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检索条件"任意字段=3rd Conference on Learning and Intelligent Optimization"

共 11827 条记录，以下是131-140 订阅

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Placement Prediction Using the Artificial Neural Network (ANN) 3

Placement Prediction Using the Artificial Neural Network (AN...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Priyadarsini, Jukta Badiger, Veena S. Sheetal Jacintha, Anitha DSouza Presidency College Department of Computer Applications Bengaluru India Presidency University Presidency College School of Engineering Bengaluru India

ISBN: (纸本)9798331515911

Placements are of utmost significance to academic organizations and college students. A strong foundation for the professional field is built up for the student beforehand, and a positive placement report gives a school or college a competitive edge in the educational marketplace. An automated method of statistical evaluation that streamlines the creation of analytical versions is machine learning. In this study, a computer that predicts whether a student might be located or not is the focus, based solely on the student's credentials, historical data, and experience. The performance is evaluated using students' datasets from presidency college and accuracy achieved for our model is 96%. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

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Research on Radar Operation Pattern Recognition Method Based on MLP optimization 24

Research on Radar Operation Pattern Recognition Method Based...

引用

3rd International conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Li, Hao Zhang, Fuqun Feng, Mingyue Zhang, Jiapeng Huang, Linxuan Tian, Tian Air Force Early Warning Acad Wuhan Peoples R China Xidian Univ Xian Peoples R China PLA Unit 93277 Shenyang Peoples R China

ISBN: (纸本)9798400716959

This article delves into an innovative radar working pattern recognition algorithm based on multi-layer perceptron (MLP). Through carefully designed optimization algorithms, we systematically searched and determined the optimal MLP network structure to solve the radar operating pattern recognition problem. In a detailed simulation experiment, we carefully analyzed the effects of various network parameters, including the number of network layers, number of neurons, learning rate, and batch rate. The experimental results show that the MLP network can exhibit optimal performance when the number of layers is 5, the number of neurons is 512, the learning rate is 0.006, and the batch rate is 10. This discovery provides us with a highly promising solution to the problem of radar working pattern recognition.

关键词： Multifunctional radar Radar working mode recognition neural network optimization algorithm multi-layer perceptron

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A Review on Oversampling Techniques for the Treatment of Class Imbalance Problem 3

A Review on Oversampling Techniques for the Treatment of Cla...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Anand, Apurv Biswas, Saroj Kr. Majumdar, Sounak Thounaojam, Dalton Meitei Department of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798331527549

The Class Imbalance Problem (CIP) is a critical challenge in machine learning, particularly in applications such as medical diagnosis and fraud detection, where minority classes are underrepresented but crucial. This research addresses the issue by introducing a novel classification of oversampling techniques into 11 distinct categories, with a unique distinction between duplication and derivation mechanisms. Through an in-depth review of each category, this study identifies specific challenges such as overfitting and noise in imbalanced datasets and demonstrates how these categorized strategies effectively enhance model performance. The outcomes highlight practical solutions to improving classification accuracy and robustness in imbalanced scenarios. © 2025 IEEE.

关键词： Contrastive learning

来源：评论

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Research on ventilation and noise reduction technology for substations based on deep learning 3

Research on ventilation and noise reduction technology for s...

引用

3rd International conference on Energy and Power Engineering, Control Engineering (EPECE)

作者： Zhu, Li Zhu, Zixuan Jiang, Wenxiu State Grid Shanghai Econ Res Inst Shanghai 200000 Peoples R China Power China Shanghai Elect Power Engn Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798350366105;9798350366099

With the accelerated urbanization process and the continuous growth of power demand, substations are a critical component of the power system. However, the main noise sources in substations, such as main transformers, reactors, and cooling fans, have a significant impact on the surrounding environment. Therefore, this paper proposes an optimization design method for ventilation and noise reduction in substations based on deep learning. Firstly, the finite element method is used to simulate the ventilation and noise data of substations under multiple operating conditions to obtain sufficient samples. Secondly, the construction of the Convolutional Neural Network (CNN) model is completed, and the obtained data is used for model training. Finally, the optimization solution of parameter design is achieved using the SAC algorithm. The results show that the designed parameters for substations using this method can effectively reduce noise and comply with national standards.

关键词： transformer station deep learning ventilation and noise reduction

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An On-site Fault Remote intelligent Diagnosis and Warning Method for Electricity Information Collection Terminal Based on Deep learning 3

An On-site Fault Remote Intelligent Diagnosis and Warning Me...

引用

3rd Asian conference on Frontiers of Power and Energy

作者： Ma, Kun Ding, Jianshun Ji, Aiqiong Lin, Fei Zhang, Wenqi Li, Xinran State Grid Anhui Elect Power Co State Grid Anhui Mkt Sevice Ctr Hefei Peoples R China

ISBN: (纸本)9798350378467;9798350367676

An on-site fault remote intelligent diagnosis and warning method for electricity information collection terminal based on deep learning is proposed to address the complex forms of faults and the difficulty of manual inspection in meeting operation and maintenance needs. Firstly, model the fault diagnosis and warning system for the electricity information collection terminal, including the state monitoring layer, prediction and diagnosis layer. Then, the deep forest algorithm is improved by setting weights and applied to remote real-time intelligent diagnosis of faults in electricity information collection terminals. Finally, a BLSTM-GRU model is constructed by combining bidirectional long short-term memory network (BLSTM) and gated recurrent unit (GRU) networks, and applied to fault prediction of electricity information collection terminals, as well as issuing warnings based on the prediction results. Based on the selected data samples, experimental analysis is conducted on the proposed method, and the results shows that its fault diagnosis accuracy reaches 96.03%, and the fault warning results are reliable.

关键词： electricity information collection terminal fault diagnosis fault warning improved deep forest algorithm BLSTM-GRU model deep learning

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Deep learning-Based Automatic Identification System for Marine Plankton 24

Deep Learning-Based Automatic Identification System for Mari...

引用

3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Xiang, Su Shandong University of Science and Technology Shandong Qingdao China

ISBN: (纸本)9798400718120

Due to their diverse species and complex morphology, the automatic identification of marine plankton has always been a challenging task. In response to this problem, this study proposes an innovative deep learning-based intelligent identification system. The system integrates advanced computer vision and artificial intelligence technologies, possessing the capability of automatic feature extraction and pattern recognition. We extensively collected marine sampling data and combined it with public datasets to construct a large-scale database containing 200 species of plankton and 500,000 images. Through the training and optimization of convolutional neural network models, the system demonstrates outstanding classification performance of 94.2% on the test set. Not only does the system outperform traditional methods in accuracy, but its inference speed has also been optimized through model compression and parallel computing, capable of processing 620 images per second. By deploying containerization and automatic scaling strategies, the system ensures high availability. This research provides strong technical support for marine ecological conservation, fisheries management, and other related fields. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Advancements in Credit Scoring, Profit Scoring, and Portfolio optimization for P2P Lending 3

Advancements in Credit Scoring, Profit Scoring, and Portfoli...

引用

3rd International conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Nayaka, Premkumar Hegde, Anusha Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Surathkal India National Institute of Technology Karnataka Brics Laboratory Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798331528201

The Peer-to-peer (P2P) lending platform allows borrowers to connect directly with lenders outside traditional banking systems. Therefore, for the sustainability of these platforms, they must accurately assess the credit risk and profitability of the loans. Various credit scoring techniques, including Logistic Regression, neural networks, and ensemble methods, can be used to estimate the likelihood of borrower default. It is imperative to analyze the profit the lenders generated and enhance the credit scoring so that the investors face minimum loss. Once the profit analysis is done, then it is crucial to advise the investors about the portfolio of loans. This paper presents recent credit scoring, profit scoring, and portfolio optimization trends for P2P lending. We highlight the significant issues in incorporating machine learning models into credit scoring systems. The analysis emphasizes the need for a data-driven approach to perfecting lending practices, thus benefiting both borrowers and investors in the rapidly changing P2P landscape. © 2024 IEEE.

关键词： Decentralized finance

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PSO Algorithm for intelligent Big Data Financial Management 3

PSO Algorithm for Intelligent Big Data Financial Management

引用

3rd International conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Wang, Mingrui Heilongjiang University of Technology Heilongjiang158100 China

ISBN: (纸本)9798331519032

intelligent big data financial management is a new industry that uses intelligent technology and big data analysis to process and manage financial data. As a key problem in this industry, how to find the optimal solution has attracted more and more attention. Particle swarm optimization (PSO) and genetic algorithm (GA) are mainstream optimization algorithms used in solving the optimal solution. In this paper, we evaluated and compared the optimal solution finding fitness value, stability, and time performance of PSO algorithm and GA algorithm in intelligent big data financial management. We found that the optimal fitness value of PSO algorithm is 2000. It performs much better than the fitness value found by GA algorithm. First, this algorithm can obtain more accurate and reliable financial data than GA algorithm. In addition, the algorithm is more stable and can find the optimal solution in a shorter time. © 2024 IEEE.

关键词： Decentralized finance

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Enhancing Solar Power Forecasting Through Hybrid Machine learning Models, Satellite Data Integration, and Transfer learning Techniques 3

Enhancing Solar Power Forecasting Through Hybrid Machine Lea...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Aneesh, M. Nischal, U. Shendige, Sathvik N. Ashwini, A. Bnm Institute of Technology Department of Cse Bangalore India Bnm Institute of Technology Department of Eee Bangalore India

ISBN: (纸本)9798331515911

This paper introduces an innovative method for improving solar power prediction accuracy by integrating realtime weather forecasting, advanced machine learning techniques, and hybrid modelling frameworks. The proposed methodology incorporates satellite-based solar irradiance data, time-series prediction leveraging Long Short-Term Memory (LSTM) networks, and transfer learning for cross-regional model adaptation. These innovations aim to address the limitations of traditional models and provide a more resilient framework for forecasting solar power in diverse geographic regions. © 2025 IEEE.

关键词： Adversarial machine learning

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Dynamic Algorithmic Configuration for Enhanced Malware Detection 3

Dynamic Algorithmic Configuration for Enhanced Malware Detec...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ramya, S. Periyasamy, M. Ramprasanth, P.S. Srinivasa Aravindh, S. Mohan kumar, B. Department of Artificial Intelligence And Data Science M Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

As edge-based decisions continue to grow in demand, organizations are increasingly seeking user data, raising privacy concerns. Federated learning (FL) addresses this problem but is typically dependent on computationally intensive Neural Network (NN) models. This work introduces a novel lightweight approach by integrating Support Vector Machines (SVMs) into FL frameworks. Optimized methodology SVM with the compatibility of SGD is used where the meta-trained controller dynamically adapts the learning rate to the improvement of models. The proposed method shows equal performance to those based on the NN-based FL methods but drastically reduces the cost of computation for being applicable to resource-constrained environments. © 2025 IEEE.

关键词： Federated learning

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