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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321-330 订阅

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Graphene-Based Wearable E-Textiles Integrated with Deep learning for Real-Time Sweat Analysis in Military and Sports applications 4

Graphene-Based Wearable E-Textiles Integrated with Deep Lear...

引用

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

作者： Sharmila, V. Ceronmani Varun, B. Sai Josh, P.A. Alvis Atmuri, Aashrith Prasanth, K. Department of Electronics and Communication Engineeering Faculty of Engineering and Technology SRM institute of Technology Kattankulathur India Department of Electronics and Communication Engineeering SRM institute of Technology Kattankulathur India

ISBN: (纸本)9798350352931

Graphene-based conductive fabrics integrated with deep learning algorithms enable real-time sweat analysis for military and sports applications. these graphene-based e-textiles, renowned for their flexibility, durability, and bio-signal capturing capabilities, are screen-printed onto fabric to create non-invasive sweat sensors. the sensors measure physiological data such as electrolyte balance, hydration levels, and sweat rates, essential for monitoring physical performance under various conditions. A deep learning model, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, processes the collected data to classify sweat composition and recommend optimal clothing based on environmental and physical stressors. Performance analysis demonstrates CNN's superior convergence speed and lower loss values compared to LSTM. this integrated system offers real-time feedback, helping to prevent dehydration, fatigue, and heat-related illnesses, thus providing a transformative approach to personalized healthcare and performance monitoring in demanding environments. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Study of Power optimization Techniques for Microcontroller based IoT applications 4

A Comparative Study of Power Optimization Techniques for Mic...

引用

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

作者： Raut, Shital Bhandari, Chirag Jain, Harshita Vishwakarma Institute of Technology Dept. of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350343670

this research paper presents a comparative study of power optimization techniques used in microcontroller applications, focusing on both hardware and software levels. It provides a thorough analysis of different techniques, comparing their power reduction effectiveness and analyzing the trade-offs associated with their implementation. Rather than focusing solely on individual techniques, we examine the principles behind each approach and how they can be used in different applications. the paper highlights key use cases and applications where power optimization techniques can be applied, and the results of the study show that different techniques are suitable for different applications. the paper concludes that a combination of hardware and software level power optimization techniques can be used to achieve the best power reduction while maintaining performance. the findings of this study can be useful for microcontroller designers and developers who are looking to optimize power consumption in their applications. the paper emphasizes the importance of understanding the trade-offs associated with each technique and how they can be applied to specific use cases to achieve the best power reduction possible. © 2024 IEEE.

关键词： DVFS Gating IoT Microcontroller Power optimization Software optimization Techniques

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Enhancing Gameplay Experience through Reinforcement learning in Games 4

Enhancing Gameplay Experience Through Reinforcement Learning...

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

作者： Zhasulanov, Dinmukhammed Marat, Bekarys Erkin, Kuanysh Omirgaliyev, Ruslan Kushekkaliyev, Alman Zhakiyev, Nurkhat Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Physics West Kazakhstan University Uralsk Kazakhstan Department of Science and Innovations Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350374865

It is important to improve the game development field since games have great influence on economics and society. One of the ways to enhance game experience is to replace non-playable characters (NPC) by machine learning (ML) agents. Artificial Intelligence (AI) trained by Reinforcement learning algorithms can adapt to the game environment and act based on users’ actions or changes in surroundings. this research project aims to test the idea of replacing the NPC by artificial intelligence agent trained by Reinforcement learning algorithms. the data for this project was collected by observation of experiments inside of the game created in Unity platform. Agent was trained in special toolkit for Unity platform called "Unity ML-Agents". the behavior of the agents was subjected to preparatory measures for replication, including preliminary data processing using behavior cloning methods. the selected algorithm of the Reinforcement learning was the Proximal Policy optimization, which is based on balance between new actions and its rewards. the training progress of the agents was monitored utilizing TensorBoard, the default package within the Unity ML Agents toolkit. During the research initialization phase, there were set null hypothesis, which states that pre-scripted bots are better for games and alternative hypothesis, which states that AI can replace NPC and enhance the game experience for a player. © 2024 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence and Machine learning algorithms to Analyse Integrated Nutrient Management Strategies for Optimal Yield and Quality of Oilseeds Crops for Sustainable Development 4

Artificial Intelligence and Machine Learning Algorithms to A...

引用

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

作者： Mithra, C. Dar, Showkat A. Preethi, A. Dept. of Computer Science and Engineering Chennai India Githam University Dept. of Computer Science and Engineeing India Dept. of Computer Science and Engineering Chidambaram India

ISBN: (纸本)9798331515720

the main objective of this research is to deduce the efficacy of integrated nutrient management (INM) technologies in production of oilseed crops for sustainable development. A great amount of experience is needed in selecting the most effective INM strategy. A new recommendation system to circumnavigate this issue is proposed. It lets farmers decide on the best INM strategy to maximize oilseed crop yield and quality. this system is built on the techniques of advanced machine learning (ML) and aritifical Intelligence (AI). Oilseed crop date in Tamil Nadu from 1961 to 2019 was used to develop the proposed algorithm. the proposed algorithm for crop yield prediction (CYP) which includes a Soft Voting Ensemble Classifier with weights (SVECWW), a Soft Voting Ensemble Classifier without weights (SVECWOW) along with the SVM technique are compared and contrasted with existing algorithms and also proves that SVECWW outperforms other ML algorithms with an accuracy rate of 97.2%. Furthermore, the Stacked Generalization Ensemble model is employed and compared with another Deep Neural Network (DNN) for the INM crop recommendation system which offers a simple graphical user interface (GUI) for farmers to use and received an accuracy of 97.5%. this GUI enables farmers to access valuable information such as the optimal timing for cultivating oilseed crops, the appropriate types and quantities of organic manures, inorganic fertilizers, and bio-fertilizers required for successful oilseed crop production. the study shows, on its whole, how to create tailored recommendation systems for farmers using GUI models with artificial intelligence and machine learning algorithms. Implementing these systems is expected to significantly improve oilseed crop production and quality significantly, benefiting the whole agricultural sector for sustainable development. Artificial intelligence (AI) makes a recommendation system more accurate and adaptable by looking through complex datasets and patterns

关键词： Adversarial machine learning

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Dual-Population Adaptive Strategy Comprehensive learning Particle Swarm optimization 5th

Dual-Population Adaptive Strategy Comprehensive Learning Par...

引用

5th international conference on Neural Computing for Advanced applications (NCAA)

作者： Chen, Yujie Fan, Mingjie Zhao, Xinchao Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Particle swarm optimization (PSO) is a classic algorithm in the field of swarm intelligence. Despite its widespread use, PSO faces challenges in complex optimization scenarios, particularly its propensity for falling into local optima and slow convergence. Comprehensive learning particle swarm optimization (CLPSO) has enhanced PSO's global search capabilities by introducing a comprehensive learning mechanism. this study presents further enhancements by proposing the dual population adaptive strategy comprehensive learning particle swarm optimization (DPAS-CLPSO), which combines a dual population framework with an adaptive learning paradigm to increase population heterogeneity and algorithm search efficiency. Experimental results on the CEC2017 standard benchmark tests have shown that DPAS-CLPSO significantly outperforms several well-known algorithms in both 30D and 50D problem spaces. Lastly, the effectiveness of the algorithm in solving complex optimization problems is also underscored by statistical validation through rank sum tests.

关键词： Particle swarm optimization Comprehensive learning Dual population strategy Adaptive learning strategy

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A Satellite Momentum Wheel Digital Twin Model Based on Multi-step Parameter optimization Method 4

A Satellite Momentum Wheel Digital Twin Model Based on Multi...

引用

4th international conference on Control theory and applications, ICoCTA 2024

作者： Ying, Kaixin Cheng, Yuehua Lu, Ningyun Yu, Ziquan University of Aeronautics and Astronautics College of Automation Engineering Nanjing Nanjing China

ISBN: (纸本)9798331506155

the satellite momentum wheel is an essential component of the satellite attitude control system. Its operating status is directly related to the normal operation of the satellite and the quality of mission completion. However, due to the limitations of the satellite's visible window and communication channels, it is difficult to obtain rich monitoring information to characterize the operating status of the momentum wheel system. therefore, this paper proposes a digital twin method based on multi-parameter optimization to track key parameters affecting the satellite's operating status, achieving monitoring of the satellite momentum wheel's operating status. Firstly, by constructing a mechanism model of the satellite momentum wheel, the simulation of the momentum wheel under the influence of different degradation factors is realized from the perspectives of mechanics and electromagnetism. then, combined with heuristic algorithms, the key parameters in the physical model are updated based on data from the physical entity, achieving tracking of the actual status of the momentum wheel by the twin model. Simulation experiments verify that the model established in this paper has higher accuracy and more data dimensions compared to traditional simulation models. through verification of the proposed parameter update strategy, it is demonstrated that the proposed method can accurately identify the operating status of the momentum wheel. © 2024 IEEE.

关键词： Heuristic algorithms

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ImConv_RNN: Improved Convolutional Recurrent Model Based Channel Estimation for 6G Networks 4

ImConv_RNN: Improved Convolutional Recurrent Model Based Cha...

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

作者： Kulkarni, Raghavendra Suresh Kumar Kondeti, Venkata Satya Pillai, Binu Sudhakaran Surendran, R. T-Mobile Usa Inc Principal Engineer Department of Network Technology BellevueWA United States Senior Systems Engineer Department of Professional Services Axyom. Core North AndoverMA United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

this research introduces a novel deep learning for channel estimation for the 6G networks. the channel estimation for the 6G networks is employed using the proposed Improved Convolutional Recurrent (ImConv-RNN) model. In this, the loss function optimization is employed using the proposed ChSO algorithm. the chaotic mapping based on Chebyshev is included in the skill optimization assist the algorithm to enhance the convergence rate. the proposed ImConv-RNN model is evaluated based on RMSE, R2, MAE and MAPE acquired the values of 0.0136, 0.9584, 0.0114, and 5.8969 respectively. © 2025 IEEE.

关键词： 6G networks channel estimation chebyshev mapping recurrent model skill optimization algorithm spatio-temporal feature extraction

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Training a Tucker Model With Shared Factors: a Riemannian optimization Approach 27

Training a Tucker Model With Shared Factors: a Riemannian Op...

引用

27th international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Peshekhonov, I Arzhantsev, A. Rakhuba, M. HSE Univ Moscow Russia

Factorization of a matrix into a product of two rectangular factors, is a classic tool in various machine learning applications. Tensor factorizations generalize this concept to more than two dimensions. In applications, where some of the tensor dimensions have the same size or encode the same objects (e.g., knowledge graphs with entity-relation-entity 3D tensors), it can also be beneficial for the respective factors to be shared. In this paper, we consider a well-known Tucker tensor factorization and study its properties under the shared factor constraint. We call it a shared-factor Tucker factorization (SF-Tucker). Since sharing factors breaks polylinearity of classical tensor factorizations, common optimization schemes such as alternating least squares become inapplicable. Nevertheless, as we show in this paper, a set of fixed-rank SF-Tucker tensors preserves a Riemannian manifold structure. therefore, we develop efficient algorithms for the main ingredients of Riemannian optimization on the SF-Tucker manifold and implement a Riemannian optimization method with momentum. We showcase the benefits of our approach on several machine learning tasks including knowledge graph completion and compression of neural networks.

关键词： Factorization

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Cannabis Revolutionizing VOC Detection: Advanced Sensors and Machine learning Innovations 4th

Cannabis Revolutionizing VOC Detection: Advanced Sensors and...

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4th international conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： Ayat, Yassine El Moussati, Ali El Aouni, Abdelaziz Mir, Ismail Oujda60000 Morocco

ISBN: (纸本)9789819606436

this paper details the conception of an electronic nose tailored for the precise detection of Volatile Organic Compounds (VOCs) emitted by cannabis and tobacco. Leveraging the Grove—Gas Sensor V2, equipped with four chemically sensitive sensors, our device offers accurate detection capabilities. through rigorous experimentation, we have demonstrated its efficacy in identifying VOCs from cannabis resins and tobacco with exceptional accuracy and reliability. Employing advanced machine learning algorithms, we’ve developed an odor recognition model to interpret sensor data effectively. Notably, our focus on performance optimization includes integrating stabilization techniques to minimize external factor-induced variations, enhancing detection accuracy and reproducibility. this innovation represents a significant step forward in VOC characterization technology, promising diverse applications across industrial and healthcare sectors. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic nose

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2024 IEEE 4th international conference in Power Engineering applications: Powering the Future: Innovations for Sustainable Development, ICPEA 2024

2024 IEEE 4th International Conference in Power Engineering ...

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4th IEEE international conference in Power Engineering applications, ICPEA 2024

ISBN: (纸本)9798350344578

the proceedings contain 73 papers. the topics discussed include: morphological study on corrosive sulfur development in transformer windings;day-ahead optimal power flow with stochastic wind and solar power plants using Harris hawk optimization;prediction of transmission losses allocation using artificial neural network and Z-Bus tracing;structure and ac breakdown strength of polypropylene/ethylene-octene copolymer/magnesium oxide nanocomposites;output power prediction of grid connected photovoltaic system using dolphin echolocation algorithm;residential customer baseline load estimation based on conditional denoising diffusion probabilistic model;optimization of automatic generation control performance for power system of two-area based on genetic algorithm-particle swarm optimization;and improved particle swarm optimization MPPT for standalone PV system under varying environmental conditions.

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