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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yu-Tung Liu Kuan-Chen Wang Rong Chao Sabato Marco Siniscalchi Ping-Cheng Yeh Yu Tsao Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Taiwan Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan University of Palermo Italy

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-based approaches, such as high-pass filtering and template subtraction, have been used to remove ECG interference but are often limited in their effectiveness. Recently, neural network-based methods have shown greater promise for sEMG denoising, but they still struggle to balance both efficiency and effectiveness. In this study, we introduce MSEMG, a novel system that integrates the Mamba state space model with a convolutional neural network to serve as a lightweight sEMG denoising model. We evaluated MSEMG using sEMG data from the Non-Invasive Adaptive Prosthetics database and ECG signals from the MIT-BIH Normal Sinus Rhythm Database. The results show that MSEMG outperforms existing methods, generating higher-quality sEMG signals using fewer parameters.

关键词： Adaptation models Databases Computational modeling Surface contamination Noise reduction Interference Electrocardiography Electromyography Speech processing Surface treatment

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Classifying Upper Extremity Motor Function Using Deep Learning and Third-Person Video Data

Classifying Upper Extremity Motor Function Using Deep Learni...

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IEEE International Conference on Big Data

作者： Tan Tran Lin-Ching Chang Peter Lum Department of Electrical Engineering and Computer Science The Catholic University of America Washington DC USA Department of Informatics New Jersey Institute of Technology Newark NJ USA Department of Biomedical Engineering The Catholic University of America Washington DC USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Post-stroke rehabilitation of upper extremity (UE) motor function is essential. Despite the widespread use of UE rehabilitation in clinical settings, assessing the success of these treatments is challenging. Methods for evaluating UE motor function include task performance under clinical supervision, patient self-reports, and data analysis from wearable devices equipped with accelerometers and gyroscopes. In prior research, we demonstrated that machine learning and deep learning models using data from a single wrist-worn accelerometer sensor could accurately differentiate between functional and non-functional UE movements in stroke patients. To overcome the limitations of single wrist-worn accelerometer sensors – challenges in capturing the full context of functional movements – this study presents a new deep learning (DL) based framework designed to classify functional and non-functional arm movements in videos captured from a conventional camera. The system is entirely automated and comprises two DL networks. The first network performs human pose estimation, extracting 2D pose key points of the paretic arm(s) and torso on 2-second sequences of the frames. The second network then uses these 2D pose landmarks to classify the movement sequence as functional or non-functional. This system offers two key benefits for rehabilitation. First, it can automatically generate initial annotations for video frames, significantly reducing the time needed for manual labeling. Second, analyzing the effectiveness of UE rehabilitation through third-person videos allows for objective outcome measurement of UE treatments in stroke survivors' home environments.

关键词： Deep learning Accelerometers Torso Pose estimation Manuals Stroke (medical condition) Motors Cameras Wearable devices Videos

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Contemporary Approach of Additive Manufacturing for Sustainable Products in the Era of Circular Economy 8th

Contemporary Approach of Additive Manufacturing for Sustaina...

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8th EAI International Conference on Management of Manufacturing Systems, MMS 2023

作者： Knapčíková, Lucia Behúnová, Annamária Tauberová, Rebeka Kaščak, Jakub Husár, Jozef The Technical University of Košice Faculty of Manufacturing Technologies with a seat in Prešov Department of Industrial Engineering and Informatics Prešov Slovakia Technical University of Kosice Faculty of Mining Ecology Process Control and Geotechnologies Institute of Earth Resources Kosice Slovakia The Technical University of Košice Faculty of Manufacturing Technologies with a seat in Prešov Department of Computer Aided Manufacturing Technology Prešov Slovakia

ISBN: (纸本)9783031531606

Considering the need for mass customization in Industry 4.0, it is necessary to develop non-traditional production methods. Therefore, additive manufacturing can become a key technology for the production of customized products thanks to its ability to create irregular objects or shapes. In addition to enabling new design possibilities, the additive manufacturing method holds promise for increasing social, economic, and environmental sustainability. It also has the potential to reduce energy consumption and lower polymer material consumption. Additive manufacturing contrasts with conventional processes, as it consists in applying materials layer by layer. Conventional manufacturing operations produce polymer waste that is difficult to reintroduce into manufacturing processes. The aim of the contribution is the possibility of using additive manufacturing in the design and processing of new effective use of the product with regard to the environmental aspect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 3D printing

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A Comparative Analysis of Tweet Analysis Algorithms Using Natural Language Processing and Machine Learning Models

A Comparative Analysis of Tweet Analysis Algorithms Using Na...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Andreas Kanavos Nikos Antonopoulos Ioannis Karamitsos Phivos Mylonas Department of Informatics Ionian University Corfu Greece Department of Digital Media and Communication NeMeCULAB Ionian University Argostoli Greece Department of Graduate and Research Rochester Institute of Technology Dubai UAE Department of Informatics and Computer Engineering University of West Attica Athens Greece

Online Social Networks (OSNs) have become integral platforms for information sharing, attracting both legitimate users and spammers. Detecting and mitigating spam within these networks pose significant challenges due to the dynamic nature of content and user behavior. In this paper, we present a comprehensive comparative analysis of algorithms for tweet analysis, focusing on Natural Language Processing (NLP) and Machine Learning (ML) models. We evaluate these algorithms through sentiment analysis and multiple attribute analysis, utilizing diverse methodologies and datasets. Our study explores feed-forward neural networks, Bayesian classifiers, and transformer-based models for NLP tasks, achieving high prediction accuracy and insightful metrics such as precision, recall, and F1 score. Furthermore, we delve into multiple attribute analysis using Random Forest, Logistic Regression, and Gradient Boosting algorithms. Through a systematic exploration of various approaches, this work contributes to a deeper understanding of spam detection and sentiment analysis within the context of OSNs, paving the way for enhanced social network security and content analysis.

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Expression of Concern for: Glaucoma Detection Using Deep Learning Approach in research Detection

Expression of Concern for: Glaucoma Detection Using Deep Lea...

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Advances in Computing, Communication and Applied informatics (ACCAI), International Conference on

作者： Anil Pandurang Shinde Ramandeep Singh Renuka Shantappa Anami Noor Mohammed R. Selvameena Ashwini Jotiram Patil Department of Computer Technology Bharati Vidyapeeth's Jawaharlal Nehru Institute of Technology (Poly) Pune Maharashtra Department of Computer Science Guru Nanak Khalsa College Daroli Kalan Jalandhar Jalandhar Punjab Department of Computer Technology Bharati Vidyapeeth's Jawaharlal Nehru Institute of Technology (Poly) Pune Department of Computer Science Engineering Graphic Era Deemed to be University Dehradun Uttrakhand Department of Computer Science and Engineering Dr.M.G.R Educational and Research Institute Chennai TamilNadu India

ISBN: (数字)9798350315905

ISBN: (纸本)9798350315912

An persistent eye infection called glaucoma causes irreversible vision loss. A crucial problem in the clinical field is glaucoma identification. There have been very few studies designed to detect glaucoma in its early stages. However, using current techniques to carry out glaucoma infection detection was ineffective. Moreover, the conventional glaucoma infection diagnosis method required additional time. Glaucoma identification in various fundus images is a challenging task that necessitates mastery and extensive training. Glaucoma is an ocular neuropathy characterized by mild retinal ganglion cell degeneration. There are several different types of glaucoma, each having a unique a etiology and set of pathogenic factors, but they all have alterations in the structure and function of the optic nerve that are essentially universal. Convolution organisations, a type of deep learning computation, have swiftly emerged as a method for making decisions while analyzing clinical images. A chronic eye condition called glaucoma causes irreversible vision loss. In this section, we discuss two state-of-the-art glaucoma detection methods in the light of deep learning. The glaucoma dataset is used in this deep learning computation proposed for glaucoma photo analysis. This finding contrasts with convolutional brain network grouping techniques in the context of deep learning. Each rating indicates that the newly proposed worldview is more favorable than the one currently in use.

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A Cloud-based Distributed Computing Approach for Extracting Molecular Descriptors 6

A Cloud-based Distributed Computing Approach for Extracting ...

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6th International Conference on Algorithms, Computing and Systems, ICACS 2022

作者： Didachos, Christos Kintos, Dionisis Panagiotis Fousteris, Manolis Gerogiannis, Vassilis C. Son, Le Hoang Kanavos, Andreas Department of Computer Engineering & Informatics University of Patras Patras Greece Department of Pharmacy Laboratory of Medicinal Chemistry University of Patras Patras Greece Department of Digital Systems University of Thessaly Larissa Greece Vnu Information Technology Institute Vietnam National University Hanoi Viet Nam Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9781450397407

Feature extraction is an initial and essential part for the development of accurate predictive machine learning classifiers. In the research field of drug discovery and development, the usage of molecular descriptors, which can be defined as mathematical representations of molecules' chemical properties, is a challenging task not only for machine learning studies but even for "classical wet lab"approaches. However, a high diversity of these descriptors is required in order to exploit all the available knowledge and, consequently, to maximize the potentially predictive power of approaches that could be applied for the discovery of new bioactive compounds against one or more molecular targets. Furthermore, the representation and normalization of these information is considered a rather time-consuming process. Herein, we present an approach that employs the power of cloud and distributing computing for the extraction, processing and representation of big datasets, leading to the generation of molecular descriptors in a reasonable time frame. © 2022 ACM.

关键词： Big data

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When Sign Language Meets Semantic Communications 35

When Sign Language Meets Semantic Communications

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Kouvakis, Vasileios Trevlakis, Stylianos E. Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros Singh, Keshav Qi, Nan InnoCube P.C. Department of Research and Development 17th Noemvriou 79 Thessaloniki55535 Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics & Telecommunications Lamia35100 Greece Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210016 China

ISBN: (纸本)9798350362244

This paper presents an American sign language (ASL) semantic communications scheme. The scheme consists of a semantic encoder that leverages a convolutional neural network to effectively utilize the ASL alphabet. The encoded information is transmitted with the 24-QAM quadrature amplitude modulation (QAM). Additionally, this paper introduces a dataset that involves the overlaying of red-green-blue landmarks and key-points onto the acquired images, thereby augmenting the depiction of hand posture. The quantification of the proposed system's training, testing, and communication performance is accomplished through numerical results, which serve to emphasize the attainable benefits and stimulate meaningful discussions. © 2024 IEEE.

关键词： Quadrature amplitude modulation

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Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks

arXiv

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arXiv 2024年

作者： Hou, Junlin Liu, Sicen Bie, Yequan Wang, Hongmei Tan, Andong Luo, Luyang Chen, Hao The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Department of Engineering Shenzhen MSU-BIT University Shenzhen China The Department of Biomedical Informatics Harvard University Cambridge United States The Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable Artificial Intelligence (XAI). Post-hoc XAI techniques, which aim to explain black-box models after training, have raised concerns about their fidelity to model predictions. In contrast, Self-eXplainable AI (S-XAI) offers a compelling alternative by incorporating explainability directly into the training process of deep learning models. This approach allows models to generate inherent explanations that are closely aligned with their internal decision-making processes, enhancing transparency and supporting the trustworthiness, robustness, and accountability of AI systems in real-world medical applications. To facilitate the development of S-XAI methods for medical image analysis, this survey presents a comprehensive review across various image modalities and clinical applications. It covers more than 200 papers from three key perspectives: 1) input explainability through the integration of explainable feature engineering and knowledge graph, 2) model explainability via attention-based learning, concept-based learning, and prototype-based learning, and 3) output explainability by providing textual and counterfactual explanations. This paper also outlines desired characteristics of explainability and evaluation methods for assessing explanation quality, while discussing major challenges and future research directions in developing S-XAI for medical image analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Pre-trained Convolutional Neural Networks and Otsu’s Multi-level Thresholding based Alzheimer’s Classification

Pre-trained Convolutional Neural Networks and Otsu’s Multi-...

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Advances in Computing, Communication and Applied informatics (ACCAI), International Conference on

作者： Nivedhitha Mahendran P M Durai Raj Vincent Duraimurugan Samiayya Venkatesan Rajinikanth Department of Networking and Communications School of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India School of Computer Science and Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Technology St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Division of Research and Innovation SIMATS Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Disorders affecting the brain and the nerves that supply the entire body are referred to as neurological diseases. Changes in the brain’s chemistry, structure, or electrical system can cause a variety of symptoms. Alzheimer’s disease (AD), a degenerative neurological condition that impairs cognitive function, is one example of this type of illness. The most prevalent type of dementia for which there is no effective treatment. Early detection and treatment, however, may help the patient’s lifestyle and reduce symptoms. Brain imaging techniques, including MRI, CT, PET, and others, are widely used to diagnose neurological problems. It has been discovered that the Artificial Intelligence based approaches, such as the deep learning algorithms and machine learning algorithms work well for assessing these photos and provide improved accuracy for identifying AD. In this paper, we classified AD and Control cases using four different Convolutional Neural Networks (CNN) versions. Additionally, in order to increase the accuracy of AD’s classification, we have segmented the images using Otsu’s thresholding technique. The outcomes demonstrate that, both before and after thresholding was implemented, the CNN AlexNet offers a significant improvement over the other deep learning models.

关键词： Neurological diseases Deep learning Thresholding (Imaging) Machine learning algorithms Magnetic resonance imaging Computed tomography Computational modeling

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Transient Session Key Derivation Protocol for Key Escrow Prevention in Public Key Infrastructure 1st

Transient Session Key Derivation Protocol for Key Escrow Pre...

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1st EAI International Conference on Internet of Everything, IoECon 2022

作者： Nyangaresi, Vincent Omollo Abduljabbar, Zaid Ameen Maolood, Ismail Yaqub Sibahee, Mustafa A. Al Ma, Junchao Aldarwish, Abdulla J. Y. Faculty of Biological and Physical Sciences Tom Mboya University Homabay40300 Kenya Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen518118 China – System Information Ministry of Higher Education and Scientific Research Erbil Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Computer Technology Engineering Department Iraq University College Basrah Iraq

ISBN: (纸本)9783031252211

The Internet of Things (IoT) devices have been deployed to realize smart environments such as smart cities, smart homes, smart health and smart grids. In these domains, the IoT devices collect and forward high volumes of sensitive and private data. It is therefore important that security schemes be developed to protect the exchanged data. In this regard, a myriad of authentication protocols have been developed over the recent past. However, these schemes deploy cryptographic primitives that result in extremely high communication, storage and computation complexities. In addition, some of these protocols still have numerous security and privacy issues that render them unsuitable for deployment in an IoT environment. For instance, untraceability, anonymity, key escrow problems and attack vectors such as de-synchronization and forgery attacks are frequently ignored. In this paper, a transient session key derivation protocol is developed to address some of these security and efficiency challenges. The security analysis executed shows that this protocol offers untraceability, device anonymity and perfect forward key secrecy. In addition, it is robust against de-synchronization, known secret key leakage, eavesdropping and forgery attacks. In terms of operational efficiency, this protocol incurs the lowest computation and communication complexities. © 2023, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Authentication

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