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SSRN 2024年

作者： Kim, JuChan Le, Duc-Tai Bum, Junghyun Son, Chang-Hwan Lee, Eun Jung Han, Jong Chul Choo, Hyunseung Department of Artificial Intelligence Systems Engineering Sungkyunkwan University Korea Republic of College of Computing and Informatics Sungkyunkwan University Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Korea Republic of Software Science and Engineering Major Kunsan National University Korea Republic of Department of Ophthalmology Samsung Medical Center Korea Republic of School of Medicine Sungkyunkwan University Korea Republic of Department of Medical Device Management and Research SAIHST Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Two major fundus imaging types, conventional fundus (CF) imaging and ultrawidefield fundus (UF) imaging, are widely used in ophthalmology. In a typical clinical setting, both imaging methods are used simultaneously to diagnose diseases depending on the case. This study introduced an enhanced method of modality transformation to boost the diagnostic support capabilities of deep-learning models in ophthalmology. The method proposed in this study to translate UF images (UFIs) to CF images (CFIs) can replace the common clinical approach of both imaging methods, thereby reducing financial and temporal burdens on patients. This study learns and transforms the relationship between these modalities using UFI–CFI image pairs obtained from the same patient on the same day to minimize distorted information and accurately convert between different modalities. Our proposed model uses scale-aware registration and gradient-guided translation methods to align the UFI–CFI pairs, The generated CFIs not only enhance the quality of images and better represent the retinal area compared to existing methods but also effectively preserve the disease-related details in UFIs, aiding in the diagnosis. In addition, we optimize the image transformation performance, resulting in an 22% improvement in image quality assessment scales compared with previous methods. Furthermore, we acquired the observation that our results are nearly impossible to discern any differences between the real CFIs and the generated CFIs in the view of ophthalmologists. © 2024, The Authors. All rights reserved.

关键词： Ophthalmology

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Image channel as an input method for deep learning ensemble

Image channel as an input method for deep learning ensemble

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2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021

作者： Javokhir, Musaev Nguyen, Ngoc Thanh Hwang, Dosam Yeungnam University Department of Computer Engineering Gyeongsan Korea Republic of Wroclaw University of Science and Technology Department of Applied Informatics Wroclaw50-570 Poland Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh City70000 Viet Nam

ISBN: (纸本)9781665436038

The application of computer vision (CV) in many fields is becoming an important factor in development, and the demand for high accuracy CV products is increasing. Therefore, a new method that ensembles three trained models with the same architecture but different inputs is proposed. Each of the different channels of the images were used as input. The first channel of the image was used in the dataset for the first model, the second channel was used for the second model, and the third channel was used for the third model. The classification probabilities were achieved after training three different models with three channels. The model with the highest accuracy was selected as the main model, and it was added to the classification probabilities of certain classes of the main model. Applying this method to the classification task resulted in 75.45% accuracy, while using the entire image for the model resulted in 70.9% accuracy. The advantage of using this ensemble method is that it can be used simultaneously with other ensemble methods and achieves better results. Dividing an image into three different channels than simply using the entire image helps the model learn the image better. © 2021 IEEE.

关键词： Probability

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Multi-Person Key Points Detection for Abnormal Human Behavior Analysis Using The ConvLSTM-AE Method

Multi-Person Key Points Detection for Abnormal Human Behavio...

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International Conference on computer engineering, Network and Intelligent Multimedia (CENIM)

作者： Sofia Ariyani Eko Mulyanto Yuniarno Mauridhi Hery Purnomo Dept. Of Electrical Engineering Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Faculty of engineering Institut Teknologi Sepuluh Nopember Universitas Muhammadiyah Jember Surabaya Indonesia Dept. Of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society Surabaya Indonesia

ISBN: (纸本)9781665476515

The detection of abnormal human behavior is an interesting issue to consider in computer vision. The problem of detecting abnormal activities carried out in this study is a problem formulated in the process of monitoring human activities. In understanding the nature of human activities, a system is needed that is applied to data training which is specifically proposed based on ConvLSTM-AE for the detection of moving objects in motion-based events. Detection of anomalies and localization that arise due to camera jitter and movement of objects in the area. With the unusual motion of the video frame, connected component analysis is exploited to provide bounding boxing to find out whether human movements whose activities are based on human poses will be classified as normal or abnormal in a set of activity data. The learning and training process so that the proposed model can capture different duration of time and more optimal processes.

关键词： Training Location awareness computer vision Surveillance Multimedia systems Computational modeling Estimation

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Large Language Models for Disease Diagnosis: A Scoping Review

arXiv

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arXiv 2024年

作者： Zhou, Shuang Xu, Zidu Zhang, Mian Xu, Chunpu Guo, Yawen Zhan, Zaifu Ding, Sirui Wang, Jiashuo Xu, Kaishuai Fang, Yi Xia, Liqiao Yeung, Jeremy Zha, Daochen Lin, Mingquan Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Nursing Columbia University New YorkNY United States Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas RichardsonTX United States Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Informatics University of California Irvine IrvineCA United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN United States Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States Shanghai China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Department of Computer Science Rice University HoustonTX United States

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the growing attention in this field, many critical research questions remain under-explored. For instance, what diseases and LLM techniques have been investigated for diagnostic tasks? How can suitable LLM techniques and evaluation methods be selected for clinical decision-making? To answer these questions, we performed a comprehensive analysis of LLM-based methods for disease diagnosis. This scoping review examined the types of diseases, associated organ systems, relevant clinical data, LLM techniques, and evaluation methods reported in existing studies. Furthermore, we offered guidelines for data preprocessing and the selection of appropriate LLM techniques and evaluation strategies for diagnostic tasks. We also assessed the limitations of current research and delineated the challenges and future directions in this research field. In summary, our review outlined a blueprint for LLM-based disease diagnosis, helping to streamline and guide future research endeavors. Copyright © 2024, The Authors. All rights reserved.

关键词： Diseases

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Preface and Acknowledgements

Preface and Acknowledgements

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作者： Favorskaya, Margarita N. Peng, Sheng-Lung Simic, Milan Alhadidi, Basim Pal, Souvik Institute of Informatics and Telecommunication Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of Creative Technologies and Product Design National Taipei University of Business Taipei City Taiwan School of Engineering RMIT University Bundoora East Campus MelbourneVIC Australia Department of Computer Information Systems Al-Balqa’ Applied University As-Salt Jordan Department of Computer Science and Engineering Global Institute of Management and Technology Kolkata India

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Mamba vision models: Automated American sign language recognition

Franklin Open

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Franklin Open 2025年 10卷

作者： Altaher, Ali Salem Bang, Chiron Alsharif, Bader Altaher, Ahmed Alanazi, Munid Altaher, Hasan Zhuang, Hanqi Department of Electrical Engineering and Computer Science Florida Atlantic University 777 Glades Road Boca Raton 33431 FL United States College of Medicine Ibn Sina University of Medical and Pharmaceutical Science Baghdad Iraq Department of Computer Science and Engineering College of Telecommunication and Information Technical and Vocational Training Corporation Riyadh 12464 Saudi Arabia Electronic Computer Center Al-Nahrain University Jadriya Baghdad Iraq Business Informatics Department College of Business King Khalid University Abha Saudi Arabia Information and Communication Technology Department Baghdad Institute of Technology Middle Technical University Baghdad Iraq

Historically, individuals with hearing impairments have faced significant challenges in effective communication due to the lack of adequate resources. Recent technological advancements have spurred the development of innovative tools aimed at enhancing the quality of life for those with hearing disabilities. This research focuses on the application of Vision Mamba Models for classifying hand gestures representing the American Sign Language (ASL) alphabet, with a detailed comparative analysis of its performance against 13 deep learning architectures. The Vision Mamba Models, namely, Vision Mamba and Remote Sensing Mamba, were trained on a substantial dataset comprising 87,000 images of ASL hand gestures, and through iterative fine-tuning of their architectural parameters, the models’ accuracy and performance were optimized. Experimental results demonstrated that the Mamba Vision Models outperformed all other models previously examined in this context scoring exceptional accuracy rate that exceeded 99.98%, with less architectural complexity. These findings highlight the potential of deep learning technologies, particularly the Mamba vision models, in advancing assistive technologies, offering a sophisticated and highly accurate tool for interpreting ASL hand gestures and promising improved communication and accessibility for individuals with hearing impairments. © 2025 The Authors

关键词： American sign language Deep learning Remote sensing mamba Transfer learning Vision Mamba.

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RIS-Assisted MIMO CV-QKD at THz Frequencies: Channel Estimation and SKR Analysis

arXiv

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arXiv 2024年

作者： Kumar, Sushil Dash, Soumya P. Ghose, Debasish Alexandropoulos, George C. the School of Electrical Sciences Indian Institute of Technology Bhubaneswar Khordha Argul752050 India School of Economics Innovation and Technology Kristiania University College Bergen Norway Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States

—In this paper, a multiple-input multiple-output (MIMO) wireless system incorporating a reconfigurable intelligent surface (RIS) to efficiently operate at terahertz (THz) frequencies is considered. The transmitter, Alice, employs continuous-variable quantum key distribution (CV-QKD) to communicate secret keys to the receiver, Bob, which utilizes either homodyne or heterodyne detection. The latter node applies the least-squared approach to estimate the effective MIMO channel gain matrix prior to receiving the secret key, and this estimation is made available to Alice via an error-free feedback channel. An eavesdropper, Eve, is assumed to employ a collective Gaussian entanglement attack on the feedback channel to avail the estimated channel state information. We present a novel closed-form expression for the secret key rate (SKR) performance of the proposed RIS-assisted THz CV-QKD system. The effect of various system parameters, such as the number of RIS elements and their phase configurations, the channel estimation error, and the detector noise, on the SKR performance are studied via numerical evaluation of the derived formula. It is demonstrated that the RIS contributes to larger SKR for larger link distances, and that heterodyne detection is preferable over homodyne at lower pilot symbol powers. © 2024, CC BY.

关键词： Heterodyne detection

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When Sign Language Meets Semantic Communications

When Sign Language Meets Semantic Communications

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

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.

关键词： Training Hands Sign language Quadrature amplitude modulation Assistive technologies Semantic communication Numerical models Convolutional neural networks Testing Land mobile radio

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Wide-range Angle-sensitive Plasmonic Color Printing with a Lossy Resonator

Wide-range Angle-sensitive Plasmonic Color Printing with a L...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Sarah N. Chowdhury Jeffrey Simon Michał P. Nowak Ludmila J. Prokopeva Karthik Pagadala Colton Fruhling Piotr Nyga Esteban Garcia Bravo Alexander V. Kildishev Vladimir M. Shalaev Alexandra Boltasseva School of Electrical and Computer Engineering and Birck Nanotechnology Center Purdue University West Lafayette IN USA Institute of Optoelectronics Military University of Technology Warsaw Poland Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Toruń Poland Department of Computer Graphics Technology Purdue University West Lafayette IN USA

We demonstrate a wide gamut of color generation by large-scale, lithography-free, and environment-friendly plasmonic structures with a resolution of 100 µm for macroscopic color printing by utilizing femtosecond ... 详细信息

ISBN: (纸本)9781957171258

关键词： Color Effective medium theory Femtosecond lasers Interference Optical elements Polarization

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Federated Deep Learning Approaches for the Privacy and Security of IoT Systems

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Wireless Communications and Mobile Computing 2022年第0期2022卷

作者： Alazzam, Malik Bader Alassery, Fawaz Almulihi, Ahmed Faculty of Computer Science and Informatics Amman Arab University Jordan Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia

Using federated learning, which is a distributed machine learning approach, a machine learning model can train on a distributed data set without having to transfer any data between computers. Instead of using a centralised server for training, the model uses data stored locally on the device itself. After that, the server uses this model to create a jointly trained model. Federated learning asserts that privacy is preserved because no data is sent. Botnet attacks are detected using on-device decentralised traffic statistics and a deep autoencoder. This proposed federated learning approach addresses privacy and security concerns about data privacy and security rather than allowing data to be transferred or relocated off the network edge. In order to get the intended results of a previously centralised machine learning technique while also increasing data security, computation will be shifted to the edge layer. Up to 98% accuracy is achieved in anomaly detection with our proposed model using features like MAC IP and source/destination/IP for training. Our solution outperforms a standard centrally managed system in terms of attack detection accuracy, according to our comparative performance analysis. © 2022 Malik Bader Alazzam et al.

关键词： Anomaly detection

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