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

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Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques 4

Enhancing Multi-Hop Knowledge Graph Reasoning through Reward...

引用

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

作者： Li, Chen Zheng, Haotian Sun, Yiping Wang, Cangqing Yu, Liqiang Chang, Che Tian, Xinyu Liu, Bo Univ Texas Dallas Dallas TX 30346 USA NYU New York NY 10012 USA Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Boston Univ Boston MA 02215 USA Univ Chicago Chicago IL 60637 USA George Washington Univ Washington DC 20052 USA Georgia Inst Technol Atlanta GA 30332 USA Zhejiang Univ Hangzhou 310058 Zhejiang Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In the realm of computational knowledge representation, Knowledge Graph Reasoning (KG-R) stands at the forefront of facilitating sophisticated inferential capabilities across multifarious domains. the quintessence of this research elucidates the employment of reinforcement learning (RL) strategies, notably the REINFORCE algorithm, to navigate the intricacies inherent in multi-hop KG-R. this investigation critically addresses the prevalent challenges introduced by the inherent incompleteness of Knowledge Graphs (KGs), which frequently results in erroneous inferential outcomes, manifesting as both false negatives and misleading positives. By partitioning the Unified Medical Language System (UMLS) benchmark dataset into rich and sparse subsets, we investigate the efficacy of pre-trained BERT embeddings and Prompt learning methodologies to refine the reward shaping process. this approach not only enhances the precision of multi-hop KG-R but also sets a new precedent for future research in the field, aiming to improve the robustness and accuracy of knowledge inference within complex KG frameworks. Our work contributes a novel perspective to the discourse on KG reasoning, offering a methodological advancement that aligns with the academic rigor and scholarly aspirations of the Natural journal, promising to invigorate further advancements in the realm of computational knowledge representation.

关键词： Knowledge Graph Reasoning Reinforcement learning Reward Shaping Transfer learning

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Enhanced DNS Cybersecurity: optimization of XGBoost Using Automated Hyperparameter Tuning via Optuna 4

Enhanced DNS Cybersecurity: Optimization of XGBoost Using Au...

引用

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

作者： Kirubavathi, G. Dhanyatha Shree, C.A. Nithish Kumar, R. School of Physical Sciences Amrita Vishwa Vidyapeetham Department of Mathematics Coimbatore India

ISBN: (数字)9798331528348

ISBN: (纸本)9798350352931

this paper presents an application of using Optuna for hyperparameter optimization in a context of improving the performance of an XGBoost model. the starting point from the baseline XGBoost model was an accuracy of around 88.89%. With Optuna-assisted automated and efficient search of the optimal combinations of some hyperparameters-like learning rate, maximum depth, no. of trees, subsampling, column subsampling, min-child-weight-the final accuracy improved considerably to around 93%. Of course, this big improvement underscores how much valuable hyperparameter tuning plays in the improvement of performance in machine learning models. the results establish the ability of Optuna to efficiently find the optimal parameter settings, leading to improved model generalization and predictive accuracy. Beyond improving the robustness of the models, the fine-tuning process enables the construction of a more solid foundation to deploy machine learning models in real-world applications where the high accuracy is obligatory. this research therefore adds to an expanding literature on automatic hyperpa-rameter optimization and the value of tools such as Optuna in optimizing complex models such as XGBoost. © 2024 IEEE.

关键词： Traffic control

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Fault Diagnosis and Localization of Power Cables Using Bi-Directional Long Short Term Memory with Adam Optimizer 4

Fault Diagnosis and Localization of Power Cables Using Bi-Di...

引用

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

作者： Song, Lingling Yantai Vocational College Department of Intelligent Control Yantai China

ISBN: (纸本)9798350352931

In recent days, the fault diagnosis and localization of power cables is one of the incredible remote ones, which have to be identified carefully and located, fixed within a short period. In existing, the Support Vector Machine (SVM) is used for the diagnosis of fault in power cables. However, these Machine learning (ML) models are designed for static data, which struggled to capture the temporal dependencies in a sequential time series signals. therefore, Bi-directional Long Short Term Memory (BiLSTM) with Adam Optimizer (AO) is proposed and named as BiLSTM-AO. Initially, the procedure is started with the data collection and further processed into preprocessing with Time Domain Reflectometry (TDR). After that, the features are extracted from the preprocessed data using Short Time Fourier Transform (STFT) to extract the apparent features in time domain such as frequency components and harmonics. Finally, the BiLSTM is utilized for the diagnosis of faults and AO is used to increase the performance of model. From the results, the proposed Bi-LSTM-AO achieved better accuracy (99.80%) when compared with existing Whale Gray Wolf optimization Algorithm-Variational Mode Decomposition-Support Vector Machine (WGWOA-VMD-SVM). © 2024 IEEE.

关键词： Support vector machines

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Research on Enhanced Hybrid Whale optimization Algorithm with Inverse learning Strategy and Lévy Flight Mechanism 4

Research on Enhanced Hybrid Whale Optimization Algorithm wit...

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2023 4th International conference on Computer Engineering and intelligent Control, ICCEIC 2023

作者： Guo, Danyang Zheng, Yinghao Lu, Jiaxin Liang, Sihua Chen, Tuyin Yang, Jialin Zhou, Chao Guangzhou City University of Technology School of Robotics Engineering 510800 China Guilin University of Electronic Technology School of Materials Science and Engineering 541000 China Guangdong Baiyun University School of Big Data and Computing 510450 China

ISBN: (纸本)9798350308877

In recent years, bio-inspired optimization algorithms have attained significant success in addressing complex global optimization issues. Nonetheless, a single bio-inspired search strategy may struggle to handle diverse and intricate problems. To surmount this constraint, this paper introduces an enhanced hybrid whale algorithm (MEHWOA) based on reverse learning strategy and Lévy flight mechanism improvement. this approach amalgamates the global search capabilities of the gray wolf algorithm with the local search prowess of the whale algorithm, further augmenting the convergence speed and optimization accuracy of MEHWOA by incorporating reverse learning strategy and Lévy flight mechanism. To assess the performance of MEHWOA, we conducted experiments on 23 general benchmark test functions and compared it with original gray wolf (GWO), whale (WOA), particle swarm (PSO), and sparrow search (SSA) optimization algorithms. the experimental outcomes reveal that MEHWOA exhibits faster convergence speed and superior accuracy across various test functions, including unimodal, multimodal, and composite benchmark test functions. these findings corroborate that MEHWOA possesses considerable potential for solving complex global optimization problems. © 2023 IEEE.

关键词： Global optimization

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A Review of Machine learning Methods to Solve Flight Scheduling Research 4

A Review of Machine Learning Methods to Solve Flight Schedul...

引用

4th International conference on Big Data Engineering and Education, BDEE 2024

作者： Pan, Yanbin Xu, Haiwen Qian, Jide Chu, Mengtao Yan, Hao Civil Aviation Flight University of China Faculty of Science Guanghan China Civil Aviation Flight University of China Key Laboratory of Civil Aviation Flight Technology and Flight Safety Guanghan China

ISBN: (纸本)9798350388268

the crew scheduling problem is a crucial component of airline operations planning. Using traditional operations research optimization methods to optimize the crew scheduling process can enhance the scientific and accurate nature of the plan, significantly impacting the airline's operational efficiency, cost savings, and ensuring the safety of flight operations. However, due to the increasing number and complexity of modern flight schedules, traditional combinatorial optimization algorithms face enormous computational pressure and cost. Additionally, the need for timely recovery of problem flights imposes limitations on traditional methods. therefore, by reviewing recent algorithms that use machine learning to address combinatorial optimization problems, this research explores and examines the integration methods, algorithmic logic, and areas for improvement. It also investigates the potential of these methods for solving flight scheduling issues, providing insights and references for future applications of machine learning in addressing flight scheduling problems. © 2024 IEEE.

关键词： Neural networks

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optimization Method for Unit Commitment Based on Deep Graph Reinforcement learning 4

Optimization Method for Unit Commitment Based on Deep Graph ...

引用

4th Power System and Green Energy conference (PSGEC)

作者： Huang, Hui Fan, Li Liu, Bosi Chang, Haijun Zhou, Hao Zhao, Yonglong Wang, Ruoyun Nari Tech Nanjing Control Syst Ltd Nanjing Peoples R China State Grid Corp China Southwest Branch Chengdu Peoples R China

ISBN: (纸本)9798350365573;9798350365580

the large-scale integration of renewable energy sources such as wind and solar energy has led to the widespread replacement of traditional power sources, significantly reducing the short-circuit capacity of the power grid and increasing the risk of transient voltage instability. Current research utilizes short-circuit ratios or capacities as constraints to optimize start-up strategies, ensuring sufficient voltage support strength in the system. However, short-circuit ratios or capacities can only estimate the system's voltage support capability and cannot accurately characterize the transient voltage insecurity risks at various nodes. therefore, a method based on deep graph reinforcement learning for optimizing unit start-up strategies is proposed. this approach combines graph-based deep learning methods with reinforcement learning. By integrating the feature extraction capabilities of deep learning with the policy-solving capabilities of reinforcement learning, the method enables rapid online acquisition of start-up strategies after offline training completion.

关键词： deep learning reinforcement learning DC blocking voltage support transient voltage safety

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Transforming IoT with Federated Multimodal learning: A Privacy-Centric Approach 4

Transforming IoT with Federated Multimodal Learning: A Priva...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Kumar, Mukesh thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350349900

Suppliers face significant challenges when discussing cloud-based health apps, such as implementing highly secure procedures and rigorous privacy safeguards. this applies to the fact that the acceptance of these applications is limited by the need for such quality of service, which is achieved mainly by reducing latency. this work is devoted to tackling these hurdles through the cutting-edge application of edge computing and model-federated learning aspects. Federated learning, a complex and distributed paradigm for machine learning helps perform computations near the data source and overcomes the constraint problem of a distant data source. Integrating the newest edge computing, our system includes an intelligent instrument that is applied to extract clinical data using Clustered Federated learning (CFL). the value of this framework is evaluated using the performance metrics of two benchmark datasets, one of which represents different kinds of problems that occur in the day-to-day life of people, which concludes the effectiveness and significance of implementing machine learning at the edge in the health care sector the presented results above strongly suggest the fact that CFL promotes this model results in improvement. the performance indicators of the CFL model showed an appraisal boost with the increments in X-ray and Ultrasound datasets by 16% and 11%, respectively, when compared to those trained conventionally. Hence, edge AI through CFL can achieve the same tasks, and the possible scenarios can also be remarkably superior compared to a centralized data system. © 2024 IEEE.

关键词： Federated learning

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optimization of Pose Recognition Algorithms in Smart Wearable Devices 4

Optimization of Pose Recognition Algorithms in Smart Wearabl...

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4th International conference on intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Luo, Mingze Huang, Jueqiao Zheng, Bowen Lin, Guofei Zhao, Tianze Cao, Kangjie School of Electronic Science and Engineering Southeast University Jiangsu Nanjing China Minzu University of China Department of Computer Science and Technology China Fok Ying Tung Graduate School Hong Kong University of Science and Technology Hong Kong School of Accounting and Finance Jiageng College Xiamen University Fujian Zhangzhou China Minzu University of China Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350343113

With the surge in popularity of smart wearable devices, the necessity for pose recognition has become paramount, especially in fields like motion analysis, health monitoring, and virtual reality. Yet, achieving efficient and precise pose recognition on devices with limited resources poses significant hurdles. Addressing this, we introduce an innovative optimization method for pose recognition algorithms, harnessing the power of the C++ compiler. Our approach uniquely integrates traditional machine learning paradigms with model simplification techniques. Furthermore, by exploiting C++ functionalities such as inline assembly, template metaprogramming, and compile-time optimization, we craft an optimization tailored explicitly for wearable hardware. Evaluations conducted on various devices testify to our method's superiority, marking a 10% surge in accuracy, 30% boost in efficiency, and a 15% dip in energy consumption relative to contemporary methods. Apart from achieving stellar accuracy and real-time performance, our method distinguishes itself in resource efficiency and scalability. this article offers an in-depth exploration of our design philosophies, optimization tactics, experimentation methodologies, and seminal discoveries, signposting the future trajectory of pose recognition in smart wearables and highlighting its commercial potential. © 2023 IEEE.

关键词： Machine learning

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Application of Virtualization Digital Technology in intelligent Platform Construction Pattern Analysis System 4

Application of Virtualization Digital Technology in Intellig...

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4th International conference on Machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Yuan, Hongyan Shandong Institute of Commerce and Technology Shandong Jinan250103 China

intelligent platforms are mainly service platforms composed of machine learning integrations that rely on continuous reasoning and learning. this technology has been widely used in today's the Internet Age, and its analysis system, as an important customer oriented feedback link, is also very important. therefore, this article would optimize the intelligent platform analysis system by applying virtual digital technology, and use intelligent platforms for ship shipping and smart home platforms as examples. © 2024 the Authors.

关键词： Smart homes

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Deep learning Based Modal for Predicting Flooding Pattern and Climate Shifts 4

Deep Learning Based Modal for Predicting Flooding Pattern an...

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

作者： Kumari, Lalita Kumar, Umesh Kumar, Niranjan Amity University Amity School of Engineering Patna India

ISBN: (纸本)9798350307757

this research presents an integrated deep learning model that combines climate pattern prediction and flood forecasting. Understanding and mitigating climate change and its impact on flooding are critical environmental concerns. Our methodology harmonizes climate and flood datasets, utilizing a neural network architecture and advanced optimization techniques. the compiled model successfully predicts climate patterns and flood events, shedding light on their potential interrelationships. this research offers a novel approach to studying climate and flooding, with significant implications for climate change mitigation and flood risk management. © 2024 IEEE.

关键词： Floods

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