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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Yu, Jin-Woo Kim, Bomin Kim, Jinmo Kim, Han-Joon Choi, Ji-Woong Dgist Department of Electrical Engineering & Computer Science Daegu Korea Republic of Kumoh National Institute of Technology Department of Medical It Convergence Engineering Gumi Korea Republic of Brain Engineering Convergence Research Center Dgist Department of Electrical Engineering & Computer Science Daegu Korea Republic of

ISBN: (纸本)9798350335385

Reducing unwanted skin irritation during transcutaneous electrical stimulation (TES) is essential for providing clear virtual sensations in virtual reality (VR) systems, leading to a more immersive experience. We investigated general characteristics of parameters in the TES, including the number of channels and pulse width of the stimulation signal, which may affect reducing skin irritation with 3D electromagnetic computer simulation with numerical neuron models. The results showed that the increasing number of channels and the declining pulse width attenuated skin irritation to 44.5 %. © 2023 IEEE.

关键词： Electromagnetic simulation

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Multi-StudentNet: a Semi-supervised Deep-Learning Framework for Endometrial Segmentation in Transvaginal Ultrasound Images

Multi-StudentNet: a Semi-supervised Deep-Learning Framework ...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Peng, Boyuan Liu, Yiyang Zhu, Xin Huang, Jie Wang, Wenwen Fang, Li Zhou, Qin Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Chiyodaku Tokyo Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350350548

Semi-supervised learning (SSL) is a promising solution for the problem of insufficient medical labeled data. However, it is still a challenging task to segment transvaginal ultrasound (TVUS) images because of their poor contrast and speckle noise. In this study, we propose Multi-StudentNet, a semi-supervised deep-learning framework for endometrial segmentation in TVUS images. This method employs multiple student models to effectively integrate labeled and unlabeled data, improving the reliability and performance of segmentation while significantly reducing the need for extensive manual labeling. By leveraging the weights of multiple models, our framework facilitates feature sharing, which enhances model robustness. Specifically, Multi-StudentNet achieves a Dice coefficient (DSC) of 0.81 and a specificity of 0.99. Our multi-student models consistently outperform single-student models across various conditions, including normal, polyp, and cancer cases. Extensive experiments demonstrate that Multi-StudentNet achieves state-of-the-art performance in both accuracy and robustness. © 2024 IEEE.

关键词： Self-supervised learning

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From Analysis to Implementation: A Comprehensive Review for Advancing Arabic-English Machine Translation 21

From Analysis to Implementation: A Comprehensive Review for ...

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21st International Learning and technology Conference, L and T 2024

作者： Sidiya, Aichetou Mohamed Alzaher, Hanin Almahdi, Razan Elkafrawy, Passant Effat University Effat College of Engineering Computer Science Department Jeddah Saudi Arabia Energy and Technology Research Center Effat University Effat College of Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

In an increasingly interconnected world, the demand for accurate Arabic-English translation has surged, highlighting the complexities in handling Arabic's intricate morphology and diverse linguistic structures. This research delves into various translation models, including Convolutional Neural Networks (CNNs), LSTM, Neural Machine Translation (NMT), BERT, and innovative fusion architectures like the Transformer-CNN. Each model's strengths and limitations are scrutinized through comprehensive evaluations and comparisons, unveiling their potential to address translation challenges. The research then builds two models, the first based on LSTM and the second on BERT, and tests their performance in translating English to Arabic. The paper then conducts an in-depth analysis of the results. The comparative analysis provides insights into the landscape of Arabic-English translation models, guiding future research toward refining models, leveraging diverse datasets, and establishing standardized evaluation benchmarks to bridge existing gaps. © 2024 IEEE.

关键词： Computational linguistics

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Mapping the Intersection of AI and science Fiction: A Data-Driven Analysis Using R-Package 21

Mapping the Intersection of AI and Science Fiction: A Data-D...

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21st International Learning and technology Conference, L and T 2024

作者： Sarirete, Akila Quotah, Najla Effat University Effat College of Engineering Computer Science Department Jeddah Saudi Arabia Effat University Effat College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

This study investigates the intersection between Artificial Intelligence (AI) and science fiction (Sci-Fi), using bibliometrix R-Package and VOSviewer network visualizations to analyze data from the Web of science (WoS) and Scopus databases. The search yielded 462 articles from WoS and a significantly larger corpus of 1029 articles from Scopus. Our findings indicate that computer science is the predominant field in AI and Sci-Fi research, accounting for 39% and 40% of the documents in WoS and Scopus, respectively. This underscores the central role of computer science in shaping AI within Sci-Fi narratives. Notably, in Scopus, engineering and Mathematics also emerged as significant categories, highlighting the research's strong technical and quantitative focus. The study also reveals a keen interest in understanding AI's societal impacts, as evidenced by numerous Humanities and Social sciences articles. Additionally, recent AI and Sci-Fi research trends, such as 'chatbots', 'ChatGPT', 'metaverse', and 'privacy', were identified. These trends align with advancements in natural language processing (NLP), virtual realities (VR), data privacy, and deep learning technologies, indicating a shift in academic focus towards these cutting-edge areas. This comprehensive study offers valuable insights into the evolving landscape of AI and Sci-Fi research, guiding future scholarly exploration in these dynamically interconnected fields. © 2024 IEEE.

关键词： Virtual reality

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Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 34-48页

作者： Hammadi, Ahmed Al Bariah, Lina Muhaidat, Sami Al-Qutayri, Mahmoud Sofotasios, Paschalis C. Debbah, Merouane Technology Innovation Institute Abu Dhabi United Arab Emirates Khalifa University Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaK1S 5B6 Canada Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Tampere University Department of Electrical Engineering Tampere33014 Finland Khalifa University KU 6G Center Abu Dhabi United Arab Emirates

Visible Light Communication (VLC) is a promising enabling technology for the next-generation wireless networks, as it complements radio-frequency (RF)-based communications by providing wider bandwidth, higher data rates, and immunity to interference from electromagnetic sources. However, due to its unique characteristics, VLC is highly sensitive to the line-of-sight (LoS) blockage. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative solution that dynamically reconfigures the wireless environment. The present contribution proposes a two-stage resource management framework in an indoor VLC system: In the first stage, a maximum possible fairness (MPF) algorithm is presented in order to maximize the fairness amongst the users. In the second stage, deep Q-learning is exploited in order to maximize the overall spectral efficiency (SE). The corresponding numerical results have shown that the proposed DQL-MPF framework exhibits superior performance in terms of both the overall SE and Jain's Fair Index, achieved at a fast convergence rate. More specifically, when the noise power is high and the number of users is relatively large, the DQL-MPF algorithm achieves a more than tenfold overall SE compared to the Baseline scheme. Moreover, the synergy between the MPF and the DQL algorithms is investigated. To this end, we demonstrate that the MPF algorithm maximizes the fairness amongst the users while the DQL algorithm maximizes the overall SE and improves the robustness against the noise. Our results also highlight the effectiveness of the proposed algorithm in leveraging the increasing number of IRS elements for optimized performance. © 2023 The Authors.

关键词： Spectrum efficiency

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Efficient Acoustic-Optical Fiber Coupling with Wire Horn Structure

Efficient Acoustic-Optical Fiber Coupling with Wire Horn Str...

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Frontiers in Optics + Laser science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser science 2023

作者： Wang, Ruixuan Theis, Logan Homa, Daniel Wang, Anbo Pickrell, Gary Center for Photonics Technology The Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States Center for Photonics Technology Department of Material Science and Engineering Virginia Tech BlacksburgVA United States

This paper reports on a wire horn structure to enhance acoustic-optical fiber coupling. Compared with the conventional schemes, it was proved to enhance the ultrasonic wave peak-to-peak amplitude coupling efficiency b... 详细信息

ISBN: (纸本)9781957171296

关键词： Coupling efficiency Fiber Bragg gratings Fiber optic sensors Mode conversion Optical fibers Tunable lasers

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Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices

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National science Review 2022年第11期9卷 172-181页

作者： Shufang Zhao Wenhao Ran Zheng Lou Linlin Li Swapnadeep Poddar Lili Wang Zhiyong Fan Guozhen Shen State Key Laboratory for Superlattices and Microstructures Institute of SemiconductorsChinese Academy of Sciences and Center of Materials Science and Optoelectronic EngineeringUniversity of Chinese Academy of Sciences Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology School of Integrated Circuits and Electronics Beijing Institute of Technology

High-accuracy neuromorphic devices with adaptive weight adjustment are crucial for high-performance computing. However, limited studies have been conducted on achieving selective and linear synaptic weight updates without changing electrical pulses. Herein, we propose high-accuracy and self-adaptive artificial synapses based on tunable and flexible MXene energy storage devices. These synapses can be adjusted adaptively depending on the stored weight value to mitigate time and energy loss resulting from recalculation. The resistance can be used to effectively regulate the accumulation and dissipation of ions in single devices, without changing the external pulse stimulation or preprogramming, to ensure selective and linear synaptic weight updates. The feasibility of the proposed neural network based on the synapses of flexible energy devices was investigated through training and machine learning. The results indicated that the device achieved a recognition accuracy of ～95% for various neural network calculation tasks such as numeric classification.

关键词： neuromorphic computing MXene flexible device high accuracy adaptability ion dynamics

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Crime Analysis and Prediction in 7 States of India Using Statistical Software RStudio 1

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4th International Conference on Advances in Information Communication technology and Computing, AICTC 2022

作者： Sharma, Aditi Sharma, Chandani Sharma, Rahul Panchal, K.D. School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Department of Computer Science and Engineering Quantum University Roorkee India Department of Computer Science and Engineering Parul Institute of Technology Parul University Vadodara India Department of Mechanical Engineering Dr. S. & S.S. Ghandhy Government Engineering College Surat India

ISBN: (数字)9789811998881

ISBN: (纸本)9789811998874

Crime analysis is a function of law enforcement. It comprises systematized analysis for understanding the movement of crime patterns. Crime is an increasing factor all over the world and the condition in India is the same. Common people feel insecure as they feel there is no safe place left in this world. It is also well known that crime increases with population and by looking at any year’s population count of India it can be said how crime rate goes a step forward from the previous year. In this paper, data for the top 3 crimes in India namely Rape, Dowry death and Domestic violence is collected and pattern is analyzed using Holt’s Linear Trend method (aka Double Exponential Smoothing). Through this paper, the respective crime patterns and future values are forecasted. The statistical computations and graphical presentation have been done with the help of the statistical software RStudio. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crime

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Non-Invasive Breast Cancer Screening: Leveraging InceptionV3 and GoogLeNet for Thermal Image Analysis 13

Non-Invasive Breast Cancer Screening: Leveraging InceptionV3...

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13th International Conference on computer Technologies and Development, TechDev 2024

作者： Jalloul, Reem Chethan, H.K. Alkhatib, Ramez Maharaja Institute of Technology Mysore Department of Computer Science & Engineering Karnataka India Leibniz Lung Center Forschungszentrum Borstel Biomaterial Bank Nord Borstel Germany

ISBN: (纸本)9798331539771

Breast cancer remains a leading cause of mortality worldwide, highlighting the need for early detection. This research demonstrates the potential of combining thermal imaging and deep learning for non-invasive, cost-effective breast cancer screening. This study applies deep learning models, specifically InceptionV3 for feature extraction and classifiers such as GoogLeNet, DenseNet121, and MobileNetV2, to analyze thermal images for breast cancer diagnosis. Using the Database for Mastology Research (DMR), thermal images were preprocessed and augmented to improve the dataset. Transfer learning with pre-trained models enhanced diagnostic accuracy. The InceptionV3 + GoogLeNet model achieved the highest performance with an accuracy of 96.49%, an AUC of 99.72%, a precision of 98.25%, and an F1-score of 96.55%, outperforming other combinations. Despite the promising results, the study has limitations, including a relatively small dataset and the need for further validation. Nevertheless, it paves the way for improved early detection strategies, potentially reducing breast cancer mortality rates globally. © 2024 IEEE.

关键词： Lung cancer

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EEG-Based Motion Intention Detection for Robotic Rehabilitation: Evaluating Classification and Regression Algorithms

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SN computer science 2024年第8期5卷 1057页

作者： Khan, Abdul Manan Khan, Fatima Dwivedi, Anshu Kumar Semwal, Vijay Bhaskar Martha, Sheshikala Bijalwan, Vishwanath Department of Aerospace Engineering University of Bristol Bristol United Kingdom Graduate School of Cancer Science and Policy National Cancer Center Ilsan South Korea School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE Maulana Azad National Institute of Technology Bhopal India School of Computer Science & amp Artificial Intelligence SR University Warangal Warangal India Department of Electrical and Electronics Engineering University of West London London United Kingdom

Rehabilitation robots have shown significant efficacy in restoring lost motor functions in stroke survivors by providing compliant assistance during rehabilitation tasks. However, accurately understanding the desired motion intention (DMI) of the subjects is a significant challenge in providing effective robotic assistance. This work aims to determine the desired motion intention (DMI) for compliant robotic assistance through the analysis of upper limb motions using electroencephalography (EEG) signals. A group of fifteen individuals who were in good health carried out six distinct exercises. These movements were categorized using decision trees, random forest, and deep learning algorithms. Using deep learning, we achieved the highest accuracy rates, reaching 82% for movement-to-movement classification and 90% for movement-to-rest classification. The random forest method achieved superior performance compared to the other algorithms in regression tests, with an accuracy rate of 83%. The results demonstrate the effectiveness of our proposed EEG-based classification and regression techniques in detecting DMI for robotic help in *** study was conducted with a small sample size, exclusively involving healthy subjects, which is a critical factor to consider. This may limit the generalizability of the results, particularly when populations with motor impairments are taken into account. However, the study offers valuable insights into the potential of the EEG-based models for real-time rehabilitation applications despite this limitation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： C-RNN Deep learning Desired motion intention EEG signals Machine learning Rehabilitation

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