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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491-500 订阅

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optimization of Relay Protection Setting for Distribution Networks Based on a Multi-scale Combinatorial Kernel Limit learning Machine 4

Optimization of Relay Protection Setting for Distribution Ne...

引用

4th international conference on Electrical Engineering and Control Science, IC2ECS 2024

作者： Shi, Hengchu Zhou, Haicheng You, Hao Hu, Xiao Chen, Xiaofan Zheng, Quanchao Yunnan Power Grid Co. Ltd. Kunming China Yunnan Pu'er Power Supply Bureau of Southern Power Grid Pu'er China Guangdong Yitaida Technology Development Co. Ltd. Guangzhou China

ISBN: (纸本)9798331510107

the conventional distribution network relay protection setting planning is generally fixed-point or distribution network target optimization, which is relatively limited, resulting in the increase of the final setting standard deviation. therefore, the design and practical implementation of the distribution network relay protection setting optimization method based on multi-scale combined kernel limit learning machine are proposed. According to the current relay protection processing requirements, feature extraction is carried out first, and multi-level method is adopted to break the restricted conditions and realize multi-level linear setting planning. On this basis, the optimization model of distribution network relay protection setting based on multi-scale combined kernel limit learning machine is designed, and the adaptive scheduling method is adopted to finally realize the optimal setting processing. the test results show that the multi-scale joint kernel extreme learning machine method proposed in this paper can better reduce the calibration relative error. Whether before DG access, before optimization, or after optimization, the calibration relative error can be maintained at a level below 1.5, and the convergence time can be effectively controlled. © 2024 IEEE.

关键词： Adversarial machine learning

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Integrated Computer Network Security System: Intrusion Detection and threat Prediction Using Machine learning algorithms 4

Integrated Computer Network Security System: Intrusion Detec...

引用

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

作者： Utarbayeva, Mariya Mukanova, Makpal Nazarbayev Intellectual School Almaty Kazakhstan Department of Computer Science Nazarbayev Intellectual School Almaty Kazakhstan

ISBN: (纸本)9798350374865

the escalating use of computer networks and associated applications has made cybersecurity a critical concern. this research presents a comprehensive approach to addressing network security issues by developing an Intrusion Detection System (IDS) based on the dataset from the Security Information and Event Management System (SIEM) using various machine learning algorithms. the study includes a detailed comparison of models such as Logistic Regression, K-Nearest Neighbors, Gusain Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, XGBoost, and Artificial Neural Networks. the results demonstrated using histograms and tables show the effectiveness of Random Forest and PCA Random Forest, emphasizing their accurate traffic classification. the research evaluates the model's performance across various cyber-security datasets containing multiple categories of cyber-attacks. It evaluates efficiency by utilizing criteria such as accuracy, precision, and recall. By applying a multilevel approach aligned with the latest trends in machine learning, the study aims to facilitate swift and precise threat analysis and response, ultimately enhancing the overall effectiveness of the cybersecurity system. the study is an effective educational resource that introduces IT students to innovative machine learning and cybersecurity ideas. through the integration of the results into IT curriculum, educators may close the knowledge gap between theory and practical application by offering students real-world experience in addressing cyber threats. this combined emphasis on innovation and education seeks to develop a new generation of IT professionals with the expertise required for enhanced cybersecurity. © 2024 IEEE.

关键词： Network intrusion

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Federated Multi-Task learning on Non-IID Data Silos: An Experimental Study 24

Federated Multi-Task Learning on Non-IID Data Silos: An Expe...

引用

4th Annual international conference on Multimedia Retrieval (ICMR)

作者： Yang, Yuwen Lu, Yuxiang Huang, Suizhi Sirejiding, Shalayiding Lu, Hongtao Ding, Yue Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798400706028

the innovative Federated Multi-Task learning (FMTL) approach consolidates the benefits of Federated learning (FL) and Multi-Task learning (MTL), enabling collaborative model training on multi-task learning datasets. However, a comprehensive evaluation method, integrating the unique features of both FL and MTL, is currently absent in the field. this paper fills this void by introducing a novel framework, FMTL-Bench, for systematic evaluation of the FMTL paradigm. this benchmark covers various aspects at the data, model, and optimization algorithm levels, and comprises seven sets of comparative experiments, encapsulating a wide array of non-independent and identically distributed (Non-IID) data partitioning scenarios. We propose a systematic process for comparing baselines of diverse indicators and conduct a case study on communication expenditure, time, and energy consumption. through our exhaustive experiments, we aim to provide valuable insights into the strengths and limitations of existing baseline methods, contributing to the ongoing discourse on optimal FMTL application in practical scenarios. the source code can be found at https://***/youngfish42/FMTL-Benchmark.

关键词： Federated learning Multi-Task learning Dense Prediction

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Research on Train Energy Saving optimization Problem Based on Multi-Objective Genetic Algorithm 4

Research on Train Energy Saving Optimization Problem Based o...

引用

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

作者： Huang, Qien Information Engineering College Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9798350359589

With the gradual development of urban rail transit, the passenger flow of public transportation facilities increases, resulting in the rapid growth of energy consumption, which brings a significant burden to the sustainable development of the city. this paper explores the marching strategy to reduce the energy consumption of train operation through the study of a single train running between two stations, so as to further explore the potential of energy-saving and carbon-reducing of the train. A single-train multi-objective energy-saving optimization model is established, and a multi-objective genetic algorithm is used to solve for the lowest energy consumption and shortest running time of the train under the consideration of complex road conditions and the influence of complex dynamic processes of the motor. the BP neural network optimized by Gray Wolf algorithm is used to verify the solutions, and the minimum energy consumption is 2.0166e+12J and the shortest running time is 211.6529s. © 2024 IEEE.

关键词： BP neural network gray wolf algorithm optimization NSGA-II Train energy efficiency optimization

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Enhanced Wastewater Treatment Plant Feature Prediction Using Edge Attention Network with Parrot optimization 4

Enhanced Wastewater Treatment Plant Feature Prediction Using...

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

作者： Sivaramkrishnan, M. Ramkumar, M. Siva Arun, M. Kanan, Mohammad Shankar, S. Giri, Jayant Karpagam College of Engnieering Dept of Eee India Sns College of Technology Dept of Ece India Saveetha University Department of Mechanical Engineering Chennai602105 India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan Cambridge Institute of Technology Department of Mechanical Engineering Bengaluru560086 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798331523923

To assist process design and controls, enhance system dependability, lower operating costs, and support overall performance optimization, an accurate prediction of wastewater treatment plant (WWTP) key features can help understand and predict plant behavior. In order to address the non-linearity and dynamic character of environmental data, deep learning technologies, which have been shown to be data-driven soft sensors, should be developed for WWTP applications. In order to predict important WWTP parameters such as influent flow, influent temperature, influent biochemical oxygen demand (BOD), effluent chloride, effluent BOD, and power consumption, this work uses deep learning-based models as soft sensors. the proposed method predicts key features of wastewater treatment plants (WWTPs) by using a Multilayer Edge Attention Network with Parrot Optimizer (MEA-Net-PO) and a Hybrid Osprey optimization Algorithm with Emperor Penguin Optimizer-Based Feature Selection (OOA-EPO). the approach adopts MinMax scaler normalization for preprocessing data to obtain optimal performance in high-dimensional datasets. the MEA-Net-PO model encompasses complex relationships among WWTP data, while OOA-EPO feature selection promotes the selection of the most suitable input features. this does not rely on data distribution assumptions but is very elastic for a given WWTP. the model improves the forecasting efficiency and accuracy. Using historical data from a municipal WWTP, located along a coastal area of Saudi Arabia, the model clearly outperforms traditional methods through RMSE, MAPE, and R2. this may be considered the most promising avenue for data-driven optimization in managing WWTP operations. © 2025 IEEE.

关键词： Effluents

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A Comparative Analysis of Various Machine learning algorithms for Handwritten Digit Recognition 4

A Comparative Analysis of Various Machine Learning Algorithm...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Akshat, Abhi Tripathi, Kritika Malik, Devanshi Fatima, Hoor Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350306118

One of the most fundamental uses of machine learning is the identification of handwritten digits. there has been a lot of compelling research in this field. Since everyone has different handwriting, it becomes very important for the algorithm to recognize all styles and ways of writing. With the advancement in science and technology, digitization of all forms of documents has become prominent. Since there are still many documents that are yet to be digitized, these applications come in handy. MNIST and SKLearn load digits datasets are prominent for the recognition process and they have a large set of data points. Many algorithms are developed over the years that are capable of performing recognition of digits. the accuracy and performance metrics of the methods Support Vector Machine (SVM), Random Forest, Gaussian Naive Bayes, and Convolutional Neural Networks (CNN) are analyzed in this study. © 2023 IEEE.

关键词： Support vector machines

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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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A Review on Cosmetic Product Recommendation Using Deep learning 4

A Review on Cosmetic Product Recommendation Using Deep Learn...

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

作者： Chouhan, Ruchika Barde, Snehlata Sage University Dept. of Computer Science Engineering Indore India

ISBN: (纸本)9798331515720

Cosmetic products play an important role in every one's life. Cosmetic products also called as makeup are substance that is applied on the body especially on face. the modern lifestyle can be demanding, leaving little time for self-care. However, it's crucial to prioritize self-care, including skincare, despite hectic schedules. Indeed, relying solely on best-seller products or in-store recommendations may not always be effective in determining compatibility between a skincare product and an individual's skin condition. this is because everyone's skin tone and type of the skin is unique, with varying sensitivities, concerns, and needs. Many people buy their goods online today, using various e-commerce sites. So, to choose the best skincare product during online purchasing is difficult, since it depends entirely on skin type and usage. the main aim of this paper is to survey and explores the existing recommendation systems designed to assist users in selecting cosmetics personalized to their unique skin type, by using advanced algorithms and machine learning. this study has reviewed various methods like face recognition, cosmetic product recognition, tag recommendation, facial skin image classification using convolution neural network, a new examples-rules guided deep neural network approach. through a comprehensive review of literature and existing research, this paper examines the methodologies, algorithms, and techniques employed in cosmetic recommendation systems. Key factors such as user preferences, skin type, skin concerns, and product attributes are analysed to understand how recommendation systems use this information to generate personalized recommendations. © 2024 IEEE.

关键词： Cosmetics

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Optimizing Multi-Task learning for Enhanced Performance in Large Language Models 4

Optimizing Multi-Task Learning for Enhanced Performance in L...

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

作者： Qi, Zhen Chen, Jiajing Wang, Shuo Liu, Bingying Zheng, Hongye Wang, Chihang Northeastern University Boston United States New York University New York United States Purdue University Indianpolis Indianapolis United States McLean United States The Chinese University of Hon Kong Hong Kong

ISBN: (纸本)9798331534622

this study aims to explore the performance improvement method of large language models based on GPT-4 under the multi-task learning framework and conducts experiments on two tasks: text classification and automatic summary generation. through the combined design of shared feature extractors and task-specific modules, we achieve knowledge-sharing and optimization of multiple tasks in the same model. the experiment uses multiple subtasks of the GLUE dataset to compare the performance of the multi-task model with the single-task GPT-4, the multi-task version of GPT-3, the BERT basic model, and the classic Bi-LSTM with Attention model. the results show that the proposed multi-task learning model outperforms other comparison models in terms of text classification accuracy and ROUGE value of summary generation, demonstrating the advantages of multi-task learning in improving model generalization ability and collaborative learning between tasks. the model maintains a stable loss convergence rate during training, showing good learning efficiency and adaptability to the test set. this study verifies the applicability of the multi-task learning framework in large language models, especially in improving the model's ability to balance different tasks. In the future, with the combination of large language models and multimodal data and the application of dynamic task adjustment technology, the framework based on multi-task learning is expected to play a greater role in practical applications across fields and provide new ideas for the development of general artificial intelligence. © 2024 IEEE.

关键词： Contrastive learning

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On Strong Anti-learning of Parity 3rd

On Strong Anti-learning of Parity

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3rd international conference on optimization, learning algorithms and applications (OL2A)

作者： Lisitsa, Alexei Vernitski, Alexei Univ Liverpool Dept Comp Sci Liverpool Merseyside England Univ Essex Dept Math Sci Colchester Essex England

ISBN: (纸本)9783031530357;9783031530364

On some data, machine learning displays anti-learning;that is, while the classifier demonstrates excellent performance on the training set, it performs much worse than the random classifier on the test set. In this paper we study what we call strong anti-learning, that is, the most surprising scenario, in which the more examples you place in the training set, the worse the accuracy becomes, until it becomes 0% on the test set. We produce a framework in which strong anti-learning can be reproduced and studied theoretically. We deduce a formula estimating anti-learning when decision trees (one of the most important tools of machine learning) solve the parity bit problem (one of the most famously tricky problems of machine learning). Our estimation formula (deduced under certain mathematical assumptions) agrees very well with experimental results (produced on random data without these assumptions).

关键词： Decision trees

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