检索结果-内蒙古大学图书馆

5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ghosh, Triyas Chakraborty, Soham Kaplun, Dmitrii Gulvanskii, Vyacheslav Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9789819638628

In this paper, we have proposed a novel model, called Bonferroni Mean Operator-aided Fusion of Neural Networks (BFuse-Net). Here, we have taken advantage of the capabilities of four deep learning models as the base learners and then applied a customized attention method to prioritize different fine-grained features by passing their outputs via four parallel pipelines. Next, we have employed a novel aggregation method, a modified Bonferroni Mean operator, to merge the decision scores acquired from every pipeline, prioritizing their individual contributions above their interactions with others in the decision scores. Three medical image datasets— LC25000 (colon cancer), Oral Squamous Cell Carcinoma, and Malaria Cell Images have been used for experimentation. The model yields state-of-the-art outcomes for the three datasets. The source codes and additional results can be found at GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Utilizing chaos game representation for enhanced classification of SARS-CoV-2 variants with stacked sparse autoencoders

引用

Neural Computing and Applications 2024年第31期36卷 19823-19837页

作者： Coutinho, Maria G. F. Câmara, Gabriel B. M. Barbosa, Raquel de M. Fernandes, Marcelo A. C. RN Natal59078-900 Brazil UFRN RN Natal59078-900 Brazil Department of Pharmacy and Pharmaceutical Technology University of Seville Seville41012 Spain Department of Computer and Automation Engineering UFRN RN Natal59078-900 Brazil

Since the beginning of the COVID-19 pandemic, the World Health Organization (WHO) has been tracking SARS-CoV-2 mutations. The SARS-CoV-2 consistently mutated throughout the pandemic, which resulted in many variants. A variant is a viral genome containing one or more genetic code mutations. Deep learning techniques have been successfully used in many viral classification problems associated with viral infection diagnosis, metagenomics, phylogenetics, and analysis. This work proposed an effective viral genome classifier for SARS-CoV-2 variants using the deep neural network based on the stacked sparse autoencoder (SSAE). Aiming to achieve the best performance of the model, we explored the utilization of image representations of the complete genome sequences as the SSAE input. The dataset based on Chaos Game Representation (CGR) images was generated and applied to the experiments of classification of SARS-CoV-2 variants of concern (VOC). The SSAE technique provided great performance results, achieving classification accuracy of 99.9% for the validation set and 99.8% for the test set. Finally, the results indicated the relevance of using this deep learning technique in genome classification problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Modeling a fuzzy expert system for predicting customers’ behavioral intention for restaurant business through parallel prediction of perceived values and satisfactions

引用

International Journal of Information Technology (Singapore) 2025年第2期17卷 1007-1014页

作者： Akhtaruzzaman, M. Shahryer, Fahim Halder, Sachitra Tanvin, Jamal Uddin Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Mirpur Cantonment Dhaka 1216 Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

This paper focuses on the modeling of a recommendation process for the restaurant industry. It is a challenge job for a restaurant owner to ascertain the intentions of customers to evaluate their services, further improve their profit margin. So, the objective of this study is to design a fuzzy-based prediction model of customers’ intentions for a fine dining experience. To facilitate a comparative analysis and to identify the optimal approach, various membership functions (MF) and de-fuzzification methods are employed along with the analysis of the correlational behavior among various input subsets. Results have shown that CoA method with triangular MF are well enough to implement the model. The fuzzy-correlation analysis presents various levels of membership strengths for a same recommendation score in some cases. Moreover, the evaluation of the proposed model based on a custom dataset shows prominent results with as 0.938. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Customer intension Customer satisfaction FIS Perceived value Recommendation

来源：评论

学校读者我要写书评

暂无评论

Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

引用

computers, Materials & Continua 2022年第11期73卷 2241-2255页

作者： El-Sayed M.El-kenawy Zeeshan Shafi Khan Abdelhameed Ibrahim Bandar Abdullah Aloyaydi Hesham Arafat Ali Ali E.Takieldeen Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Science Department College of Engineering and Computer ScienceMustaqbal UniversityBuraidahSaudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Mechanical Engineering Qassim UniversityBuraidah51452Saudi Arabia

Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous *** is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital *** route planning methods for the mobile robot have been developed and *** to our best knowledge,no method offers an optimum solution among the existing *** Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental *** the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile *** paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)*** is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with *** order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are *** experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.

关键词： Mobile robot swarm optimization robot route planning neural networks

来源：评论

学校读者我要写书评

暂无评论

Real Time Optimization for Operation of Water Distribution Systems to Water Leakage Reduction 14

Real Time Optimization for Operation of Water Distribution S...

引用

14th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2022

作者： Dai, Pham Duc Viet, Nguyen Hoang Thuyloi University Department of Control Engineering and Automation Hanoi Viet Nam

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

Water loss occurring in water distribution systems (WDSs) can be reduced by regulating operations of Pressure Reducing Valves (PRVs) installed in WDSs. This practical problem can be casted into a nonlinear program (NLP). The quality control of WDSs depends on how fast the solution of the NLP is deduced. It is due to the fact that solving such the optimization normally requires a large computation time, the fully accurate solution of the NLP is thus not suitable for real time control. In this paper, we applied the sensitivity-based method for deriving the real time pressure settings for PRVs, which is appropriate for real time control of the WDS. One benchmark WDS is taken for optimal pressure control to demonstrate the efficacy of the sensitivity-based method. The results have shown that the real time solution by the sensitivity-based method while requiring a negligible time computation have acceptable accuracy. © 2022 IEEE.

关键词： Water distribution systems

来源：评论

学校读者我要写书评

暂无评论

Feasibility Analysis of Path Planning Algorithms 16

Feasibility Analysis of Path Planning Algorithms

引用

16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022

作者： Allozi, Elbera Yilmaz, Abdurrahman Ervan, Osman Temeltas, Hakan Istanbul Technical University Control and Automation Engineering Department Istanbul Turkey

ISBN: (数字)9781665498104

ISBN: (纸本)9781665498104

Path planning, which aims to find the most suitable route between source and destination, is a challenging problem in mobile robotics. Although there are several study ideas on the path planning challenges of mobile robots in the literature, there is no approach that evaluates path quality qualitatively and quantitatively. It is significant to choose the optimal path by analyzing the properties of the path and the planning algorithm for effectiveness and efficiency. In this paper, we proposed a feasibility analysis to evaluate the quality of the path planning algorithm from various perspectives. This paper defines criteria for path planning algorithms and investigates traceability, smoothness, and shortness factors. Furthermore, by using these test results, a novel artificial neural network-based method has been proposed to enhance path planning algorithms without altering them. © 2022 IEEE.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

Drone-Based Predictive Structure Analysis: A Review

Drone-Based Predictive Structure Analysis: A Review

引用

2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Patil, Trushant Nandan, Durgesh Sharma, Kanhaiya Parashar, Deepak Department of Robotics and Automation Pune India Department of Electronics and Telecommunication Engg. Pune India Department of Computer Science & Engineering Pune India Faculty of Engineering Pune India

ISBN: (纸本)9798350329186

Drone-based predictive structure analysis is a rapidly growing field that has the potential to revolutionize infrastructure and building inspection. In this review of the research, we give a general overview of how drones could increase the effectiveness and precision of structural analysis, particularly for keeping an eye on difficult-to-reach locations. The usage of drones for 3D mapping, inspection, and monitoring of diverse constructions, such as concrete buildings, bridges, tunnels, and dams. The investigations show that drone-based inspection can produce 3D models and high-resolution photos, allowing for remote monitoring and the early identification of possible problems. The findings show that drones may greatly boost infrastructure management's safety and effectiveness, making them an important tool for predictive structural research. © 2023 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Circularity Reinforcement of Critical Raw Materials in Europe: A Case of Niobium

引用

Circular Economy and Sustainability 2024年第4期4卷 2449-2472页

作者： von Rennenberg, Theresa Yu, Yifei Fraccascia, Luca Yazan, Devrim Murat Department of Industrial Engineering and Business Information Systems University of Twente Drienerlolaan 5 Enschede 7522 NB Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome 00185 Italy

Critical Raw Materials attract increasing attention due to their depleting reserves and low recyclability. Niobium, one of the most rare and vital elements, is primarily found in Brazil. This research explores the potential impact of Circular Economy (CE) strategies on mitigating niobium's criticality within Europe. First, a niobium supply chain is designed and analysed by Enterprise Input–Output modelling. Second, the supply risk is calculated based on the criticality matrix proposed by the European Commission under three scenarios associated with resources, technologies, and policies. The results show that urban mining is a potential solution to reduce niobium’s criticality and mitigate its environmental impacts. A higher recycling input rate and/or a mix of recycling and substitution strategies is necessary to offset niobium’s criticality. Aligned with the CE action plan, the research offers a scientific foundation to strategically prevent the risk of niobium supply shortages. © The Author(s) 2024.

关键词： Circular economy Critical raw materials Enterprise input–output Niobium supply chain Scenario analysis

来源：评论

学校读者我要写书评

暂无评论

A Day-Ahead Market Clearing Model Under System Inertia Constraints 16

A Day-Ahead Market Clearing Model Under System Inertia Const...

引用

16th IEEE PES Asia-Pacific Power and Energy engineering Conference, APPEEC 2024

作者： Ma, Yuting Jin, Jiangliang Hao, Liangliang Huang, Qisheng Shen, Liye College of Information Scienc and Technology Donghua University Shanghai China Shandong University Department of Control Science and Engineering Shandong China School of Mechanical Engineering and Automation Harbin Institute of Technology Shanghai China

ISBN: (纸本)9798350386127

We study the market clearing model for electric energy, inertia, and reserve in the day-ahead market, with a particular focus on the virtual inertia provided by wind units. In this paper, we consider synchronous inertia and virtual inertia as sources of system inertia. We incorporate system inertia constraints into the unit commitment (UC) and the economic dispatch (ED) models, and assign economic value to virtual inertia, so as to incentivize wind power to provide virtual inertia. Numerical results demonstrate that the proposed model enhances the participation of wind power units, increases the wind utilization rate and reduces the total costs. © 2024 IEEE.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

引用

Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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：