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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181-190 订阅

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Construction of E-Commerce User Profile Design Model Based on Deep learning Algorithm 4

Construction of E-Commerce User Profile Design Model Based o...

引用

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

作者： Li, Jiahua School of Information Engineering Guangzhou Vocational University of Science and Technology Guangzhou China

ISBN: (纸本)9798350352931

In rural e-commerce platforms, user behaviors are complex and diverse, and it is difficult for the recommendation system to accurately predict demand, resulting in a low conversion rate of the platform. Based on deep learning, this paper uses deep learning models, that is, models, to analyze user browsing, purchasing, and feedback behaviors, and optimize recommendation algorithms. the research process mainly includes user portrait design and model construction, model description recommendation and optimization, system integration, and application. According to the experiment, the recommendation accuracy of the rural e-commerce user portrait recommendation system increased by 20% or more, and the purchase conversion rate increased by 44.23%. It can be seen that deep learning, especially the recommendation system, can significantly improve the user experience and sales performance of rural e-commerce platforms, especially in personalized recommendation and real-time feedback optimization. On the whole, deep learning can show good performance and high application value in the rural e-commerce recommended user portrait. © 2024 IEEE.

关键词： Marketplaces

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Object detection in videos using deep learning methods: A survey 4th

Object detection in videos using deep learning methods: A su...

引用

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

作者： Kumar, Sunil Kumar, Sanjeev Singh, Ashutosh Kumar Department of CSE United University Prayagraj UP Rawatpur India Department of CSE UCER Prayagraj UP Naini India

ISBN: (纸本)9781032911571

In computer vision, video object detection process is crucial. Many applications including video surveillance, robot navigation, self-driving cars or automatic modes of transportation, etc. use video object detection. Nowadays, advanced learning techniques like deep learning approaches are used to apply for video object detection. However, researchers face many challenges during implementation, become bottleneck in this research field. this problem occurs due to position (partially hidden object and object complexity of vegetation) and visualization (blur and illumination). In this paper, we present a review that attempts to cover the latest research methodologies of object detection in video and classify video object detection algorithms. Also, we list the issues and challenges of object detection in video that will help for peoples those will research in this field around the globe. © 2025 the Author(s).

关键词： Video analysis

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Research on Resource Scheduling Algorithm optimization in Cloud Computing Environment Based on Deep learning 4

Research on Resource Scheduling Algorithm Optimization in Cl...

引用

4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Wang, Chuanchen Guo, Ruifeng Xiu, Pengfei Li, Xuerao Wang, Hongliang Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

ISBN: (纸本)9798331534622

With the rapid advancement of cloud computing technology, user demands for Quality of Service (QoS) are continuously increasing. As a crucial link between user requirements and resource provisioning, resource scheduling has become a key focus in algorithm optimization research. Deep learning, a core technique in artificial intelligence, has been widely adopted to improve the efficiency of resource allocation in cloud computing, enabling intelligent and dynamic solutions. this paper provides a comprehensive review of deep learning-based resource scheduling algorithms, analyzing current progress and identifying existing challenges. It begins by introducing the fundamental concepts of resource scheduling and its pivotal role in maintaining service quality. Common deep learning models and their application scenarios in this domain are summarized. Next, recent research advances are discussed in detail, covering design principles, model training techniques, optimization strategies, and real-world outcomes. the strengths and limitations of various algorithms are compared, with a particular focus on metrics such as real-time responsiveness, accuracy, and computational complexity. Furthermore, this paper explores ongoing challenges, including data imbalance, limited generalization capabilities, constraints in real-time performance, and the need for energy efficiency. Finally, it envisions future research directions, emphasizing the potential of reinforcement learning and transfer learning to address these challenges. It is posited that continuous breakthroughs in deep learning will drive novel approaches for resource scheduling, advancing cloud computing services toward greater intelligence and efficiency. © 2024 IEEE.

关键词： Contrastive learning

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Road Target Detection Method Based on Improved YOLOv8 4

Road Target Detection Method Based on Improved YOLOv8

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Chen, Yuejun School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350359589

With the rapid development of artificial intelligence technology, machine vision, as one of its key branches, has shown great application potential and value in many fields such as industrial automation, intelligent security, unmanned driving, and medical diagnosis. In this context, target detection, as the core task of machine vision, aims to accurately identify and locate objects of interest from complex scene images. For unmanned driving technology, target detection technology is very important, and whether it can accurately judge road targets determines whether unmanned driving can be realized. With the breakthrough progress of deep learning technology, many target detection algorithms have emerged, and the target detection performance has realized significant improvement in both accuracy and speed. However, there are still some shortcomings such as difficulty in detecting small targets and lack of robustness. In this thesis, a variety of driving scene datasets with weather and location are selected, and the YOLOv8 algorithm is optimized with the BRA algorithm, and its effectiveness is verified after comparing it with a variety of other algorithms. © 2024 IEEE.

关键词： deep learning target detection YOLOv8

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Model of Sentiment Analysis in Different Platforms on Social Media 4

Model of Sentiment Analysis in Different Platforms on Social...

引用

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

作者： Vasanthi, P. Madhu Viswanatham, V. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

Reviews provide useful information for making decisions. Online stores allow customers to express their opinionson goods and services. Many of these opinions are available tothe public as reviews. Customer reviews are very interesting to manufacturers, retailers, and customers. Such reviews are valuable to consumers since they help them choose which brand or product to purchase. these subjective reviews can help manufacturers identify areas where their product needs improvement. Because there are so many reviews available online foranalysis, manually reading them is not cost effective. An automated system that offers a summary of user sentiments is required to optimize this laborious task. Sentiment analysis is a branch of study that examines how reviews and opinionated literature reflect the sentiments or opinions of readers. In the process of sentiment analysis, machine learning, deep learning, and optimized algorithms are used to assess sentiments and emotions and generate a distilled output for use in decision making. © 2024 IEEE.

关键词： Adversarial machine learning

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Using Federated learning Techniques to Generalize Reinforcement learning Approaches 19th

Using Federated Learning Techniques to Generalize Reinforcem...

引用

19th international conference on Hybrid Artificial Intelligence Systems

作者： Tellaeche Iglesias, Alberto Fidalgo Astorquia, Ignacio Vazquez, Juan-Ignacio Gaviria de la Puerta, Jose Univ Deusto Grp D4K Avda Univ 24 Bilbao 48007 Spain

ISBN: (纸本)9783031741852;9783031741869

Artificial Intelligence (AI) is becoming more prevalent in industrial applications and, specifically, in advanced robotics, both industrial and mobile. this research work explores federated learning techniques to generate more robust and efficient learning models, combining the strengths of different existing reinforcement learning algorithms compensating for their less efficient aspects. To this end, reinforcement learning algorithms that represent the current state of the art have been selected, trained using standard evaluation environments, and then hybridized using federated learning techniques. Tests carried out show a better performance in terms of reward and specially in episode length of the federated algorithm with respect the original reinforcement learning approaches.

关键词： Reinforcement learning Federated learning optimization

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An Artificial Intelligence Approach for Analyzing Students' Academic Performance Using Machine learning algorithms 4

An Artificial Intelligence Approach for Analyzing Students' ...

引用

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

作者： Turan, Ahmet Serdar Koksoy, Bedirhan Pasaoglu, Ali Istanbul Rumeli University Computer Engineering Engineering and Natural Science Istanbul Turkey

ISBN: (纸本)9798331523923

this paper investigates the effect of midterm and short quizzes on the success scores at the end of the course through machine learning methods. Data from 3,427 students in the Faculty of Engineering and Natural Sciences of Istanbul Rumeli University, recorded from the year 2019 to 2024, were analyzed, and of that, 1,547 records were qualified for machine learning after applying specific filtering criteria. Due to insufficient data, 6,300 synthetic records were generated using the Generative Adversarial Networks (GAN) method. To ensure high-quality data generation and stable training, a Wasserstein GAN with Gradient Penalty (WGAN-GP) was implemented, enhancing the synthetic dataset's diversity and reliability. In this paper, the algorithms of Random Forest (RF), AdaBoost, Linear Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) were used for the classification and prediction of course success. Among these, the most accurate performance, quantified for example by R2 score, Mean Absolute Error and Mean Squared Error, was given by RF, with a probability of being correct at 83.81%. the results indicate that midterm exams have a significantly stronger influence on predicting course success, while short quizzes play an important role in maintaining consistent student engagement and reinforcing foundational concepts. Future studies will aim for data diverseness by adding more features and comparing new methods, such as Artificial Neural Networks with hyperparameter optimization. © 2025 IEEE.

关键词： Generative adversarial networks

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Unlocking the Secrets of Online Engagement: A Deep Dive into Website Traffic 4

Unlocking the Secrets of Online Engagement: A Deep Dive into...

引用

4th international Mobile, Intelligent, and Ubiquitous Computing conference

作者： AbdElminaam, Diaa Salama Mostafa, Donia Ibrahim, Abdelrahman Ahmed Bakry, Youssef Abdelrahman Ibrahim, Farah Mohamed Fekry, Nour Hesham Misr Int Univ Fac Comp Sci Cairo Egypt

ISBN: (纸本)9798350367782;9798350367775

Accurately predicting web traffic is crucial for optimizing website performance and understanding user behavior. this study investigates various machine learning and statistical models to forecast web traffic trends. We apply a range of algorithms, including XGBoost, Random Forest, SARIMA, ARIMA, Prophet, and Moving Average with Seasonal Index, to two distinct datasets: "Daily Website Visitors" and "Future Traffic to Wikipedia Pages." the datasets are preprocessed and analyzed to uncover underlying trends and seasonal patterns, with models evaluated using key performance metrics such as MSE, RMSE, and MAPE. Our results demonstrate that the Moving Average model with Seasonal Index outperforms other models in terms of accuracy, achieving 95.87% accuracy on the "Daily Website Visitors" dataset and 98.23% accuracy on the "Future Traffic to Wikipedia Pages" dataset. these findings highlight the importance of leveraging advanced machine learning techniques alongside traditional statistical methods to improve web traffic forecasting, providing valuable insights for website optimization and digital marketing strategies.

关键词： Web traffic prediction machine learning time series forecasting XGBoost SARIMA ARIMA Prophet website optimization

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Accelerating Drug Discovery: Ai Powered Approaches in Pharmaceutical Research 4

Accelerating Drug Discovery: Ai Powered Approaches in Pharma...

引用

4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Chintala, Satishkumar Fisher Investments TX United States

ISBN: (纸本)9798350307757

the use of machine learning (ML) and deep learning (DL) in the process of drug development marks the beginning of a new chapter in the creation of pharmaceuticals. Researchers now have the ability to manage massive amounts of information, discover complex biomolecular interactions, and accelerate the development of novel therapies thanks to the use of cutting-edge computational approaches. the purpose of this study is to investigate the applications of deep learning (DL) and machine learning (ML) in the field of drug development. Specifically, the research investigates the impacts of these two types of learning on target selection, chemical screening, and lead candidate optimization. there are still problems with the quality of the data, the interpretability of the data, and the processing requirements, despite the fact that these approaches offer a lot of potential. By conducting a thorough analysis of the relevant literature and case studies, this research addresses ethical concerns, provides evidence of successful implementations, and elucidates the many methodologies that are now in use. In the conclusion, the need of continuous innovation and collaboration across disciplinary lines is stressed. this is necessary in order to fully exploit the transformative potential of deep learning and machine learning in the field of drug development. © 2024 IEEE.

关键词： Deep learning

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Using Machine learning-based algorithms to Predict Academic Performance - A Systematic Literature Review 4

Using Machine Learning-based Algorithms to Predict Academic ...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Wu, Meng Subramaniam, Geetha Zhu, Dan Li, Cailing Ding, Hongyuan Zhang, Yirong Sanda University School of Information Science and Technology Shanghai China SEGi University Faculty of Education Languages Psychology and Music Selangor Malaysia

ISBN: (纸本)9798350307757

the exploration of machine learning applications for predicting students' academic performance has become a pivotal research domain. this systematic literature review screened 83 indexed research articles between 2020-2023 that examined machine learning applications in predicting academic achievement. the findings highlighted that ensemble learning outperformed other methods in predicting academic performance, achieving an average accuracy rate of 87.67%. this was closely followed by the support vector machine (SVM) approach, which attained an average accuracy of 84.30%. Demographic, academic, and behavioral factors were found to be significant predictors of academic achievement. this research emphasized the significance of early identification of students with issues and the necessity for timely interventions to enhance their educational outcomes. Predictive factors are essential as they can assist educators and policymakers in figuring out students who may be at risk of underperformance and in providing targeted interventions to enhance the quality of educational outcomes. this is in line with SDG 4, which focuses on quality education. © 2024 IEEE.

关键词： Students

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