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

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the Application of Imaging Methods and Machine learning in the Agroindustry Sector at Production Activity 4th

The Application of Imaging Methods and Machine Learning in t...

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4th international conference on Advanced Research in Technologies Information Innovation and Sustainability-ARTIIS-Annual

作者： Vardasca, Ricardo Pratas, Antonio Tereso, Marco Bento, Fernando ISLA Santarem Santarem Portugal Univ Porto LAETA INEGI Porto Portugal Univ Evora VISTA Lab ALGORITMI Evora Portugal ISCTE IUL ISTAR Lisbon Portugal

ISBN: (纸本)9783031832093;9783031832109

the agricultural production sector is of great interest to national economies, their GDP and the population's food chain. During digitalization and modernization, imaging techniques and machine learning are important driving factors. In this research, a narrative critical literature review was conducted on the most used imaging modalities and their use with machine learning methods, which are examined along with their applications in the agricultural industry in production activities, as well as their limitations and existing challenges. this research is intended to support a further development of a technological framework for intelligent precision agroindustry production. It was found that the most used imaging methods are hyperspectral and multispectral imaging, infrared thermal imaging, magnetic resonance imaging, X-ray imaging, scanning electron microscopy and ultraviolet imaging. the majority of employments of machine learning along with imaging was using supervised learning algorithms, there were a few applications using unsupervised and reinforcement learning algorithms. From the results and analysis, it can be concluded that the use of imaging techniques enables an increase in quality and profit maximization of agro-industrial products and their characterization data, which can be better analyzed with the help of machine learning methods and lead to a more sustainable and innovative agro-industrial productive sector.

关键词： Agroindustry Imaging Machine learning Production activity

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Smart Management of Electric Vehicle Chargers through Reinforcement learning 4

Smart Management of Electric Vehicle Chargers Through Reinfo...

引用

4th international conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Abdullah, Heba M. Gastli, Adel Ben-Brahim, Lazhar Qatar University Department of Electrical Engineering Qatar Qatar University College of Engineering Qatar

ISBN: (纸本)9798350306262

A reinforcement learning (RL) based EV charging management system is developed for the charger coordination problem. RL can handle system uncertainties, requires no historical data, and is not affected by future changes to the system. the EV charging coordination reduced peak demand, required charging infrastructure and increased solar power utilization. the proximal policy optimization (PPO) is used to train the RL to reach targets and the results are compared with other benchmark algorithms. © 2024 IEEE.

关键词： Reinforcement learning

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Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement learning 24

Research on Autonomous Driving Decision-making Strategies ba...

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4th international conference on Internet of things and Machine learning

作者： Wang, Zixiang Yan, Hao Wei, Changsong Wang, Junyu Bo, Shi Xiao, Minheng Syracuse Univ Coll Engn & Comp Sci Syracuse NY 13244 USA Univ Calif Los Angeles UCLA Comp Sci Dept Los Angeles CA USA Boston Univ Grad Sch Arts & Sci Boston MA 02215 USA Ohio State Univ Dept Integrated Syst Engn Columbus OH 43210 USA

ISBN: (纸本)9798400710353

the behavior decision-making subsystem is a key component of the autonomous driving system, which reflects the decision-making ability of the vehicle and the driver, and is an important symbol of the high-level intelligence of the vehicle. However, the existing rule-based decision-making schemes are limited by the prior knowledge of designers, and it is difficult to cope with complex and changeable traffic scenarios. In this work, an advanced deep reinforcement learning model is adopted, which can autonomously learn and optimize driving strategies in a complex and changeable traffic environment by modeling the driving decision-making process as a reinforcement learning problem. Specifically, we used Deep Q-Network (DQN) and Proximal Policy optimization (PPO) for comparative experiments. DQN guides the agent to choose the best action by approximating the state-action value function, while PPO improves the decision-making quality by optimizing the policy function. We also introduce improvements in the design of the reward function to promote the robustness and adaptability of the model in real-world driving situations. Experimental results show that the decision-making strategy based on deep reinforcement learning has better performance than the traditional rule-based method in a variety of driving tasks.

关键词： Autonomous Driving Decision-making Strategy Deep Reinforcement learning Proximal Policy optimization

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applications of GA and Hybrid GA in various domains: Design and Prospects 4

Applications of GA and Hybrid GA in various domains: Design ...

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4th IEEE international conference on Data Engineering and Communication Systems, ICDECS 2024

作者： Darius, Preethi S H Devadason, Joshua Rajah Solomon, Darius Gnanaraj Cmr Institute of Technology Department of Cse Bengaluru India Coimbatore Institute of Technology Department of Cse Coimbatore India School of Mechanical Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350393354

One of the most widely adapted algorithms in bioinspired optimization techniques is the Genetic Algorithm (GA). It has been used extensively in solving problems that require a Pareto-optimal solution for bi-objective optimization problems. the general working of GA followed by diverse applications, particularly in resource scheduling is exemplified in this work. the application of GA in flexible job shop scheduling problems (FJSSP), University Course scheduling, the role of GA in preventive maintenance, automated manufacturing systems, cross-deck scheduling, and conflict-free scheduling of Automatic Guided Vehicles (AGV) are studied. the formulation of the fitness function and various strategies used to improve the existing crossover or mutation are examined. Some heuristics that are inspired by GA and the effect of combining GA with other heuristics are also reported. GA with ANN, GA with Ant-Colony optimization, Multi-objective GA (MOGA), and GA with random forest learning algorithm are explored. the chromosome representation, the various selection, crossover, and mutation strategies are compared. the hybrid versions of GA show an improvement in the quality of solutions and comparatively lesser deviation from the optimal solutions. the increasing time complexity is a demerit in such versions and there is scope for further work in proposing a better algorithm with lower time complexity. the merits and shortcomings of GA and the scope for further research in this area are put forth. © 2024 IEEE.

关键词： Genetic algorithms

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Research on intelligent adaptive optimization model system of computing system based on reinforcement learning 4

Research on intelligent adaptive optimization model system o...

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

作者： Ma, Jun Sun, Qian Chengdu Neusoft University Sichuan Chengdu611844 China PowerChina Sichuan Electric Power Engineering Co. Ltd. Sichuan Chengdu610041 China

ISBN: (纸本)9798350368239

this paper achieves the dynamic optimization of system performance through the application of Q-learning and deep reinforcement learning (DQN). In traditional systems, decision-making mechanisms often lack self-adjusting capabilities for various requirements. By using adaptive optimization techniques, the system can make real-time adjustments according to the feedback from the operating environment, thus optimizing the decision-making process. the system is mainly composed of two parts: algorithm design and model simulation. Simulation results indicate that in multiple test scenarios, this system significantly enhances the operational efficiency and performance. Data analysis reveals that compared with traditional static mechanisms, the system's task completion rate has increased by 15% and the operation time has been reduced by 12%. Moreover, the universality of the adaptive optimization mechanism in different operating scenarios has been verified through precise analysis of simulation data. In conclusion, this study demonstrates that the adaptive system optimization based on reinforcement learning can not only remarkably improve the performance of individualized decision-making, but also provide effective theoretical support and technical reference for the design of future intelligent systems. ©2024 IEEE.

关键词： Deep reinforcement learning

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Enhancing Wireless Communication Efficiency: Channel Models and optimization Techniques 4

Enhancing Wireless Communication Efficiency: Channel Models ...

引用

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

作者： Shekdar, Rohit Rayar, Vijay Kle technological University's Dr. Msscet Department of Electronics and Communication Engineering Belagavi India

ISBN: (纸本)9798350352931

Wireless communication technology is an emerging field with a wide range of practical applications, such as environmental monitoring and the development of smart cities. Various factors such as signal transmission, path loss, interference, and environmental conditions have become major factors that affect channel efficiency in communication channels for such applications. this paper provides an exhaustive review of different channel models, including deterministic, empirical, and stochastic ones. the paper discusses their specific applications, the mathematical formulations associated with them, and presents a comprehensive analysis of these models that are largely based on key parameters. Additionally, this paper examines several optimization techniques that could potentially be applied to enhance channel modeling while reducing prediction errors that can enhance the reliability and efficiency of wireless communication systems. © 2024 IEEE.

关键词： interference optimization in channel modeling path loss Signal attenuation Wireless communication

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Logo Detection Using Machine learning Algorithm: A Survey 4

Logo Detection Using Machine Learning Algorithm: A Survey

引用

4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023

作者： Sanghvi, Jay Rathod, Jay Nemade, Sakshi Panchal, Hasti Pavate, Aruna Department of Information Technology St. Francis Institute of Technology Mumbai India

ISBN: (纸本)9781665459877

As more and more logos are produced, logo detection has gradually grown in popularity as study across numerous jobs and sectors. Deep learning-based solutions, which make use of numerous data sets,learning techniques, network designs, etc., have dominated recent advancements in this field. this research examines the progress made in the field of logo detection using deep learning approaches. In order to evaluate the efficacy of logo detection algorithms, which tend to be more diversified, difficult, and realistically reflective of real life, we first discuss a thorough background of the topic. the pros and disadvantages of each learning approach are then thoroughly analysed, along with the current logo detection *** wrap up this study, we examine probable obstacles and provide the future directions for logo detecting development. © 2023 IEEE.

关键词： Computer vision

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An Embedded 3D Object Detection in Real-Time Using Deep learning Techniques 4

An Embedded 3D Object Detection in Real-Time Using Deep Lear...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Aravinda, K. New Horizon College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350300888

the field of computer vision is currently experiencing significant growth in the area of real-time 3D object identification. this research aims to efficiently and precisely identify and locate objects in a three-dimensional space. the demand for efficient detection approaches has escalated due to the growing prevalence of sensors such as LiDAR and depth cameras in various applications, including autonomous vehicles and augmented reality. this study explores the most recent developments in utilizing deep learning algorithms for real-time 3D object recognition. It is worth mentioning that algorithms such as PointRCNN, VoteN et, and 3D-CNN s have exhibited the capability to effectively handle large and sparse point cloud data. Nevertheless, there are ongoing challenges that continue to exist in this field, including the issue of limited availability of data and the high computational demands required for real-time processing. By utilizing optimal neural network designs and integrating multi-modal sensor input, the systems presented demonstrate the potential to overcome these problems. this study provides novel research insights on the prospective trends by highlighting the potential of end-to-end learning and domain adaptability. © 2023 IEEE.

关键词： Object detection

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Resnet-53 for Alzheimer's Disease Detection from MRI Images and Analysis with SVM Tuning with Hyper optimization Technique 4

Resnet-53 for Alzheimer's Disease Detection from MRI Images ...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Rama Lakshmi, B. Radhika, Y. Department of Computer Science and Engineering AP Visakhapatnam India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

the research utilizes a dataset containing biomarkers and cognitive traits, utilizing advanced machine learning algorithms for early detection and accurate diagnosis of Alzheimer's disease globally. Gaussian filter and bilateral filter to produce an adaptive filter, combining it with Gaussian filtering allows the model to optimize the smoothing and enhancement process depending on local characteristics by adaptively filtering various sections of the picture. After reducing the noise from the images, this output is given to the RESNET for extraction of features from the images. Here, the customized three layers of RESENT-50 to RESNET-53. As a result, feature learning may become more efficient and discriminative. Brain biomarkers can be used to classify patients using information from multi-modal imaging, such as DTI and structural MRI. CNN techniques have been shown to be effective tools for enhancing image-based classification recently. In order to reduce residual errors, the process begins with the extraction of features using ResNet, with a preference for skipping connections. ResNet50 was selected because to its outstanding picture analysis and classification skills. the model's parameters are adjusted in accordance with the difference between the predicted and actual class scores. the SVM model is fine-tuned in the final layer, particularly with regard to Alzheimer's detection for binary and multiclass assignments. In order to improve the model's performance on the validation set, Bayesian optimization using Hyperopt methodically investigates the hyperparameter space by improving parameters like regularization and kernel selection. In this proposed work, we have used transfer learning with Resnet-Based Image Analysis with SVM Tuning to conduct different classification of Alzheimer disease. these kinds of representations are learnable from the data using deep learning algorithms. the SVM model is fine-tuned in the final layer, particularly with regard to Alzheime

关键词： Adaptive filtering

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Optimizing Feature Selection for Industrial Casting Defect Detection Using QLESCA Optimizer 1

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12th international conference on Robotics Vision Signal Processing and Power applications

作者： Hamad, Qusay Shihab Salehe, Sami Abdulla Mohsen Suandi, Shahrel Amin Samma, Hussein Hamad, Yasameen Shihab Al Amoudi, Ibrahim Univ Sains Malaysia Sch Elect & Elect Engn Intelligent Biometr Grp Engn Campus Nibong Tebal 14300 Penang Malaysia Univ Informat Technol & Commun UOITC Baghdad Iraq King Fand Univ Petr & Minerals SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran Saudi Arabia Minist Educ Baghdad Iraq

ISBN: (数字)9789819990054

ISBN: (纸本)9789819990078;9789819990054;9789819990047

Feature selection is critical in fields like data mining and pattern classification, as it eliminates irrelevant data and enhances the quality of highly dimensional datasets. this study explores the effectiveness of the Q-learning embedded sine cosine algorithm (QLESCA) for feature selection in industrial casting defect detection using the VGG19 model. QLESCA's performance is compared to other optimization algorithms, with experimental results showing that QLESCA outperforms the other algorithms in terms of classification metrics. the best accuracy achieved by QLESCA is 97.0359%, with an average fitness value of - 0.99124. the proposed method provides a promising approach to improve the accuracy and reliability of industrial casting defect detection systems, which is essential for product quality and safety. Our findings suggest that using powerful optimization algorithms like QLESCA is crucial for obtaining the best subsets of information in feature selection and achieving optimal performance in classification tasks.

关键词： Machine learning Support vector machine (SVM) classifier Convolutional neural networks Swarm intelligence Feature engineering

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