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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

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Semantic Feature-Driven Automatic Parameter optimization of Apache Spark 4

Semantic Feature-Driven Automatic Parameter Optimization of ...

引用

4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Zou, Qianggui Rong, Xiaohui Yu, Jianjun Chinese Academy of Sciences Computer Network Information Center Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350394375

Big data analysis frameworks such as Spark are widely used in various scenarios, and the configuration of Spark significantly affects the execution time of the application. How to select appropriate configuration from the huge parameter space is a challenging problem currently. In the previous configuration optimization models, the semantic information of the Spark application was rarely considered. We combined the semantic information of the code to build a performance prediction model to improve the accuracy of performance prediction. For each application, parameters that have a critical impact on performance are selected to reduce the parameter space. Experimental results show that our model significantly improves the performance of Spark applications compared to Bayesian optimization and reinforcement learning-based parameter optimization models. © 2024 IEEE.

关键词： optimization

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Enhancing Cloud Performance with AI-Driven Load Balancing and optimization Algorithms 4

Enhancing Cloud Performance with AI-Driven Load Balancing an...

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4th International conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Singhal, Amit Kumar Goel, Pawan Garg, Devesh Sharma, Chhaya Raj Kumar Goel Institute of Technology Department of Cse U.P. Ghaziabad India Rkgit Department of Cse UP Ghaziabad India

ISBN: (纸本)9798350364729

Cloud computing has become a crucial technology for handling large-scale data and delivering scalable, on-demand services across various industries. As cloud environments continue to grow in complexity, efficient load balancing has emerged as a critical factor in maintaining performance and minimizing resource wastage. In this paper, we address the challenge of dynamic load balancing in cloud systems, with a focus on leveraging AI-driven optimization algorithms to enhance overall system performance. Although traditional algorithms like round-robin and heuristic-based methods have been widely studied, they face limitations in scalability, adaptability, and energy efficiency, leaving many challenges unresolved. Our research proposes a novel AI-driven load balancing framework that integrates machine learning models with optimization algorithms to dynamically adjust workloads in real-time, optimizing resource utilization and reducing latency. this innovative approach represents the first of its kind in applying AI for real-time decision-making in cloud load balancing, providing a more adaptive and efficient solution. Experimental results demonstrate that our framework outperforms existing methods by achieving up to 25% improvement in response time and 30% reduction in resource overhead. this first-of-its-kind solution shows significant potential for future cloud computing advancements, offering a more intelligent and responsive method for managing cloud resources effectively. © 2024 IEEE.

关键词： Cloud platforms

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Dimensionality Reduction via Geometric Modeling of Topology-Optimized Devices for Machine learning-based Inverse Design 4

Dimensionality Reduction via Geometric Modeling of Topology-...

引用

4th International conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Chen, Yuchen Qiu, Jifang Guo, Hongxiang Wu, Jian Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China

ISBN: (纸本)9798350394375

Machine learning techniques have been increasingly applied in the inverse design of nanophotonics. A well-trained neural network can generate devices with diverse functions, obviating the requirement for repeated optimization processes for each type of devices. Topology optimization is a mainstream optimization strategy in inverse design, which excels in designing compact, high-performance devices, yet its high degree of freedom introduces challenges for the application of machine learning techniques. We present a novel scheme for adapting machine learning techniques into topology optimization of nanophotonic devices, breaking the convention where machine learning techniques were applied only in the design of devices with limited parameters. this scheme reduces the parameter dimension of topology-optimized devices from the order of 103 to 101 through a structure simplification process and B-spline geometric modeling, and subsequently utilizes a conditional generative network to generate devices with various functions in the new parameter space. As a demonstration, we present for the first time a set of T-junctions with continuous power splitting ratios ranging from 0 to 0.5, achieving transmissions exceeding 90% at 1550 nm across all ratios within a compact footprint of 2.8×2.8 μm2 © 2024 IEEE.

关键词： Degrees of freedom (mechanics)

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Enhancing Customer Segmentation and Behavior Prediction through Machine learning and Medium Gaussian SVM 4th

Enhancing Customer Segmentation and Behavior Prediction Thro...

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4th International conference on Advanced Network Technologies and intelligent Computing-ANTIC-Annual

作者： Pandyan, Uma Maheswari Usha, Gandhimathi Alias S. Manoj, S., V Chandru, M. Abinandan, S. Velammal Coll Engn & Technol Dept ECE Madurai 625009 Tamil Nadu India

ISBN: (纸本)9783031837951;9783031837968

Customer segmentation and prediction, driven by machine learning algorithms, plays a vital role in today's fast-paced and data-driven market. this paper focuses on applying machine learning techniques, specifically the k-means clustering algorithm and Medium Gaussian Support Vector Machine(SVM) regression learning, for customer segmentation and prediction. the process starts with data col-lection and cleaning to ensure the reliability of the data clarity gained. the k-means clustering algorithm plays a central role in identifying patterns within customer data, revealing natural groupings that may not be apparent through traditional analysis methods. Using machine learning algorithms, with a focus on k-means clustering, the segmentation process is refined, allowing for the discovery of complex patterns in customer behavior. this, in turn, enables the creation of personalized interactions through predefined customer segments. Furthermore, the segmented customer information is analyzed using different regression techniques to predict segmented customers better. Experimental results show that the proposed approach achieves the lowest mean average error of 15.723 with a model size of 12KB. the advantages of this approach are substantial, including the development of tailored marketing strategies, personalized recommendations, and the optimization of resource allocation. this understanding allows companies to create customized approaches for each group, leading to happier customers, more people recommending their services or products, and a better overall image for the brand.

关键词： Customer segmentation Machine learning Medium Gaussian SVM

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intelligent evaluation of carbon emission reduction level in thermal power plants based on hybrid particle swarm optimization algorithm 4

Intelligent evaluation of carbon emission reduction level in...

引用

4th International conference on Smart Grid and Energy Engineering, SGEE 2023

作者： Song, Li Nanjing Institute of Tourism and Hospitality Nanjing China

ISBN: (纸本)9798350359619

the carbon emissions of thermal power plants are affected by operating conditions, load changes and other factors, which may lead to fluctuations in carbon emissions. therefore, an intelligent evaluation method of carbon emission reduction level in thermal power plants based on hybrid particle swarm optimization algorithm is proposed. By analyzing the balance relationship of carbon emission reduction in thermal power plants, an intelligent evaluation model of carbon emission reduction level in thermal power plants is constructed based on chain network. Combining particle swarm optimization with simulated annealing algorithm to form a hybrid particle swarm optimization algorithm, the intelligent evaluation model of carbon emission reduction level in thermal power plants is solved, and the intelligent evaluation results of optimal carbon emission reduction level in thermal power plants are output. the experimental results show that the proposed method has high accuracy and efficiency in intelligent assessment of carbon emission reduction level in thermal power plants. © 2023 IEEE.

关键词： Simulated annealing

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Enhancing Crop Health in Smart Greenhouse through IoT-Based Data optimization and Deep learning Algorithms 4

Enhancing Crop Health in Smart Greenhouse Through IoT-Based ...

引用

4th International conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Slimani, Hicham El Mhamdi, Jamal Jilbab, Abdelilah Mohammed V University In Rabat Rabat Morocco

ISBN: (纸本)9798350309508

Integrating convolutional neural networks (CNNs) with the Internet of things (IoT) is paramount in agriculture, particularly greenhouses. By leveraging IoT capabilities, operators can collect agro-environmental information inside the greenhouse based on installed sensor nodes. this data-driven approach minimizes water, fertilizer, and energy waste. Simul-taneously, CNNs enhance the monitoring systems by facilitating early detection and classification of crop diseases. Our research proposes a comprehensive solution: an online technology platform for intelligent greenhouses based on IoT and CNNs. this platform effectively collects environmental and physical variables and detects diseases in real time using image-based analysis. the results of our study demonstrate that the system architecture is a reliable IoT platform, leading to significant energy savings. Moreover, the disease identification accuracy and classification process achieved an impressive rate of over 98 %, ensuring the system's efficacy in identifying and categorizing diseases. Additionally, the system exhibits a recall rate of over 90 %, indicating its ability to identify and recall crop disease instances accurately. © 2024 IEEE.

关键词： Greenhouses

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Deep Reinforcement learning-Driven Path optimization in Multi-UAV Edge Computing Systems 4th

Deep Reinforcement Learning-Driven Path Optimization in Mult...

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4th International conference on Advanced Network Technologies and intelligent Computing-ANTIC-Annual

作者： Adhikari, Pronab Kumar Tomar, Abhinav Netaji Subhas Univ Technol Dept Comp Sci & Engn New Delhi India

ISBN: (纸本)9783031837821;9783031837838

Aerial drones, in general known as Unmanned Aerial Vehicles (UAVs), holds a longstanding history of being utilized within mobile networks as network processors;however, a shift has been observed where they are now being utilized as mobile servers within the framework of Mobile Edge Computing (MEC). this evolution is primarily attributed to their inherent flexibility, portability, robust line-of-sight communication capabilities, and cost-effectiveness, which allows for adaptable usage scenarios, thereby contributing to an increase in their utilization within both research and commercial settings. the essential characteristics of aerial drones have made them increasingly popular across a wide spectrum of civilian services, such as transportation, industrial monitoring, agriculture, forest fire management, and wireless services. Within the scope of this project, the focus lies on exploring MEC networks utilizing Unmanned Aerial Vehicles, where these UAVs undertake computational tasks provided by mobile terminal users (TUs). In order to guarantee the Quality-of-Service (QoS) for each TU, the UAV makes real-time modifications to its flight path by taking into consideration the positions of the mobile TUs, with the ultimate goal of improving the overall performance of the network and enhancing the user experience.

关键词： UAV Mobile Edge Computing Quality-of-service

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Automated Penetration Testing Based on MATD Algorithm 4

Automated Penetration Testing Based on MATD Algorithm

引用

4th International conference on Artificial Intelligence, Virtual Reality and Visualization, AIVRV 2024

作者： Ren, Qiankun Xiong, Xinli Liu, Jingju Wang, Zhen National University of Defense Technology College of Electronic Engineering Hefei China

ISBN: (纸本)9798331528744

With the rapid advancement of internet technology, cybersecurity risks are continuously increasing, and malicious cyberattacks have caused significant losses to users, making it crucial to maintain network security. Automated penetration testing plays a vital role in current cybersecurity efforts. However, existing automated penetration testing methods suffer from inefficiencies in information utilization, inaccurate action decision-making, and an overly large action space. To address these issues, we propose a new algorithm called Multi-Agent Task Decomposition(MATD), based on historical experience information and task decomposition. MATD enhances the efficiency of utilizing historical observation data through the use of Long Short-Term Memory(LSTM) networks and reduces the action space of individual agents by employing a multi-task decomposition method. Experimental results demonstrate that the proposed method outperforms the traditional Proximal Policy optimization(PPO) algorithm by achieving higher cumulative rewards in fewer steps, thereby improving decision-making in intelligent penetration testing. this approach holds promise for increasing the efficiency of penetration testing and enhancing overall network security. © 2024 IEEE.

关键词： Reinforcement learning

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Enhancing Cardiac Health: Digital Twin Technology for Real-Time Monitoring and Abnormality Detection of Human Heart 4

Enhancing Cardiac Health: Digital Twin Technology for Real-T...

引用

4th International conference on Machine learning and intelligent Systems Engineering (MLISE)

作者： Vidula, Bala B. Pravin, P. S. Coimbatore Inst Technol Dept Artificial Intelligence & Machine Leaming Coimbatore Tamil Nadu India Natl Inst Technol Silchar Dept Elect & Instrumentat Engn Silchar Assam India

ISBN: (数字)9798350375077

ISBN: (纸本)9798350375084;9798350375077

As simulation technology is increasingly integrated into medical practice, the idea of diagnosis and treatments to individual patients through Digital Twin technology is gaining prominence in the field of precision medicine. this paper aims to develop a digital twin model of the human heart for simulating its function and predicting abnormalities through the application of machine learning techniques. Our method expands this frame-work to concentrate specifically on digital twin-based monitoring and abnormality detection of cardiac health by utilizing the readily accessible data from smartwatches. this method uses digital twin technology to create personalized real-time virtual heart models, detecting abnormalities and monitoring cardiac health using machine learning. We explore the use of a digital twin model enhanced with machine learning to forecast heart function abnormalities, relying solely on age as the input. We validate these forecasts through a brief two-minute assessment, covering three distinct heart conditions, including scenarios involving resting, walking, and normal heart rates. Furthermore, we examine the connection between normal heart rate and step count, aiming to identify any possible relationships. the findings illustrate its effectiveness in accurate diagnosis and tailored treatment. the incorporation of Digital Twins into healthcare holds the potential to revolutionize medical practice, facilitating precise detection tailored to individual patients.

关键词： Digital twin Heart abnormality detection Machine learning Healthcare

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SIST 2024 - 2024 IEEE 4th International conference on Smart Information Systems and Technologies, Proceedings

SIST 2024 - 2024 IEEE 4th International Conference on Smart ...

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4th IEEE International conference on Smart Information Systems and Technologies, SIST 2024

ISBN: (纸本)9798350374865

the proceedings contain 110 papers. the topics discussed include: review of machine learning models in cyberbullying detection problem;quantum computing in the object-oriented model of quality management;regression and machine learning methods for predicting human movements based on skeletal data;application of game theory methods to optimize the stakeholder management process;conceptualizing the ICT project management in the sustainability context;human-computer testing of students’ knowledge;fuzzy inference system for test case prioritization in software testing;mobile application of convolutional neural networks for melanoma classification;an investigation of sensing and technologies for supporting the intelligent transport management system in urban area;and a comparative study of supervised machine learning and deep learning techniques with feature selection methods for classifying Parkinson’s disease based on speech impairments.

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