检索结果-内蒙古大学图书馆

2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Arnia, Fitri Saddami, Khairun Muharar, Rusdha Dwi Pratiwi, Dea Ananda Nurdin, Yudha Universitas Syiah Kuala Department of Electrical and Computer Engineering Indonesia Universitas Syiah Kuala Telematics Research Center Indonesia FernUniversität in Hagen Faculty of Mathematics and Informatics Germany

ISBN: (纸本)9798350382822

Diabetes mellitus, causing substantial global mortality with millions affected in 2019, necessitates early detection methods to aid Diabetic Foot Ulcer (DFU) patients. We proposed a mobile application for early DFU identification using a Convolutional Neural Network (CNN) classifier. The application embedded a fine-tuned MobileNetV2 model specifically adapted for DFUs, trained on a plantar thermogram dataset through transfer learning. We evaluate the model's classification performance pre- and post-embedded into the application. Results reveal that the pre-embedded model achieved 90% accuracy, 100% specificity, and 80% sensitivity. After embedding, the model retained 85% accuracy, 80% specificity, and 90% sensitivity. Notably, a slight accuracy decrease post-embedded is likely attributed to the normalization of the input image. This study emphasizes that the interplay between model performance and mobile embedded might influence the model's performance. It underscores the meticulous consideration of mobile platform-specific processes crucial for accurate and effective early DFU detection. © 2023 IEEE.

关键词： Infrared imaging

来源：评论

学校读者我要写书评

暂无评论

Green Measurements for Software Product Based on Sustainability Dimensions

引用

computer Systems Science & engineering 2022年第4期41卷 271-288页

作者： Komeil Raisian Jamaiah Yahaya Aziz Deraman Department of Computer Isfahan(Khorasgan)BranchIslamic Azad UniversityIsfahanIran Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Ocean Technology Engineering&Informatics Universiti Malaysia Terengganu21030TerengganuMalaysia

Software is a central component in the modern world and vastly affects the environment’s *** demand for energy and resource require-ments is rising when producing hardware and software *** study reveals that many studies focused on green hardware;however,limited efforts were made in the greenness of software *** software products are necessary to solve the issues and problems related to the long-term use of soft-ware,especially from a sustainability *** a proper mechanism for measuring the greenness of a particular software product executed in a speciﬁc environment,the mentioned beneﬁts will not be ***,there are not enough works to address this problem,and the green status of software products is uncertain and *** paper aims to identify the green measurements based on sustainable dimensions in a software *** second objective is to reveal the relationships between the elements and measurements through empirical *** study is conducted in two ***ﬁrst phase is the theoretical phase,where the main components,measurements and practices that inﬂuence the sustainability of a software product are identiﬁ*** second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achiev-ing green software *** study has revealed seven green measurements of software product:Productivity,Usability,Cost Reduction,Employee Support,Energy Efﬁciency,Resource Efﬁciency and Tool *** relationships are statistically signiﬁcant,with a signiﬁcance level of less than 0.01(p=0.000).Thus,the hypothesised relationships were all *** contributions of this study revolve around the research perspectives of the measurements to attain a green software product.

关键词： Green software product sustainability dimension empirical study green measurements

来源：评论

学校读者我要写书评

暂无评论

Spatial, Spatiotemporal, and Feature Analysis of Electroencephalogram During Imagined Speech with Visual vs Audio Cues 3

Spatial, Spatiotemporal, and Feature Analysis of Electroence...

引用

3rd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2023

作者： Fitriah, Nilam Budikayanti, Astri Zakaria, Hasballah Latifah Erawati Rajab, Tati Universitas Indonesia Faculty of Computer Science Depok Indonesia Universitas Indonesia Department of Neurology Jakarta Indonesia Institut Teknologi Bandung Department of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350327762

People with speech disorders have difficulties in social life and welfare. To overcome this barrier, researchers have conducted studies to recognize imagined speech from biosignals. The essential biosignals in producing speech are brain signals. This study aimed to determine activated brain regions during the imagined speech by spatial and spatiotemporal analysis to assist feature selection in imagined speech recognition. We recorded the normal subjects' brain signals using electroencephalography (EEG) when they imagined saying a word without vocalization. The protocol ran in two different-time sessions to cover the EEG nonstationary. We utilized visual cues in the first sub-session and audio cues in the second one. One word per sub-session had seven trials, each consisting of a rest state and cue presentation, followed by four repetitions of the imagined speech state, each preceded by a preparation state. We calculated power spectral entropy (PSE) values from the acquired dataset and used them as a feature since they captured signals' activity. The normalized PSE above 0.5 (middle value) was considered as high activity. The results showed that repeated-trial protocol induced brain activity during imagined speech. For visual or audio-cued imagined speech, the selected channels were in the central and left frontal regions, with additional right temporal, left parietal, and left occipital regions for visual-cued imagined speech. The highest PSE was found in the central region. Meanwhile, the lowest PSE was shown in the preparation state, confirming that cue presentation was not affecting imagined speech states. © 2023 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

AdaptVR: An Adaptive Learning VR System for Dental Students Training

AdaptVR: An Adaptive Learning VR System for Dental Students ...

引用

International Conference on computer and Applications (ICCA)

作者： Meriam Sherif Nahla Barakat Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Artificial Intelligence Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The paper proposes “AdaptVR” a virtual reality (VR) system designed to enhance dental training through realistic real tile simulations and adaptive learning environment, to overcome traditional training challenges in dental education. The system aims to provide personalized learning experiences, facilitating skill development and comprehensive assessments for students. The proposed system was tested by 17 dentistry students and 5 experts. AdaptVR framework is done for operative dentistry which is removing decay especially Class I and Class II of classification of caries. Results from the experiment revealed that 77.3% of participants exceed their expectations, 59.1% saw a positive effect on their dental skills, 90.9% reported higher levels of engagement, and 65% indicated that vibration and force feedback provided students with a genuine sensation. AdpatVR's experimental findings show an average accuracy of 50.85% for Class I, 51.7% for Class II, and 82.09% for the Class II box technique. The error percentage averages 49% for Class I, 48% for Class II, and 18% for Class II box technique.

关键词： Training Vibrations Drilling Solid modeling Adaptive learning Adaptation models Force feedback Virtual reality Teeth Dentistry

来源：评论

学校读者我要写书评

暂无评论

A review of federated learning in renewable energy applications:Potential,challenges,and future directions

引用

Energy and AI 2024年第3期17卷 444-457页

作者： Albin Grataloup Stefan Jonas Angela Meyer Bern University of Applied Sciences School of Engineering and Computer ScienceQuellgasse 21Biel2501Switzerland Universitàdella Svizzera italiana Faculty of InformaticsVia la Santa 1Lugano-Viganello6962Switzerland Delft University of Technology Department of Geoscience and Remote SensingStevinweg 1Delft2628The Netherlands

Federated learning has recently emerged as a privacy-preserving distributed machine learning *** learning enables collaborative training of multiple clients and entire fleets without sharing the involved training *** preserving data privacy,federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation,research and *** paper provides an overview of federated learning in renewable energy *** discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and *** also evaluate the potential and the challenges associated with federated learning applied in power and energy ***,we outline promising future research directions in federated learning for applications in renewable energy.

关键词： Federated learning Renewable energy Dis tributed learning Data heterogeneity Privacy Data sharing Wind energy Solar energy Buildings Review

来源：评论

学校读者我要写书评

暂无评论

An Attention-Based Deep Network for Plant Disease Classification

引用

Machine Graphics and Vision 2024年第1期33卷 47-67页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Králové Czech Republic Škoda Auto University Mladá Boleslav Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method. © 2024 Institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Sliding window based deep ensemble system for breast cancer classification

引用

Journal of Medical engineering and Technology 2021年第4期45卷 313-323页

作者： Alqudah, Amin Alqudah, Ali Mohammad Department of Computer Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid Jordan

Breast cancer is a severe problem for women around the world especially in developing countries, according to recent reports from the World Health Organization (WHO). High accuracy and early detection of breast cancer reduces the mortality rate, in the other hand, recognition of breast cancer is a complicated issue. Various studies and methods have been carried out to overcome this problem and to obtain accurate screening of breast cancer. One of the most recent methods with high performance is deep learning;it has been used to classify breast cancer using mammograms or histopathological images. This paper proposes a new using the concept of sliding window, and using the ensemble of four pre-trained convolutional neural networks (CNN) in order to classify breast cancer into eight classes. In this study, each image produces 4 non-overlapped sliding windows which are fed to GoogleNet, AlexNet, ResNet50, and DenseNet-201 CNNs, and an ensemble is then done to find the major class of each window, the ensemble is then applied again to find the class of the whole histopathological image. Breast Cancer Histopathological Database (BreakHis) database has been employed in this paper with eight classes (Adenosis, Ductal Carcinoma, Fibroadenoma, Lobular Carcinoma, Mucinous Carcinoma Papillary Carcinoma, Phyllodes Tumour, Tubular Adenoma). The proposed method is applied to four magnification cases: 40x, 100x, 200x, and 400x images. The proposed ensemble technique achieved an accuracy of 99.3325%. The results of the proposed system are comparable to recent studies results. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Developing countries

来源：评论

学校读者我要写书评

暂无评论

An enhanced donkey and smuggler optimization algorithm for choosing the precise job applicant

引用

Iran Journal of computer Science 2023年第3期6卷 233-243页

作者： Hasan, Nazir M. Rashid, Tarik A. Alsadoon, Abeer Qosaeri, Ahmed S. Abualigah, Laith Yaseen, Zaher Mundher Computer Science and Engineering Department University of Kurdistan Hawler KR Erbil Iraq Charles Sturt University Sydney Australia Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

Throughout the last few decades, Nature-Inspired Algorithms (NIA) have become very popular in solving real-world problems by getting inspiration from nature. This work suggests the Modified Donkey and Smuggler Optimization (MDSO) algorithm for solving the selection problem to choose suitable job applicants for a specific position. The original Donkey and Smuggler Optimization algorithm (DSO) has two modes: smuggler and donkey mode (non-adaptive and adaptive). In the smuggler mode, the algorithm tries to find the best solutions, which is the path to send the donkey to the destination;once the best path is found, the smuggler will send the donkey through the selected path. The donkey's actions will start when the smuggler mode's best solution is no longer the best one. Certain modifications have been made in the smuggler mode, including replacing the original fitness with a new fitness equation since that method can work more accurately. The Human Resource (HR) department in Korek-telecom has been used as a resource to achieve real-world data to test the original and Modified DSO. Using MDSO, not only will organizations be able to choose suitable job applicants more accurately but also will manage to do so at a faster pace as well. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Donkey and smuggler algorithms Human resource management MDSO Nature-inspired algorithms Optimization algorithm Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Hardware-in-the-Loop Simulation for Satellite System Verification

Hardware-in-the-Loop Simulation for Satellite System Verific...

引用

2022 International Electrical engineering Congress, iEECON 2022

作者： Saroj, Tanin Sreesawet, Suwat Channumsin, Sittiporn Kaewkhuntod, Nisarat Tandayya, Pichaya Geo-Informatics and Space Technology Chonburi20230 Thailand Prince of Songkhla University Faculty of Engineering Department of Computer Engineering Songkhla90110 Thailand

ISBN: (纸本)9781665402064

The satellite simulator platform is necessary for the satellite development. It is an essential tool to verify the operations of flight software. The Controller-in-the-Loop simulation with Simulation Third Generation (SimTG) existing at Thailand's Geo-informatics and Space Technology Development Agency (GISTDA) can help verify the flight software along with the on-board computer (OBC). However, it is rather rigid and cannot adjust other devices arbitrarily. This paper presents the requirements of the Hardware-in-the-Loop Simulation (HILS) platform along with the proposed solution to solve this problem. The interface module is developed by using the Python EtherSpaceLink-API along with socket programming. The essential tool in the development is the Parsl, which is a promising Python parallel library. The experimental testing of the proposed HILS is done by using a cloned simulator working as physical devices. Then, the response times are measured adjusting the frequency of the flight software. The proposed design achieved the expected results, respecting to the Controller-in-the-Loop simulator. © 2022 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

A Neutrosophic Bezier Curve Model using Control Points

引用

Neutrosophic Sets and Systems 2023年 60卷 455-466页

作者： Mahmud, Maziah Wahab, Abd. Fatah Zulkifly, Mohammad Izat Emir Broumi, Said Faculty of Computer Science and Mathematics Universiti Teknologi MARA Cawangan Kelantan Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Terengganu Malaysia Malaysia Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia Malaysia Laboratory of Information processing Faculty of Science Ben M’Sik University Hassan II B.P 7955 Sidi Othman Casablanca Morocco Casablanca-Settat Morocco

Uncertain and ambiguous data is usually presented as a data point. When dealing with uncertain data, it is challenging to deal with incompleteness, imprecision, and incertitude;therefore, various mathematical models were developed to resolve issues concerning uncertainty points. To overcome this difficulty, a mathematical model with redefined control points characterised by three important components of the neutrosophic set was produced. The neutrosophic set can then be translated by creating models based on the neutrosophic theory and its relationships to produce control points. Hence, this paper represents a curve generated by combining neutrosophic control points with the Bezier basic functions using their relation as a Neutrosophic Bezier Curve. With an illustration example, we show how to visualise neutrosophic data sets into Neutrosophic Bezier Curve and their relationship. © (2023), (University of New Mexico). All Rights Reserved.

关键词： Data visualization

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：