检索结果-内蒙古大学图书馆

Journal of Membrane Computing 2025年 1-14页

作者： Orellana-Martín, David Cabarle, Francis George C. Paul, Prithwineel Zeng, Xiangxiang Freund, Rudolf Research Group on Natural Computing Department of Computer Science and Artificial Intelligence SCORE lab I3US Universidad de Sevilla Sevilla Spain Department of Computer Science Quezon City Philippines Department of Computer Science and Engineering Institute of Engineering and Management University of Engineering and Management Kolkata India Department of Computer Science Hunan University Changsha China Faculty of Informatics TU Wien Vienna Austria

Spiking neural P systems (SN P systems) are computing models based on the third generation of neuron models known as spiking neurons. Recent results in neuroscience highlight the importance of extrasynaptic activities of neurons, that is, features and functioning of neurons outside their synapses. Previously it was thought that signals such as neuropeptides only assist neurons, but recently such signals have been given additional importance. Inspired by recent results, we define wireless SN P systems (WSN P systems). In WSN P systems, no synapses exist: regular expressions associated with each neuron are used to decide which spikes it receives. We provide two semantics of how to “interpret” the spikes released by neurons. A specific register machine is simulated to show the different style of programming WSN P systems compared to programming standard SN P systems and other variants. This style emphasizes a trade-off: WSN P systems can be more “flexible” since they are not limited by their synapses for sending spikes; however, losing the useful directed graph structure requires careful design of rules and expressions associated with each neuron. We use linear prime number encodings in constructing the expressions and rules of the neurons to prove that WSN P systems are Turing-complete in both spike semantics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of a Voice-Enabled Automated Sleep Survey System 46

Development of a Voice-Enabled Automated Sleep Survey System

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Qiu, Yang Mohanty, Anwesh Owens, Robert L. Nemati, Shamim University of California San Diego Computer Science and Engineering La Jolla United States University of California San Diego Department of Biomedical Informatics La Jolla United States University of California San Diego Department of Medicine La Jolla United States

ISBN: (纸本)9798350371499

To enhance sleep quality in hospitalized patients, we developed a conversational agent that streamlines the collection and analysis of sleep data. The system employs the Richards-Campbell Sleep Questionnaire, supplemented by additional questions about environmental factors such as room temperature and lighting, to comprehensively evaluate sleep disturbances experienced by patients. By processing patients' spoken responses, the agent identifies environmental and care-related factors impacting sleep, which may allow for non-pharmacological interventions to enhance sleep quality. The integration of advanced conversational AI technologies, including GPT-4 and large language models, is a key feature of this system, enabling nuanced interpretation and structuring of patient feedback. This approach not only streamlines sleep assessment in hospital settings but also aligns with the shift towards patient-centric healthcare. By offering detailed insights into factors affecting sleep, the system showcases its potential with a high recognition accuracy, underscoring its potentially valuable role in advancing healthcare sleep quality management. © 2024 IEEE.

关键词： Sleep research

来源：评论

学校读者我要写书评

暂无评论

Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model

引用

Biomedical Signal Processing and Control 2024年 87卷

作者： Putro, Nur Achmad Sulistyo Avian, Cries Prakosa, Setya Widyawan Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Indonesia Department of Electronics and Informatics Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia

Human-machine interfaces frequently use electromyography (EMG) signals. Based on previous work, feature extraction has a great deal of influence on the performance of EMG pattern recognition. Furthermore, the Deep Learning method is supposed to increase performance and not depend on feature engineering. However, directly processing raw signals will require a higher computation rate. This study proposed a new method that combines feature extraction and Deep Learning to address those issues while improving performance, reducing architecture size, and producing a more representative output. The proposed architecture employs the Transformer model as the backbone to get the correlation between elements and focus on the important information for estimating the flexion-extension of finger joint angles. This study uses experiment three of the NinaPro (Non-Invasive Adaptive Hand Prosthetics) DB5 dataset. Each experiment produces 16 Surface EMG data streams from two Myo Armbands devices representing 22 finger joint angles as output. This study compares the windowing process, feature extraction, execution time, and results with previous studies. The results show that the proposed model outperforms the previous study, from 0.957 in the previous study to become 0.970 in this study for the R-Square score. This result is obtained using 100 data points for the windowing process and Median Frequency for the best feature extraction method. © 2023

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Cyber Risk Identification and Classification-Based Load Forecasting Tool for Pandemic Situations

引用

IET Cyber-Physical Systems: Theory and Applications 2025年第1期10卷

作者： Shivran, Kuldeep Singh Swire-Thompson, Kyle Saxena, Neetesh Das, Sarasij Department of Electrical Engineering Indian Institute of Science Bangalore India School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

Smart grid operators use load forecasting algorithms to predict energy load for the reliable and economical operation of the electricity grid. COVID-19 pandemic-like situations (PLS) can significantly impact energy load demand due to uncertainties in factors such as regulatory orders, pandemic severity and human behavioural patterns. Additionally, in a smart grid, cyberattacks can manipulate forecasted load data, leading to suboptimal decisions, economic losses and potential blackouts. Forecasting load during these situations is challenging for traditional load forecasting tools, as they struggle to identify cyberattacks amidst uncertain load demand, where cyberattacks may mimic pandemic-like load patterns. Traditional forecasting methods do not incorporate factors related to pandemics and cyberattacks. Recent studies have focused on forecasting by considering factors such as COVID-19 cases, social distancing, weather, and temperature but fail to account for the impact of regulatory orders and pandemic severity. They also lack the ability to differentiate between normal and anomalous forecasts and classify the type of attack in anomalous data. This paper presents a tool for short-term load forecasting, anomaly detection and cyberattack classification for pandemic-like situations (PLS). The proposed short-term load forecasting algorithm uses a weighted moving average and an adjustment factor incorporating regulatory orders and pandemic severity, making it computationally efficient and deterministic. Additionally, the proposed anomaly detection and cyberattack classification algorithm provides robust options for detecting anomalies and classifying various types of cyberattacks. The proposed tool has been evaluated using K-Fold cross-validation to improve generalisability and reduce overfitting. The mean squared error (MSE) was used to measure prediction accuracy and detect discrepancies. It has been analysed and tested on real-load data from the State Load Dispatch Ce

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Volumetric Hippocampus Segmentation Using 3D U-Net Based On Transfer Learning

Volumetric Hippocampus Segmentation Using 3D U-Net Based On ...

引用

IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Ramadhan Sanyoto Sugiharso Widodo I Ketut Eddy Purnama Reza Fuad Rachmadi Department of Electrical Engineering Department of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

The hippocampus, a crucial component of the human brain, is involved in fundamental cognitive processes such as learning, memory, and spatial navigation. However, it is susceptible to several neuropsychiatric disorders, including epilepsy, Alzheimer’s disease, and depression. Utilizing Magnetic Resonance Imaging (MRI) techniques with efficient spatial navigation capabilities is crucial for assessing the physiological condition of the hippocampus. Labeling the hippocampus on MRI images primarily depends on manual methods, which are time-consuming and prone to errors between observers. The issue with MRI image processing lies in its demanding computational requirements and lengthy duration. Furthermore, there is a need for more three-dimensional hippocampal datasets for training deep-learning models, in which 3D labeled medical datasets are often scarce in medical imaging. This paper introduces a 3D U-Net architecture that utilizes a transfer learning model to segment the hippocampus from different pre-trained model scenarios. The results of all test scenarios indicate that the suggested model exhibits an average Dice Score, Intersection over Union (IoU) Score, and Sensitivity exceeding 0.85, 0.75, and 0.80, respectively. The proposed methodology enhances the model’s ability to generalize within a shorter timeframe, even when dealing with limited volumetric datasets. These results are achieved through transfer learning, which decreases computational complexity by utilizing pre-learned characteristics from previous tasks.

关键词： Solid modeling Three-dimensional displays Navigation Magnetic resonance imaging Computational modeling Volume measurement Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Trustworthy and Load-Balancing Routing Scheme for Satellite Services with Multi-Agent DRL

Trustworthy and Load-Balancing Routing Scheme for Satellite ...

引用

2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Song, Jiaxin Ju, Ying Liu, Lei Pei, Qingqi Wu, Celimuge Jan, Mian Ahmad Mumtaz, Shahid School of Telecommunications Engineering Xidian University Xi'an710071 China Graduate School of Informatics and Engineering The University of Electro-Communications Chofu182-8585 Japan Abdul Wali Khan University Mardan Department of Computer Science Mardan23200 Pakistan Silesian University of Technology Department of Applied Informatics Akademicka16 44-100 Poland Nottingham Trent University Engineering Department Nottingham999020 United Kingdom

ISBN: (纸本)9781665494274

Massive computing tasks of various applications have been generated in 6G space-air-ground integrated networks, and need to be transmitted securely and reliably. Nevertheless, the mobility of satellites and the untrusted nodes bring new challenges to the routing scheme design in low earth orbit (LEO) satellite networks. To improve the system trust and elevate the service quality, this paper proposes a fully distributed trustworthy load-balancing routing scheme for satellite services with a multi-agent dueling double deep Q network (D3QN)-based learning algorithm. Our scheme organizes multiple agents to generate hop-by-hop routes and makes decisions based on the trust value of the nodes, which has good scalability to deploy on various satellite constellations and can meet the trust requirements of the services. Besides, we add a variable delay constraint into the load minimization objective to meet various delay-sensitive satellite quality of service (QoS) requirements. We demonstrate that the proposed scheme dramatically reduces the link queue utilization rate and enhances the system capability of handling delay-sensitive services. The packet loss rate of our scheme is 24% lower than that of the benchmark scheme when the system has 30% malicious nodes. © 2023 IEEE.

关键词： Satellites

来源：评论

学校读者我要写书评

暂无评论

Enhancing the effectiveness of intelligent tutoring systems using adaptation and cognitive diagnosis modeling 1

Enhancing the effectiveness of intelligent tutoring systems ...

引用

1st International Conference on Novelties in Intelligent Digital Systems, NIDS 2021

作者： Krouska, Akrivi Troussas, Christos Giannakas, Filippos Sgouropoulou, Cleo Voyiatzis, Ioannis Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (纸本)9781643682044

This paper presents a novel framework for developing educational hypermedia systems incorporating adaptation techniques and tailored feedback. In particular, the adaptation techniques refers to the content presentation;where the system hides/displays information according to students' knowledge level and learning goals, and to the navigation design;where the system proposes the learning path that is better to be followed based on their profile. Finally, the framework embodies a diagnostic model that analyzes the students' misconceptions and provides tailored feedback and advices on bridging students' knowledge gap. This framework aims to enhance the effectiveness of learning process, increasing student engagement through the adaptive content and navigation and improving student performance through the tailored feedback. © 2021 The authors and IOS Press.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Analysis of Histopathology Images for Breast Cancer Detection: A Comparative Study of ResNet and VGG Architectures

Deep Learning Analysis of Histopathology Images for Breast C...

引用

International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Andreas Kanavos Phivos Mylonas Department of Informatics Ionian University Corfu Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

Medical image analysis has undergone significant advancements with the emergence of deep learning techniques, offering great promise in improving diagnostic precision and expediting patient care. This research investigates the effectiveness of ResNet and VGG architectures in detecting breast cancer through the analysis of histopathology images. By meticulously fine-tuning hyperparameters and optimizers, we establish robust and accurate deep learning models. Our findings reveal that the ResNet model with the SGD optimizer excels, surpassing the performance of VGG in terms of accuracy and F1-score. However, employing transfer learning with pre-trained VGG16 and ResNet50 networks does not yield competitive results, potentially due to disparities in input image size and data distribution. The primary focus of this study is to address the critical challenge of early breast cancer detection, ultimately leading to enhanced patient outcomes. By exploring state-of-the-art deep learning architectures and methodologies, we contribute to the growing body of research aimed at leveraging artificial intelligence for medical diagnosis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Detecting Blood Transfusion Needs Using Biosignals

引用

computer Systems Science & engineering 2023年第8期46卷 2369-2381页

作者： Hoon Ko Chul Park Wu Seong Kang Yunyoung Nam Dukyong Yoon Jinseok Lee Department of Biomedical Engineering College of Electronics and InformationKyung Hee UniversityYongin17104Korea Department of Internal Medicine Wonkwang University School of MedicineIksan54538Korea Department of Trauma Surgery Jeju Regional Trauma CenterCheju Halla General HospitalJeju63127Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Biomedical Systems Informatics Yonsei University College of MedicineYongin03722Korea

Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain *** those patients requiring blood,blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous ***,detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed,such as internal *** study considered physiological signals such as electrocardiogram(ECG),photoplethysmogram(PPG),blood pressure,oxygen saturation(SpO2),and respiration,and proposed the machine learning model to detect the need for blood transfusion *** the model,this study extracted 14 features from the physiological signals and used an ensemble approach combining extreme gradient boosting and random *** model was evaluated by a stratified five-fold crossvalidation:the detection accuracy and area under the receiver operating characteristics were 92.7%and 0.977,respectively.

关键词： Blood transfusion ECG PPG pulse transit time blood pressure machine learning

来源：评论

学校读者我要写书评

暂无评论

Health Risk Assessment with Federated Learning

Health Risk Assessment with Federated Learning

引用

2022 International Balkan Conference on Communications and Networking, BalkanCom 2022

作者： Diamantoulaki, Ioanna Diamantoulakis, Panagiotis D. Bouzinis, Pavlos S. Sarigiannidis, Panagiotis Karagiannidis, George K. Aristotle University of Thessaloniki Department of Electrical and Computer Engineering ThessalonikiGR-54124 Greece University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani501 00 Greece

ISBN: (数字)9781665487641

ISBN: (纸本)9781665487641

The latest growth of storage capabilities has led to an accumulating volume of medical data stored locally by various healthcare entities. Given the recent progress observed in the domain of artificial intelligence, these data could be efficiently exploited, leading to improved and less expensive healthcare conditions. However, the common practice is medical data to be solely locally used, ending up poorly exploited, due to strict sharing restrictions stemming mainly from privacy limitation of their sensitive nature. Considering the centralized character of conventional machine learning approaches, it is apparent that they cannot reassure the privacy required. On the other hand, federated learning (FL) can be regarded as an upcoming and promising answer to efficient exploitation of medical data, considering its decentralized approach. In more details, FL can enable collaboration among various participating entities on the development and training of a common, central and fully shared model without need of sharing owned sensitive data. Thus, apparently FL approach not only can mitigate the privacy-preservation issues but can lead to the development of reliable and robust healthcare tools. Indicatively, FL can facilitate the development of a model capable of assessing the health risk, which can be a vital tool for medical sciences. To this end, in this work we present a tool capable of assessing the occurrence of different diseases or complications. This tool is based on FL technique utilizing deep neural network model. The FL model developed herein is indicatively applied to four different medical applications proving its generality in the healthcare domain. The FL approach discussed herein is compared with a corresponding centralized learning. According to the demonstrated results, FL can consist a useful health risk assessment tool exhibiting acceptable performance while preserving privacy in sensitive medical data. © 2022 IEEE.

关键词： Medical applications

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：