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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61-70 订阅

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4th international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2024

4th International Mobile, Intelligent, and Ubiquitous Comput...

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4th international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2024

ISBN: (纸本)9798350367775

the proceedings contain 69 papers. the topics discussed include: a comprehensive survey on dimension reduction for medical big data using optimization algorithms;software product discovery as a crucial part of software product success;a comparative study of different smart university frameworks;intelligent decision support system for loan evaluation using machine learning;real-time boundary detection for continuous Arabic sign language translation;trade-offs in performance and optimization techniques in Unity 3D to achieve realism;transfer learning based indoor object recognition for visually impaired people;truth revealed: enhancing deception detection using long-term recurrent convolutional networks;and developing a medical question-answering chatbot for Egyptian Arabic: a comparative study.

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4th international conference on Data, Engineering, and applications, IDEA 2022

4th International Conference on Data, Engineering, and Appli...

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4th international conference on Data, Engineering, and applications, IDEA 2022

ISBN: (纸本)9789819700363

the proceedings contain 74 papers. the special focus in this conference is on Data, Engineering, and applications. the topics include: Using OpenNLP and GraalVM to Detect Sentences in Kubernetes While Comparing Helidon and Spring Boot’s Metrics;an Efficient Hybrid Model to Summarize the Text Using Transfer learning;automatic Detection of Learner’s learning Style;construction of an Intelligent Knowledge-Based System Using Transformer Model;machine learning-Based Disease Diagnosis Using Body Signals: A Review;finite Difference and Finite Volume 1D Steady-State Heat Conduction Model for Machine learning algorithms;Sign Language Detection through PCANet and SVM;A Novel Surface Roughness Estimation and optimization Model for Turning Process Using RSM-JAYA Method;effective Prediction of Coronary Heart Disease Using Hybrid Machine learning;feature Extraction Using Levy Distribution-Based Salp Swarm Algorithm;plant Disease Detection Using Machine learning Approaches: A Review;copy–Move Forgery Detection Algorithm: A Machine learning-Based Approach to Detect Image Forgery;a Machine learning-Based Approach to Combat Hate Speech on Social Media;Prediction of SARS-COVID-19 Based on Transfer Machine learning Techniques Using Lungs CT Scan Images;online Document Identification and Verification Using Machine learning Model;Mitigating Partial Shading Condition in PV System for MPPT Using Evolutionary algorithms;road Safety Modeling: Safe Road for All;AI-Enabled Road Health Monitoring System for Smart Cities;multi-objective Biofilm Algorithm to Resolve optimization Problems;comparative Analysis of Fake News Identification Using Machine learning Methods;a Review of Pre-processing Techniques for Weed-Plant Detection and Classification in Precision Agriculture;utilizing a Finger Vein in Biometric Authentication Mechanism;local Binary Patterns-Based Retinal Disease Screening.

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4th international conference on Data, Engineering, and applications, IDEA 2022

4th International Conference on Data, Engineering, and Appli...

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4th international conference on Data, Engineering, and applications, IDEA 2022

ISBN: (纸本)9789819724505

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optimization of Antenna Dimensions for Improved Frequency Response using Machine learning: A Survey 6

Optimization of Antenna Dimensions for Improved Frequency Re...

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6th international conference on Communications Signal Processing and their applications

作者： ElAbbasi, Mona K. Madi, Mervat Kabalan, Karim Y. Amer Univ Beirut Elect & Comp Engn Dept Beirut Lebanon Higher Coll Technol Elect & Elect Engn Sch Dept Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350384826;9798350384819

Machine learning techniques have widespread applications in optimization across various domains. this survey delves into their specific utilization in antenna design, where dimensions such as height and width play critical roles in performance metrics. Additionally, this study investigates the performance of several regression methods for predicting nonlinear relationships within antenna design variables affecting resonant frequency. Our analysis highlights the superiority of support vector regression (SVR), polynomial regression, and neural networks over linear regression and genetic algorithms in terms of predictive accuracy. In essence, the paper underscores the importance of selecting appropriate regression techniques based on the complexity of antenna design variables, recommending SVR, polynomial regression, and neural networks for modeling intricate patterns effectively and providing accurate predictions.

关键词： Machine learning performance metrics support vector regression (SVR) polynomial regression neural networks linear regression genetic algorithms

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Generative Machine learning for Multivariate Equity Returns 23

Generative Machine Learning for Multivariate Equity Returns

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4th ACM international conference on AI in Finance (ICAIF)

作者： Tepelyan, Ruslan Gopal, Achintya Bloomberg New York NY 10022 USA

ISBN: (纸本)9798400702402

the use of machine learning to generate synthetic data has grown in popularity with the proliferation of text-to-image models and especially large language models. the core methodology these models use is to learn the distribution of the underlying data, similar to the classical methods common in finance of fitting statistical models to data. In this work, we explore the efficacy of using modern machine learning methods, specifically conditional importance weighted autoencoders (a variant of variational autoencoders) and conditional normalizing flows, for the task of modeling the returns of equities. the main problem we work to address is modeling the joint distribution of all the members of the S&P 500, or, in other words, learning a 500-dimensional joint distribution. We show that this generative model has a broad range of applications in finance, including generating realistic synthetic data, volatility and correlation estimation, risk analysis (e.g., value at risk, or VaR, of portfolios), and portfolio optimization.

关键词： Stock Returns Generative Modeling Variational Autoencoders Normalizing Flows Risk Forecasting Portfolio optimization

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Lung Cancer Classification Framework using Hybrid Bat Chicken Swarm optimization (Bat-CSO) Algorithm and Machine learning 4

Lung Cancer Classification Framework using Hybrid Bat Chicke...

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4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Sreeprada, V Vedavathi, K. GITAM Deemed Univ Dept Comp Sci Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9798350386356;9798350386349

Lung cancer is the biggest cause of mortality across the globe, and it affects both men and women. Early identification is critical to improving long-term survival rates and enhancing the prospects of recovery. On medical imaging for illness identification, deep learning has recently shown great potential when used. Lung cancer detection and classification utilizing Hybrid Bat-CSO and SVM algorithms are presented in this article. It is recommended to use a threshold-based method to differentiate the candidate nodules from other structures. Different statistical and shape-based features are extracted for these nodule candidates. Using these different features, a discriminative feature vector is designed. We apply the Hybrid Bat and Chicken Swarm optimization (CSO) algorithm for optimized feature selection. Based on the feature vector, the selected regions are classified using SVM algorithm. Based on SVM, the training features are estimated and trained. Once the nodules are detected, they are classified as severe, moderate and very severe. the proposed framework is implemented in Python. Experimental results have shown that Bat-CSO-SVM framework outperforms the existing methods in terms of accuracy, sensitivity, and specificity and F1-score metrics.

关键词： Cancer diagnosis Machine learning optimization algorithm

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4th international conference on Digital Technologies and applications, ICDTA 2024

4th International Conference on Digital Technologies and App...

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4th international conference on Digital Technologies and applications, ICDTA 2024

ISBN: (纸本)9783031686740

the proceedings contain 53 papers. the special focus in this conference is on Digital Technologies and applications. the topics include: Efficient KPIs Analysis: Harnessing the Power of Excel and VBA Programming for Data Visualization and Analysis;A Deep learning-Powered TinyML Model for Gesture-Based Air Handwriting Simple Arabic Letters Recognition;applications of Machine and Deep learning in Funding Decision: A Review;a Deep learning Approach to Potato Late Blight Detection: Developing and Evaluating a Lightweight Single-Stage Detection Model;deep Neural Networks for Automated Metadata Extraction;performance optimization in Agro-Vision by Assessing the Impact of Data Normalization and Standardization on Deep learning Models;digital Revolution and Emerging Opportunities of the Blockchain in Finance;design, and optimization Using Genetic algorithms of a Dual-Band Microstrip Antenna Based on Defective Ground Structure for Wireless Power Transmission applications;analysis and Design of Power Management System for Wireless Power Transfer Circuits;Multiband Antenna Design Using Giuseppe Peano’s Fractal with Graphene for thz applications;blockchain applications in Emergency Logistics: A Bibliometric Study and Future Research Horizons;Towards Efficient Computation Offloading in Multi-user MEC Environments: A Game-theoretic Approach;the Relationship Between Differentiation Strategy and the Achievement of Competitive Advantage;pre-service Teachers’ Perceived Level of Digital Literacy: A Quantitative Study from a Developing Country;navigating Between Conditions and Convictions: Investigating the Influence of Socio-geographical Factors on Interest and Attitudes Toward Artificial Intelligence Among Secondary School Teachers;mapping the Evolution of Open Innovation and Digital Transformation: A Bibliometric Analysis;digital Innovations for Good: A Bibliometric Study of Social Entrepreneurship Digitalization.

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Research on AGV Path Planning Design Based on Reinforcement learning-Ant Colony Algorithm 4

Research on AGV Path Planning Design Based on Reinforcement ...

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4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Chen, Yi Yu, Kaisen Southwest Jiaotong University SWJTU-Leeds Joint School Chengdu China

ISBN: (纸本)9798350361643

In this study, we develop and evaluate a novel path planning methodology that integrates reinforcement learning (RL) with Ant Colony optimization (ACO) to significantly enhance the operational efficacy of Automated Guided Vehicles (AGVs) within complex and dynamic environments. Traditional path planning algorithms, such as A∗ and Dijkstra's, while effective in static and simplistic settings, often fail to deliver optimal performance in environments characterized by their dynamic nature and complexity, primarily due to their inherent limitations including inefficiencies, slow convergence rates, and a tendency to fall into local optima. Addressing these challenges, our proposed methodology leverages the adaptive learning capabilities of RL to intelligently configure the parameters of ACO, eliminating the dependence on empirical parameter selection. this is further complemented by the incorporation of local optimization techniques derived from artificial potential field algorithms, aimed at enhancing path smoothness and improving the capability of AGVs to navigate around obstacles efficiently. Empirical evaluations conducted across a variety of environments laden with obstacles of different configurations underscore the robustness of our approach. © 2024 IEEE.

关键词： Reinforcement learning

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4th international conference on Digital Technologies and applications, ICDTA 2024

4th International Conference on Digital Technologies and App...

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4th international conference on Digital Technologies and applications, ICDTA 2024

ISBN: (纸本)9783031686528

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A Reinforcement learning Driven Iterated Greedy Algorithm for Energy-Efficiency Flexible Job-Shop Scheduling Problem 4

A Reinforcement Learning Driven Iterated Greedy Algorithm fo...

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4th international conference on Computer, Control and Robotics (ICCCR)

作者： Bao, Haizhu Wang, Chuang Pan, Quanke Wang, Bingtao Rong, Miao Yang, Aolei Wang, Xiaohua Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China

ISBN: (纸本)9798350373141;9798350373158

the escalating environmental challenges have sparked significant interest in energy-efficient scheduling as a potent strategy for realizing sustainable development and fostering green manufacturing. this approach is characterized by its dual focus on economic efficiency and energy conservation. this paper tackles the energy-efficient scheduling of the flexible job-shop scheduling problem (EFJSP) with the dual objective of minimizing both makespan and total energy consumption. the mixed-integer linear programming (MILP) model of EFJSP is devised. A reinforcement learning driven iterated greedy algorithm (RLIGA) is proposed to solve the EFJSP. Furthermore, upon a thorough analysis of the problem's characteristics, two optimization strategies-a energy-saving strategy and an acceleration strategy-are developed to enhance the solution further. Extensive benchmark tests have substantiated the superior efficiency and significance of the RLIGA over state-of-the-art algorithms in addressing the EFJSP.

关键词： flexible job-shop energy-efficient scheduling iterated greedy algorithm reinforcement learning multi-objective optimization

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