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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71-80 订阅

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Estimate and Predict the Foreign Currency Exchanging Rates by Using Adaptive Neuro Fuzzy Inference System and Deep learning algorithms 4

Estimate and Predict the Foreign Currency Exchanging Rates b...

引用

4th international conference on Information Communication and Software Engineering (ICICSE)

作者： Bai, Ying Wang, Dali Johnson C Smith Univ Dept Comp Sci & Math Charlotte NC 28216 USA Christopher Newport Univ Dept Phys & Comp Sci Newport News VA 23606 USA

ISBN: (纸本)9798350351705;9798350351699

To correctly and accurately predict and estimate the exchanging rates among various currencies to get the maximum profit is a challenging task, and it is critical important to all financial institutions under the current fluctuation situation. In this study, we try to use different AI methods and algorithms, such as Adaptive Neuro Fuzzy Inference System ( ANFIS) and Deep learning (DL), to easily and correctly predict and estimate the current currency exchanging rate. Combining with some appropriate pre-data-processing techniques, the current currency exchanging rates could be accurately and quickly estimated via those models. In this research, both algorithms are designed and built to help decision makers working in the financial institutions to easily and conveniently predict the current exchanging rates. the minimum training and checking RMSE values for ANFIS model can be 0.0009828 and 0.001713. the minimum MSE value for DL model is 0.0000047 with a regression value of 0.9958.

关键词： ANFIS algorithm deep learning estimate and predict exchanging rates for currencies AI applications in financial implementations

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optimization of Machine learning Models Applied to Robot Localization in the RobotAtFactory 4.0 Competition 1

引用

4th international conference on optimization learning algorithms and applications

作者： Klein, Luan C. Mendes, Joao Braun, Joao Martins, Felipe N. Fabro, Joao Alberto Costa, Paulo Pereira, Ana I. Lima, Jose Univ Tecnol Fed Parana UTFPR PR Campus Curitiba Toledo Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Inst Politecn Braganca Associated Lab Sustainabil & Technol Mt Reg SusTE Braganca Portugal Univ Porto Fac Engn Porto Portugal INESC Technol & Sci Porto Portugal Hanze Univ Appl Sci Sensors & Smart Syst Grp Inst Engn Groningen Netherlands

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

Several approaches have been developed over time aiming to improve the localization aspects, especially in mobile robotics. Besides the more traditional techniques, mainly based on analytical models, artificial intelligence has emerged as an interesting alternative. the current study proposes to explore the machine learning model structure optimization for pose estimation, using the RobotAtFactory 4.0 competition as the main context. Using a Bayesian optimization-based framework, the parameters of a Multi-Layer Perceptron (MLP) model, trained to estimate the components of the 2D pose (x, y, and theta) of the robot were optimized in four different scenarios of the same context. the results obtained showed a quality improvement of up to 60% on the estimation when compared with the modes without any optimization. Another aspect observed was the different optimizations found for each model, even in the same scenario. An additional interesting result was the possibility of the reuse of optimization between scenarios, presenting an interesting approach to reduce time and computational resources.

关键词： Localization Machine learning optimization Robotics

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Augmented Reality in Industrial Training: A Comparative Analysis of Teaching Methods 1

引用

4th international conference on optimization learning algorithms and applications

作者： Barros, Pedro Leite Goncalves, Gil Pinto, Vitor H. Univ Porto SYSTEC DIGI2 ARISE Fac Engn Rua Dr Roberto Frias P-4200465 Porto Portugal Univ Porto Dept Elect & Comp Engn Fac Engn Rua Dr Roberto Frias P-4200465 Porto Portugal

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

this paper presents a comparative analysis of three distinct training methods - Augmented Reality, Guided Instruction, and Self-Directed Instruction - within an industrial setting, specifically a car manufacturing plant. the study assesses the efficacy of these methods across several metrics including learning efficiency, user satisfaction, and skill retention. the research highlights how Augmented Reality (AR) can enhance the training process by reducing time to competency, improving engagement, and fostering a deeper understanding of complex tasks. the findings indicate that AR, despite its initial learning curve, can outperform traditional methods in several key areas, notably in reducing the time required for training and enhancing participant motivation. the study also explores the emotional responses of participants, providing insights into the user experience associated with each training method. this paper argues for the broader adoption of AR in training modules, suggesting that AR technology can lead to more efficient, engaging, and effective training solutions, thus supporting the ongoing transformation towards Industry 4.0.

关键词： learning theory Augmented Reality Education

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Computer Vision for Detecting Attentional Behaviors 1

引用

4th international conference on optimization learning algorithms and applications

作者： Piza, Caio Bombacini, Marcos Roberto Lima, Jose Inst Politecn Braganca Campus Santa Apolonia P-5300253 Braganca Portugal Univ Tecnol Fed Parana Rua Cristo Rei 19 Toledo PR Brazil Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Inst Politecn Braganca Lab Sustentabilidade & Tecnol Regioes Montanha Su Braganca Portugal

ISBN: (数字)9783031774324

ISBN: (纸本)9783031774317;9783031774324

Nowadays, there is the paradox of technology: although smartphones have revolutionized our way of living, bringing convenience and connectivity, they have also introduced new challenges, notably distracted driving. this paper addresses the issue of visual distraction, one of the main contributors to traffic accidents, through the development of an innovative system that combines the application of convolutional neural networks and the functionality of mobile devices. the adopted methodology focused on the collection of a broad set of images to train an artificial intelligence model capable of classifying a qualitative variable with two distinct categories: attention and distraction of a driver. In particular, the study concentrated on creating a mobile application that uses a smartphone's camera to monitor the driver and issue auditory alerts if it detects prolonged distraction. the achieved results highlighted the efficacy of the model, especially after its optimization for the TensorFlow Lite format, suitable for implementation on mobile devices due to its efficiency in terms of speed and resource consumption.

关键词： traffic safety convolutional neural networks mobile applications distraction detection

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Survey of Gradient Descent Variants and Evaluation Criteria 4

Survey of Gradient Descent Variants and Evaluation Criteria

引用

4th international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Hajji, Morad Benhala, Bachir Hamdi, Imad Moulay Ismail University Image Laboratory Superior School Of Technology Meknes Morocco Sidi Mohammed Ben Abdellah University Lisac Laboratory Faculty Of Sciences Dhar El Mahrz Fez Morocco

ISBN: (纸本)9798350309508

Gradient descent is a fundamental optimization algorithm widely used in artificial intelligence to minimize the loss function and find the optimal parameters of a model, so optimize the learning process. It is one of the most important algorithms for training various types of models, such as neural networks, support vector machines, linear regression, and so on. It arouses the keen interest and enthusiasm of a very large group of researchers. Indeed, there has been a surge in research on gradient descent variants and their applications in various fields to enhance the efficiency, convergence speed and rate, accuracy of the optimization process, and generalization performance of the algorithm over the years. the large number of gradient descent variants, their variety and diversity lead to confusion and ambiguity. In addition, these variants are scattered and dispersed in the mass of scientific research, which is a source of undecidability in choosing an appropriate variant. In order to overcome this issue and address this challenge, we provide a comprehensive survey of gradient descent variants and evaluation criteria to support researchers, practitioners, readers, and others to make informed decisions when choosing an optimization algorithm for their artificial intelligence project, leading to more efficient and effective optimization processes. © 2024 IEEE.

关键词： optimization

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ST - Minitrack Quantum Computing applications 57

ST - Minitrack Quantum Computing Applications

引用

57th Hawaii international conference on System Sciences (HICSS)

作者： Maass, Wolfgang Wilhelm-Mauch, Frank Struckmeier, Frederick German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany Forschungszentrum Julich PGI Julich Germany TRUMPF SE Co KG Ditzingen Germany

ISBN: (纸本)9780998133171

Quantum computing is a rapidly evolving field with a wide range of applications in diverse industries. Hybrid quantum computing combines classical and quantum computing to solve complex problems, quantum machine learning is a subset of machine learning that leverages quantum computing for optimization, and quantum embeddings use quantum algorithms to process high dimensional data. Quantum computers have the potential to revoltionize finance, industry, production, drug research, and even consumer-facing services. With quantum computing's ability to handle massive amounts of data and perform complex calculations at incredible speeds, it holds great promise for addressing some of the world's most pressing problems in these areas.

关键词： Quantum algorithms Quantum Machine learning Quantum optimization Quantum Cryptography Quantum Simulation

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learning to Beamform for Unmanned Aerial Vehicle Assisted Dual-Functional Radar-Communication Systems 4

Learning to Beamform for Unmanned Aerial Vehicle Assisted Du...

引用

4th international conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2024

作者： Qiu, Qianwei Wang, Peng Guangdong University of Technology Guangzhou China

ISBN: (纸本)9798350378542

this paper addresses the challenge of designing joint transmit and receive beamforming for dual-function radar-communication (DFRC) systems. the system model under consideration involves multiple communication users and an unmanned aerial vehicle (UAV). An optimization problem is formulated to maximizing the achievable sum rate for communication user and Base Station (BS) receiver while satisfying transmit and receive beamforming constant modulus constraints. this represents a standard nonconvex optimization problem. To solve it, we introduce an online learning approach by configuring adjustable network parameters and creating the concerned loss function. Simulation results show that the proposed learning algorithm achieves a higher achievable sum rate for communication user and BS receiver compared to existing algorithms. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Patterns Recognition of Sentimental Patterns from the X-Dataset using Deep learning Algorithm 4

Patterns Recognition of Sentimental Patterns from the X-Data...

引用

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

作者： Konduri, Praveen S R Department of Data Science School of Computing Mohan Babu University Tirupati India

ISBN: (纸本)9798331515720

this research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. the study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. the results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. this research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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H Yperparameter Tunning to Improve Deep learning Model Performance 4

H Yperparameter Tunning to Improve Deep Learning Model Perfo...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Bhandari, Manav Pawar, Mrunmayee Kulkarni, Smita School of E&tc Engineering Mit Academy of Engineering Alandi Pune India

ISBN: (纸本)9798331540364

Deep learning models have become a powerful tool in various domains, including fraud detection in credit card transactions. In this paper, we explore the crucial aspect of hyperparameter tuning to enhance the performance of deep learning models in the context of credit card fraud detection. We investigate four different combinations of optimization algorithms (Adam and Adagrad) and weight initializers (Xavier and He) with and without batch normalization. through comprehensive experimentation using Convolutional Neural Networks (CNNs) on a credit card fraud detection dataset, we find that the combi-nation of Adam optimization with batch normalization and the He initializer consistently yields the highest accuracy compared to the other configurations. Our study not only highlights the significance of selecting appropriate hyperparameters but also provides insights into achieving superior performance in fraud detection tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Reinforcement learning Algorithm Based Stress Wave Modal optimization and Deicing Efficiency of Transmission Line 4

Reinforcement Learning Algorithm Based Stress Wave Modal Opt...

引用

4th international conference on Energy Engineering and Power Systems, EEPS 2024

作者： Ding, Qi Deng, Shicong Luo, Yu Niu, Xiaolei Wu, Rongle Wuzhou Bureau of China Southern Power Grid Uhv Transmission Company Guangxi Wuzhou543000 China

ISBN: (纸本)9798350366914

this study introduces a reinforcement learning-based stress wave modal optimization method to improve the de-icing efficiency of transmission lines. Detailed experiments demonstrated that the optimized stress wave parameters significantly enhanced de-icing performance, reducing de-icing time by 30%, decreasing energy consumption by 25%, and increasing de-icing area by 40%. the reinforcement learning algorithm exhibited strong convergence and stability, as indicated by consistent reductions in the loss function and increases in reward values under various conditions. these findings validate the proposed method's effectiveness and reliability in optimizing stress wave applications for de-icing processes. © 2024 IEEE.

关键词： Adversarial machine learning

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