检索结果-内蒙古大学图书馆

IEEJ Transactions on Electronics, Information and systems 2024年第9期144卷 894-900页

作者： Hadi, Danang Kumara Song, Zequn Rahmadya, Budi Sun, Ran Takeda, Shigeki Graduate School of Science and Engineering Ibaraki University 4-12-1 Nakanarusawa Ibaraki Hitachi316-8511 Japan Department of Agroindustrial Technology Universitas Muhammadiyah Jember Jember68124 Indonesia Computer System Department Faculty of Information Technology Andalas University Padang25175 Indonesia

This research introduces real-time monitoring and localizing product stock using the First-In-First-Out (FIFO) method with radio frequency identification (RFID) pressure sensing tags. The proposed FIFO system has RFID pressure sensing tags that detect pressure changes, generate sensor codes, and estimate a stock location. Integrating artificial intelligence (AI) improves location estimation accuracy of up-to-date stock visibility. The ability of RFID pressure sensing tags and AI to detect stock from sensor code data enables more effective asset management, optimizes stock management, reduces product waste, and contributes to better efficiency in product inventory management. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Inventory control

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Thyroid Diagnosis system Utilizing Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2840-2855页

作者： Sutradhar, Ananda Al Rafi, Mustahsin Ghosh, Pronab Shamrat, F M Javed Mehedi Moniruzzaman, Md. Ahmed, Kawsar Azad, A.K.M. Bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada University of Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK S7N 5A9 Canada Imam Mohammad Ibn Saud Islamic University Faculty of Science Department of Mathematics and Statistics Riyadh13318 Saudi Arabia Charles Stuart University AI and Cyber Futures Institute BathurstNSW2795 Australia

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used as classifiers on the training data set. Third, a local interpretable model-agnostic explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a partial dependence plot (PDP). Finally, the proposed system is validated with extensive experiments, where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector. © 2020 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

引用

computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.Usman Ashraf Rabia Zafar Ahmed M.Alghamdi Ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 201.,627,000 women had died of breast cancer(World Health Organization Report 201.).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

来源：评论

学校读者我要写书评

暂无评论

Synergistic effects of heating and traction during fibrous tissue elongation

引用

Journal of Biomechanical science and {1. 2023年第2期18卷

作者： Yagi, Ippei Koike, Kazuki Uchida, Satoshi Department of Electrical Engineering and Computer Science Faculty of System Design Tokyo Metropolitan University 1-1 Minami-Osawa Hachioji-shi Tokyo192-0397 Japan

Fibrosis involves the abnormal accumulation of components of the extracellular matrix, such as collagen, and can lead to organ failure, which accounts for approximately one-third of all deaths worldwide. Accordingly, there is an urgent need to develop low-invasive treatment strategies for fibrosis as there is currently a lack of anti-fibrotic pharmacological agents available. In the present study, we tested the hypothesis that applying traction to fibrous tissues while using heat to denature proteins can be used to adjust the shape and flexibility of tissues. Bovine Achilles tendon samples as a model of fibrous tissue contracted by approximately 25% compared to the original length in response to heating, in line with previous findings. In contrast, the application of traction during heating and cooling increased tissue length by 25% and decreased Young’s modulus by 1.%. Tissue staining and detection of unfolded collagen using collagen-hybridizing peptide demonstrated thermal degradation of collagen fibers and conversion to gelatinous material throughout samples, confirming that traction, and heating damages collagen. These results indicate that this technique can be used to alter the shape and reduce the rigidity of organs affected by fibrosis. © 2023 The Japan Society of Mechanical Engineers. This is an open access article under the terms of the Creative Commons Attribution 4.0 International license (https://***/licenses/by/4.0/).

关键词： Collagen

来源：评论

学校读者我要写书评

暂无评论

Streamflow Prediction in the Mekong River Basin Using Deep Neural Networks

引用

IEEE Access 2023年 11卷 97930-97943页

作者： Nguyen, Thi-Thu-Ha Vu, Duc-Quang Mai, Son T. Dang, Thanh Duc National Taiwan University Department of Bioenvironmental Systems Engineering Taipei10617 Taiwan National Central University Department of Computer Science and Information System Taoyuan320317 Taiwan Thai Nguyen University of Education Thai Nguyen250000 Viet Nam Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Institute for Computational Science and Artificial Intelligence Van Lang University Laboratory of Environmental Sciences and Climate Change Ho Chi Minh City70000 Viet Nam Van Lang University Faculty of Environment Ho Chi Minh City70000 Viet Nam

In recent years, the Mekong River Basin (MRB), one of the largest river basins in Southeast Asia, has experienced severe impacts from extreme droughts and floods. Streamflow forecasting has become crucial for effective risk management strategies in the region. However, this task presents significant challenges due to rapid climate changes and the presence of numerous newly constructed upstream dams, which disrupt the natural flow. In this paper, we develop multiple deep learning models (incl. Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long short-term Memory (LSTM), and Transformer) to predict streamflow with different lead time forecasts based on observed meteorological variables and climatic indices (i.e., discharge, water level, precipitation, and temperature) from 1.79 to 201.. The results indicate that LSTM obtains high performance for streamflow prediction in both dry and wet seasons while Transformer is not recommended for long-term prediction, especially in the dry season. The proposed deep learning models capture well the fluctuation of river flow in the MRB during the period of high-dam development, especially LSTM (NSE ≥ 0.8). The models' performances are enhanced with the adding of temperature for short-term prediction while precipitation was the most sensitive variable for long-term one. Such proposed models are essential for government agencies to plan mitigation and adaptation strategies at different periods, which can range from days to years. © 201. IEEE.

关键词： Floods

来源：评论

学校读者我要写书评

暂无评论

Multilayer CVD graphene electrodes using a transfer-free process for the next generation of optically transparent and MRI-compatible neural interfaces

引用

Microsystems & Nano{1. 2022年第5期8卷 183-196页

作者： Nasim Bakhshaee Babaroud Merlin Palmar Andrada Iulia Velea Chiara Coletti Sebastian Weingärtner Frans Vos Wouter A.Serdijn Sten Vollebregt Vasiliki Giagka Department of Microelectronics Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyMekelweg 4Delft 2628 CDThe Netherlands Technologies for Bioelectronics Group Department of System Integration and Interconnection TechnologiesFraunhofer Institute for Reliability and Micro-integration IZMGustav-Meyer-Allee 25Berlin 13355Germany Department of Imaging Physics Faculty of Applied ScienceDelft University of TechnologyLorentzweg 1Delft 2628 CJThe Netherlands Erasmus University Medical Center(Erasmus MC) dr.Molewaterplein 40Rotterdam 3015 GDThe Netherlands

Multimodal platforms combining electrical neural recording and stimulation,optogenetics,optical imaging,and magnetic resonance(MRI)imaging are emerging as a promising platform to enhance the depth of characterization in neuroscientific *** conductive,optically transparent,and MRI-compatible electrodes can optimally combine all *** as a suitable electrode candidate material can be grown via chemical vapor deposition(CVD)processes and sandwiched between transparent biocompatible ***,due to the high graphene growth temperature(≥900℃)and the presence of polymers,fabrication is commonly based on a manual transfer process of pre-grown graphene sheets,which causes reliability *** this paper,we present CVD-based multilayer graphene electrodes fabricated using a wafer-scale transfer-free process for use in optically transparent and MRI-compatible neural *** fabricated electrodes feature very low impedances which are comparable to those of noble metal electrodes of the same size and *** also exhibit the highest charge storage capacity(CSC)reported to date among all previously fabricated CVD graphene *** graphene electrodes did not reveal any photo-induced artifact during 1.-Hz light pulse ***,we show here,for the first time,that CVD graphene electrodes do not cause any image artifact in a 3T MRI *** results demonstrate that multilayer graphene electrodes are excellent candidates for the next generation of neural interfaces and can substitute the standard conventional metal *** fabricated graphene electrodes enable multimodal neural recording,electrical and optogenetic stimulation,while allowing for optical imaging,as well as,artifact-free MRI studies.

关键词： transparent transfer process

来源：评论

学校读者我要写书评

暂无评论

A two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization

引用

Information sciences 2024年 674卷

作者： Zou, Juan Tang, Li Liu, Yuan Yang, Shengxiang Wang, Shiting Hunan Engineering Research Center of Intelligent System Optimization and Security Xiangtan University Hunan Province Xiangtan411105 China Faculty of School of Computer Science School of Cyberspace Science of Xiangtan University Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Large-scale multiobjective optimization problems (LSMOPs) have exponential growth in the search space as the decision variables increase, and the vast search space poses a challenge to the performance of multiobjective evolutionary algorithms (MOEAs). Many current large-scale MOEAs need to consume a large amount of computational resources to get good performance. This paper proposes a two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization (LMOEA-S2D) to balance the performance and computational resource overhead. The algorithm exploits the Pareto-optimality property of domination and the diversity-preserving property of decomposition to optimize the performance in the two stages, respectively, and designs a corresponding direction-guided mechanism to improve search efficiency. LMOEA-S2D designs global direction search and local direction search in the domination-based stage for efficient exploitation to accelerate population convergence. To promote greater population diversity, a hybrid direction search was devised to aid diversity exploration in the decomposition-based stage, and this facilitates even distribution of candidate solutions across the Pareto optimal frontier. LMOEA-S2D is compared with five state-of-the-art large-scale MOEAs on some large-scale multiobjective test suites with 1.0 to 5,000 decision variables. The experimental results show that LMOEA-S2D significantly outperformed all compared algorithms under limited computational resources. © 2024 Elsevier Inc.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

A Scalable Power Splitter using High Sheet Polysilicon Resistor Suitable for RFIC Application

A Scalable Power Splitter using High Sheet Polysilicon Resis...

引用

2022 International Conference on Intelligent Technology, system and Service for Internet of Everything, ITSS-IoE 2022

作者： Uddin, Jasim Shakib, M.N. Islam, Syed Zahurul Hasan, Md. Mahadi Ullah, Habib Rahman, Md Arafatur Cardiff Metropolitan University Cardiff School of Technologies Department of Applied Computing and Engineering Wales CardiffCF5 2YB United Kingdom Green University of Bangladesh Faculty of Science and Engineering Dhaka1207 Bangladesh Universiti Tun Hussein Onn Malaysia Power Integration System Faculty of Electrical and Electronic Engineering Johor Batu Pahat Parit Raja86400 Malaysia Asian University of Bangladesh Department of Computer Science and Engineering Dhaka1341 Bangladesh University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia University of Wolverhampton Faculty of Science and Engineering WV1 1LY United Kingdom

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

Integrated circuits employing CMOS RF technology are increasingly common and widely deployed. There are currently a wide range of materials available for use in the process that are optimised for RF noise, matching characteristics, and improved resistivity. In this paper, the highly resistive passive resistor component is used in the power splitter circuits, where a high sheet polysilicon resistor is the best choice. The high sheet polysilicon resistor and its extracted parasitic capacitance are shown to be the best match among the various resistors mentioned in the literature. Power splitter circuits use these resistor characteristics to get the best possible results, such as low return and insertion losses and good isolation. In order to get the desired resistor values for the power splitter's individual components, twelve parallel high sheet poly resistors are employed. A total of six electronic componentsâtwo metal finger capacitors and four lumped element spiral inductorsâare employed. Power splitter and probe pad components from the layout are on display. The correctness of the layout is confirmed by simulations run before to and after layout. The design schematic, pre-layout and post layout simulation results confirms that high sheet polysilicon resistors are the best selected potential candidate for RFIC application. © 2022 IEEE.

关键词： Resistors

来源：评论

学校读者我要写书评

暂无评论

A Comparison Study of FC-RDVM and RIWM Router Placement Methods for WMNs: Performance Evaluation Results by WMN-PSOHC Simulation system Considering Chi-Square Distribution and Different Instances 1

引用

37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Sakamoto, Shinji Barolli, Admir Liu, Yi Kulla, Elis Barolli, Leonard Takizawa, Makoto Department of Information and Computer Science Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Computer Science National Institute of Technology Oita College 1666 Maki Oita870-0152 Japan Department of System Management Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University Tokyo Kajino-Machi Koganei-Shi184-8584 Japan

ISBN: (数字)9783031290565

ISBN: (纸本)9783031290558

In this work, we deal with the node placement problem in Wireless Mesh Networks (WMNs). We present a hybrid intelligent simulation system called WMN-PSOHC, which combines Particle Swarm Optimization (PSO) and Hill Climbing (HC). We implement in WMN-PSOHC system the Fast Convergence Rational Decrement of Vmax Method (FC-RDVM) and Random Inertia Weight Method (RIWM) router replacement methods. By using WMN-PSOHC system, we carry out simulations the access the performance of these two methods considering Chi-Square distribution of mesh clients and two different instances. The simulation results show that FC-RDVM performs better then RIWM in the considered scenario. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Novel Classification Algorithm for Complex Class Structures

SSRN

引用

SSRN 2024年

作者： El Melhaoui, Ouafae Baghaz, E. Benchaou, S. Research Center High Studies of Engineering School EHEI Electronic and System Laboratory Faculty of Science Mohamed First University Oujda Morocco Laboratory of Electronics Instrumentation and Energetic Department of Physics Faculty of Sciences Chouaïb Doukkali University El Jadida24010 Morocco Labo MATSI EST. University Mohammed 1. Oujda Morocco Oujda Morocco Electronic and System Laboratory Faculty of Science Mohamed First University Oujda Morocco

omplex data classification is a very important research topic in the field of data mining, which has been a wide concern for a long time. In recent years, with the rapid development of information technology, the research on complex data classification is full of opportunities and challenges. The present paper proposes a new complex data classification technique that addresses the challenge of classification accuracy, time consuming. This method is also designed to be more straightforward for programming unlike more intricate methods such as the fuzzy min-max classifier. Our novel approach is structured around two main phases: the initial phase focuses on prototypes creation and noise elimination, while the subsequent phase involves merging prototypes into classes. Each prototype is represented by its centroid, and outliers are identified and filtered out based on their distance from neighbors. The merging phase, iteratively combines the closest prototypes until the desired number of classes is achieved. The proposed algorithm is compared to the K-means, the fuzzy C-means, the competitive neural networks and the fuzzy min-max classifier methods through a number of simulations. The proposed approach demonstrates promising results, outperforming established methods in terms of classification accuracy and computational efficiency. The integration of innovative strategies for prototype creation, noise elimination, and class merging distinguishes our approach, making it a valuable contribution to the field of complex data classification © 2024, The Authors. All rights reserved.

关键词： Merging

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：