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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381-390 订阅

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A Music Recommender System to Identify the Emotional State of a Person Using Machine learning algorithms 4

A Music Recommender System to Identify the Emotional State o...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. the system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. this study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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On optimization of Traffic Sign Recognition using Improved YOLOv5 Algorithm 24

On Optimization of Traffic Sign Recognition using Improved Y...

引用

4th international conference on Artificial Intelligence, Automation and algorithms

作者： Zhou, Yunpeng Mondal, Subrota Kumar Macau Univ Sci & Technol Sch Comp Sci & Engn Macau Peoples R China

ISBN: (纸本)9798400717840

Traffic sign recognition technology has a very important role in modern intelligent transportation systems. It can help vehicles to automatically identify and understand traffic signs on the road, so as to better maintain traffic order and improve driving safety. However, we observe that some of the traffic sign recognition algorithms cannot meet the needs of network deployment on in-vehicle devices, these algorithms are typically larger in size and have higher computational requirements and hardware limitations. To address these issues, we design a traffic sign recognition method with an improved YOLOv5 algorithm. First, we improve the C3 module, which is a convolutional neural network module for extracting features from input images in YOLOv5. We modify the C3 module based on the FasterNet block in FasterNet and name the modified module as C3-Faster. then the C3-Faster module is introduced to make the model lightweight. Secondly, Efficient Channel Attention (ECA) mechanism is introduced to improve the capability of feature extraction. Experiments are conducted with the TT100K Chinese traffic sign dataset. According to the results, the algorithm exhibits substantial enhancements in many respects. Compared to the standard YOLOv5 algorithm, the main Average Precision (mAP), Precision (P) and Recall (R) are improved by 2.9, 2, and 4 percent respectively. Alongside the model size, parameters and computational effort are reduced by 17, 19 and 17 per cent respectively. the algorithm also achieves 93.3 frames per second (FPS), which satisfies the need for real-time detection in vehicles. the results of this algorithm have practical implications for intelligent traffic applications for in-vehicle devices.

关键词： Traffic Sign Recognition YOLOv5 Attention Mechanism FasterNet

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optimization of Vegetable Replenishment and Pricing Strategies Based on Data Mining 4

Optimization of Vegetable Replenishment and Pricing Strategi...

引用

4th IEEE international conference on Electronic Communications, Internet of things and Big Data, ICEIB 2024

作者： Wang, Xiaolong Guan, Junlin Cao, Yang Zhao, Jing Chen, Taotao Jinxin, L. College of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350360721

In the digital era, vegetable sales forecasting and strategy optimization are important for superstores. therefore, we developed an innovative integrated method combining Long Short-Term Memory Network (LSTM), Random Forest (RF), and Genetic Algorithm (GA) to optimize the pricing and replenishment decisions of vegetables on sale. First, the LSTM model was used to accurately predict the future vegetable sales trend to provide a scientific basis for decision-making. Next, the key factors in the sales data were analyzed using RF to identify the variables that affect the sales volume. Finally, GA was used to optimize the price and replenishment strategy based on the prediction results to maximize profits and balance market demand. this integrated approach improves the accuracy of sales prediction and optimizes the replenishment and pricing decision-making process through intelligent algorithms. then, the operational efficiency of superstores improves significantly. the developed effectively responds to market changes, supports superstore management, and demonstrates its potential for applications in the retail industry. © 2024 IEEE.

关键词： Genetic algorithms

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Short-term load forecasting for smart grids based on NARX neural networks 4

Short-term load forecasting for smart grids based on NARX ne...

引用

4th international conference on Advanced algorithms and Neural Networks, AANN 2024

作者： Ning, Yongjie Song, Ningxi Yuan, Fang State Grid Henan Electric Power Company Information Communication Branch Henan Zhengzhou450052 China

ISBN: (纸本)9781510686106

Accurate load forecasting is of great significance for the planning and operation of smart grids. In order to further improve the accuracy of short-term load forecasting, an AGPSO-NARX load forecasting model is proposed, and the input information of the neural network is selected. Firstly, the particle swarm optimization algorithm is optimized by introducing variable weights, variable learning factors, and selection, crossover, and mutation operations, and the initial thresholds and weights of the NARX neural network are optimized based on this algorithm. Secondly, through experiments, suitable influencing factors are selected as inputs to the network to further improve the prediction accuracy. Finally, two scenarios, winter and summer, are considered to test the predictive ability of the neural network model through examples. the results show that the overall error and single-point error of the model are significantly reduced, indicating a high prediction accuracy. © 2024 SPIE.

关键词： Particle swarm optimization (PSO)

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Analysis of Machine learning Based Credit Card Transaction and its applications 4

Analysis of Machine Learning Based Credit Card Transaction a...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Venkatachalam, S. Priyadarsini, P. Jayasree, K. Pawar, Vikas Bhimrao Sasikala, R. Loganathan, S. Karpagam Academy of Higher Education Department of Management Coimbatore India Tagore Engineering College Department of Management Studies Chennai India Panimalar Engineering College Department of Master of Business Administration Chennai India Bharati Vidyapeeth College of Engineering Department of Applied Science Navi Mumbai India Department of Mba Chennai600077 India Cse Vinayaka Mission's Kirupananda Variyar Engineering College Salem India

ISBN: (纸本)9798350343670

In this paper fraudulent crediting of amounts is the primary challenge that clients encounter in the finance sector. On the other side, frauds have accompanied credit card innovation since it began. Many rule-based techniques that were used in the past to identify fraud were ineffective at managing the large number of variables. However, in order to prevent customers from paying for more credit, it is imperative to detect fraud. Machine learning techniques in an effort to combat corruption, the government is also pushing digital currency in this day and age. Although many people use credit cards and ATM cards to complete their transactions, they are not aware of the potential for fraud. An attacker utilizes the information of others to perform a fraudulent transaction, which results in billions of dollars' worth of losses annually. To lower the losses, effective fraud detection algorithms can be applied. the sophisticated machine learning methods these algorithms rely on can be useful to fraud investigators. © 2024 IEEE.

关键词： Credit Customers Finance Machine learning Savings

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4th international conference on Energy Material and Energy Technology

4th International Conference on Energy Material and Energy T...

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4th international conference on Energy Material and Energy Technology, EMET 2024

the proceedings contain 5 papers. the topics discussed include: the removal of ammonia nitrogen from wastewater by using modified zeolite particles under ultrasonic cavitation;CNN-BiLSTM combined with Bayesian optimization for short-term wind power prediction;preparation of CaSrSiOx: nCe4+ luminous material with rice husk as the silicon source;development and optimization of a CMOS-based energy management circuit for piezoelectric energy harvesting in IoT applications;and enhancing in-situ air injection conversion: catalytic roles of metal oxides in kerogen-rich shale oxidation.

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Current Situation and Strategy of Sports Equipment Procurement Based on Improved Ant Colony optimization Algorithm 4

Current Situation and Strategy of Sports Equipment Procureme...

引用

4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Li, Guangpeng Kong, Fanrong Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350333558

With the development of the Internet, more and more sports equipment has entered people's lives. However, due to the late start of sports venue procurement work and the lack of corresponding theoretical knowledge and management experience, there are many problems in its practical application. this article is based on the improved ant colony optimization algorithm and combines traditional heuristic search and random programming to explore a new procurement model for sports equipment in universities based on the improved ant colony optimization algorithm. It introduces new methods and applies them to practical applications. When considering factors such as genetic operations and simulated annealing, analysis and calculation were conducted. In the solving process, this article uses the particle swarm evolutionary optimization model to set parameters and design constraint conditions for it. In this paper, Tabu search strategy is combined to achieve the optimal solution, and heuristic search and random sexual selection are combined to design a procurement platform, and the platform is functional tested. the test results show that the automation level of the platform is over 90%. Improved ant colony optimization algorithms can automatically complete some optimization tasks during user registration and login processes, such as automatically detecting the legitimacy of user input information, thereby improving user experience and procurement efficiency. © 2023 IEEE.

关键词： Simulated annealing

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Joint Secure Beamforming Design Based on Deep Reinforcement learning 4

Joint Secure Beamforming Design Based on Deep Reinforcement ...

引用

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

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

ISBN: (纸本)9798350378542

Reconfigurable Intelligent Surface (RIS) has become a key technology that play a vital role in communication or radar systems. Consider a multiple-input single-output (MISO) downlink system assisted by RIS with a legitimate user and eavesdropper, we propose a learning algorithm to maximize the system secrecy rate by joint design the transmit beamforming and the phase shift matrix. First, a secrecy rate maximization problem of this secure communication system is formulated, subject to a power budget constraint and the unit-modulus phase shift constraints. then, we develop a learning algorithm based on deep reinforcement learning (DRL) to address the non-convex optimization problem, rather than utilizing the alternating optimization (AO) techniques (which alternatively search the locally optimal solution) in literature. Simulation result confirm that the proposed DRL-based method can deliver an even higher secrecy rate, while also maintaining an acceptable speed of convergence. © 2024 IEEE.

关键词： Deep reinforcement learning

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Deep learning Based Automatic Modulation Recognition Using GELU Activation Function 4

Deep Learning Based Automatic Modulation Recognition Using G...

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4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Altuntas, Bilge Erdem Aksu, Omer Celik, Melih Yigitcan Durak, Mehmet Hakan Erzurum Tech Univ Dept Elect & Elect Engn Erzurum Turkiye Univ Oulu CWC RT Oulu Finland

ISBN: (纸本)9798350354140;9798350354133

One promising technique for detecting signal modulation schemes in cognitive radio networks is automatic modulation recognition (AMR). AMR based on highperformance deep learning (DL) techniques have been made easier recently by the growing research on DL. But, as DL is evolving daily, AMR approaches must perform better, and new approaches must be developed. this research presents a new DL based technique for AMR used in modern communication systems' cognitive radio networks. To simultaneously learn the spatio-temporal signal correlations with Gaussian Error Linear Unit (GELU) activation function, the network architecture is built with multiple distinct convolutional blocks. the suggested technique achieves an overall 6-modulation classification rate of 80% at 20 dB SNR in the simulations performed with the generated dataset.

关键词： automatic modulation recognition deep learning convolutional neural network Gaussian Error Linear Unit GELU

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Assessment of Hyperparameter optimization Techniques for Cross-Stitched Multi-Task learning 11

Assessment of Hyperparameter Optimization Techniques for Cro...

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11th international conference on Computer, Control, Informatics and its applications, IC3INA 2024

作者： Yaniasih, Yaniasih Yaman, Aris Pebiana, Siska Research Center for Data and Information Sciences National Agency for Research and Innovation Jakarta Indonesia Research Center for Artificial Intelligence and Cybersecurity National Agency for Research and Innovation Jakarta Indonesia

ISBN: (纸本)9798331542313

Hyperparameter optimization is critical to successful machine learning model development. Conducting experiments to evaluate various hyperparameter optimization techniques across different models and datasets is essential for advancing knowledge and providing practical guidance. this study aims to compare state-of-the-art hyperparameter optimization algorithms in the developing cross-stitched multitask deep learning models, focusing on a dataset encompassing three citation meanings in text: function, role source, and sentiment. the hyperparameters examined include embedding output dimensions, convolutional filters and kernel size, dense layer configurations, dropout rates, learning rates, and batch sizes. the techniques evaluated are (1) Grid Search, (2) Random Search, (3) Tree-structured Parzen Estimator (TPE), (4) Gaussian Process (GP)-based algorithm, (5) Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) based algorithm, (6) Nondominated Sorting Genetic Algorithm II, (7) a method enabling partially fixed parameters (PF), and (8) Quasi-Monte Carlo (QMC) sampling algorithm. the findings indicate that the TPE and CMA-ES techniques are particularly effective in selecting hyperparameters that obtained the optimal performance (the highest average improvements: 2.48 and 2.42, respectively), with the learning rate emerging as the most important hyperparameter. these results contribute to the literature on hyperparameter optimization, specifically within the case of cross-stitched multi-task learning. © 2024 IEEE.

关键词： Genetic algorithms

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