检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Shati, Asmaa Hassan, Ghulam Mubashar Datta, Amitava Department of Computer Science and Software Engineering University of Western Australia Australia Department of Information Systems King Khalid University Saudi Arabia

A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people’s daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various respiratory illnesses and lung disorders. The traditional diagnosis of such sounds requires specialized knowledge, which can be costly and reliant on human expertise. Recently, cough audio recordings have been used to automate the process of detecting respiratory conditions. This research aims to examine various acoustic features that enhance the performance of machine learning (ML) models in detecting COVID-19 from cough signals. This study investigates the efficacy of three feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, on two ML algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and thus proposes an efficient COVID-19 detection system. The proposed system produces a practical solution and demonstrates higher state-of-the-art classification performance with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the Virufy dataset for COVID-19 detection. Copyright © 2023, The Authors. All rights reserved.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Benefits and drawbacks of radiofrequency ablation via percutaneous or minimally invasive surgery for treating hepatocellular carcinoma

引用

World Journal of Gastrointestinal Surgery 2024年第11期16卷 3400-3407页

作者： Ching-Lung Hsieh Cheng-Ming Peng Chun-Wen Chen Chang-Hsien Liu Chih-Tao Teng Yi-Jui Liu Department of Computer Science and Information Engineering Feng Chia UniversityTaichung 40724Taiwan Department of Surgery Chung Shan Medical University HospitalTaichung 40201Taiwan School of Medicine Chung Shan Medical UniversityTaichung 40201Taiwan Department of Radiology Taichung Armed Forces General HospitalTaichung 406Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and TechnologyTaichung 406Taiwan Department of Radiology School of MedicineNational Defense Medical CenterTaipei 11490Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital and China Medical UniversityHsinchu 302Taiwan Institute of Nuclear Engineering and Science National Tsing Hua UniversityHsinchu 300Taiwan Department of Radiology Nantou Hospital of the Ministry of Health and WelfareNantou 540Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia UniversityTaichung 407Taiwan Department of Automatic Control Engineering Feng Chia UniversityTaichung 407Taiwan

The management of early stage hepatocellular carcinoma(HCC)presents significant *** radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter hospital stays,its high recurrence rate remains a significant ***,achieving improved survival solely through RFA is challenging,particularly in retrospective studies with inherent *** is commonly used for guiding percutaneous RFA,but its low contrast can lead to missed tumors and the risk of HCC *** enhance the efficiency of ultrasound-guided percutaneous RFA,various techniques such as artificial ascites and contrast-enhanced ultrasound have been developed to facilitate complete tumor *** invasive surgery(MIS)offers advantages over open surgery and has gained traction in various surgical *** studies suggest that laparoscopic intraoperative RFA(IORFA)may be more effective than percutaneous RFA in terms of survival for HCC patients unsuitable for surgery,highlighting its ***,combining MIS-IORFA with these enhanced percutaneous RFA techniques may hold greater significance for HCC treatment using the MIS-IORFA *** article reviews liver resection and RFA in HCC treatment,comparing their merits and proposing a trajectory involving their combination in future therapy.

关键词： Percutaneous radiofrequency ablation Minimally invasive surgery Hepatocellular carcinoma Intraoperative radiofrequency ablation Contrast-enhanced ultrasound

来源：评论

学校读者我要写书评

暂无评论

An Asymmetrical Amplitude Digitized Modulation Scheme for TP-IBMC in Unbalanced Three-Phase IPT Systems

An Asymmetrical Amplitude Digitized Modulation Scheme for TP...

引用

IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Zhihao He Duleepa J. Thrimawithana Martin Neuburger Grant A. Covic Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Faculty of Mobility and Technology Esslingen University of Applied Sciences Goppingen Germany

ISBN: (数字)9798350351156

ISBN: (纸本)9798350351163

This paper proposes a three-phase asymmetrical amplitude digitized modulation (TP-AADM) scheme for a three-phase integrated boost multi-level converter (TP-IBMC) utilized on an inductive power transfer (IPT) system with a three-to-three coupler and the double-sided LCL tuning network. It can control these output voltages driving the coupler independently in order to generate balanced currents in the secondary coils or the rectifier with the misalignment situation while the conventional solutions require three separated H-bridge converters. A simulation model in accordance with the SAEJ2954 WPT3/Z3 indicates the performance in the misaligned IPT system.

关键词： Multilevel converters Modulation Rectifiers Couplers Wireless power transfer Rendering (computer graphics) Regulation Voltage control Tuning Standards

来源：评论

学校读者我要写书评

暂无评论

IoT System for Floating Raft Hydroponics: Nutrient Monitoring and Automation 13

IoT System for Floating Raft Hydroponics: Nutrient Monitorin...

引用

13th International Physics Seminar 2024, IPS 2024

作者： Purwalaksana, A.Z. Wahyu, S. Ndari, W.E.W. Syah, S.A. Rismawati, E. Kusumadjati, A. Suharmanto, P. Department of Physics Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta13220 Indonesia Department of Physics Education Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta13220 Indonesia Physics Study Program Indonesia Defense University IPSC Sentul Area West Java16810 Indonesia Department of Industrial Engineering Faculty of Engineering and Computer Science Universitas Indraprasta PGRI Jl. Nangka Raya No.58 C Jakarta12530 Indonesia

The utilization of hydroponic cultivation in agriculture is increasing due to its high productivity even in limited spaces. However, in practice, monitoring and controlling nutrients in hydroponic systems often require intensive human supervision and intervention. This issue can be addressed through technological advancements, particularly with the adoption of microcontrollers, sensors, and the Internet of Things (IoT). Previous research has developed IoT-based monitoring and control systems for DFT hydroponics using the Blynk platform. This study aims to develop an IoT-based nutrient monitoring and automation system for floating raft hydroponics. Floating raft hydroponics was chosen for its simplicity, lower installation costs, and space optimization. The system developed in this study uses the HC-SR04 ultrasonic sensor with an average error of 1.229%, the DS18B20 water temperature sensor with an error of 0.415%, and the TDS sensor with an error of 2.055%. The sensor and actuator measurement data are presented via the Antares Web. This research has the potential to minimize the necessity for manual intervention in hydroponic systems, thereby enhancing both efficiency and consistency in nutrient delivery. © Published under licence by IOP Publishing Ltd.

关键词： Hydroponics

来源：评论

学校读者我要写书评

暂无评论

AI Decision Assistant ChatBot for software Release Planning and Optimized Resource Allocation

AI Decision Assistant ChatBot for Software Release Planning ...

引用

IEEE International Conference on Artificial Intelligence Testing (AITest)

作者： Ehab Ebrahim Mazen Sayed Mahmoud Youssef Hisham Essam Salma Abd El-Fattah Dina Ashraf Omar Magdy Reem ElAdawi Computer and Systems Engineering Department Ain Shams University Cairo Egypt Siemens Digital Industries Software Cairo Egypt

This paper presents Alfred, a web-based artificial intelligence (AI) chatbot powered by the Rasa framework and designed to facilitate Agile software release planning by using two machine learning (ML) models to estimate the time for each task selected by the project managers and recommend the optimal resource to be assigned to each task. Extreme gradient boosting (XGBoost) is used for time estimation, and the model’s performance evaluated using accuracy, adjusted coefficient of determination (R2), and fitting a line between the estimation and actual values. The results obtained were 82% accuracy and 98% adjusted R2 score. K-nearest neighbors (K-NN) is used for resource recommendation in a one-shot learning manner. The application integrates a dashboard with useful visualizations and an interactive smart assistant AI-based chatbot, providing project managers with a tool to make more informed decisions. Additionally, an approach is used to indicate the model’s confidence in tasks estimation of given tasks based on the amount of available data showing the history in the system to give project managers more insights. The approach uses a keywords frequency indication algorithm based on the summary tokens to group the task estimates into one of three categories of confidence: high, medium, or low.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Parallel Change Detection Method for Spatiotemporally Multi-Temporal SAR Image Based on Q-Learning and Wavelet 14

A Parallel Change Detection Method for Spatiotemporally Mult...

引用

14th International Conference on Digital Image Processing, ICDIP 2022

作者： Peng, Jinxi Su, Yuanqi Xue, Xiaorong Song, Donghong Xie, Zhengbo Xue, Xiaoyong Fan, Wenlong Department of Computer Science and Technology Xían JiaoTong University Shaanxi Xi'an710049 China Department of software Engineering South China institute of software Engineering Guangdong Guangzhou510990 China Liaonin Jinzhou121001 China The Middle Training School of Zhenan County Zhen'an Shaanxi 711500 China The Middle School of Luonan County Luonan Shaanxi 726100 China

ISBN: (纸本)9781510657564

In order to make full use of multi-source remote sensing image resources in the application of remote sensing image change detection, a parallel computing for coarse-grained computing data is a very important choice. For the large-scale explosive growth of remote sensing image change which is contained coherent speckles detection sensor data, it’s the amount of data calculation is large and time-consuming. With the development of modern remote sensing big data and Artificial Intelligence (AI), however, it is difficult for traditional methods to solve an efficient algorithm for change detection in synthetic aperture radar application. In connection with acquiring SAR (Polarimetric Synthetic Aperture Radar) Image to accurately obtain an effectively computing power. Looking for data processing and task computing as the basis for parallelization, we propose a parallel change detection method for multi-temporal SAR images based on wavelet transform, and comprehensively apply the Q-learning model of parallel computing with intelligent processing. Firstly, according to the statistical features of SAR images and the semantics of Convolutional Neural Network Pixel (CNN-Pixel) analysis for an efficient change detection method based on Q-Learning semantic analysis and wavelet transform is proposed. Secondly, on the basis of Pixel-Data is achieved the accuracy of change detection, probabilistic and statistical conjugate multi-distribution function features, which is pixels and tasks are pre-processed, pixel-space data calculation of Gaussian mixture model and conjugate gradient transform task model for parallel calculation. Thirdly, the change-sequence data with obstacles at the time series model of Q-learning in the changing target object data on which the surrounding path is an obstacle to achieving that image-pixel sequence data of conjugate gradient calculation data and threshold iteratively optimizes the feature extraction of the changing area, the multi-sample training se

关键词： Change detection

来源：评论

学校读者我要写书评

暂无评论

Meta-optic accelerators for object classification

Meta-optic accelerators for object classification

引用

2023 Flat Optics: Components to Systems, FLatOptics 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in high-speed classifiers that are demonstrated for hand... 详细信息

ISBN: (纸本)9781957171289

关键词： Imaging systems Machine vision Neural networks Optical components Optical neural systems Optical systems

来源：评论

学校读者我要写书评

暂无评论

Predicting Modalities of Dyslexic Students using Neuro-Linguistic programming to Enhance Learning Method

Predicting Modalities of Dyslexic Students using Neuro-Lingu...

引用

2022 IEEE International Conference on Data Science and Information System, ICDSIS 2022

作者： Choube, Gaurav Dudhmande, Gauri Rahul Pushparaj, Jagalingam Anand, Christopher Suresh, Shilpa Software Engineer Optum Global Solutions Pvt Ltd Hyderabad India Software Engineer Intel Technology Pvt Ltd Aurangabad India Vellore Institute of Technology School of Computer Science and Engineering Vellore India McMaster University Department of Computing and Software Ontario Canada Manipal Institute of Technology Department of Mechatronics Manipal India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

Dyslexia causes difficulty in reading, writing and learning. The children at a tender age have always suffered due to dyslexia. Dyslexia deceives student's perception and makes it difficult in the process of learning. In this paper, machine learning techniques like multi-layer perceptron, Decision tree and Gaussian NB approaches were implemented for the prediction of modalities. To enhance the learning approach for the students suffering from dyslexia, the predicted modalities can be adopted. The sampled data was trained, and the target labels were classified into three classes as visual, auditory, and kinesthetic. The data was processed and fed into multi-layer perceptron, decision tree and naive bayes machine learning algorithms using scikit-learn. Confusion matrix was used to evaluate the performance measure of the algorithms. It was observed that models achieved accuracy of 81.41% for MLP Classifier, 63.82% for Decision tree and 79.25% for Naive bayes. The best result was achieved by MLP Classifier. © 2022 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A Context-Driven Approach for Co-Auditing Smart Contracts Using Gpt-4 Code Interpreter

SSRN

引用

SSRN 2024年

作者： Bouafif, Mohamed Salah Zheng, Chen Qasse, Ilham Zulkoski, Ed Hamdaqa, Mohammad Khomh, Foutse Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada Department of Computer Science Reykjavik University Reykjavík Iceland Department of Computer Science The Hong Kong Polytechnic University Hung Hom Hong Kong Quantstamp Dover United States

Context: The surge in the adoption of smart contracts necessitates rigorous auditing to ensure their security and reliability. Manual auditing, although comprehensive, is time-consuming and heavily reliant on the auditor's expertise. With the rise of Large Language Models (LLMs), there is growing interest in leveraging them to assist auditors in the auditing process (co-auditing). However, the effectiveness of LLMs in smart contract co-auditing is contingent upon the design of the input prompts, especially in terms of context description and code ***: This paper introduces a novel context-driven prompting technique for smart contract co-auditing. The primary objective is to improve the effectiveness of LLMs in detecting vulnerabilities in smart contracts through enhanced prompt ***: Our approach employs three techniques for context scoping and augmentation, namely: Code Scoping, which involves chunking long code into self-contained code segments based on code interdependencies;Assessment Scoping, which enhances context description based on the target assessment goal to limit the search space;and Reporting Scoping, which forces a specific format for the generated ***: Through empirical evaluations on publicly available vulnerable contracts, our method demonstrated a detection rate of 96\% for vulnerable functions, outperforming the native prompting approach, which detected only 53\%. To assess the reliability of our prompting approach, manual analysis of the results was conducted by two expert auditors from Quantstamp, a world-leading smart contract auditing company. The experts' analysis indicates that, in unlabeled datasets, our proposed approach enhances the proficiency of the GPT-4 code interpreter in detecting ***: The context-driven prompting technique significantly improves the effectiveness of LLMs in smart contract co-auditing. This method not only enhances the detection rate of vulnerabilities but

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Transforming Healthcare Diagnostics With Tensorized Attention and Continual Learning on Multi-Modal Data

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Shabaz, Mohammad Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Algamdi, Shabbab Ali Li, Yang Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Model Institute of Engineering and Technology J and K Jammu India Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia Prince Sattam bin Abdulaziz University College of Computer Science and Engineering Department of Software Engineering Al Kharj Saudi Arabia Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China

Analyzing multi-modal medical data in the setting of uncertain healthcare situations continues to be a major topic in medical image analysis and healthcare big data. Traditional machine learning algorithms are severely hampered by inaccurate data fusion, a lack of adaptability to changing patient data, and challenges managing uncertainty. These difficulties are made worse by complicated medical images and diverse data sources, which results in less accurate diagnosis and worse-than-ideal healthcare choices. To tackle these urgent problems, this paper suggests two new approaches: Continual Learning using Progressive Neural Networks (PNNs) and Tensorized Attention Mechanism for Data Fusion. The Tensorized Attention Mechanism improves multi-modal data fusion by using dynamic, task-specific attention to improve feature alignment across modalities, and the PNNs framework uses continual learning, memory augmentation, and domain adaptation to ensure robust learning under data uncertainty. We test these methods on a variety of multi-modal datasets, such as MIMIC-IV, CheXpert, MOST, OAI, and Heart Murmur, which offer a comprehensive representation of medical data from clinical reports, chest X-rays, heart murmurs, and other heterogeneous data sources. Our experimental results show notable improvements in diagnostic performance, with notable results like a CFI of 0.10, a KR score of 90.4%, and an MMC score of 0.097, indicating superior generalization and robustness across domains. Healthcare AI applications could be revolutionized by the use of specialized losses, such as Conditional Variational Autoencoder (CVAE), Adversarial Contrastive Learning (ACL), Reciprocal Regularization, and domain adaptation losses, which are essential for preventing forgetting and guaranteeing learning stability across shifting data streams. © 1975-2011 IEEE.

关键词： Sensor data fusion

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：