检索结果-内蒙古大学图书馆

Novel vision transformer and data augmentation technique for efficient detection of monkeypox disease

Multimedia Tools and Applications 2024年 1-19页

作者： Alarfaj, Aisha Ahmed Ahmad, Salman Hakeem, Abeer M. Alabdulqader, Ebtisam Abdullah PERO, Chiara Alsubai, Shtwai Innab, Nisreen Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Post Graduate Resident Department of Urology Allama Iqbal Teaching Hospital Dera Ghazi Khan Pakistan Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Via Giovanni Paolo II 132 Fisciano Salerno Italy Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Recent technological advancements have paved the way for the optimization of medical processes, particularly automated disease detection. Moreover, the adoption of machine learning (ML) has greatly helped in automating disease detection. Such approaches can detect various diseases early, enabling timely treatment to save countless lives. Early and accurate diagnosis is very important for diseases like monkeypox, to curb its spread. Monkeypox is a viral disease caused by double-stranded DNA and can be transmitted through close contact with infected humans or animals. It’s early identification and accurate lesion diagnosis are critical to contain the disease. This study proposes an automated approach to optimize the diagnosis of monkeypox disease using a novel vision transformer, which is utilized due to its effectiveness for feature extraction. The Proposed approach’s efficiency and accuracy are tested on a public benchmark dataset comprising a variety of skin lesions of different ages and genders. In addition, data augmentation involves rotation, scaling, and flipping thereby enhancing the density of the training data set for better generalization of ML models. Experiments involve binary, as well as, multi-class classification. For the binary class, the proposed model achieves an accuracy of 97.63%, outperforming traditional ML and deep learning (DL) techniques. In the case of multi-class classification with monkeypox, measles, normal, HFMD, cowpox, and chickenpox classes, the proposed model archives an accuracy of 90.61% while precision, recall, and F1 scores are 91.39%, 89.17%, and 90.28%, respectively. Furthermore, the proposed approach shows average accuracy, precision, recall, and F1 scores of 97.54%, 96.19%, 95.16%, and 95.67%, respectively for five-fold cross-validation. Experiments demonstrate that the combination of data augmentation techniques and the vision transformer model significantly optimizes diagnostic performance. In brief, advanced DL architectur

关键词： Medical image processing

来源：评论

学校读者我要写书评

暂无评论

2019 Novel Coronavirus Disease (Covid-19): Toward a Novel Design for Disinfection Robot to Combat Coronavirus (Covid-19) Using IoT Based Technology 12

2019 Novel Coronavirus Disease (Covid-19): Toward a Novel De...

引用

12th IEEE Control and System graduate Research Colloquium, ICSGRC 2021

作者： Mohammed, M.N. Hazairin, Nurul Aslamiah Arif, Arif Sameh Al-Zubaidi, S. Alkawaz, Mohammed Hazim Sairah, A.K. Jazlan, Ahmad Izzul Iman Md Yusuf, Eddy Management Science University Faculty of Information Sciences and Engineering Department of Engineering and Technology Shah Alam40100 Malaysia Management Science University School of Graduate Studies Shah Alam40100 Malaysia Middle Technical University Institute of Administration-Al Rusafa Department of Computer Systems Techniques Baghdad10047 Iraq University of Baghdad Al-Khwarizmi College of Engineering Department of Automated Manufacturing Engineering Baghdad10071 Iraq Jakarta Global University Faculty of Pharmacy Depok16412 Indonesia

ISBN: (纸本)9781665440110

Coronavirus Disease 19 or well known as Covid-19 is infectious disease that attack human respirator system. This virus is contagious due to its fast transmission from small droplets that come to people that sneezing, coughing, or even talking. This virus can stay in air for long time and it also can survive on the inanimate surface things. During this time, some places like hospital, mall, and station are places where crowds gathered. People in those places have possibility to spread the virus not only through close contact but also due to touching the infected surfaces. That is why device that able to clean the infected surfaces needed. The Internet of Things based robots may give big impact in combating the coronavirus that stay on inanimate surfaces. The proposed system is the robot that able to disinfect the surfaces of things using UV-C lights. The implementation of UV disinfectant robot will indeed help health authorities in reducing the transmission. © 2021 IEEE.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Cardiovascular Disease Prediction Using Machine Learning Algorithms 8

Cardiovascular Disease Prediction Using Machine Learning Alg...

引用

8th IEEE International Conference on engineering Technologies and Applied Sciences, ICETAS 2023

作者： Bagadi, Kalapraveen Annepu, Visalakshi Jameel Al-Tamimi, Adnan Naji Challa, Naga Raju Aljibori, H.S.S. Mohammed, M.N. Abdullah, Oday I. Abbas Helmi, Rabab Alayham Alfiras, M. School of Electronics Enineering VIT-AP University Amaravati India School of Comp. Science & Eng. VIT-AP University Amaravati India College of Technical Engineering Al-Farahidi University Baghdad Iraq Bapatla Engineering College Dept. of Elec. and Comm. Eng. Baptla India College of Engineering University of Warith Al-Anbiyaa Karbala Iraq College of Engineering Gulf University Mechanical Engineering Department Sanad26489 Bahrain University of Baghdad Dept of Energy Engineering Iraq College of Engineering Gulf University Mechanical Engineering Dept Sanad26489 Bahrain Hamburg University of Technology Institute of Laser and Systems Technologies Hamburg Germany School of Graduate Studies Management and Science University Selangor Shah Alam Malaysia College of Engineering Gulf University Electrical and Electronic Engineering Department Sanad26489 Bahrain

ISBN: (纸本)9798350327090

The fact that cardiovascular disease (CVD) is a major cause of death worldwide highlights the significance of accurate prediction for successful preventative and treatment measures. Machine learning algorithms, which use the analysis of vast patient data to reveal hidden patterns and risk variables, have recently come to light as potential techniques for CVD prediction. This study intends to analyse how machine learning (ML) techniques are used in CVD prediction and evaluate how well they perform in comparison to conventional approaches. The study makes use of a sizable patient cohort's medical history, demographic data, and clinical factors in a complete dataset. Predictive models are built using a variety of machine learning algorithms, including support vector machine (SVM), gradient boosting, K-nearest neighbours, naive Bayes classifier, and logistic regression. To find the most important factors influencing CVD risk, feature selection techniques are used. Metrics like accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve are used to assess how well the machine learning models work. The outcomes are contrasted with well-known risk prediction models and clinical guidelines in order to assess the added value of machine learning methods. This work intends to improve CVD prediction capabilities and offer useful insights for better risk assessment and management strategies by utilizing the power of machine learning. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

COMPETITIVE ADVANTAGE MODEL THROUGH KNOWLEDGE management systems IMPLEMENTATION TO OPTIMIZE BUSINESS SUSTAINABILITY 9

COMPETITIVE ADVANTAGE MODEL THROUGH KNOWLEDGE MANAGEMENT SYS...

引用

9th International Conference on Information Technology: IoT and Smart City, ICIT 2021

作者： Sardjono, Wahyu Lusia, Erma Utomo, Hargo Sukardi, Samudra Rahmasari, Aninda Regent Montororing, Yuri Delano Information Systems Management Department Binus Graduate Program Bina Nusantara University Indonesia Research Capacity Building Research & Development Bina Nusantara University Indonesia Department of Management Faculty of Economic and Business Gajah Mada University Indonesia Graduate Program Faculty of Economy and Business University of Indonesia Indonesia International Relations Department Faculty of Humanities Bina Nusantara University Indonesia Industrial Engineering Study Program Bhayangkara University Indonesia

ISBN: (纸本)9781450384971

Complaint resolution that arise due to internal and external factors in a company can be monitored through the Service Recovery Index (SRI), and SRI is developed through a number of factors that influence it. Meanwhile, the SRI model needs to be developed to see the business sustainability through implementation knowledge management system in the organization. The data collection method was carried out, starting with building a research instrument based on knowledge management theory and continued with observation and distributing questionnaires to respondents who were involved in the business process of managing SRI. The data analysis method was used to process the data from the questionnaire, factor analysis method was used and the method to build the model used was the regression analysis method. The results obtained from this study are the discovery of 4 (four) factors with a number of indicators that are grouped on each formed factor, which can then be made and built a readiness model that reflects current conditions, so that it can be used to build predictions in the future related to readiness. implementation of knowledge management system development in order to support increased understanding of the service recovery index in the organization. © 2021 ACM.

关键词： Knowledge management

来源：评论

学校读者我要写书评

暂无评论

Laterality Effect on Visual Orientation Processing: An Event-Related Potential Study

Laterality Effect on Visual Orientation Processing: An Event...

引用

IEEE/ICME International Conference on Complex Medical engineering, CME

作者： Fengxia Wu Yanna Ren Jinglong Wu Meng Wang Chang Qu Jingjing Yang School of Artificial Intelligence Changchun University of Science and Technology Changchun China College of Humanities and Management Guizhou University of Traditional Chinese Medicine Guiyang China Cognitive Neuroscience Laboratory Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama University Okayama Japan

ISBN: (数字)9798350316117

ISBN: (纸本)9798350316124

Although previous studies have demonstrated visual spatial frequency processing in human, how orientation discrimination alter visual spatial frequency processing in temporal mechanism has not yet been elucidated completely. To investigate this, we design to performing a classical perceptual tasks of grating orientation discrimination. Participants were told to identify the orientation of the stimulus and pressed relevant button as quickly and accurately as possible. We assessed the diversity by measuring the magnitude of reaction times (RT) and event-related potential (ERP) results, which showed that the subject's response was strongly depended on spatial frequency (p < 0.05). The evoked brain activity during orientation discrimination was significant hemispheric asymmetry, the amplitude of middle stage (270–410 ms) for right hemisphere was larger than that for left hemisphere. In addition, the laterality effect was depended on spatial frequency, significant difference was found between low and high spatial frequency (p < 0.05). These results provide unique insight about how the brain processes visual orientation information and provide some basic data for human hemispheric asymmetry.

关键词： Visualization Time-frequency analysis Brain Atmospheric measurements Particle measurements Frequency diversity Frequency measurement

来源：评论

学校读者我要写书评

暂无评论

An explainable framework based on counterfactual explanations for multi-class financial distress prediction of small and medium enterprises

An explainable framework based on counterfactual explanation...

引用

IEEE International Conference on Big Data

作者： Renon Ando Yuji Kawamata Toshihiko Takeda Yukihiko Okada Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Center for Artificial Intelligence Research University of Tsukuba Tsukuba Japan Operations Management Dept. Mito Shinkin Bank Mito Japan Institute of Systems and Information Engineering / Tsukuba Institute for Advanced Research / Center for Artificial Intelligence Research University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Small and medium enterprises (SMEs) play a crucial role in supporting the global economy by contributing significantly to employment and value creation. Therefore, accurately predicting early signs of financial distress in SMEs and taking timely management improvement actions is of paramount importance. However, there is a lack of research on developing multi-class financial distress prediction (MFDP) models specifically for SMEs. Moreover, traditional MFDP models often lack interpretability. To address this, this study proposes definitions for multi-class financial distress in SMEs and constructs a MFDP model using machine learning. Additionally, it introduces an explainable framework to interpret the constructed model. Specifically, the study classifies SMEs' financial conditions into three categories: healthy, mild financial distress, and severe financial distress. It conducts a comparative analysis of six machine learning models. Furthermore, it proposes new interpretation methods for the MFDP model using SHapley Additive exPlanations (SHAP) and counterfactual explanations (CE), both of which are explainable artificial intelligence techniques. The empirical results reveal that the MFDP model, which adjusts data balance using random oversampling and integrates LightGBM with a one-versus-rest decomposition method, demonstrates the highest performance. Moreover, the proposed explainable framework demonstrates that it can provide practical and concrete improvement strategies to the model users, such as managers and financial institutions.

关键词： Economics Accuracy Biological system modeling Employment Decision making Machine learning Predictive models Data models Risk management Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

DISENTANGLING CONTENT AND FINE-GRAINED PROSODY INFORMATION VIA HYBRID ASR BOTTLENECK FEATURES FOR VOICE CONVERSION

arXiv

引用

arXiv 2022年

作者： Zhao, Xintao Liu, Feng Song, Changhe Wu, Zhiyong Kang, Shiyin Tuo, Deyi Meng, Helen Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Shenzhen International Graduate School Tsinghua University Shenzhen China Huya Inc Guangzhou China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Hong Kong

Non-parallel data voice conversion (VC) have achieved considerable breakthroughs recently through introducing bottleneck features (BNFs) extracted by the automatic speech recognition(ASR) model. However, selection of BNFs have a significant impact on VC result. For example, when extracting BNFs from ASR trained with Cross Entropy loss (CE-BNFs) and feeding into neural network to train a VC system, the timbre similarity of converted speech is significantly degraded. If BNFs are extracted from ASR trained using Connectionist Temporal Classification loss (CTC-BNFs), the naturalness of the converted speech may decrease. This phenomenon is caused by the difference of information contained in BNFs. In this paper, we proposed an any-to-one VC method using hybrid bottleneck features extracted from CTC-BNFs and CE-BNFs to complement each other advantages. Gradient reversal layer and instance normalization were used to extract prosody information from CE-BNFs and content information from CTC-BNFs. Auto-regressive decoder and Hifi-GAN vocoder were used to generate high-quality waveform. Experimental results show that our proposed method achieves higher similarity, naturalness, quality than baseline method and reveals the differences between the information contained in CE-BNFs and CTC-BNFs as well as the influence they have on the converted speech. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Smart hospital services: Health 4.0 and opportunity for developing economies 29

Smart hospital services: Health 4.0 and opportunity for deve...

引用

29th International Conference of the International Association for management of Technology: Towards the Digital World and Industry X.0, IAMOT 2020

作者： Oliver, Nwauka Arnesh, Telukdarie Tak, Igusa University of Johannesburg Post Graduate School of Engineering Management South Africa John's Hopkins University Department of Civil and Systems Engineering Baltimore United States

ISBN: (纸本)9781775921950

The exponential dysfunction of public hospital emanates from an inability to manage real-time systems with complex dynamic behavior. The service delivery dissatisfaction results in poor resource allocation, human factors, poor energy transition and demand-supply imbalance. Smart hospital, therefore, stands as the only state-of-art option that addresses these defects. This concept supports big data applications, ambient automation, social control and management of the environment;handling a huge amount of data based on daily operations, services, and resources. Smart hospital connects different units of the hospital, manages energy consumption and facilitates the use of medical care for patients and other stakeholders;communication platform enabler, real-time drug inventory monitor, a welfare facilitator, protection and security enabler that initiates treatment, creates identity for each patient, doctor, nurse and other stakeholders with the use of digital. This paper presents a digital framework for a smart hospital system that processes services for optimal healthcare delivery. The framework further underpins smart hospital as a partner of smart grid for energy transition;in consideration of the load consumption of the medical devices (and beyond) within the facility. Finally, the paper enlists the value potentials of Health 4.0 to healthcare service delivery particularly to developing economies. © 2020 Towards the Digital World and Industry X.0 - Proceedings of the 29th International Conference of the International Association for management of Technology, IAMOT 2020. All rights reserved.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

Automated Two-Stage Approach for Damage Detection of Surface Defects in Historical Buildings

Automated Two-Stage Approach for Damage Detection of Surface...

引用

IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Wanpeng Shao Ken' ichi Kakizaki Shunsuke Araki Tomohisa Mukai Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Fukuoka Japan National Institute for Land and Infrastructure Management Ministry of Land Infrastructure Transport and Tourism Tsukuba Ibaraki Japan

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Damages of reinforced concrete buildings caused by aging or complicated environmental factors have become a worldwide problem. It is critical to accurately obtain damage information to determine the current state of the aging structure or its levels of decay. However, the inspection of multiple damages o the whole surface of a concrete structure is challenging. In this study, we present a two-stage method for damage detection of surface deflects of reinforced concrete buildings in point clouds captured by a terrestrial laser scanner using a 3D neural network and a novel cluster analysis technique. In the first stage, we divide the whole building into 3D grids, then a classification of multiple damages is performed using PointNet++ along with 3D data with color information mapped on it. In the second stage, we propose a four-step post-processing method based on cluster analysis including removal of isolated damaged clusters, dilation, and filling to develop the detection results. Experimental results show that the voxel-based recall rate reaches 0.928 and the precision rate reaches 0.753. The proposed method offers an acceptable damage detection performance on aging concrete surfaces.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Characterization of Mood and Emotion Regulation in Females with PMS/PMDD Using Near-Infrared Spectroscopy to Assess Prefrontal Cerebral Blood Flow and the Mood Questionnaire

arXiv

引用

arXiv 2024年

作者： Aoki, Makiko Suzuki, Masato Suzuki, Satoshi Oiwa, Kosuke Maeda, Yoshitaka Okayama, Hisayo Faculty of Health Science and Nursing Juntendo University Shizuoka Japan Graduate School of Science University of Hyogo Hyogo Japan Department of Clinical Engineering Kanagawa Institute of Technology Kanagawa Japan Department of Information and Management Systems Engineering Nagaoka University of Technology Niigata Japan Medical Simulation Centre Jichi Medical University Tochigi Japan Faculty of Medicine University of Tsukuba Ibaraki Japan

Many sexually mature women experience premenstrual syndrome (PMS) or premenstrual dysphoric mood disorder (PMDD). This discomfort, being recurrent and cyclical, significantly impacts women's quality of life. Current approaches for managing PMS/PMDD rely on daily mental condition recording, which many discontinue due to its impracticality. Hence, there's a critical need for a simple, objective method to monitor mental symptoms. One of the principal symptoms of PMDD is a dysfunction in emotional regulation, which has been demonstrated through brain-function imaging measurements to involve hyperactivity in the amygdala and a decrease in functionality in the prefrontal cortex (PFC). However, most research has been focused on PMDD, leaving a gap in understanding of PMS. Near-infrared spectroscopy (NIRS) measures brain activity by spectroscopically determining the amount of hemoglobin in the blood vessels. The NIRS device is compact and imposes minimal physical and psychological stress on participants. This study aimed to characterize the emotional regulation function in PMS. We measured brain activity in the PFC region using NIRS when participants were presented with emotion-inducing pictures. Furthermore, moods highly associated with emotions were assessed through questionnaires. Forty-six participants were categorized into non-PMS, PMS, and PMDD groups based on the gynecologist’s diagnosis. The mood of participants was collected by a self-administered questionnaire (POMS2) and NIRS was used to obtain cerebral blood flow. POMS2 scores revealed higher negative mood and lower positive mood in the follicular phase for the PMS group, while the PMDD group exhibited heightened negative mood during the luteal phase. NIRS results showed reduced emotional expression in the PMS group during both phases, while no significant differences were observed in the PMDD group compared to non-PMS. It was found that there are differences in the distribution of mood during the luteal and fol

关键词： Near infrared spectroscopy

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：