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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是311-320 订阅

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Enhancing Submersible Trajectory Prediction Using Grey Relational Analysis and Reinforcement learning 4

Enhancing Submersible Trajectory Prediction Using Grey Relat...

引用

4th international conference on Computer Communication and Artificial Intelligence (CCAI)

作者： Shi, Zian Gao, Rongjian Ling, Zihan Fuzhou Univ Maynooth Int Engn Coll Fuzhou Fujian Peoples R China

ISBN: (纸本)9798350362770;9798350362763

In this study, we present a comprehensive approach to enhancing submersible trajectory prediction in deepsea environments by integrating grey relational analysis and reinforcement learning techniques. the utilization of grey relational evaluation models and Multi-Agent Reinforcement learning with the MADDPG method allows for the optimization of search and rescue equipment selection for deep-sea submersibles. By considering factors such as equipment availability, maintenance, preparation, and usage-related costs, the proposed methodology aims to improve the efficiency and effectiveness of search and rescue operations in challenging underwater conditions. Furthermore, the integration of grey relational analysis and reinforcement learning offers a novel and advanced strategy for predicting submersible trajectories with increased accuracy and reliability. By leveraging the capabilities of these analytical tools, this research contributes to the development of more robust and adaptive systems for deep-sea exploration and recovery missions. the findings of this study have significant implications for enhancing the safety and success of submersible operations in complex underwater environments, ultimately advancing the field of deep-sea exploration and rescue efforts.

关键词： artificial intelligence Fluid Mechanics Reinforcement learning deep learning machine learning

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A Comprehensive Analysis of AI applications in Bone Fracture Detection: A Systematic Review 4

A Comprehensive Analysis of AI Applications in Bone Fracture...

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

作者： Sharmila Yadav, R.K. Tyagi, Priyanka Tiwari, Richa Bhushan, Shashi Abhraham, Robin Raj Kumar Goel Institute of Technology Department of Ece Ghaziabad India Gl Bajaj Institute of Technology and Management Dept. of Ece Greater Noida India Universiti Teknologi Petronas Department of Computer & Information Sciences Perak Malaysia

ISBN: (纸本)9798350364729

the human body relies significantly on bones, enabling movement and structural support. Fractures within bones are frequently observed, prompting doctors to utilize X-ray imaging for accurate diagnosis. However, manually detecting fractures is both time-consuming and prone to a higher chance of errors. In the realm of medical diagnostics, advancements in image processing techniques play a pivotal role in enhancing the accuracy and efficiency of fracture detection in X-ray images. Due to the recent advancement of artificial intelligent and increased the power of computational hardware, researchers is working on various machine learning and deep learning algorithms which can assists radiology and orthopedics in their diagnosis and reduce the possibility of misdiagnosis. this paper presents the progress in deep learning specifically related to bone fracture and addressing the challenge of precise localization and characterization. © 2024 IEEE.

关键词： Deep learning

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Accelerated Stochastic optimization Methods under Quasar-convexity 40

Accelerated Stochastic Optimization Methods under Quasar-con...

引用

40th international conference on Machine learning

作者： Fu, Qiang Xu, Dongchu Wilson, Ashia Sun Yat Sen Univ Guangzhou Peoples R China Harvard Univ Cambridge MA 02138 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA

Non-convex optimization plays a key role in a growing number of machine learning applications. this motivates the identification of specialized structure that enables sharper theoretical analysis. One such identified structure is quasar-convexity, a non-convex generalization of convexity that subsumes convex functions. Existing algorithms for minimizing quasar-convex functions in the stochastic setting have either high complexity or slow convergence, which prompts us to derive a new class of stochastic methods for optimizing smooth quasar-convex functions. We demonstrate that our algorithms have fast convergence and outperform existing algorithms on several examples, including the classical problem of learning linear dynamical systems. We also present a unified analysis of our newly proposed algorithms and a previously studied deterministic algorithm.

关键词： Convex optimization

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A Systematic Study of Machine learning applications in E-Commerce UX Personalization 4

A Systematic Study of Machine Learning Applications in E-Com...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Nishamali, M.K.C.P. Pathirage, Gihan Saminda Herath, Samitha Herath, Penusha Wickramarathne, Jagath Sri Lanka Institute of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350352931

this study provides a systematic analysis of the role of machine learning in personalizing e-commerce user experiences (UX). through a review of selected recent literature, the study explores various machine learning techniques applied to e-commerce, including supervised learning, unsupervised learning, deep learning, and ensemble methods. the research discusses these approaches' effectiveness in enhancing UX through personalized recommendations, search results, dynamic pricing models, and virtual try -on experiences. the analysis examines four main areas: recommendation systems for improved product discovery and user engagement, dynamic pricing optimization for maximizing revenue and customer satisfaction, virtual try-on technologies for enhancing fashion retail experiences, and personalized search. the study investigates how these technologies contribute to creating engaging and efficient shopping experiences while examining implementation challenges such as data privacy and algorithmic bias. Additionally, the research discusses emerging trends, technological advancements, and future research directions to further advance personalization in e-commerce. Overall, this research contributes to the understanding of machine learning in e-commerce personalization for both researchers and practitioners. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Satellite Image Retrieval with RNN Algorithm and DNN -Based Bat optimization Classifier 4

Enhancing Satellite Image Retrieval with RNN Algorithm and D...

引用

4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Ezhilarasan, D. Bhavani, N.P.G. Saveetha School of Engineering Chennai India

ISBN: (纸本)9798350352931

Remote sensing has been predominantly used in assessing environmental changes, disaster management, and studies of urban structure, among others, and as the quality of satellite images increases, methods for efficient storage and retrieval become important. this research finds itself right at addressing the challenge of satellite image retrieval through proposing an efficient search method that comprises RNN based algorithm and classifier, as well as DNN based BAT optimization. the methodology builds on RNN, notably LSTM to capture temporal dependencies and to identify relevant features of satellite image sequences. this in turn helps to improve the retrieval efficiency of an article and a DNN-based BAT optimization classifier is applied to fine-tune the weights and bias terms of the network. Based on an extensive broad satellite image dataset used in the experimental assessment, the effectiveness enhancement was confirmed. the proposed method gives an F1 score of 92.4% which shows that the proposed method yields a superior result over the compared method such as RNN-, CNN BAT and NN classifiers. the findings establish that applying RNN algorithms with BAT optimization in constructing satellite image search system is accurate and efficient to establish a sophisticated geographical data analysis, which holds potential for future applications. © 2024 IEEE.

关键词： Satellite imagery

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Physics-Informed Machine learning for Hybrid optimization of Microwave and RF Devices 25

Physics-Informed Machine Learning for Hybrid Optimization of...

引用

24th international conference on Electromagnetics in Advanced applications (ICEAA) / 12th IEEE-APS Topical conference on Antennas and Propagation in Wireless Communications (IEEE APWC)

作者： Liu, Yanan Jin, Jian-Ming Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798350320589

Design optimization of microwave and RF devices has been of great interest to researchers and engineers in the microwave and RF communities for decades. To achieve an effective optimization, one often faces two challenges. the first is to choose a robust optimization technique. In the past, a wide variety of optimization techniques have been proposed, and they can be classified into two main categories: search-based algorithms and gradient-based methods. the genetic algorithm (GA) is one of the most popular search-based algorithms, based on the principle of natural evolution. While GA offers the potential to search for global optima in the presence of a large number of design variables, the method can be prohibitively expensive. Gradient-based optimization methods, on the other hand, are based on the analytical evaluation of gradients of the objective function with respect to design variables. Fast result can be achieved provided that the initial design is in the vicinity of the optimal solution. the second challenge is to obtain very efficient forward solutions required in the optimization process. this is particularly true in the GA optimization which requires the evaluation of many designs. In the gradient-based optimization, one not only needs to provide forward solutions, but also their gradients with respect to design parameters, which is by no means trivial. © 2023 IEEE.

关键词： Genetic algorithms

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DeepWS: Dynamic Workflow Scheduling in Heterogeneous Cloud Clusters with Edge-Aware Reinforcement learning and Graph Convolution Networks 4

DeepWS: Dynamic Workflow Scheduling in Heterogeneous Cloud C...

引用

4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Weng, Canbin Wan, Yitian Xie, Hongbo East China Normal University Software Engineering Institute Shanghai China East China Normal University Computer Science & Technology Institute Shanghai China

ISBN: (纸本)9798350394375

this paper introduces DeepWS, a dynamic scheduling algorithm that leverages the A2C (Actor-Critic) reinforcement learning algorithm and Graph Convolution Network (GCN) techniques. Unlike existing models, DeepWS does not necessitate prior knowledge of the entire Directed Acyclic Graph (DAG), enhancing scalability and enabling versatility across diverse DAG graphs. Employing reinforcement learning and GCN, DeepWS adeptly adapts task scheduling in dynamic environments. the primary focus of DeepWS is to address dynamic workflow scheduling in heterogeneous cloud computing clusters. the algorithm accounts for task dependencies, node and edge weights, as well as the computational power of host machines and router bandwidth. the optimization objective is to minimize makespan. Experimental validation conducted in a heterogeneous clustering environment, using classical datasets, demonstrates DeepWS's superior performance compared to benchmark algorithms like HEFT (Heterogeneous Earliest Finish Time) and MCT (Minimum Completion Time). Results illustrate that DeepWS not only outperforms traditional algorithms significantly but also exhibits enhanced scalability, making it adaptable to different DAG graphs without requiring complete graph information in advance. this research offers a flexible and efficient solution for complex task scheduling challenges in cloud computing, particularly in environments characterized by scheduling uncertainties. © 2024 IEEE.

关键词： Reinforcement learning

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Integrating Deep learning with Ensemble Approach for Anomaly Detection in Network Traffic 4

Integrating Deep Learning with Ensemble Approach for Anomaly...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sangeetha, V. Naidu, R. China Appala Bhat, Avinash Kulkarni, Pranav Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350352931

Intrusion Detection Systems (IDS) is frequently automated using expert systems and applied machine learning methods. Because of the interplay between different industrial control systems and the Internet environment that is inherent to IoT technology, critical infrastructure applications of communication technologies are vulnerable to attacks. Conventional machine learning techniques used in network anomaly detection are ineffective due to the massive network traffic present in crucial Cyber-Physical Systems. As a result, the detection and categorization of abnormalities at the network and host levels are successfully utilizing recently developed machine learning algorithms, with a focus on deep learning. It is very crucial in today's network traffic to distinguish between normal and malicious activities. With the rise of cyber threats, machine learning models have become popular for IDS due to their adaptability and high detection rates. this paper introduces a hybrid Network Intrusion Detection System (NIDS) that combines Long Short-Term Memory (LSTM) networks and XGBoost classifiers. By leveraging LSTM's temporal learning and XGBoost's boosting capabilities. the proposed model achieves 99% detection accuracy on the NF-BoT-IoT dataset, making it a robust solution for real-time anomaly detection. © 2024 IEEE.

关键词： Network intrusion

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Ending Affirmative Action Harms Diversity Without Improving Academic Merit 24

Ending Affirmative Action Harms Diversity Without Improving ...

引用

4th ACM conference on Equity and Access in algorithms, Mechanisms, and optimization, EAAMO 2024

作者： Lee, Jinsook Harvey, Emma Zhou, Joyce Garg, Nikhil Joachims, thorsten Kizilcec, René F. Cornell University United States Cornell Tech United States

ISBN: (纸本)9798400712227

Each year, selective American colleges sort through tens of thousands of applications to identify a first-year class that displays both academic merit and diversity. In the 2023-2024 admissions cycle, these colleges faced unprecedented challenges to doing so. First, the number of applications has been steadily growing year-over-year. Second, test-optional policies that have remained in place since the COVID-19 pandemic limit access to key information that has historically been predictive of academic success. Most recently, longstanding debates over affirmative action culminated in the Supreme Court banning race-conscious admissions. Colleges have explored machine learning (ML) models to address the issues of scale and missing test scores, often via ranking algorithms intended to allow human reviewers to focus attention on 'top' applicants. However, the Court's ruling will force changes to these models, which were previously able to consider race as a factor in ranking. there is currently a poor understanding of how these mandated changes will shape applicant ranking algorithms, and, by extension, admitted classes. We seek to address this by quantifying the impact of different admission policies on the applications prioritized for review. We show that removing race data from a previously developed applicant ranking algorithm reduces the diversity of the top-ranked pool of applicants without meaningfully increasing the academic merit of that pool. We further measure the impact of policy change on individuals by quantifying arbitrariness in applicant rank. We find that any given policy has a high degree of arbitrariness (i.e. at most 9% of applicants are consistently ranked in the top 20%), and that removing race data from the ranking algorithm increases arbitrariness in outcomes for most applicants. © 2024 ACM.

关键词： Adversarial machine learning

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Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine learning 24

Recent and Upcoming Developments in Randomized Numerical Lin...

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30th ACM SIGKDD conference on Knowledge Discovery and Data Mining

作者： Derezinski, Michal Mahoney, Michael W. Univ Michigan Ann Arbor MI 48109 USA LBNL ICSI Berkeley CA USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9798400704901

Large matrices arise in many machine learning and data analysis applications, including as representations of datasets, graphs, model weights, and first and second-order derivatives. Randomized Numerical Linear Algebra (RandNLA) is an area which uses randomness to develop improved algorithms for ubiquitous matrix problems. the area has reached a certain level of maturity;but recent hardware trends, efforts to incorporate RandNLA algorithms into core numerical libraries, and advances in machine learning, statistics, and random matrix theory, have lead to new theoretical and practical challenges. this article provides a self-contained overview of RandNLA, in light of these developments.

关键词： Matrix computations Randomization optimization Statistics

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