antwerp - imec IDLab - Faculty of Applied Engineering Belgium

ISBN: (纸本)9798331521110

On the threshold of a new technological era, Sixth Generation (6G) networks promise to revolutionize global connectivity, bringing mobile communications to data speeds in the terabits per second range and ultra-low latency. These networks will enhance the user experience enable a wide range of advanced applications and emerging services. Artificial Intelligence (AI)-powered network functions and services, also known as Network Intelligence Functions (NIF) and Network Intelligence Service (NIS), are essential to achieve this vision. In this study, we present the design and development of an end-to-end framework for orchestrating AI-based functions. Utilizing Kubernetes (K8s) and Prefect, we showcase its implementation through an AI-driven Traffic Classification (TC) use case. Our results confirm the feasibility of the proposed framework, offering valuable insights in the lifecycle management design, such as data collection, decision-making, and critical performance metrics, including deployment time and model performance in terms of accuracy and inference times among three different Machine Learning (ML)-based TC models. © 2024 IEEE.

关键词： Decision making

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A Comprehensive Review of Bio-Medical Portable Devices for Blood Coagulation Detection Device 2

A Comprehensive Review of Bio-Medical Portable Devices for B...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bore, Joy Dhale, Trishul Kumar, Praveen Deshmukh, Gauri S. Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Meghe Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Department of Applied Chemistry Nagpur India

ISBN: (纸本)9798331528713

The paper provides a synoptic view of portable biomedical point-of-care devices for blood coagulation detection, emphasising the state-of-the-art technology adopted and its use in the medical industry. These devices give fast and precise results at the point of care and are indispensable for diagnosing coagulation and perceiving thrombotic states. It brought into focus the issues of monitoring blood coagulation across different areas of practice, such as the ICU, operative and perioperative services, and ambulatory care. This paper also highlights new trends in the development of sensors, miniaturisation and the integration of digital health solutions. Further, it explains how these devices are advantageous and disadvantageous in terms of speed, sample requirement, standardisation, calibration, and user-friendliness. Last but not least, the regulation and market uptake issue is discussed, focusing on how portable coagulation detection devices can revolutionise patient care and access to healthcare. Finally, the authors state that the paper should inspire further research to develop new solutions and collaboration to address obstacles and underpin advancements in the field. © 2024 IEEE.

关键词： Portable equipment

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Revolutionizing Urban Waste Management: Cutting-Edge Image Processing for Precise Solid Waste Classification 5

Revolutionizing Urban Waste Management: Cutting-Edge Image P...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rama Krishna, S. Patan, Muzeeb Khan Sudarsan, J.S. Padmapriya, R. Bhagavathiperumal, S. Balaram, Allam Department of Computer Science & Engineering Hyderabad502329 India College of Engineering and Technology University of Technology and Applied Sciences Department of EEE Muscat Oman India Sathyabama Institute of Science and Technology Department of Civil Engineering Chennai India Sri Krishna College of Engineering and Technology Coimbatore India MLR Institute of Technology Department of Computer Science and Engineering Hyderabad500043 India

ISBN: (纸本)9798331530013

Due to rapid urbanization and population growth, urban waste management has become one of the major challenges faced by cities around the world. Solid waste classification and sorting are necessary for intensifying recycling, safeguarding environment and manage additional landfilling of materials that are reusable or recyclable, and are getting in landfill of solid waste in certain extent. In this study, an original ensemble model using the VGG19 and EfficientNet architectures is proposed to classify solid waste in urban area. This proposed model was applied on a dataset consists of 2,527 number of images in to six classes to increase the accuracy of classification as well as to maximize the efficiency of waste. It preprocesses the incoming data using various techniques such as image resizing, normalization and augmentation to make the dataset more diverse and robust. The ensemble mechanism performed better combining the detailed feature extraction skills of VGG19 and versatile and scalable architecture of EfficientNet. On test set, it outperformed individual models and the accuracy for the proposed model was 98.75% which closely followed, previously obtain the best result. © 2024 IEEE.

关键词： Population statistics

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Lexical Complexity Prediction using Word Embeddings 35

Lexical Complexity Prediction using Word Embeddings

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35th Conference on Computational Linguistics and Speech Processing, ROCLING 2023

作者： Yang, Cheng-Zen Li, Jin-Jian Lin, Shu-Chang Department of Computer Science and Engineering Department of Foreign Languages and Applied Linguistics Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9789869576963

Lexical complexity is crucial for reading comprehension. In the past, research work of lexical complexity prediction mainly focuses on differentiating the complexity difference between two words. Moreover, most of the previous lexical complexity prediction approaches only consider traditional lexically relevant features. In this paper, we propose a novel supervised approach using word embeddings features to tackle the lexical complexity prediction problem as a single-label multiclassification problem. We discuss four word embeddings techniques including Word2Vec, fastText, GloVe, and BERT. We also discuss five classification models including k-Nearest Neighbors, Support Vector Machines, Multilayer Perception, Random Forest, and XGBoost. The prediction models are evaluated with three datasets in English, Traditional Chinese, and Japanese. The results show that SVM with fastText can achieve the highest accuracy of 66.23% for the English dataset. SVM with GloVe can achieve the highest accuracy of 53.84% for the Traditional Chinese dataset. SVM with Word2Vec can achieve the highest accuracy of 49.96% for the Japanese dataset. © 2023 ROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing. All rights reserved.

关键词： Embeddings

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