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

共 13504 条记录，以下是581-590 订阅

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Design, and optimization Using Genetic algorithms of a Dual-Band Microstrip Antenna Based on Defective Ground Structure forWireless Power Transmission applications 4th

Design, and Optimization Using Genetic Algorithms of a Dual-...

引用

4th international conference on Digital Technologies and applications (ICDTA)

作者： En-Naghma, Walid Halaq, Hanan El Ougli, Abdelghani Univ Sidi Mohamed Ben Abdellah Fac Sci Dept Phys Comp Sci Signal Automat & Cognitivism Lab Fes Morocco

ISBN: (纸本)9783031686740;9783031686757

this paper investigates an improved microstrip patch antenna array (MPAA) design using the defective ground structure (DGS) technique operating at a dual-band (at 2.45 GHz and 5.8 GHz) in the ISM (Industrial, Scientific, and Medical) band. this improved MPAA based on DGS contains two identical and symmetrical microstrip rectangular patches fed by an inset feed technique using a single microstrip feed line of each patch. the dimensions of each layer (radiating element, substrate, and ground) are calculated using the transmission line model coded in MATLAB software. then, these dimensions are optimized based on the genetic algorithm (GA) technique which is available in CST MSW (computer simulation technology microwave studio). this novel MPAA design printed on a Rogers RT/Duroid 5880 substrate. the effectiveness of this optimized MPAA using GA is proved by the results obtained by CST software and confirmed by another software called HFSS (high-frequency structure simulator) in terms of its high performance: coefficient reflection, voltage standing wave ratio, directivity, gain, efficiency, and dual-band functionality. the MPAA conception is effective in various wireless power transmission applications to supply different systems ecologically.

关键词： Defective ground structure Microstrip patch antenna Radio-frequency energy harvesting Renewable energy Wireless power transmission

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Research on Intelligent Seedling Environment Monitoring And Early-warning System Based on Machine learning Algorithm 4

Research on Intelligent Seedling Environment Monitoring And ...

引用

4th IEEE international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Yu, Caiping Ren, Dongyan Xie, Zongzhi Guangxi Vocational Normal University School of Computer and Information Engineering China

ISBN: (纸本)9798350363586

With the rise of intelligent agriculture, the intelligent transformation of agriculture has been realised, bringing new development to agricultural production. However, in traditional agriculture, the regulation of seedling growth conditions mainly relies on manual management, and it is difficult to accurately predict and monitor complex ecological changes manually, which increases operational uncertainty and leads to economic losses brought about by low production efficiency. therefore, this study is based on the machine learning-based environmental monitoring and warning system for seedlings, introducing the hardware design, technical framework, machine learning algorithms in the random forest and convolutional neural network applications. through the experimental data, the speed and accuracy of the system in environmental abnormality detection are verified. the experimental results show that the system can effectively warn of environmental risks, provide scientific basis and reduce unnecessary losses. In the future, with the promotion and use of the system, it can greatly promote the effective combination of artificial intelligence and intelligent agriculture, and promote the sustainable development of agriculture. © 2024 IEEE.

关键词： Environmental monitoring

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FSENet: Applying Deep learning to Transparent Lens Counting 24

FSENet: Applying Deep Learning to Transparent Lens Counting

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4th international conference on Computer, Internet of things and Control Engineering

作者： Ai, Shiquan Liu, Chun Hubei Univ Technol Sch Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9798400711848

the accurate identification and counting of transparent lenses is critical in automated production processes, as it directly impacts overall production efficiency. However, current neural network models exhibit limitations in feature extraction, robustness against light interference, and handling sparse features of transparent lenses, making them inadequate for practical applications. Moreover, the task of counting transparent lenses presents several challenges, including blurred edges, lack of distinct textures, and interference from optical reflections. To address these challenges, we propose FSENet. Experimental results demonstrate that our model surpasses existing approaches in the counting task, yielding significant improvements in the MAE and RMSE metrics, with values of 14.54 and 18.47, respectively. these results indicate that FSENet offers enhanced accuracy and stability, positioning it as a more reliable solution for transparent lens counting tasks.

关键词： deep learning object counting Transparent lens counting Multi-scale feature extraction Density map regression

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Comparative Analysis of Encoding Methods in Regression Predicting Models of Bachelor's Final Marks 4th

Comparative Analysis of Encoding Methods in Regression Predi...

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4th international conference on Digital Technologies and applications (ICDTA)

作者： Oqaidi, Khalid Aouhassi, Sarah Mansouri, Khalifa Univ Hassan II Casablanca ENSET Mohammedia Lab M2S2I Casablanca Morocco

ISBN: (纸本)9783031686597;9783031686603

this study assesses the impact of One-Hot and Target Encoding techniques on the accuracy of predicting bachelor's degree final marks in economics and management. Employing regression models across six machine learning algorithms -Linear Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, and Neural Network- we analyze a comprehensive dataset from Hassan II University of Casablanca. this dataset includes both demographic and academic performance data of students. We focus on the application of encoding methods to categorical variables and evaluate model performances based onMean Squared Error (MSE) and Mean Absolute Error (MAE). Our findings highlight the differential effectiveness of encoding techniques in enhancing the precision of predictive models for academic outcomes.

关键词： Regression Academic Performance Feature Engineering Predictive Machine learning Higher Education

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A Method for Anomaly Detection of Encrypted Traffic in Power IoT Base on Security Baseline learning 4

A Method for Anomaly Detection of Encrypted Traffic in Power...

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4th international conference on Digital Data Processing, DDP 2024

作者： Peng, Zhou Qiang, Xu Lizhi, Hu Menglin, Li State Grid Zhejiang Co. Ltd. Information and Communication Branch Hangzhou China China Electric Power Research Institute Information and Communication Branch Beijing China

ISBN: (纸本)9798331515706

As numerous power IoT terminals require secure access to the power grid, how to detect the encrypted malicious traffic in power IoT becomes a challenge. Following an in-depth investigation of potential security risks in power, IoT encrypted traffic, an unsupervised learning method is designed to establish a security baseline for normal business encrypted traffic and to identify the anomaly encrypted traffic. Firstly, a partially seeded K-means clustering algorithm is proposed for security baseline learning to construct a set of clusters for normal encrypted traffic. Secondly, an algorithm tailored for comparing similarity within a cluster is introduced to minimize the costs associated with anomaly detection. At last, experiments are performed to evaluate the effectiveness and performance of this method on real-power IoT encrypted traffic and open VPN encrypted traffic. the results demonstrate that this method has the ability not only to detect the anomaly in business encrypted traffic caused by terminal device error or manual wrong operation but also to identify unknown cyberattacks targeting internal applications to penetrate the secure access gateway by encrypted traffic © 2024 IEEE.

关键词： Encryption algorithms

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Multi-Behavioral Recommender System Based on Multi-View Contrastive learning 4

Multi-Behavioral Recommender System Based on Multi-View Cont...

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4th international conference on Machine learning and Intelligent Systems Engineering (MLISE)

作者： Zhang, Haiyang Gao, Rong Liu, Donghua Wan, Xiang Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China China Waterborne Transport Res Inst Beijing Peoples R China Wuhan Second Ship Design & Res Inst Wuhan Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In modern recommendation systems, diverse user behavior data such as browsing, clicking, and purchasing provide rich information for personalized recommendations. However, effectively integrating and utilizing these varied behavioral data remains a challenge. this paper proposes a multi-behavior recommendation approach based on multi-behavior and contrastive learning. Firstly, multiple user and item views are generated through different masking mechanisms to capture diverse user behavioral characteristics. Subsequently, the LightGCN model is employed to generate embedding representations for users and items, effectively learning the interaction information between them. Next, leveraging contrastive learning methods for the same behaviors across different views involves pulling embedding vectors of similar behaviors closer while pushing those of dissimilar behaviors farther apart, thereby enhancing the model's discriminative power. Finally, aggregation of multiple behavior views and optimization using the Bayesian Personalized Ranking (BPR) loss function aim to maximize ranking differences, further improving recommendation accuracy. Experimental results demonstrate that the proposed approach effectively leverages diverse user behavior data, significantly outperforming traditional single-view and non-contrastive learning-based recommendation methods in terms of recommendation precision and user satisfaction.

关键词： multi-behavior view generation contrastive learning recommendation systems

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Machine learning methods and resources: An overview 4th

Machine learning methods and resources: An overview

引用

4th international conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Goyal, S. Kaur, A. Batra, N. Haryana Mullana-Ambala India

ISBN: (纸本)9781032911571

Machine learning tasks are made simpler by a variety of tools that provide an extensive selection of features and ease of use. One renowned framework that excels at deep learning is TensorFlow, which makes model creation and execution quick and easy. With its extensive library of conventional methods, Scikit-learn make experimentation and implementation easier. PyTorch is a popular tool for dynamic graph analysis that may be used for both production and research purposes. A high-level API called Keras facilitates quick prototyping by streamlining deep learning workflows. Scalability is a key feature of Apache Spark, enabling networked machine learning on big datasets. Together, these technologies improve accessibility and quicken machine learning innovation across domains and sectors. this paper will expose a variety of methods within this paper that may be applied at various points of creating a robust machine learning model. It will also show you through the tasks the program completes, give case studies of a few applications, and describe which methods from machine learning are used to achieve the main objectives of the application. © 2025 the Author(s).

关键词： Adversarial machine learning

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Central Limit theorem for Two-Timescale Stochastic Approximation with Markovian Noise: theory and applications 27

Central Limit Theorem for Two-Timescale Stochastic Approxima...

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27th international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Hu, Jie Doshi, Vishwaraj Eun, Do Young North Carolina State Univ Raleigh NC 27695 USA IQVIA Inc Durham NC USA

Two-timescale stochastic approximation (TTSA) is among the most general frameworks for iterative stochastic algorithms. this includes well-known stochastic optimization methods such as SGD variants and those designed for bilevel or minimax problems, as well as reinforcement learning like the family of gradient-based temporal difference (GTD) algorithms. In this paper, we conduct an in-depth asymptotic analysis of TTSA under controlled Markovian noise via central limit theorem (CLT), uncovering the coupled dynamics of TTSA influenced by the underlying Markov chain, which has not been addressed by previous CLT results of TTSA only with Martingale difference noise. Building upon our CLT, we expand its application horizon of efficient sampling strategies from vanilla SGD to a wider TTSA context in distributed learning, thus broadening the scope of Hu et al. (2022). In addition, we leverage our CLT result to deduce the statistical properties of GTD algorithms with nonlinear function approximation using Markovian samples and show their identical asymptotic performance, a perspective not evident from current finite-time bounds.

关键词： Asymptotic analysis

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Leveraging Machine learning for Signal Processing in Surface Electromyography (sEMG) for Prosthetic Control 4th

Leveraging Machine Learning for Signal Processing in Surface...

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4th international conference on Digital Technologies and applications (ICDTA)

作者： Mhiriz, Zakariae Bourhaleb, Mohammed Rahmoune, Mohammed Mohammed First Univ Natl Sch Appl Sci Res Lab Appl Sci Oujda Morocco

ISBN: (纸本)9783031686498;9783031686504

In this study, we delve into enhancing prosthetic limb control through surface electromyography (sEMG) via machine learning techniques, addressing challenges like signal selectivity and noise. With the backdrop of exponential growth in computational power and artificial intelligence, we employ convolutional neural networks (CNNs), among other algorithms, for feature extraction and movement classification from sEMG data, showcasing CNNs' superiority in automating feature extraction despite higher computational requirements. Our innovative approach introduces an estimation method incorporating joint angle measurements to refine hand movement estimation, aiming to overcome the limitations of traditional classification methods. this method marks a significant step towards achieving more nuanced and adaptable prosthetic control. Findings reveal that machine learning enhances the precision and flexibility of prosthetic control systems, with the potential to significantly improve amputee patients' quality of life by providing more responsive and naturalistic limb control. this study not only contributes to advancing prosthetic control technology but also sets the stage for future research to further optimize prosthetic limb functionality.

关键词： Prosthetic control Surface electromyography (sEMG) Machine learning Signal processing Movement classification

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Agility of in Deep learning algorithms in Developing Conflicting Attacks Protections and to Secure Vulnerabilities 4

Agility of in Deep Learning Algorithms in Developing Conflic...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Asaad, Renas Rajab Abdulrahman, Saman Mohammed Hani, Ahmed Alaa Ahmad, Hawar Bahzad Sallow, Amira Bibo Zeebaree, Subhi R. M. Nawroz University Department of Computer Science Duhok Iraq Nawroz University College of Science Department of Computer Science KRG Duhok Iraq Duhok Polytechnic University Technical College of Engineering Energy Engineering Department Duhok Iraq

ISBN: (纸本)9798350360165

Deep learning methods have brought revolution to a variety of applications: from image recognition to natural language processing, and many others. However, their most important vulnerability, when used in adversarial conditions, is a matter of concern that remains up to today. Most importantly, adversarial attacks insinuate the design of input in a way to be capable of bringing errors in the model, hence exposing significant vulnerabilities. this paper investigates the nature of such vulnerabilities and proposes the enhancement of model robustness through comprehensive methodologies. First of all, adversaries would be considered under the threat landscape and classified according to their complexity and impact, should one materialize. the second section would detail all available defense mechanisms, again classified based on strengths and limitations. We propose using the ensemble of adversaries, regularization methods, and dynamic network architecture. We validate this using the empirical testing of the technique over several datasets and models, subjected to a battery of rigorous tests. © 2024 IEEE.

关键词： Adversarial machine learning

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