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检索条件"机构=Dept. of Information Engineering and Computer Science and Mathematics"

共 8635 条记录，以下是91-100 订阅

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Analyzing Cyberbullying Word Distribution Across Social Media to Develop Lexicon Dictionary 2

Analyzing Cyberbullying Word Distribution Across Social Medi...

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2nd IEEE International Conference on Networks, Multimedia and information Technology, NMITCON 2024

作者： Sahana, V. Anil Kumar, K.M. Jss Academy of Technical Education Dept. of Information Science and Engineering Bengaluru560060 India Jss Science and Technology University Dept. of Computer Science and Engineering Mysuru570006 India

ISBN: (纸本)9798350372892

Social media platforms have become essential tools for communication, collaboration, and exchanging information, ideas, and knowledge among users worldwide. Despite their benefits, the anonymity offered by these platforms has unfortunately led to a rise in hate speech and cyberbullying, causing concern across the globe. This issue has drawn the attention of researchers and scholars now focused on devising methods for automatically detecting cyberaggression and hate speech. The goal is to mitigate these harmful behaviors and create safer online environments [1]. This study specifically aims to identify and analyze the usage of cyberbullying language within the English Tweet corpus, focusing on distinguishing between bullying and non-bullying language. This research contributes to developing more effective cyberbullying detection models by examining the frequency and patterns of cyberbullying words. These findings are vital for enhancing the efficacy of cyberbullying detection on social networks in future research efforts. The conclusions of the experiments indicate that the SVM achieved the highest average accuracy, approximately 84 %. © 2024 IEEE.

关键词： Anonymity

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Automated Bengali Sign Language Character Classification with Deep Learning Techniques 15

Automated Bengali Sign Language Character Classification wit...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Akter, Tahmina Mahmud, Tanjim Barua, Tikle Naher, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with 94% and ResNet variants achieving around 88%. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals. © 2024 IEEE.

关键词： Bengali sign language CNNs Deaf and hard-of-hearing community Image classification

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Federated Learning for Enhanced Cell Nuclei Segmentation in Histopathological Images

Federated Learning for Enhanced Cell Nuclei Segmentation in ...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Usai, Marco Loddo, Andrea Putzu, Lorenzo Ruberto, Cecilia Di University of Cagliari Dept. of Mathematics and Computer Science Cagliari Italy University of Cagliari Dept. of Electrical and Electronic Engineering Cagliari Italy

ISBN: (纸本)9798350362480

This study addresses the critical challenge of cell nuclei segmentation in histopathological image analysis, which is essential for cancer diagnosis and prognosis. Traditional segmentation methods often struggle with complexities such as overlapping nuclei and variations in shape and size. Recent advancements in machine learning and deep learning, particularly convolutional neural networks, have improved segmentation accuracy but face limitations due to the need for large annotated datasets, often constrained by privacy regulations in the medical field. This research explores federated learning as a solution, enabling collaborative model training across institutions without sharing sensitive patient data. By leveraging diverse datasets while maintaining privacy, federated learning enhances model robustness and generalization capabilities. The study evaluates various federated learning techniques on in- and out-of-domain test sets, aiming to improve the reliability of segmentation models in clinical settings. The findings suggest that federated learning techniques can effectively address the challenges of data scarcity and domain shift, paving the way for more accurate and widely applicable segmentation algorithms in computational pathology. © 2024 IEEE.

关键词： Federated learning

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Towards Empathetic Care Robots

Towards Empathetic Care Robots

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2023 Italia Intelligenza Artificiale - Thematic Workshops, Ital-IA 2023

作者： Costantini, Stefania Dell'Acqua, Pierangelo Dyoub, Abeer Monaldini, Andrea Department of Information Engineering Computer Science and Mathematics University of L'Aquila Italy Dept. of Science and Technology Linköping University Sweden

Care Robots are the future of healthcare giving for patients with chronic diseases and disabilities. For improving the quality of care-giving and the trustworthiness of those robots, they should be equipped with emotion recognition capabilities and empathetic behavior. In this work, we propose an framework for an empathy module to be incorporated in every care robot, and then demonstrate the effectiveness of our proposal by means of an example. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Emotion Recognition

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Ultra-Wideband Pattern Reconfigurable Antenna for Base station Applications 5

Ultra-Wideband Pattern Reconfigurable Antenna for Base stati...

引用

5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Bhickta, Shalini Jaglan, Naveen Kumar, Amit Jaypee University of Information Technology Dept. of Electronics and Communication Engineering Waknaghat Solan India Jaypee University of Information Technology Dept. of Computer Science & Engineering and Information Technology Waknaghat Solan India

ISBN: (纸本)9798350376753

The proposed work aims at 5G base station antenna communication applications. The Reconfigurable Antenna is built with an idea of parasitic loaded elements in antenna. The proposed design has a zig-zag S-shape structure with a driven Monopole antenna located along the central axis and a parasitic structure enfolding around the monopole. Reconfiguration is accomplished by adjusting the PIN diodes bias voltages. The 2.44-9.46 GHz frequency range i.e., the Ultra-Wide-Band frequency spectrum is used at which this reconfigurable antenna is designed to operate. This paper has three radiation pattern which are obtained by two PIN diodes. Beam-steering capability in the elevation plane is achieved i.e., θ (0°-180°). The outcome shows the design's potential and its wide range concurrent applications for base station antennas. © 2024 IEEE.

关键词： Monopole antennas

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Enhancing VANET Efficiency with ACRR Technique for Improved Traffic Management and Network Scalability

Enhancing VANET Efficiency with ACRR Technique for Improved ...

引用

2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Karthick, K. Talari, Praveen Nishadevi, V. Krishna Prakash, R. Asokan, R. Velusamy, A. Dept. of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Computer Science and Engineering Vignan Bharathi institute of technology Telangana Hyderabad India Dept. of Computer Science and Engineering Mahendra College of Engineering Tamilnadu Salem India Dept. of Information Technology Sona College of Technology Salem India Dept. of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Dept. of Information Technology Sona College of Technology Tamil Nadu Salem India

ISBN: (纸本)9798350376913

The role of Vehicular Ad Hoc Networks (VANETs) is crucial in enabling Intelligent Transportation System (ITS) technologies such as safe financial transactions, media applications, and effective traffic control. As traffic increases, the topology of vehicular networks is in constant flux, and the sparse distribution of vehicles, particularly on highways, presents challenges for network scalability. This situation makes it difficult for cars to keep consistent routes inside the network, which affects the stability of the network. To address these challenges, the developed Adaptive-ant Colony based Randomized Recommendation (ACRR) technique emerges as a unique solution for enhancing VANETs by reducing travel time. In instances of high traffic density on busy roads, the ACRR algorithm is effectively utilized to group vehicles. Leveraging data collected from these densely populated road segments, the system identifies congestion-prone areas and formulates optimal vehicle routes based on customized vehicle groupings. The framework's performance evaluation encompasses various parameters, including packet loss, message transmission rate, energy consumption, and average cluster growth. The proposed VANET framework, empowered by the ACRR algorithm, achieves an impressive message transmission rate of approximately 80%. In comparison, alternative methods like Re-RouTE exhibit a limited transmission rate of 70%, while others such as Net Run Rate (NRR), DIVERT 30, and DIVERT-60 demonstrate rates below 20%. Furthermore, the framework's parcel loss is significantly reduced to only 33% of that observed in the standard VANET framework. As a result, the ACRR algorithm integrated into the VANET framework demonstrates notable efficiency when compared to other approaches. It is crucial to recognize that, even with a refined technique, managing traffic congestion remains challenging if drivers disregard the recommended routing suggestions. Overall, this research offers insights into the p

关键词： Traffic congestion

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Device Identification using Network Traces in a Smart Home IoT Network 17

Device Identification using Network Traces in a Smart Home I...

引用

17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Brahma, Jwngfu Matam, Rakesh Choudhury, Nikumani Indian Institute of Information Technology Dept. of Computer Science & Engineering Guwahati India Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad India

ISBN: (纸本)9798350307672

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques. © 2023 IEEE.

关键词： Internet of things

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Automated Road Extraction in Urban and Rural Regions from Remote Sensing Imagery with U-Net and ResNet-Based Architectures 1

Automated Road Extraction in Urban and Rural Regions from Re...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Palvi Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Dept. of Computer Science & Engineering Punjab India Chandigarh University Dept. of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350356816

Accurate road extraction from Remote Sensing Imagery (RSI) is crucial for various applications, including urban planning, navigation, and disaster response. This study investigates automated road extraction in rural, semi-urban and urban regions using deep learning architectures, specifically UNet and Deep Residual U-Net (ResNet) models. We leverage High-Resolution Satellite Imagery (HRSI) to train and validate our models, focusing on the distinct characteristics and challenges presented by urban and rural environments. Our methodology involves comprehensive data preprocessing and augmentation techniques to enhance model robustness. We evaluate the performance of both architectures using metrics and achieved an accuracy of 90.29%, recall of 80.95%, precision of 74.89%, and F1- score of 77.80% for the Deep Residual U-Net. Experimental results demonstrates that while both models perform well, the Deep Residual U-Net exhibits superior accuracy and generalization, particularly in complex urban landscapes. © 2024 IEEE.

关键词： Urban planning

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CELL SEGMENTATION IN FLUORESCENCE MICROSCOPY IMAGES

CELL SEGMENTATION IN FLUORESCENCE MICROSCOPY IMAGES

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2024 IET International Conference on engineering Technologies and Applications, ICETA 2024

作者： Yin, Shao-Yun Hsu, Wei-Wen Hsia, Chih-Hsien Master Program of Artificial Intelligence National Ilan University Ilan Taiwan Dept. of Computer Science and Information Engineering National Taitung University Taitung Taiwan Dept. of Computer Science and Information Engineering National Ilan University Ilan Taiwan

ISBN: (纸本)9781837242672

For microscopic cells images, cells’ morphologies are widely varied among different imaging modalities and staining methods. For fluorescence microscopy images, Poisson noises present in images due to the lightening, causing photobleaching, which makes it challenging in analysis with the deep learning models. Traditionally, median filtering can be applied to the original image for denoising, however, it also results in a blurry image, which is harmful to segmentation. In this study, the Haar Wavelet Transform is used to alleviate the photobleaching problem for cell segmentation, integrated to the framework without interfering with feature extraction by the backbone model. For our experiments, instance segmentation for fluorescence-stained cells was performed based on U-Net architecture with the method of gradient flow tracking proposed by Cellpose. And the Haar wavelet downsampling is embedded in the skip connections to verify if it can improve the segmentation performance for fluorescence images. The experimental results show that the approach of Haar wavelet downsampling embedded in the skip connections achieve superior performance among the other settings. Compared with the performance of the baseline model, the performance of the proposed model reached 0.732 in F1-score, which surpassed the one of baseline model (0.6341) by almost 0.1. © The Institution of engineering & Technology 2024.

关键词： Fluorescence microscopy

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How to Spatial Audio with the WebXR API: a comparison of the tools and techniques for creating immersive sonic experiences on the browser

How to Spatial Audio with the WebXR API: a comparison of the...

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2023 Immersive and 3D Audio: from Architecture to Automotive, I3DA 2023

作者： Tomasetti, Matteo Boem, Alberto Turchet, Luca University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350311044

The WebXR Device API provides a powerful set of functionalities that can be used for creating immersive experiences that can be accessed directly from a web browser. However, while creating a compelling visual experience is easy to implement using the WebXR API and related tools, creating the audio part of the experience is less clear and with more open questions and possibilities. This paper presents a comparison of six tools oriented to practitioners and used for implementing and delivering interactive spatial audio experiences in XR applications running on a browser. These tools are selected and evaluated based on their compatibility with the WebXR Device API, ease of use, provided support and documentation, and specific features like reverberation, head-related transfer functions (HRTFs), sound scene rotation, and real-time microphone input. The paper then discusses the integration of these spatial audio tools into WebXR applications, highlighting their potential and limitations. It also addresses the absence of standardized spatial audio frameworks for the web. The challenges of choosing the right tool regarding resource allocation, learning curve, and compatibility with XR systems are acknowledged. This overview aims to provide insights into the current state of spatial audio tools for the web, making them more accessible to developers, musicians, and researchers seeking guidance on selecting suitable 3D spatial audio tools for experiences based on WebXR. © 2023 IEEE.

关键词： Audio acoustics

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