检索结果-内蒙古大学图书馆

19th International Conference on Intelligent systems Design and Applications, ISDA 2019

作者： Khaskhoussy, Rania Ayed, Yassine Ben MIRACL: Multimedia InfoRmation System and Advanced Computing Laboratory Sfax University Sfax Tunisia

ISBN: (纸本)9783030493417

Parkinson’s disease (PD) is a neurodegenerative disease ranked second after Alzheimer’s disease. It affects the central nervous system and causes a progressive and irreversible loss of neurons in the dopaminergic system, that insidiously leads to cognitive, emotional and language disorders. But until day there is no specific medication for this disease, the drug treatments that exist are purely symptomatic, that’s what encourages researchers to consider non-drug techniques. Among these techniques, speech processing becomes a relevant and innovative field of investigation and the use of machine-learning algorithms that provide promising results in the distinction between PD and healthy people. Otherwise many other factors such as feature extraction, number of feature, type of features and the classifiers used they all influence on the prediction accuracy evaluation. The aim of this study is to show the importance of this last factor, a model is suggested which include feature extraction from 3 types of features (acoustic, prosodic and phonetic) and classification is achieved using several machine learning classifiers and the results show that the proposed model can be highly recommended for classifying PD in healthy individuals with an accuracy of 99.50% obtained by Support Vector Machine (SVM). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Speech processing

来源：评论

学校读者我要写书评

暂无评论

Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

引用

Neural computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

来源：评论

学校读者我要写书评

暂无评论

Biometric individual identification system based on the ECG signal 19th

Biometric individual identification system based on the ECG ...

引用

19th International Conference on Intelligent systems Design and Applications, ISDA 2019

作者： Hamza, Sihem Ben Ayed, Yassine Higher Institute of Computer Science and Multimedia ISIMS University of Sfax Sfax Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory MIRACL University of Sfax Sfax Tunisia

ISBN: (纸本)9783030493417

Human biometric identification based on the ElectroCardioGram (ECG) is relatively new. This domain is intended to recognize individuals since the ECG has unique characteristics for each individual. These features are robust against forgery. In this study, feature extraction from ECG signals was performed using a combination of three new types of characteristics: MFCC, ZCR, and entropy. We proposed to apply classification methods: K Nearest Neighbors (KNN) and support vector machines (SVM) for human biometric identification. For evaluation we used two bases, namely MIT-BIH arrhythmia and normal sinus rhythm obtained from the Physionet database. For the MIT-BIH database, we used 47 individuals, each recording contains ECG data recorded for 15 s and in the SNR database, we used 18 individuals, the duration of each recording is 10 s. The analysis of the results obtained shows that the combination of all the features proposed makes it possible to improve the efficiency of our identification system to reach a performance rate equal to 100% for the two bases. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

引用

Journal of Computer Science & Technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

来源：评论

学校读者我要写书评

暂无评论

Parameter-efficient fine-tuning of Whisper for low-resource speech recognition

Parameter-efficient fine-tuning of Whisper for low-resource ...

引用

Artificial Intelligence, Networking and information Technology (AINIT), International Seminar on

作者： Yunpeng Liu Dan Qu Department of Artificial Intelligence Laboratory for Advanced Computing and Intelligence Engineering School of Information Systems Engineering University of Information Engineering Henan China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Limited data availability remains a significant challenge for Whisper's low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully reduced the recognition error rates of target language speech through fine-tuning, a comprehensive exploration and analysis of Whisper's parameter-efficient fine-tuning capabilities, the advantages and disadvantages are still lacking. This paper aims to fill this gap by conducting comprehensive experimental exploration for Whisper's low-resource speech recognition performance using two parameter-efficient fine-tuning strategies with limited supervised data from multiple low-resource languages. The results and analysis demonstrate that all fine-tuning strategies explored in this paper significantly enhance Whisper's performance. However, different strategies vary in their suitability and practical effectiveness, highlighting the need for careful selection based on specific use cases and resources available.

关键词： Seminars Costs Target recognition Error analysis Speech recognition Data models Task analysis

来源：评论

学校读者我要写书评

暂无评论

Semantic Heads Segmentation and Counting in Crowded Retail Environment with Convolutional Neural Networks Using Top View Depth Images

引用

SN Computer Science 2023年第1期4卷 61页

作者： Abed, Almustafa Akrout, Belhassen Amous, Ikram Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Multimedia Information Systems and Advanced Computing Laboratory (MIRACL) Sfax University Sfax 3021 Tunisia ENET’COM University of Sfax National School of Electronics and Telecommunications of Sfax Road Tunis City El Ons Sfax 3018 Tunisia

In recent years, researchers have developed several techniques to accurately count the number of people in a crowded retail environment for human behavior analysis, ranging from vision and feature based approaches to machine-learning and deep learning approaches. Due to the availability and affordability of recent advanced technologies such as depth sensors and high computing powers as well as the availability of big data, the need for building accurate models to detect and count people in crowded environments arises. Deep learning approaches has proven to be highly accurate especially when there is enough data to train the model and high computation powers. People detection and counting is a challenging problem. due to several issues such as occlusion, light variations, complex backgrounds to name a few. To cope with these issues, we utilize a top-view configuration depth data to train our model. We propose a convolutional encoder decoder architecture consisting of a resnet50 encoder trained on the ImageNet dataset as a transfer learning technique and we built the decoder part of the model as a novel contribution, for segmenting and counting customers heads in a crowded retail stores where there are more than 6 individuals per square meter without compromising people privacy because the camera does not record people’s faces. The objective of our method is to segment and count people using the publicly available TV-Head Dataset and People Counting DataSet (PCDS). The results demonstrate that our model is robust and can be used for real time people counting with accurate results. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Computer vision Convolutional neural networks Intelligent retail environment People counting Top-view configuration

来源：评论

学校读者我要写书评

暂无评论

New Dynamic Bayesian Network for Time-to-Event Prediction

New Dynamic Bayesian Network for Time-to-Event Prediction

引用

ACS/IEEE International Conference on Computer systems and Applications

作者： Walid Ben Abdallah Hasna Njah Salma Jamoussi National Engineering School of Gabes Gabes University Tunisia Multimedia InfoRmation systems and Advanced Computing Laboratory Sfax University Tunisia Higher institute of Computer sciences and multimedia of Gabes Gabes University Tunisia Higher institute of Computer sciences and multimedia of Sfax Sfax university Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Time-to-event prediction is an important analytical approach in medical research and personalized medicine that aims to predict the timing of clinically relevant occurrences and find associated risk variables. In the context of chronic diseases such as multiple sclerosis (MS), precisely modelling time to disease progression, adverse events, or therapeutic responses can have a significant impact on patient management strategies and clinical results. This study investigate the use of Bayesian networks(BN), augmented with a temporal factor, for time-to-event prediction in predicting disease progression in MS patients. We suggest using dynamic Bayesian networks (DBNs) as a versatile temporal modeling framework to capture the complex dynamics and inter-dependencies found in time-to-event processes.

关键词： Multiple sclerosis Accuracy Precision medicine Probabilistic logic Bayes methods Timing Medical diagnostic imaging Context modeling

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

引用

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

来源：评论

学校读者我要写书评

暂无评论

Two-mode squeezing over deployed fiber coexisting with conventional communications

Two-mode squeezing over deployed fiber coexisting with conve...

引用

Optica Quantum 2.0 Conference and Exhibition, Quantum 2.0

作者： Chapman, Joseph C. Miloshevsky, Alexander Lu, Hsuan-Hao Rao, Nageswara Alshowkan, Muneer Peters, Nicholas A. Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Advanced Computing Methods for Engineered Systems Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We demonstrate distribution of two-mode squeezing to two separate locations and implement joint homodyne detection. We also show multiplexing with multiple classical signals. We distribute squeezing over several 5-km ... 详细信息

ISBN: (纸本)9781957171272

关键词： Analog to digital converters Homodyne detection Multiplexing Optical networks Phase noise Squeezed states

来源：评论

学校读者我要写书评

暂无评论

Deep support vector machines for speech emotion recognition 19th

Deep support vector machines for speech emotion recognition

引用

19th International Conference on Intelligent systems Design and Applications, ISDA 2019

作者： Aouani, Hadhami Ben Ayed, Yassine National School of Engineers ENIS University of Sfax Sfax Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory MIRACL University of Sfax Sfax Tunisia

ISBN: (纸本)9783030493417

Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: zero crossing rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Support vector machines

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：