检索结果-内蒙古大学图书馆

4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Prabha, B. Vishnu Manikanda Kumaran, K. Manikandan, S. Murugan, S. Palani Jeppiaar Institute of Technology Department of Artificial Intelligence and Data Science Chennai India E.G.S. Pillay Engineering College Department of Information Technology Nagapattinam India E.G.S. Pillay Engineering College Department of Artificial Intelligence and Data Science Nagapattinam India

ISBN: (纸本)9798350364729

Healthcare has increasingly embraced machine learning (ML) algorithms, which have been shown to enhance diagnostic accuracy, treatment effectiveness, and ultimately improve patient outcomes. Despite the diversity of medical data and application requirements, it is still difficult to determine which algorithm will work best for a given healthcare task. the focus of this paper is on comparing various medical laboratory (ML) algorithms that are commonly used in the healthcare domain. the performance of algorithms is evaluated across a range von der Gesundheitswesen Aufgabenstellungen (disease forecasting, medical imaging analysis, treatment recommendation), and are then used to evaluate them using publicly available datasets. the efficacy of algorithms is measured using performance metrics like accuracy, sensitivity (for some algorithms even faster than the Precise Lotion Function), specificity and area under the receiver operating characteristic curve (AUC-ROC) Our analysis provides insight into the strengths and limitations of each algorithm and helps healthcare professionals and researchers choose algorithms for specific healthcare applications. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Communication 4

CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Commun...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Aggarwal, Vaishali Goel, Kashvi Gupta, Ishika Ajmani, Prerna Vivekananda Institute of Professional Studies School of Information Technology Technical Campus Delhi India

ISBN: (纸本)9798331518981

Wireless sensor networks (WSNs) and their applications are on an increasing trend owing to the users' demands with interest in environmental activities, intelligent cities, and medical assistance. However, because of the inefficiency and limitations of wireless sensor networks, it is still very difficult to optimize the network parameters and performance (energy consumption, area coverage and reliable information transfer). In this paper optimization algorithms including Ant Colony optimization (ACO), Moth Flame optimization (MFO) and Whale optimization Algorithm (WOA) to enhance the effectiveness of wireless sensor systems are presented. In this approach, ACO captures and explores functional properties, MFO coordinates the optimization and exploration processes, and WOA concentrates on global exploration. Wide-ranging simulations demonstrate that the recommended approach is more effective than the existing approaches in enhancing energy efficiency, network lifetime and effectiveness of packet delivery. the findings in this paper indicate that the proposed hybrid algorithms support the various requirements of the WSN, thus promoting development of networks that are secure and dependable. © 2024 IEEE.

关键词： Ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

Enhancing Deep Vein thrombosis Diagnosis with Multi-Objective Evolutionary Algorithm and Machine learning 4

Enhancing Deep Vein Thrombosis Diagnosis with Multi-Objectiv...

引用

4th international conference on Applied Artificial Intelligence (ICAPAI)

作者： Sorano, Ruslan Ripon, Kazi Shah Nawaz Magnusson, Lars Vidar Halstensen, thor-David Ghanima, Waleed Ostfold Univ Coll Dept Comp Sci & Commun Halden Norway Oslo Metropolitan Univ Dept Comp Sci Oslo Norway Ostfold Univ Coll Dept Hlth Welf & Org Halden Norway Ostfold Hosp Trust Dept Res Gralum Norway

ISBN: (纸本)9798350349764;9798350349771

Deep vein thrombosis is a serious medical condition requiring prompt and accurate diagnosis. the identification of thrombosis presents a challenging task characterized by conflicting objectives. Maintaining a delicate balance between optimizing overall diagnostic accuracy and averting the misclassification of ill patients as healthy is paramount in the diagnostic process. Our earlier works focused on optimizing the disease prediction accuracy in machine learning models by experimenting with different techniques. We employed single-objective optimization to fine-tune the classification threshold. Additionally, we applied a multi-objective evolutionary algorithm for hyperparameter optimization, both independently and in combination with feature reduction. Expanding on our previous works, this work employs a multi-objective evolutionary algorithm that concurrently tunes hyperparameters, reduces features, and adjusts the classification threshold. By addressing the inherent conflicting objectives in thrombosis diagnostics, the proposed approach generates a set of Pareto-optimal solutions, representing a balance between maximizing overall diagnostic accuracy and minimizing false negatives. Experimental results indicate that this approach enhances the outcomes of the deep vein thrombosis diagnosis prediction, effectively navigating the trade-off in competing objectives for improved clinical efficacy.

关键词： multi-objective evolutionary algorithms healthcare applications decision support deep vein thrombosis hyperparameter tuning feature reduction classification threshold

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis Of Deep learning for Robust Deepfake Detection 4

Comparative Analysis Of Deep Learning for Robust Deepfake De...

引用

4th international conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Kapoor, Rakshit Mehndiratta, Vandana Singh, Jaspreet Christ University Bangalore India

ISBN: (纸本)9798350364729

With Deepfake technology rapidly growing and its use on the rise, there is an increased demand for effectiveness of its detection. this paper performs a comparative analysis of 4 deep learning algorithms - Meso-4, MesoInception-4, Resnet50, DenseNet-121. We will investigate each algorithm's architecture, performance validation and versatility. the indepth overview will shed some light on accuracy, precision, recall, F1 score, etc. the dataset will encompass the real and fake community videos for a well-rounded assess on each algorithm's effectiveness. the results of the research will help to with the potential practical applications of the reviewed algorithms, and mention possible changes and improvements. the results of the research have shown both strengths and weaknesses of each algorithm, giving clarity as to the potential use in practice, and changes that could be made in future. the overall aim of the dissolution is to guide the reader through the process of understanding the difference between the 4 algorithms - Meso-4, MesoInception-4, Resnet-50 and DenseNet-121. © 2024 IEEE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Research on digital advertising precision delivery system based on reinforcement learning algorithm 4

Research on digital advertising precision delivery system ba...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Zhao, Te Nottingham Trent University Nottingham United Kingdom

ISBN: (纸本)9798331528676

this study proposes a new reinforcement learning framework for Trans-Proximal Policy (TPP), which aims to combine the advantages of Transformer network and Proximal Policy optimization (PPO) to realize efficient and precise digital advertising. TPP uses the powerful sequence processing capability of Transformer network to capture historical patterns of user behavior, and combines the efficient policy optimization algorithm of PPO to achieve fast and stable policy learning. In the digital advertising scenario, TPP can capture the user's historical behavior sequence data to predict the future interaction probability, and optimize the advertising strategy accordingly. Finally, through comparative experiments and analysis between the model framework and traditional optimization algorithms, this paper verifies that the TPP proposed in this paper can significantly improve the click-through rate, accuracy rate and conversion rate of advertisements while maintaining the stability of policy update, especially in the processing of large-scale user behavior data. this model is proved to be feasible and effective in the digital advertising precision delivery system. ©2024 IEEE.

关键词： Marketing

来源：评论

学校读者我要写书评

暂无评论

Analysis of effective resource scheduling optimization algorithms in IoT-fog-cloud environment 4th

Analysis of effective resource scheduling optimization algor...

引用

4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Sharma, Hritik Goel, Gaurav Ummat, Amandeep Deparment of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

the number of sensors and IoT devices has increased dramatically in last several years. In recent years, the number of IoT devices and sensors has increased significantly. Solving the purpose of fog computing processing requirements of widely distributed IoT devices and sensors in real-time, latency-sensitive applications. In order to effectively distribute resource layers for numerous application IoT networks, it is imperative to optimize fog infrastructure resources, as they are scarce and erratic. Generally speaking, fog computing brings cloud-based networking, processing, and storage closer to IoT device and detection devices. To enhance the capacity to search globally, the Firefly and Crow search algorithms are merged. the proposed hybrid algorithm (FA-CSA) results improved the QoS factor of cost and makespan while compared with PSO, ACO and genetic algorithm. © 2025 the Author(s).

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Structure parameters optimization of metasurface for broadband polarization conversion based on deep learning and optimization algorithm 5

Structure parameters optimization of metasurface for broadba...

引用

5th international conference on Computer Engineering and Application (ICCEA)

作者： Guo, Shengzhuo Du, Qingguo Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Metasurface can be used to manipulate the polarization, amplitude, phase of electromagnetic waves which has been applied in various fields such as holography technology, imaging and sensing. However, traditional metasurface optimization methods such as parameters scanning require a considerable amount of time and computing power and cannot cover every set of parameters due to the setting of step size, which may lead to the optimization result falling into a local optimal solution. To address the above-mentioned issues, in this paper, we propose a method that combines transfer learning optimization network with grey wolf optimization algorithm for structural parameters optimization. Using deep learning networks can significantly improve the speed of spectral prediction, while transfer learning algorithms can enhance the prediction accuracy of the networks. the grey wolf optimization algorithm belongs to a global optimization algorithm and its performance is superior to other traditional algorithms, thus enabling it to achieve a wider bandwidth. the results show that the bandwidth of the transmission spectrum obtained through the grey wolf optimization algorithm is 87.37 nm, which is wider than that achieved through traditional method of parameter scanning. At the same time, it only takes 45 minutes, which is one-seventieth of the time required by traditional optimization methods.

关键词： Metasurface Transfer learning optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A Euclidean Distance Matrix Model for Convex Clustering

引用

JOURNAL OF optimization thEORY AND applications 2025年第1期205卷 1-22页

作者： Wang, Z. W. Liu, X. W. Li, Q. N. Beijing Inst Technol Sch Xuteli Beijing 100081 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China Beijing Inst Technol Sch Math & Stat Beijing Key Lab MCAACI Beijing 100081 Peoples R China

Clustering has been one of the most basic and essential problems in unsupervised learning due to various applications in many critical fields. the recently proposed sum-of-norms (SON) model by Pelckmans et al. (in: PASCAL workshop on statistics and optimization of clustering, 2005), Lindsten et al. (in: IEEE statistical signal processing workshop, 2011) and Hocking et al. (in: Proceedings of the 28th international conference on international conference on machine learning, 2011) has received a lot of attention. the advantage of the SON model is the theoretical guarantee in terms of perfect recovery, established by Sun et al. (J Mach Learn Res 22(9):1-32, 2018). It also provides great opportunities for designing efficient algorithms for solving the SON model. the semismooth Newton based augmented Lagrangian method by Sun et al. (2018) has demonstrated its superior performance over the alternating direction method of multipliers and the alternating minimization algorithm. In this paper, we propose a Euclidean distance matrix model based on the SON model. Exact recovery property is achieved under proper assumptions. An efficient majorization penalty algorithm is proposed to solve the resulting model. Extensive numerical experiments are conducted to demonstrate the efficiency of the proposed model and the majorization penalty algorithm.

关键词： Clustering Unsupervised learning Euclidean distance matrix Majorization penalty method

来源：评论

学校读者我要写书评

暂无评论

A Novel Federated learning Framework for Healthcare applications Using Wearable Devices 4

A Novel Federated Learning Framework for Healthcare Applicat...

引用

4th IEEE international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Natarajan, Rajesh Krishna, Sujatha Ranjith, Christodoss Prasanna University of Technology and Applied Sciences Al-Aqr 324 College of Computing and Information Sciences Information Technology Department Shinas Oman Messiah University Computer Science Department MechanicsburgPA United States

ISBN: (纸本)9798331518882

A key necessity in managing signals for processing and generating useful information in the healthcare domain is the use of many algorithms. Traditional computing methods have been successful but may not be effective in the future as secure privacy matters. this has led to the increasing popularity of machine learning technologies. However, we present a novel device for devices and sensors for federated learning (FL), which is aimed at establishing a new, safer framework. this pattern uses machine learning with a lot of emphasis on the availability of the devices. In such a case, models for the detection, monitoring, and management of diseases can be created. the FL approach is also believed to be applicable to several healthcare contexts. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

learning algorithms for Breast Cancer Classification and Diagnosis 1

引用

4th international conference on optimization learning algorithms and applications

作者： Miranda Valentin, Ana Beatriz Bressan, Glaucia Maria Lizzi, Elisangela Ap da Silva Lopes, Fabricio Martins Univ Tecnol Fed Parana UTFPR Grad Program Bioinformat Ave Alberto Carazzai 1640 BR-86300000 Cornelio Procopio PR Brazil

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

the comprehension about the key features of breast tumors that lead to their classification as benign or malignant is fundamental to improving the detection and diagnosis of breast cancer, contributing significantly to survival rates and treatment effectiveness. this study proposes a multidisciplinary approach that combines analytical methods and graphical visualizations to classify breast tumors as benign or malignant, using supervised and unsupervised learning algorithms. the adopted dataset in this study is from the repository of the University of Wisconsin (USA). It comprises 569 breast tumor biopsy samples, with 32 features measured from digitized images of biopsy slides. Initially, for unsupervised learning, Pearson correlation was used as a similarity metric for hierarchical grouping, resulting in the formation of six clusters through a dendrogram. In supervised learning, the Principal Component Analysis (PCA) technique was performed to reduce the number of features, m achieving the 10 most relevant. the Support Vector Machine (SVM) model was applied with and without the PCA results. the comparison between hierarchical grouping and SVM methods demonstrated a notable advantage of SVM in terms of accuracy in classifying breast tumors. the use of cross-validation showed the superiority of SVM over clustering for this specific purpose. the analysis of breast tumor features and the classification approaches offer important perspectives on improving breast cancer diagnosis and treatment practices. the use of classifiers such as SVM, together with dimensionality reduction techniques such as PCA, can result in significant improvements in diagnostic accuracy and effectiveness, directly benefiting patient care in this critical area of medicine.

关键词： Breast cancer Tumor classification Feature selection efficient diagnosis

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：