检索结果-内蒙古大学图书馆

Telemonitoring Parkinson’s disease using machine learning by combining tremor and voice analysis

Brain Informatics 2020年第1期7卷 12页

作者： Sajal, Md. Sakibur Rahman Ehsan, Md. Tanvir Vaidyanathan, Ravi Wang, Shouyan Aziz, Tipu Mamun, Khondaker Abdullah Al Department of Computer Science and Engineering United International University Dhaka Bangladesh Institute of Advanced Research United International University Dhaka Bangladesh Department of Mechanical Engineering Imperial College London London United Kingdom Fudan University Shanghai China Functional Neurosurgery and Experimental Neurology Group University of Oxford Oxford United Kingdom

Background: With the growing number of the aged population, the number of Parkinson’s disease (PD) affected people is also mounting. Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients’ symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work aims to combine more than one symptom (rest tremor and voice degradation) by collecting data remotely using smartphones and detect PD with the help of a cloud-based machine learning system for telemonitoring the PD patients in the developing countries. Method: This proposed system receives rest tremor and vowel phonation data acquired by smartphones with built-in accelerometer and voice recorder sensors. The data are primarily collected from diagnosed PD patients and healthy people for building and optimizing machine learning models that exhibit higher performance. After that, data from newly suspected PD patients are collected, and the trained algorithms are evaluated to detect PD. Based on the majority-vote from those algorithms, PD-detected patients are connected with a nearby neurologist for consultation. Upon receiving patients’ feedback after being diagnosed by the neurologist, the system may update the model by retraining using the latest data. Also, the system requests the detected patients periodically to upload new data to track their disease progress. Result: The highest accuracy in PD detection using offline data was 98.3 % from voice data and 98.5 % from tremor data when used separately. In both cases, k-nearest neighbors (kNN) gave the highest accuracy over support vector machine (SVM) and naive Bayes (NB). The application of maximum relevance minimum redundancy (MRMR) feature selection method showed that by selecting different feature sets based on the patient’s gender, we could improve the detection accuracy. This study’s novelty is the appli

关键词： Accelerometers

来源：评论

学校读者我要写书评

暂无评论

Towards Transmission-Friendly and Robust CNN Models over Cloud and Device

arXiv

引用

arXiv 2022年

作者： Ding, Chuntao Lu, Zhichao Juefei-Xu, Felix Boddeti, Vishnu Naresh Li, Yidong Cao, Jiannong The School of Computer and Information Technology Beijing Jiaotong University Beijing China The School of Software Engineering Sun Yat-sen University Zhuhai519082 China Alibaba Group United States The Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States The Department of Computing The Hong Kong Polytechnic University Hong Kong

Deploying deep convolutional neural network (CNN) models on ubiquitous Internet of Things (IoT) devices has attracted much attention from industry and academia since it greatly facilitates our lives by providing various rapid-response services. Due to the limited resources of IoT devices, cloud-assisted training of CNN models has become the mainstream. However, most existing related works suffer from a large amount of model parameter transmission and weak model robustness. To this end, this paper proposes a cloud-assisted CNN training framework with low model parameter transmission and strong model robustness. In the proposed framework, we first introduce MonoCNN, which contains only a few learnable filters, and other filters are nonlearnable. These nonlearnable filter parameters are generated according to certain rules, i.e., the filter generation function (FGF), and can be saved and reproduced by a few random seeds. Thus, the cloud server only needs to send these learnable filters and a few seeds to the IoT device. Compared to transmitting all model parameters, sending several learnable filter parameters and seeds can significantly reduce parameter transmission. Then, we investigate multiple FGFs and enable the IoT device to use the FGF to generate multiple filters and combine them into MonoCNN. Thus, MonoCNN is affected not only by the training data but also by the FGF. The rules of the FGF play a role in regularizing the MonoCNN, thereby improving its robustness. experimental results show that compared to state-of-the-art methods, our proposed framework can reduce a large amount of model parameter transfer between the cloud server and the IoT device while improving the performance by approximately 2.2% when dealing with corrupted data. The code is available at https://***/zhichao-lu/mono-cnn-pytorch. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A performance comparison of graph analytic methods for supporting enterprise architecture model maintenance

A performance comparison of graph analytic methods for suppo...

引用

2019 Practice of Enterprise Modelling Conference Forum, PoEM 2019 Forum

作者： Rajashekar, Nikhitha Hacks, Simon Silva, Nuno Research Group Software Construction RWTH Aachen University Germany Division of Network and Systems Engineering KTH Royal Institute of Technology Sweden Department of Computer Science and Engineering Technical University of Lisbon Lisbon Portugal

Enterprise Architecture (EA) models help enterprise architects to make business decisions and to support organisations to understand and analyse its structure. Creating and maintaining such an EA model is expensive and time-consuming in large organisations. In this paper, the authors provide a review of how graph analytic methods can be used to support EA model maintenance. To achieve the best results for the duplicate identification, it is necessary to evaluate and optimise different graph analytic and Machine Learning (ML) algorithms to support the maintenance process. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Self-Healing Secure Blockchain Framework in Microgrids

arXiv

引用

arXiv 2022年

作者： Rath, Suman Nguyen, Lam Duc Sahoo, Subham Popovski, Petar The Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Software Systems Research Group CSIRO's Data61 Sydney2015 Australia The Department of Energy Aalborg University Aalborg9220 Denmark The Department of Electronic Systems Aalborg University Aalborg9220 Denmark

Blockchain has recently been depicted as a secure protocol for information exchange in cyber-physical microgrids. However, it is still found vulnerable to consensus manipulation attacks. These stealth attacks are often difficult to detect as they use kernel-level access to mask their actions. In this paper, we firstly build a trusted and secured peer-to-peer network mechanism for physical DC microgrids' validation of transactions over Distributed Ledger. Secondly, we leverage from a physics-informed approach for detecting malware-infected nodes and then recovering from stealth attacks using a self-healing recovery scheme augmented into the microgrid Blockchain network. This scheme allows compromised nodes to adapt to a reconstructed trustworthy signal in a multi-hop manner using corresponding measurements from the reliable nodes in the network. Additionally, recognizing the possible threat of denial-of-service attacks and random time delays (where information sharing via communication channels is blocked), we also integrate a model-free predictive controller with the proposed system that can locally reconstruct an expected version of the attacked/delayed signals. This supplements the capabilities of Blockchain, enabling it to detect and mitigate consensus manipulation attempts, and network latencies. © 2022, CC BY.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

A Guide to Image and Video based Small Object Detection using Deep Learning: Case Study of Maritime Surveillance

arXiv

引用

arXiv 2022年

作者： Rekavandi, Aref Miri Xu, Lian Boussaid, Farid Seghouane, Abd-Krim Hoefs, Stephen Bennamoun, Mohammed The Department of Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia The Department of Electrical Electronics and Computer Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia The School of Mathematics and Statistics The University of Melbourne Melbourne Australia submarine optronics undersea combat systems and undersea command and control maritime division Defence Science and Technology Group Australia

Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided. © 2022, CC BY-NC-SA.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

A conceptual framework for SPI evaluation

arXiv

引用

arXiv 2023年

作者： Unterkalmsteiner, Michael Gorschek, Tony Islam, A.K.M. Moinul Cheng, Chow Kian Permadi, Rahadian Bayu Feldt, Robert Software Engineering Research Lab School of Computing Blekinge Institute of Technology KarlskronaSE-371 79 Sweden Software Engineering: Process and Measurement Research Group Department of Computer Science University of Kaiserslautern PO Box 3049 Kaiserslautern67653 Germany General Electrics Healthcare Healthcare IT. Munzinger Straße 5 Freiburg79111 Germany Amadeus S.A.S. Product Marketing and Development 485 Route du Pin Montard Boite Postale 69 Sophia Antipolis06902 Cedex France

software Process Improvement (SPI) encompasses the analysis and modification of the processes within software development, aimed at improving key areas that contribute to the organizations’ goals. The task of evaluating whether the selected improvement path meets these goals is challenging. Based on the results of a systematic literature review on SPI measurement and evaluation practices, we developed a framework (SPI-MEF) that supports the planning and implementation of SPI evaluations. SPI-MEF guides the practitioner in scoping the evaluation, determining measures and performing the assessment. SPI-MEF does not assume a specific approach to process improvement and can be integrated in existing measurement programs, refocusing the assessment on evaluating the improvement initiative’s outcome. Sixteen industry and academic experts evaluated the framework’s usability and capability to support practitioners, providing additional insights that were integrated in the application guidelines of the framework. Copyright © 2013 John Wiley & Sons, Ltd. © 2023, CC BY-NC-SA.

关键词： software design

来源：评论

学校读者我要写书评

暂无评论

Prediction of Parkinson's Disease by Analyzing fMRI Data and using Supervised Learning

Prediction of Parkinson's Disease by Analyzing fMRI Data and...

引用

IEEE Region 10 Symposium

作者： Ahmed Hasin Neehal Md. Nura Azam Md. Sazzadul Islam Md. Ishrak Hossain Mohammad Zavid Parvez Software Engineering and HCI Research Group Brac UniversityDepartment of Computer Science and EngineeringBangladesh

ISBN: (数字)9781728173665

ISBN: (纸本)9781728173672

Parkinson's disease is the second most common neurodegenerative disorder after Alzheimer's disease. Almost 10 million people are estimated to have the disorder of Parkinson's disease. However, Parkinson's symptoms appear gradually and get worse over time. Therefore, the detection of Parkinson's disease at an early stage might significantly improve lifestyle by giving proper treatment. In recent years, the use of Functional Imaging in neurodegenerative diseases has increased. As Functional Imaging seems very efficient in the case of brain disorders, we used Functional Magnetic Resonance Imaging (fMRI) data for conducting our research. Furthermore, SVM classifier was used for the classification and prediction of Parkinson's disease. Using our proposed method, we have achieved 100% sensitivity, specificity, and accuracy considering seven subjects. However, one subject was exceptional whereas we have achieved 99.76% accuracy, 100% specificity, and 99.53% sensitivity. Finally, this process is a well-structured model for predicting the early stages of PD. It may help the doctors for diagnosis of the disease at its early stages and the patients should receive better treatment.

关键词： Functional magnetic resonance imaging Feature extraction Time series analysis Diseases Support vector machines Time-frequency analysis Training

来源：评论

学校读者我要写书评

暂无评论

Prediction of Epileptic Seizures using Support Vector Machine and Regularization

Prediction of Epileptic Seizures using Support Vector Machin...

引用

IEEE Region 10 Symposium

作者： Shaikh Rezwan Rafid Ahmad Samee Mohammad Sayeed Zaziba Ahmed Nusayer Masud Siddique Mohammad Zavid Parvez Software Engineering and HCI Research Group Department of Computer Science and Engineering Brac University Bangladesh

ISBN: (数字)9781728173665

ISBN: (纸本)9781728173672

Epilepsy is a neurological disorder that causes abnormal behavior and recurrent seizures due to unusual brain activity. This study has attempted to predict seizures in epileptic patients through the process of feature extraction from EEG signals during preictal/ictal and interictal periods, classification and regularization. EEG signals from various parts of the brain from 10 epileptic patients are considered. Fast Fourier Transform (FFT) is used to determine the three features-the phase angle, the amplitude and the power spectral density of the signals. To classify the signals, these features are then used along with Support Vector Machine (SVM) as the classifier. Furthermore, regularization is used to make better predictions i.e. increase prediction accuracy and decrease the rate of false alarm. Finally, the proposed approach is tested on CHB-MIT Scalp EEG data set and it is able to predict epileptic seizures 25 minutes on average before the onset of the seizure with 100% accuracy and a low false-alarm rate of 0.46 per hour. This study intends to contribute to the development of better and advanced seizure predicting devices in the medical field.

关键词： Support vector machines Electroencephalography Feature extraction Brain modeling Predictive models Epilepsy Training

来源：评论

学校读者我要写书评

暂无评论

Towards the statistical construction of hybrid development methods

arXiv

引用

arXiv 2021年

作者： Tell, Paolo Klünder, Jil Küpper, Steffen Raffo, David MacDonell, Stephen Münch, Jürgen Pfahl, Dietmar Linssen, Oliver Kuhrmann, Marco Department of Computer Science IT University Copenhagen Denmark Software Engineering Group Leibniz University Hannover Hannover Germany Center for Digital Technologies Clausthal University of Technology Clausthal-Zellerfeld Germany Department of Computer Science School of Business Portland State University PortlandOR United States Department of Information Technology and Software Engineering Auckland University of Technology Auckland New Zealand Department of Computer Science Reutlingen University Reutlingen Germany Institute of Computer Science University of Tartu Tartu Estonia FOM Hochschule für Oekonomie & Management Essen Germany Software Engineering I University of Passau Passau Germany

Hardly any software development process is used as prescribed by authors or standards. Regardless of company size or industry sector, a majority of project teams and companies use hybrid development methods (short: hybrid methods) that combine different development methods and practices. Even though such hybrid methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this article, we make a first step towards a statistical construction procedure for hybrid methods. Grounded in 1467 data points from a large-scale practitioner survey, we study the question: What are hybrid methods made of and how can they be systematically constructed? Our findings show that only eight methods and few practices build the core of modern software development. Using an 85% agreement level in the participants' selections, we provide examples illustrating how hybrid methods can be characterized by the practices they are made of. Furthermore, using this characterization, we develop an initial construction procedure, which allows for defining a method frame and enriching it incrementally to devise a hybrid method using ranked sets of practice. Copyright © 2021, The Authors. All rights reserved.

关键词： software design

来源：评论

学校读者我要写书评

暂无评论

Level of Trust and Privacy Management in 6G Intent-based Networks for Vertical Scenarios

Level of Trust and Privacy Management in 6G Intent-based Net...

引用

6G Networking (6GNet), International Conference on

作者： Jesú s A. Alonso-Lupez Luis Alberto Martí nez Herná ndez Sandra Pé rez Arteaga Ana Lucila Sandoval Orozco Luis Javier Garcí a Villalba Antonio Pastor Diego Ló pez Garcí a Department of Software Engineering and Artificial Intelligence (DISIA) Faculty of Computer Science and Engineering Office 431 Group of Analysis Security and Systems (GASS) Universidad Complutense de Madrid (UCM) Madrid Spain CTIO Unit Telef&#x00F3 nica I+D Ronda de la Comunicacion S/N Madrid Spain

Security is one of the main concerns when designing future 6G networks. Most of the components of service chains are software-based and their security must be ensured. In this work we propose a system that will check the level of trust of 6G services following 3 complementary approaches: attestation, generation of proofs of transit and usage of smart contracts whose execution will be published via Distributed Ledger Technology in order to enable their traceability. The service orchestration function will use reinforcement learning to formulate optimised service orchestration decisions. Services will be specified in a declarative model, following a privacy-aware flavour of intent-based networking. The proposed architecture will take into account privacy of every stakeholder: final users, service providers and infrastructure providers.

关键词： 6G mobile communication Privacy Distributed ledger Network topology Smart contracts Reinforcement learning Service-oriented architecture

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：