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Deep Reinforcement learning in Stock Trading: Evaluating DDPG and DQN Strategies 6

Deep Reinforcement Learning in Stock Trading: Evaluating DDP...

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6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Kodurupaka, Nithin Basavadeepthi, H.M. Pecheti, Shiva Teja Amudha, J. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350306613

This study presents a comparative analysis of the Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithms in the context of stock trading, focusing on historical stock price data of S&P 500 stock index. The primary objective is to evaluate the effectiveness of these RL algorithms in making trading decisions and achieving financial performance relative to a passive buy-And-hold strategy. The assessment revolves around key metrics, with a special emphasis on the buy, hold, and sell indications as essential signals for trading actions. Results indicate that DQN outperforms DDPG, showcasing a smaller total loss during the evaluation period. This suggests that DQN may offer superior capabilities in limiting financial losses and making effective trading decisions compared to DDPG, providing valuable insights into algorithm selection for dynamic trading environments. It is crucial to recognize the significance of buy, hold, and sell indications as key elements in assessing the performance of RL algorithms in stock trading scenarios. © 2024 IEEE.

关键词： Algorithmic Trading Buy-And-Hold Strategy DDPG Trading Agent Deep Neural Networks Deep Reinforcement learning DQN Portfolio Performance Total Return

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Edge computing Power Optimization Method in Container Cloud Environment 3

Edge Computing Power Optimization Method in Container Cloud ...

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3rd international conference on Electronic Information engineering and Computer Science, EIECS 2023

作者： Zhao, Yuchao Jiang, Tianhui Xiamen Ocean Vocational College Xiamen China

ISBN: (纸本)9798350316674

Due to slow CPU speed and small battery capacity, intelligent mobile devices cannot perform computing themselves, which causes many problems such as resource scheduling security technology. To reduce these problems, edge computing techniques are needed. However, moving some computing tasks from mobile devices to edge servers increases the additional energy consumption of transport and server computation. Considering the four factors affecting mobile devices and servers, as well as the energy consumption of data transmission, a particle swarm optimization algorithm based on hierarchical learning was proposed to solve the problems of computing speed, power consumption of downloaded data, and computing security of mobile devices. The proposed algorithm optimizes these four parameters for each mobile device to allocate computing resources more rationally, minimize the total energy consumption, and ensure security during scheduling. When modeling computing resources, the maximum energy consumption, computation cycle, storage, bandwidth, and delay constraints are also considered. Compared to the other algorithms in the container cloud, the proposed particle swarm optimization algorithm optimized by hierarchical learning can get the optimal solution of resource scheduling satisfying the constraints faster, with lower energy consumption and higher security. © 2023 IEEE.

关键词： computing resources container cloud hierarchical learning resource scheduling technology security

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Deep learning-Based Web Application for Real-Time Apple Leaf Disease Detection and Classification 2

Deep Learning-Based Web Application for Real-Time Apple Leaf...

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2nd international conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Kumar, Satish Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of Computer Science and Engineering Punjab India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq Technical Engineering College Al-Ayen University Department of Computer Technical Engineering Thi-Qar Iraq

ISBN: (纸本)9798350370188

Agriculture is the most significant industry in the economy of India. Various kinds of diseases affect the leaves of plants and influence the productivity of crops. Apple farmers are also constantly facing challenges in boosting their yield and protecting apple trees from diseases. The prevalence of diseases and pests significantly hampers apple production, leading to substantial financial losses for the industry each year. Farmers may not have expertise in leaf disease prediction. Detecting Apple Leaf Diseases (ALD) swiftly and accurately is crucial for effectively handling and curbing these issues within orchards. Specifically, advancements in computer vision methods utilizing Deep learning (DL) have opened up avenues for identifying and understanding these diseases at an early stage directly on the leaves. Web application based on the DL model is proposed to address this issue, which can predict Healthy and Alternaria, Leaf Spot, Marssonina Blotch, and Powdery mildew disease of the affected leaf. © 2024 IEEE.

关键词： Forecasting

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Achieving 5G Network Energy Efficiency by Hybrid Machine learning Models for Channel and Power Allocation 5

Achieving 5G Network Energy Efficiency by Hybrid Machine Lea...

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5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Ramya, H. Jaya, T. Rajendran, V. Department of Electronic and Communication Engineering Tamil Nadu Chennai India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522100

High data speeds, low latency, and huge connection are becoming increasingly important in 5G networks, making energy efficiency a significant concern. In order to achieve the twin objectives of lowering energy consumption and keeping network performance intact, our research centers on creating hybrid machine learning models to improve channel and power allocation. 5G networks are complex and dynamic, with varying user requirements, fluctuating traffic loads, and different environmental circumstances. Traditional resource allocation algorithms have a hard time keeping up. The authors of this work offer a hybrid strategy that is both adaptable and scalable by integrating supervised and unsupervised learning with reinforcement learning (RL). To begin, the hybrid model uses supervised learning to allocate channels and power using past network data. Next, reinforcement learning takes into account network circumstances, user mobility, and traffic variations to make real-time adjustments to the allocation. To improve the distribution of resources in dense networks, unsupervised learning is used to find clusters and patterns in the data. The system is able to optimize energy consumption and anticipate and respond to changes in the network environment thanks to the integration of these models. With throughput and quality of service (QoS) unaffected, the simulation results show that the hybrid machine learning model is 30% more energy efficient than traditional techniques. In light of increasing data demands and strict energy requirements, the suggested technology provides a viable option for 5G networks that are both environmentally friendly and highly efficient. © 2025 IEEE.

关键词： Energy utilization

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A DEVELOPED PLAN-DO-CHECK-ACT AND CONTEXTUAL teaching AND learning MODEL 14

A DEVELOPED PLAN-DO-CHECK-ACT AND CONTEXTUAL TEACHING AND LE...

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14th international conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2024

作者： Hu, Jun Tang, Chaoju Department of Foreign Languages University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9781837242672

Despite some application of Contextual teaching and learning (CTL) theories in practice, their effectiveness has not been prominent, necessitating comprehensive optimization. To enhance the quality and the success of typical processes like education activities, this study integrates CTL with Deming's PDCA (Plan-Do-Check-Act) cycle theory. Through the stages of planning, implementation, and evaluation, the CTL model has been significantly improved. Methodologically, the study employs an inductive approach and experimental methods for data analysis. Results show significant improvements in the mentioned activities and enhances of performance and satisfaction of stakeholders like the population involved with the optimized PDCA & CTL model. The results of this paper also confirmed the efficacy of the proposed method in process quality improvement. © The Institution of engineering & Technology 2024.

关键词： CTL MODEL PDCA PROCESSES QUALITY IMPROVEMENT TRAINING

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A Method to Reduce the Incidence of Wheat Leaf Diseases

A Method to Reduce the Incidence of Wheat Leaf Diseases

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2024 international conference on Advances in computing Research on Science engineering and Technology, ACROSET 2024

作者： Verma, Riya Rathore, Narendra Pal Singh Sethi, Kamal Kumar Acropolis Insitute of Technology and Research Department of Computer Science Indore India

ISBN: (纸本)9798350388800

Numerous studies have delved into the domain of wheat disease prediction, frequently leveraging meticulously curated datasets compiled by researchers. This review comprehensively examines a spectrum of research endeavours, with a primary focus on elucidating classification methodologies and the breadth of disease manifestations analysed. By systematically surveying diverse studies, we scrutinize the approaches employed in interpreting disease patterns and the methodologies employed for data collection, while also addressing their inherent limitations. Through this thorough analysis, we shed light on the intricate facets of wheat disease prediction, providing invaluable insights into the current state of the field and delineating promising avenues for future research and development. © 2024 IEEE.

关键词： CNNs (Convolutional Neural Network) DI (Deep learning) Leaf Diseases VGG Model

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Apparel Customization Using Generative AI 10

Apparel Customization Using Generative AI

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Menon, Gokulraj Peter, Amal P. Jomon, Ebin Varghese, Joe Anly Antony, M. Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350384369

Our project pioneers a transformative platform, marrying artificial intelligence with fashion customization. Users engage with an intuitive interface, seamlessly translating their style visions into unique apparel designs. Leveraging machine learning, the system analyzes content and styles images, intelligently transferring selected stylistic elements to create a harmonious blend. The output showcases personalized, stylized objects each a reflection of the user's chosen style fused with the original content subset. This endeavor not only redefines fashion personalization but also marks a distinctive application of AI in creative expression. We invite collaboration and support as we shape a future where technology reimagines the landscape of personalized apparel design. © 2024 IEEE.

关键词： Economic and social effects

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Reinforcement learning-based Optimization of EBike Charging infrastructure 11

Reinforcement Learning-based Optimization of EBike Charging ...

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11th IEEE/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2024

作者： Sharif, Muddsair Seker, Huseyin Birmingham City University Faculty of Computing Engineering and the Built Environment BirminghamB5 5JU United Kingdom The University of Sharjah College of Computing and Informatics Department of Information Systems Sharjah United Arab Emirates

ISBN: (纸本)9798350367300

This paper presents a novel technique for enhancing the allocation of resources in the charging infrastructure for e-bikes by employing deep reinforcement learning (DRL) in a context-specific manner. With the evolving transportation land-scape and the increasing popularity of electric micro-mobility solutions, efficient resource management plays a vital role. The methodology involves utilizing deep Q networks (DQN) in virtual environments to dynamically allocate charging resources based on contextual parameters. Through empirical analysis, we demonstrate the efficacy of our approach in achieving optimal resource allocation while considering the needs of diverse stakeholders. Findings suggest that the DQN model, when integrated with context-awareness and stakeholder objectives, enables efficient resource allocation, maximize fleet utilization, ensures charging station availability, enhances user satisfaction, and promotes environmental sustainability. The findings of this study offer valuable insights for policymakers, urban planners, and industry stakeholders seeking to enhance the sustainability and effectiveness of micro-mobility services in urban settings. Overall, this study contributes to the advancement of sustainable urban mobility solutions and underscores the potential of DRL in addressing complex urban transportation challenges. © 2024 IEEE.

关键词： Federated learning

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Research on Laboratory Unsafe Behavior Recognition Based on Robust Time Feature learning Advancement 2

Research on Laboratory Unsafe Behavior Recognition Based on ...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Li, Xinran Chen, Liang Liu, Wenhong Shenyang Ligong University College of Automation and Electrical Engineering Shenyang China Northeastern University College of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

In order to solve the problem of dangerous accidents caused by unsafe behaviors in university laboratories, We propose an improved method based on the RTFM model. This method combines Dilated convolution and multi-head attention mechanism to form a new feature aggregation module and improve the sensitivity to abnormal features. In addition, the Gated Recurrent Unit (GRU) is introduced to capture the timing information in the video, effectively solving the problem of false detection when RTFM detects anomalies. Experimental results show that compared with the original model, the method in this paper improves AUC by 4%. Effectively improves the accuracy of identifying abnormal behaviors of laboratory personnel. © 2024 IEEE.

关键词： Problem solving

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A Classification Method Based Deep learning for Educational News Comments 3

A Classification Method Based Deep Learning for Educational ...

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3rd international conference on Cloud computing, Big Data Application and Software engineering, CBASE 2024

作者： Xu, Liluxi Xi'an University of Architecture and Technology Shaanxi China

ISBN: (纸本)9798331506582

This article designs a classification method for long educational news texts based on labeled educational news reports. Firstly, CNN is used to extract local features of text information. Then, a bidirectional long short-term memory network is used to extract contextual information from the CNN output vector, thereby achieving effective extraction of text feature information. Secondly, in order to enhance the feature expression of the text, an attention layer is added at the end of the model. The model obtained through fusion not only solves the problem of different words in the text having different impacts on the classification results. Finally, the improved classification efficiency of this method was verified through education news review data. © 2024 IEEE.

关键词： Classification (of information)

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