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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1-10 订阅

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4th international conference on optimization, learning algorithms and applications, OL2A 2024

4th International Conference on Optimization, Learning Algor...

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4th international conference on optimization, learning algorithms and applications, OL2A 2024

ISBN: (纸本)9783031774317

the proceedings contain 41 papers. the special focus in this conference is on optimization, learning algorithms and applications. the topics include: Facial Expression Recognition in Virtual Reality Simulations;augmented Reality in Industrial Training: A Comparative Analysis of Teaching Methods;comparison Between Single and Multi-objective Clustering algorithms: MathE Case Study;learning algorithms for Breast Cancer Classification and Diagnosis;fuzzy c-Means as a Decision Support Tool for Liver Disease Diagnosis Based on Data Analysis;optimization of Machine learning Models Applied to Robot Localization in the RobotAtFactory 4.0 Competition;p-Fuzzy System Applied to Population Dynamics: A Case Study;enhancing K-Way Circuit Partitioning: A Deep Reinforcement learning Methodology;optimizing Olive Disease Classification through Hybrid Machine learning and Deep learning Techniques;a Neural Network-Based Approach to Identifying Wrinkles and Recommending Cosmetic Products;colorectal Polyp Segmentation: Impact of Combining Different Datasets on Deep learning Model Performance;A Comparative Analysis of MATLAB and Python Neural Networks for Diabetes Prediction;cochineal Colony Detection in Cactus Pear: A Deep learning Approach;enhancing Quadruped Robot Performance through Gait optimization;assessment of Logistics Performance Indicators in Southern European Countries Optimizing the Decision Making;Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine learning Models;route optimization for Urban Last-Mile Delivery: Truck vs. Drone Performance;speeding up Line Search for Composite Objective Function with a Linear Inside Part;the Infodemic Issue: Numerical Modelling;a Multi-objective Approach for Solving Distributed Job Shop Scheduling Problems.

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4th international conference on optimization, learning algorithms and applications, OL2A 2024

4th International Conference on Optimization, Learning Algor...

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4th international conference on optimization, learning algorithms and applications, OL2A 2024

ISBN: (纸本)9783031774256

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optimization of Antenna Performance for IoT applications Using Machine learning Techniques 4

Optimization of Antenna Performance for IoT Applications Usi...

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4th IEEE international conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Rao, Kota Srinivasa Panda, Ribhu Abhusan Bellamkonda, Pragathi GIET University Department of ECE Rayagada India DVR and Dr. HS MIC College of Technology Department of ECE Kanchikacherla India

ISBN: (纸本)9798331528171

the integration of machine learning (ML) into antenna optimization has revolutionized the design and enhancement of antenna systems. this paper provides an in-depth review of the latest advancements in antenna design optimization through ML techniques. the goal of this survey is to highlight various ML optimization methods and their effectiveness, along with the challenges they pose in terms of efficiency. A comprehensive literature review is presented, detailing the optimization algorithms and procedures employed to achieve specific antenna performance metrics, including gain, bandwidth, radiation pattern, and impedance matching. the paper begins with an overview of machine learning and its algorithms, followed by a concise explanation of the antenna optimization process. Subsequently, it introduces different printed antenna designs optimized using ML algorithms. the techniques discussed in this survey are poised to significantly impact the future development of antennas for diverse wireless applications. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Survey on Dimension Reduction for Medical Big Data Using optimization algorithms 4

A Comprehensive Survey on Dimension Reduction for Medical Bi...

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4th international Mobile, Intelligent, and Ubiquitous Computing conference

作者： Abdelminaam, Diaa Salama Abu Alheijat, Ahmed Taha, Mohamed Benha Univ Fac Comp & Artificial Intelligence Banha Egypt Misr Int Univ Fac Comp Sci Cairo Egypt Obour High Inst Informat Dept Comp Sci Cairo Egypt

ISBN: (纸本)9798350367782;9798350367775

this paper surveys dimension reduction techniques in medical big data using optimization algorithms to address challenges like computational inefficiency, overfitting, and interpretability in high-dimensional datasets. As medical data from sources like electronic health records, genomics, and imaging grow, efficient processing is essential for personalized healthcare. the paper explores feature extraction (PCA, LDA) and feature selection methods, emphasizing metaheuristic algorithms like Genetic algorithms (GA), Particle Swarm optimization (PSO), and Ant Colony optimization (ACO). these algorithms enhance machine learning model accuracy by selecting relevant features, reducing computational costs, and handling nonlinear relationships in medical data. applications in diagnosis, treatment prediction, and disease classification are discussed. Future research aims to integrate various optimization strategies and deep learning for more effective dimensionality reduction in healthcare.

关键词： Dimension Reduction Medical Big Data feature selection optimization algorithms machine learning Meta-heuristics

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optimization of Machine Vision Dynamic Monitoring System Based on Deep learning algorithms 24

Optimization of Machine Vision Dynamic Monitoring System Bas...

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4th international conference on Internet of things and Machine learning

作者： Wang, Yuan Chen, Lei Nie, Jiacheng Ma, Wenkai Song, Pengyu Mei, Di China Automot Parts Technol Tianjin Co Ltd Tianjin Peoples R China

ISBN: (纸本)9798400710353

In order to improve the automation and intelligence level of monitoring systems, an algorithm optimization method based on deep learning was analyzed. the results indicate that the comprehensive application of video preprocessing, object detection, object tracking, and anomaly event detection can effectively improve monitoring accuracy and response speed, demonstrating good adaptability and robustness. this provides practical guidance and broad application prospects for the development of intelligent monitoring technology.

关键词： Deep learning Monitoring system Video preprocessing

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A Study of optimizations in Rainfall Prediction using Machine learning Models 4

A Study of Optimizations in Rainfall Prediction using Machin...

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

作者： Sinha, Chaitanya Kumar Sharma, Udit Maragatharajan, M. Manikandan, J. VIT Bhopal University School of Computing Science and Engineering Madhya Pradesh India

ISBN: (纸本)9798331515720

Rainfall prediction is essential for many industries, such as agriculture, water resource management, and disaster relief. Recently, there has been a lot of interest in machine learning techniques to increase the accuracy of rainfall forecasts. To improve rainfall prediction models' accuracy, we provide a hybrid strategy in this study that combines conventional algorithms with cutting-edge optimization techniques. We utilize the XG Boost, Random Forest, and Linear Regression algorithms as baseline models. these are then combined with the Crow Search Algorithm (CSA) optimization technique. Crow collective behavior inspired the nature-inspired metaheuristic algorithm, CSA, which has demonstrated promising results in solving complicated problems. this study intends to improve the baseline models' performance by dynamically adjusting their parameters through the use of CSA. the proposed approach incorporates CSA with the chosen machine learning algorithms, which adaptively modify parameters to increase prediction accuracy. We validate the suggested approach with historical rainfall data and conduct extensive experimentation to show its efficacy. © 2024 IEEE.

关键词： Prediction models

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A Survey of Neural Network optimization algorithms 4

A Survey of Neural Network Optimization Algorithms

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4th IEEE international conference on Data Science and Computer Application, ICDSCA 2024

作者： Ji, Chengwei School of Data Science Fudan University Shanghai China

ISBN: (纸本)9798350368239

this paper aims to explore seven commonly used optimization algorithms in deep learning: SGD, Momentum-SGD, NAG, AdaGrad, RMSprop, AdaDelta, and Adam. Based on an overview of their theories and development histories, this paper constructs convolutional neural networks and BERT to conduct numerical experiments on these optimization algorithms. the experimental results show that among the seven optimization algorithms, SGD may get stuck in saddle points, while the other six algorithms developed based on SGD have the ability to escape from saddle points. Compared to other algorithms, Adam converges the fastest and the parameters obtained perform better in practice. ©2024 IEEE.

关键词： optimization algorithms

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Covariance Control of an Earth-to-Mars Transfer with Control Actuation Errors 1

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4th international conference on optimization learning algorithms and applications

作者： Benedikter, Boris Zavoli, Alessandro Univ Arizona Tucson AZ 85721 USA Sapienza Univ Rome Rome Italy

ISBN: (数字)9783031774324

ISBN: (纸本)9783031774317;9783031774324

this paper presents a convex optimization approach to design a covariance control policy for an interplanetary mission of a spacecraft subject to non-Gaussian control actuation errors. the goal is to develop a closed-loop policy that minimizes the control effort and ensures that the system's final state aligns with a desired probability distribution. this is achieved by formulating an optimal covariance control problem, which is then transformed into a sequence of deterministic convex optimization problems using state-of-the-art convexification techniques. Two noise models are devised to conveniently incorporate the perturbations in an optimal control framework with low computational complexity. the first is a (conservative) additive white Gaussian noise model, while the second is a more advanced multiplicative noise model, better suited to capture the system's noise characteristics.

关键词： Stochastic Optimal Control Covariance Control Convex optimization Trajectory optimization

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Efficient optimization in Dispensation of Cloud applications using Novel Stochastic Gradient with Forward Back Propagation Algorithm 4

Efficient Optimization in Dispensation of Cloud applications...

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

作者： Anithaashri, T. P. Loganathan, V Lalitha, R. Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Saveetha Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9798350386356;9798350386349

the effective use of cloud server is a major challenge in the recent days. To handle the computation speed, space complexity, network accessibility is playing vital role in achieving the efficiency of cloud server. the objective is to obtain the energy efficiency in cloud servers using a novel stochastic gradient with forward back propagation algorithm. Two sample groups with number of iterations 20 each have been tested with G-power of 80% with the total sample size of 360, divided into two groups with Group 1=180, Group 2=180 and an independent sample t-test has been done. To improve the accuracy of energy optimization, a novel stochastic gradient with forward back propagation algorithm is proposed and compared with the machine learning algorithm. the results prove that the novel stochastic gradient with forward back propagation algorithm has a high accuracy of 92.1%, which is significantly better than the other machine learning algorithms. the level of significance p=0.02 (p<0.05) shows that there is a significant difference between these two algorithms. thus, the cloud server optimization is achieved using stochastic gradient with forward back propagation algorithm through various testbeds.

关键词： Cloud applications Energy optimization Energy Consumption Novel Stochastic Gradient Cloud Server

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Traditional Ceramic Manufacturing Process optimization Method Based on Cloud Computing and Deep learning algorithms 24

Traditional Ceramic Manufacturing Process Optimization Metho...

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4th international conference on Internet of things and Machine learning

作者： Yang, Shiyu Liu, Dazhi Dankook Univ Yongin South Korea Xiamen Acad Arts & Design Xiamen Peoples R China Fuzhou Univ Fuzhou Peoples R China

ISBN: (纸本)9798400710353

In order to improve the quality of the products and the efficiency of production, the traditional process for making ceramics has some limit on cost, yield, and a product conformity rate, so the traditional process needs to be optimized. In this paper, the performance of various optimization methods such as CNN, RNN, and traditional process were empirically compared in ceramic fabrication processes. By conducting the conformity test 20 times, the conformity of product, cost, and yield was evaluated. the experimental results show the proposed optimization techniques using CNN and RNN significantly improved ceramic fabrication processes with respect to the traditional process. the CNN-based method further achieved the highest pass rate, with rates ranging from 97.1% to 98.9%, followed by the RNN-based method from 96.6%, to 98.5%, and the traditional-based method from 94.7% to 95.9%. thus, it is suggested that deep learning algorithms may have the potential to improve the conformity of products and reduce costs in manufacturing ceramic products.

关键词： Ceramic Making Process Cloud Computing Deep learning Algorithm Loss Function

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