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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Odeh, Yousra Software Engineering Department Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman11733 Jordan

ISBN: (纸本)9783031876462

Following the emergence of the COVID-19 pandemic, the entire healthcare community moved to adopt rapid digital healthcare solutions to avoid and reduce the adverse effects of pandemics. Although launching any digital healthcare solution or tool contributes to resolving or reducing the impact of the dilemma, still the agile development challenges behind this are not well structured or categorized yet. The current agile development of digital healthcare solutions is surrounded by difficulties that must be well-identified and synthesized to overcome and to specify and develop more robust solutions that are digitally ready for transformation. This research proposes a review-driven and expert-validated comprehensive classification for challenges that emerge under the agile development of digital healthcare applications, as the first deliverable in this research. The review covers the years 2020–2024. The classification contributes to deriving another deliverable in this research, that is a digital readiness framework for the agile development of digital healthcare solutions. The digital readiness framework contributes to act as a foundation for sustainable digital transformation and improving healthcare outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Project management

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Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

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Computers, Materials & Continua 2023年第7期76卷 849-859页

作者： Baowei Wang Neng Wang Yuxiao Zhang Zenghui Xu Junhao Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044JiangsuChina Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044JiangsuChina

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data *** speaking,EHRs are widely used in blockchain-based medical data *** are valuable private assets of patients,and the ownership belongs to *** recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit *** order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate *** paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted *** the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical *** addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical *** the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the *** analysis and security analysis illustrate the completeness and feasibility of the scheme.

关键词： Electronic health records cross-chain medical certificate blockchain data deletion and recovery

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Sri Potha: A Voice-Based Sinhala Document Maker

Sri Potha: A Voice-Based Sinhala Document Maker

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： De Silva, D.I. Piyadali, D.S.P. Dilrukshi, M.G.S. Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9798350319200

This study discusses the development of a web application aimed at facilitating Sinhala document creation, with a specific emphasis on Sinhala voice-to-text conversion and the handling of Sinhala commands through vocal input. Leveraging machine learning techniques, including convolutional neural networks and Natural Language Processing, the application's core features were established. Extensive research was conducted to tailor the application's content to the needs of its primary users, ensuring maximum effectiveness. The user-friendly interfaces of the web application are designed for clarity, simplicity, and consistency. The primary objective of this research is to comprehensively analyze the implementation of Sinhala voice-to-text conversion and Sinhala command handling systems. These systems are primarily designed to benefit diverse users, including journalists, content writers, and differently abled individuals with verbal abilities, by enhancing the efficiency of creating Sinhala documents. Through a detailed exploration of the research methodology, this study offers insight into the development process of the web-based system. The outcomes of the linguistic model training, presented within the study, reveal achievements and advancements that address the limitations inherent in existing solutions. Key findings from this research demonstrate the successful functionality of the Sinhala voice-to-text converter and the efficacy of the Sinhala command handler. The voice-to-text conversion system achieved an impressive accuracy rate of over 80%, while the Sinhala command handler exhibited an accuracy of approximately 80%. Moreover, this research envisions potential applications that extend beyond document creation. The technology showcased in the web application holds promise for broader language-based applications, impacting education, accessibility, and communication across the native Sinhala-speaking community in Sri Lanka. In summary, this research showcases th

关键词： convolutional neural network machine learning natural language processing

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Digital Assistant for Visually Impaired People 5

Digital Assistant for Visually Impaired People

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Thayaparan, Thuvaraka Jayakumar, Sanjay Fernando, B.A.I. Ananthanadarajan, Gobisan Abeywardana, Lakmini Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research paper presents a comprehensive exploration of various components to enhance the capabilities of a digital assistant tailored for visually impaired individuals. The first component explores how various image captioning architectures and Vision Encoder Decoder models can be used for better environmental interaction. The second component concerns with facial expression recognition for better social interaction and the third component is a currency note identification component that aims to aid visually impaired individuals with cash transaction whilst the final component that explores a voice bot aims to improve a voice bot and how to manage accents, speech accuracy and naturality using modern deep learning techniques. By investigating these components, this research advances assistive technology, empowering visually impaired individuals with a conversational digital assistant that enhances environmental interaction, supports social interactions, and aids in currency identification. © 2023 IEEE.

关键词： Deep learning

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Fair Machine Learning-An Analytical Study Based on CiteSpace 3

Fair Machine Learning-An Analytical Study Based on CiteSpace

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3rd International Conference on Consumer Electronics and Computer engineering, ICCECE 2023

作者： Luo, Xiangxi Cui, Jianfeng Ma, Shuai College of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Software Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350331578

With the development of machine learning, fair machine learning has started to receive gradual attention. How to mitigate or eliminate the possible unfair decision results of machine learning has become a popular research topic in this field. At present, the research on fair machine learning is still in its initial stage. In this paper, we analyzed the research and articles related to fair machine learning (January 2011 to December 2022) using CiteSpace visualization software, explored research collaboration networks (authors, institutions, and countries), keyword co-occurrence and clustering networks, and literature co-citation and clustering networks, and analyzed and constructed knowledge graphs. To understand the research foundation, related research progress, the latest research directions, and the research methods receiving attention in the field of fair machine learning through the analysis of the knowledge graph. Relevant key articles are discussed, and future research directions are envisioned. © 2023 IEEE.

关键词： Machine learning

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Memristive Binary Firefly Algorithm for White Lung Images Segmentation

Memristive Binary Firefly Algorithm for White Lung Images Se...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Shen, Jiarun Yu, Yongbin Wang, Xiangxiang Feng, Xiao Zhong, Xuefeng Wang, Jingya Han, Xinyi University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China

ISBN: (纸本)9798350316889

This paper investigates the feasibility of designing and employing swarm intelligence algorithms using memristors. As a representative swarm intelligence algorithm, the firefly algorithm is binarized and implemented through a memristor crossbar array to facilitate image threshold segmentation. Firstly, a memristor crossbar array with an external control circuit is meticulously designed, presenting both elevated device density and the potential for parallel computing. Secondly, the binary firefly algorithm is enacted through this memristor crossbar array. Finally, the method is put to practical use by applying it to the segmentation of medical images, the threshold can be directly calculated from the current and charge within the crossbar array. Notably, the error rate of our crossbar-based binary firefly algorithm implementation is significantly low compared to the brute-force method. © 2023 IEEE.

关键词： Medical imaging

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A Fine-Grained Detector of Face Mask Wearing Status Based on Improved YOLOX

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1816-1830页

作者： Xiao, Hongli Wang, Bingshu Zheng, Jiangbin Liu, Licheng Philip Chen, C.L. Northwestern Polytechnical University School of Software Taicang Campus Suzhou215400 China Shanghai Jiaotong University School of Electronic Information and Electrical Engineering Shanghai200240 China Hunan University College of Electrical and Information Engineering Changsha410082 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

The fast outbreak of coronavirus disease 2019 (COVID-19) and rapid proliferation of its variants have continued to pose a huge challenge to people around the world. Wearing medical masks properly in public and private settings can protect people from COVID-19, which brings a growing demand for automatic detection services of face mask wearing conditions. In this article, we propose a fine-grained detector called ECA_YOLOX-S to identify the wearing status of face masks. Efficient channel attention is introduced into YOLOX-S to reach a tradeoff between effectiveness and efficiency. To demonstrate the performance of our proposed method, a Fine-grained Face Mask (FineFM) dataset is created, which covers four classes of mask wearing status. The proposed FineFM dataset has 16 955 annotated images and covers multiple realistic scenarios. To the best of authors' knowledge, it has the largest number of improper mask wearing images among all similar datasets for realistic scenes. Experiments conducted on the FineFM dataset demonstrate that ECA_YOLOX-S achieves an overall mean average precision (mAP)@.50:95 of 86.80% for moderate scenes and an overall mAP@.50:95 of 73.20% for complex scenes, outperforming its benchmark model. Moreover, experiments conducted on other realistic and simulated datasets indicate that the proposed detector has advantages over other methods. © 2020 IEEE.

关键词： COVID-19

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RF Fingerprint Recognition Method Based on Lightweight Deep Learning for Doppler Resilience 20

RF Fingerprint Recognition Method Based on Lightweight Deep ...

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20th International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2023

作者： Zeng, Xin Liu, D.I. Yu, Can Lin, D.I. School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

Radio Frequency (RF) Fingerprint Recognition has emerged as an area of significant promise within the research community. However, its effective deployment in scenarios characterized by vehicular movement or pedestrian traffic remains a challenge, primarily due to the Doppler effect inducing frequency shifts in RF signals. To counter this, a novel Channel-Independent Spectrum Construction methodology has been proposed, aimed at mitigating the influence of Doppler frequency shifts. This study integrates this innovative approach with advanced data augmentation techniques, thereby pioneering an enhanced methodology for bolstering the resistance of RF signal fingerprint features against Doppler interference. Employing sophisticated deep learning algorithms, this research effectively extracts complex, nonlinear characteristics, thereby capturing the nuances of Doppler variations present within signals. Furthermore, this paper conducts a comprehensive comparison between traditional Convolutional Neural Networks, Deep Residual Networks, and our optimized lightweight PruMobileNet model, an adaptation of MobileNetV1. The empirical results from our study underscore the robustness of our approach in handling Doppler channels, while simultaneously achieving a paradigm shift towards low-cost, environmentally sustainable communication through model lightweighting. Notably, this methodology substantially boosts inference speed and computational efficiency, establishing a new benchmark for reliability in RF fingerprint recognition, particularly in environments with low-speed Doppler effects. © 2023 IEEE.

关键词： Doppler effect

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CNN-Transformer Architecture Solution for Compound Facial Expression Recognition 9

CNN-Transformer Architecture Solution for Compound Facial Ex...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Ullah, Sana Xie, Yuanlun Ou, Jie Tian, Wenhong University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu61173 China

ISBN: (纸本)9798350317251

True emotions can be indicated by the human facial expression of emotions. Facial expression recognition has vast applications in healthcare, security, artificial intelligence, e-learning, sports, agriculture and various other fields. Although significant research has been conducted on basic emotions, there is currently a surge of interest in recognizing compound facial expressions of emotions in the field of image processing. In this paper, we present a method that employs the vision transformer (ViT) and utilizes the DenseNet-121 as a backbone for recognizing compound facial expressions of emotions on the CFEE and RAFDB datasets. The proposed method has outperformed and improved the recognition accuracy of compound emotions compared to the state-of-the-art (SOTA) models. The obtained results demonstrate the accuracy rates of 66.4% for the CFEE dataset and 72.05% for the RAFDB dataset. The recognition accuracy was enhanced by around 9.05% on the CFEE dataset and approximately 3.62% on the RAFDB dataset in comparison to the current state of the art (SOTA), thanks to the proposed methodology. This approach tackles the current task and paves the way for future investigations in detecting complex emotions using ViT models. © 2023 IEEE.

关键词： Convolutional neural networks

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VisualRWKV: Exploring Recurrent Neural Networks for Visual Language Models 31

VisualRWKV: Exploring Recurrent Neural Networks for Visual L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hou, Haowen Zeng, Peigen Ma, Fei Yu, Fei Richard Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shool of Information Technology Carleton University Canada

ISBN: (纸本)9798891761964

Visual Language Models (VLMs) have rapidly progressed with the recent success of large language models. However, there have been few attempts to incorporate efficient linear Recurrent Neural Networks (RNNs) architectures into VLMs. In this study, we introduce VisualRWKV, the first application of a linear RNN model to multimodal learning tasks, leveraging the pre-trained RWKV language model. We propose a data-dependent recurrence and sandwich prompts to enhance our modeling capabilities, along with a 2D image scanning mechanism to enrich the processing of visual sequences. Extensive experiments demonstrate that VisualRWKV achieves competitive performance compared to Transformer-based models like LLaVA-1.5 on various benchmarks. Compared to LLaVA-1.5, VisualRWKV has a speed advantage of 3.98 times and can save 54% of GPU memory when reaching an inference length of 24K tokens. To facilitate further research and analysis, we have made the checkpoints and the associated code publicly accessible at the following GitHub repository: https://***/howard-hou/VisualRWKV. © 2025 Association for Computational Linguistics.

关键词： Recurrent neural networks

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