检索结果-内蒙古大学图书馆

7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Ekellem, Elizabeth Ango Fomuso Köhler, Lutz Applied University of Cologne Computer Science and Engineering Gummersbach Germany

ISBN: (纸本)9798350383065

Medical imaging with convolutional neural networks (CNNs) has the potential to improve early disease detection. However, the success of CNNs relies heavily on the quality and diversity of the training data. Our study focused on training CNNs using datasets for conditions like eczema, psoriasis, and melanoma across various skin tones. We found that Black skin data was underrepresented. This lack of data led to lower accuracy when analyzing African skin samples, highlighting a clear bias in medical imaging. Many believe that just using more data can solve these problems. But our results, along with studies by Romero Lopez et al. (2017) [11] and Yu et al. (2017) [13], show that having a diverse and high-quality dataset is more important. In summary, there's a need to include data from all skin tones to ensure fairness in medical imaging. This isn't just a technical problem, but a matter of equal healthcare for all. We hope for a future where medical AI is inclusive and represents everyone fairly. © 2023 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Arbitrary style transfer system with split-and-transform scheme

引用

Multimedia Tools and Applications 2024年第23期83卷 62497-62517页

作者： Tu, Ching-Ting Lin, Hwei Jen Tsai, Yihjia Lin, Zi-Jun Department of Applied Mathematics National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan

For the subject of arbitrary image style transfer, there have been some proposed architectures that directly compute the transformation matrix of the whitening and coloring transformation (WCT) to obtain more satisfactory transformation results. However, calculating the transformation matrix of WCT is time-consuming. Li et al. trained a linear transformation module to generate a WCT transformation matrix for any pair of images, i.e., content image and style image, to avoid complex calculations and improves time efficiency. In this work, we introduce a flexible arbitrary image style transfer framework based on the LST, which uses deep neural networks to train a linear transformation matrix as the standard matrix for WCT. For the first part, inverse relationship between the Whitening matrix and the Coloring matrix w.r.t. the same image is enforced during the training of the linear transformation matrix, so that the resulting matrix will be more accurate and closer to the standard matrix of WCT. For the second part, a split-and-transform scheme is proposed. Unlike LST, which transforms the block of feature maps as a whole, the split-and-transform scheme divides the feature block into several smaller blocks and transforms them individually, so that the transformation is more localized, and the more the number of divided blocks, the more localized. In addition, the proposed split-and-transform scheme allows users to determine the number of divided blocks to flexibly control the locality of the transformations. Experimental results demonstrate the effectiveness and flexibility of the proposed framework by the high-quality stylized images and adjustable balance between globality and locality of transformations. The use of the split-and-transform scheme can reduce the computational time while preserving or even improving the stylization results. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Linear transformations

来源：评论

学校读者我要写书评

暂无评论

An Adaptive on Demand Modified Ant Colony Optimization Routing for VANETs 23th

An Adaptive on Demand Modified Ant Colony Optimization Routi...

引用

23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Kumari, J. Jesy Janet Thangam, S. Raja, A. Saleem Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering University of Technology and Applied Science-Shinas Shinas Oman

ISBN: (纸本)9783031648496

Around the world, vehicle accidents claim many priceless lives. To tackle this problem, the Vehicular ad hoc networks (VANETs) are the most effective solution. Effective communication between vehicular nodes in VANETs is extremely important because of the rapid mobility of vehicles and different network topology. Currently, VANET mostly serves the public security, stability, information for voyageurs, and traffic Management, control of congestion, guidance, etc. Chaos on roadways can occur at any moment, anyplace. It is a situation in which the flow of traffic regulations might cease to function. Collisions, maintaining roads, violation of legal rules, etc., so to evade the congestion in the surrounding region. VANETs represent a dynamic and challenging communication environment characterized by high mobility, intermittent connectivity, and rapid changes in network topology. Efficient and reliable routing is crucial for enabling diverse applications such as traffic management, safety communication, and infotainment services in VANETs. In this research, propose a multi-faceted approach to improve routing performance in VANETs. An adaptive routing algorithm that considers vehicle mobility patterns and dynamically adjusts communication routes based on the anticipated movement of neighboring vehicles such as Modified Ant Colony Optimization (MACO) technique. This adaptability enhances the robustness of the network against frequent topology changes and improves the overall packet delivery ratio, end-to-end delay, and network reliability compared to traditional routing protocols. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Competing with autonomous model vehicles:a software stack for driving in smart city environments

引用

Autonomous Intelligent Systems 2024年第1期4卷 166-178页

作者： Julius Bächle Jakob Häringer Noah Köhler Kadir-KaanÖzer Markus Enzweiler Reiner Marchthaler Institute for Intelligent Systems Faculty of Computer Science and EngineeringEsslingen University of Applied SciencesFlandernstraße 101Esslingen am Neckar73732Germany

This article introduces an open-source software stack designed for autonomous 1:10 scale model *** developed for the Bosch Future Mobility Challenge(BFMC)student competition,this versatile software stack is applicable to a variety of autonomous driving *** stack comprises perception,planning,and control modules,each essential for precise and reliable scene understanding in complex environments such as a miniature smart city in the context of *** the limited computing power of model vehicles and the necessity for low-latency real-time applications,the stack is implemented in C++,employs YOLO Version 5 s for environmental perception,and leverages the state-of-the-art Robot Operating System(ROS)for inter-process *** believe that this article and the accompanying open-source software will be a valuable resource for future teams participating in autonomous driving student *** work can serve as a foundational tool for novice teams and a reference for more experienced *** code and data are publicly available on GitHub.

关键词： Autonomous model vehicle Software architecture Embedded real-time systems Bosch Future Mobility Challenge Autonomous driving

来源：评论

学校读者我要写书评

暂无评论

引用

Research on Biomedical engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Implementation and Comparative Analysis of Static and Dynamic Load Balancing Algorithms in SDN 2

Implementation and Comparative Analysis of Static and Dynami...

引用

2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Shona, M. Sharma, Rinki Ramaiah University of Applied Science Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665475174

In the contemporary years, the Internet usage has full-fledged and has made the advance of networking techniques to accomplish the necessities of better bandwidth, throughput and less latency. Software defined networking (SDN) is a technique which organizes the network functions intellectually by using software applications. One of the foremost challenges is load balancing amongst the manifold servers. Most of the applications cannot be performed well without employing load balancers. The existing load balancing mechanisms are basically either of two types: Static and Dynamic. In this paper the performance of static and dynamic load balancing algorithms are scrutinized. The simulation results of few static algorithms and a Dynamic algorithm are exposed in this paper. The relative analysis of the results of Static and Dynamic load balancing algorithms based on latency, throughput, Packet Delivery Ratio and Packet loss percentage are primed in this paper. © 2023 IEEE.

关键词： Software defined networking

来源：评论

学校读者我要写书评

暂无评论

A location-based fog computing optimization of energy management in smart buildings:DEVS modeling and design of connected objects

引用

Frontiers of computer science 2023年第2期17卷 179-195页

作者： Abdelfettah MAATOUG Ghalem BELALEM Saïd MAHMOUDI Computer Science Department Faculty of Exact and Applied SciencesUniversity of Oran 1 Ahmed Ben BellaOran 31000Algeria Science and Technology Department Faculty of Science and TechnologyUniversity of TIARETTiaret 14000Algeria Computer Science Department Faculty of EngineeringUniversity of MonsMons 7000Belgium

Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected *** is characterized by low latency with a wider spread and geographically distributed nodes to support mobility,real-time interaction,and location-based *** provide optimum quality of user life in moderm buildings,we rely on a holistic Framework,designed in a way that decreases latency and improves energy saving and services efficiency with different *** EVent system Specification(DEVS)is a formalism used to describe simulation models in a modular *** this work,the sub-models of connected objects in the building are accurately and independently designed,and after installing them together,we easily get an integrated model which is subject to the fog computing *** results show that this new approach significantly,improves energy efficiency of buildings and reduces ***,with DEVS,we can easily add or remove sub-models to or from the overall model,allowing us to continually improve our designs.

关键词： smart building energy consumption IoT fog computing Framework DEVS simulation models

来源：评论

学校读者我要写书评

暂无评论

LSTM and Linearized Segmented Model for Short-Term Power Output Forecasting of Wind Turbines 10

LSTM and Linearized Segmented Model for Short-Term Power Out...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hontinfinde, Regis Donald Houenou, Amavi G. Hounkpe Pazou, Mahugnon G. Azehoun Houessou, Faith Y. P. J. Didavi, K.B. Audace Agbomahena, MacAire B. Laboratory of Science Engineering and Mathematics Abomey Benin Laboratory of Electronics Telecommunication and Applied Computer Science Lokossa Benin

ISBN: (纸本)9798350384369

In this paper, an indirect model for predicting wind turbine output power is proposed, based on a LSTM and a mathematical model for wind power estimation. The LSTM model is used to forecast future changes in wind speed and direction, and the linearized segmented model to estimate wind turbine output power based on forecast wind speeds. To train the LSTM model, NASA meteorological data were used for a period from January 1,2015 to December 31, 2022 for a coastal site in Benin. The results obtained showed that the forecasts made by the LSTM model are fairly close to the actual values. Values of 0.36 m/s and 8.1° for the Mean Square Error (RMSE) and 0.92 and 0.71 for the Coefficient of Determination (R2) were obtained for wind speed and direction forecasts respectively. The predicted speeds were then used to estimate the power output of the NE-200S wind turbine. © 2024 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

引用

Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

来源：评论

学校读者我要写书评

暂无评论

TG-SMR:AText Summarization Algorithm Based on Topic and Graph Models

引用

computer Systems science & engineering 2023年第4期45卷 395-408页

作者： Mohamed Ali Rakrouki Nawaf Alharbe Mashael Khayyat Abeer Aljohani Applied College Taibah UniversityMedina42353Saudi Arabia College of Computer Science and Engineering University of JeddahJeddah21959Saudi Arabia

Recently,automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text ***,most of the computing methods that are used in real systems are based on graph models,which are characterized by their simplicity and ***,this paper proposes an improved extractive text summarization algorithm based on both topic and graph *** methodology of this work consists of two ***,the well-known TextRank algorithm is analyzed and its shortcomings are ***,an improved method is proposed with a new computational model of sentence *** experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization ***,through experiments on the DUC2004 and DUC2006 datasets,our proposed improved graph model algorithm TG-SMR(Topic Graph-Summarizer)is compared to other text summarization *** experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE *** is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.

关键词： Natural language processing text summarization graph model topic model

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：