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Kernel Extreme learning Machine bearing fault diagnosis based on empirical wavelet transform feature extraction and Sparrow search algorithm optimization 4

Kernel Extreme Learning Machine bearing fault diagnosis base...

引用

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

作者： Bahati, Maidina Abulizi, Maimaitireyimu Xinjiang University School of Electrical Engineering Urumqi China

ISBN: (纸本)9798350394375

Bearings are installed on mechanical equipment, and the environment of mechanical equipment is usually very noisy, and the vibration signal propagation path is very complex, so that the signal is always not pure enough, it is difficult to extract useful information, and the identification of faults brings some difficulties. It is proposed to use empirical wavelet transform to decompose fault signals, extract kurtosis as fault features, and input the established fault feature vector into a SSA-KELM for training and testing. According to experimental evidence, the method proposed in this article can quickly and better distinguish the fault state of the bearing at this time, which can bring many conveniences to our production and life. Compared with unoptimized kernel extreme learning machines, it has fast convergence speed and strong optimization ability. © 2024 IEEE.

关键词： Wavelet transforms

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FASTER FEDERATED optimization UNDER SECOND-ORDER SIMILARITY 11

FASTER FEDERATED OPTIMIZATION UNDER SECOND-ORDER SIMILARITY

引用

11th international conference on learning Representations, ICLR 2023

作者： Khaled, Ahmed Jin, Chi Princeton University United States

Federated learning (FL) is a subfield of machine learning where multiple clients try to collaboratively learn a model over a network under communication constraints. We consider finite-sum federated optimization under a second-order function similarity condition and strong convexity, and propose two new algorithms: SVRP and Catalyzed SVRP. this second-order similarity condition has grown popular recently, and is satisfied in many applications including distributed statistical learning and differentially private empirical risk minimization. the first algorithm, SVRP, combines approximate stochastic proximal point evaluations, client sampling, and variance reduction. We show that SVRP is communication efficient and achieves superior performance to many existing algorithms when function similarity is high enough. Our second algorithm, Catalyzed SVRP, is a Catalyst-accelerated variant of SVRP that achieves even better performance and uniformly improves upon existing algorithms for federated optimization under second-order similarity and strong convexity. In the course of analyzing these algorithms, we provide a new analysis of the Stochastic Proximal Point Method (SPPM) that might be of independent interest. Our analysis of SPPM is simple, allows for approximate proximal point evaluations, does not require any smoothness assumptions, and shows a clear benefit in communication complexity over ordinary distributed stochastic gradient descent. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： optimization

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Tifinagh Characters Recognition Using Deep CNN LSTM 4

Tifinagh Characters Recognition Using Deep CNN LSTM

引用

4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Chaabi, Youness Royal Inst Amazigh Culture IRCAM Ctr Comp Studies Informat & Commun Syst CEISIC Rabat Morocco

ISBN: (纸本)9798350354140;9798350354133

Optical Character Recognition (OCR) has been a prominent subject of study for many years. through research in this field, systems dedicated to the Latin alphabet have significantly improved in terms of accuracy, even for handwritten texts. However, there is relatively little literature on OCR systems for the Amazigh language, which remains of interest due to the complexity of the language in terms of grammar, writing, and spelling. this article presents an innovative approach to optical character recognition, focusing specifically on the Amazigh language with particular attention to Tifinagh characters. the main objective of this research is to design a system based on deep learning techniques to achieve automatic recognition of these specific characters. the methodology adopted integrates a deep neural network, carefully tailored to the peculiarities of Tifinagh characters, and includes image preprocessing steps to optimize the model's performance. To evaluate the effectiveness of the proposed system, it was subjected to a battery of tests on a representative corpus of images. the results obtained revealed a promising accuracy of approximately 94.1%. this promising performance reflects advancements in the field of optical character recognition for languages with non-Latin scripts, underscoring the relevance of the deep learning approach for scripts as complex as Tifinagh. the prospects offered by this research pave the way for future developments aimed at further optimizing the system's performance, while exploring practical applications of this technology for the preservation and promotion of the Amazigh language.

关键词： OCR Amazigh Tifinagh CNN LSTM Deep learning.

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Research and Application of Deep learning Based on Transfer learning in Image Classification Tasks 4

Research and Application of Deep Learning Based on Transfer ...

引用

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

作者： Bai, Jingyuan Anhui University Hefei China

ISBN: (纸本)9798350359589

With the continuous improvement of big data and computing power, deep learning models have achieved remarkable results in the field of image recognition, but building and training a deep neural network from scratch often requires a large amount of annotated data and expensive computing resources. this article first outlines the basic principles and challenges of deep learning in image classification tasks. Especially for task scenarios with small samples or scarce annotations, traditional deep learning models are prone to overfitting and insufficient generalization performance. Transfer learning is introduced into this study as an important strategy. through deep models (such as ResNet, VGG, etc.) pre-trained on large-scale image data sets (such as ImageNet), universal feature representations are extracted. And we transfer these pre-trained model parameters to specific target image classification tasks for fine-tuning. Furthermore, this article elaborates on several typical applications of transfer learning in deep learning models, and analyzes how transfer learning can effectively help improve the accuracy of image classification on the target data set based on actual cases. the experimental part compares the results of directly training a new model and using the transfer learning method to initialize the model and then train on a variety of target data sets. the experiment proves that transfer learning can significantly improve the learning efficiency and final classification performance of the model under limited samples. © 2024 IEEE.

关键词： convolutional neural network Deep learning transfer learning

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Artificial Intelligence for Climate Sustainability: A Comprehensive Review of applications, Challenges, and Future Prospects 4

Artificial Intelligence for Climate Sustainability: A Compre...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Parameshwari, M. Gnanaguru, Gnaneswari Vtu Department of Computer Applications Belagavi India Cmr Institute of Technology Department of Computer Applications Bengaluru India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Climate sustainability plays a very crucial role in environmental sustainability and by considerable use of natural resources, and reducing pollution, we can make our planet beautiful and sustaining for the life of future generations. Climate changes constitute increasingly severe challenges, and with the use of Artificial Intelligence (AI), the necessity for innovation in fostering climate sustainability has become increasingly evident. this review organizes an exhaustive inventory of which AI affects climate science. the challenges remain unmet in AI's various applications across sectors such as optimization of renewable energy, climate modeling, disaster resilience and environmental monitoring. this paper discusses the important roles of applying AI in overcoming climate change difficulties. these include energy management or conservation, cap-and-trade for carbon emissions, climate and weather forecasting, grid management, transportation, and agriculture infrastructure, or city planning. It also looks into how and the extent to which AI is adopted in resource management, increasing the adoption of renewable energy, fostering sustainable development, and overseeing resources such as water and forests. this study acknowledges various barriers that impede the effective application of Artificial Intelligence in combating climate change which include data restrictions, algorithms' bias, and ethical issues with AI operations. To solve the major challenges described, we improve the major findings of this extensive literature review and distill a set of practical recommendations for policy-makers, industrial actors and researchers. Which will in return assist in overcoming challenges and enhance the capacity of AI in combating climate change and environmental conservation. © 2024 IEEE.

关键词： Zero-carbon

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A new attacks intrusion detection model based on deep learning in Software-Defined Networking Environments 4

A new attacks intrusion detection model based on deep learni...

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4th international conference on Machine learning and Intelligent Systems Engineering (MLISE)

作者： Yang, Jikun Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia

ISBN: (纸本)9798350375084;9798350375077

Software-defined networking (SDN) has transformed the landscape of network communication. SDN separates the control plane from the data plane, offering a centralized management system and dynamic resource allocation. Nevertheless, SDN is susceptible to security risks, necessitating the deployment of sophisticated Intrusion Detection Systems (IDS). Several researchers have recently employed machine learning and other cutting-edge technologies to analyze and identify rapidly growing attacks and anomalies. However, the majority of these techniques exhibit low accuracy and poor scalability. In response to this challenge, this paper proposes an Intrusion Detection System (IDS) framework based on the Convolutional Neural Network-Gated Recurrent Unit (CNN- GRU) network. this framework leverages Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to identify real-time network intrusions. the framework was trained and evaluated on the UNSW-NB15 and InSDN datasets using Bayesian optimization (BO), achieving exceptional accuracy and F1 scores exceeding 99.93% on the UNSW-NB15 dataset. Similarly, on the InSDN dataset, the framework achieved an accuracy of 99.93%, with precision, recall, and F1 score values of 99.89%, 99.97%, and 99.93%, respectively. these demonstrate the framework's effectiveness in discerning between normal and malicious network behavior.

关键词： SDN IDS CNN-GRU

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Advanced Machine learning Techniques for Optimizing Renewable Energy Systems in Healthcare Facilities 4

Advanced Machine Learning Techniques for Optimizing Renewabl...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Pradeep, S. Kalpana, P. Sreeram, G. Adgaonkar, Amarja Roy, Devika Rani Namdev, Arpit Dept. of Cse Telangana Secunderabad India Christ University Dept. of Computer Science Karnataka Bangalore India Vignana Bharathi Institute of Technology Dept. of Cse Telangana Hyderabad India K.C. College of Engineering & Management Studies & Research Thane India University Institute of Technology Rgpv Dept. of It MP Bhopal India

ISBN: (纸本)9798350387490

Optimizing renewable energy systems in healthcare facilities via use of modern ML algorithms to improve energy efficiency along with sustainability is goal of this research. Integrating machine learning provides a potential solution in case of optimizing energy consumption along with management, which is particularly relevant for healthcare institutions that are looking to decrease their operating expenses and carbon footprint by using renewable energy sources. Optimal deployment of renewable ES like solar along with wind may be achieved by the analysis of consumption patterns, prediction of future energy demands, and the investigation of different ML methods, such as neural networks, reinforcement learning, and predictive analytics. Research in this area aims to increase hospital renewable energy systems' efficiency and dependability by creating models that take use of environmental factors, real-time energy consumption indicators, and historical data. the results imply that healthcare facilities may save money, have less of an effect on the environment, and be more resilient if they use sophisticated machine learning methods to perk upcompetence of their renewable energy systems. the study emphasizes the importance of hyperparameter configuration in improving machine learning model accuracy, with a proposed approach achieving 99 % training and 96 % testing accuracy, outperforming conventional methods and outperforming other techniques. © 2024 IEEE.

关键词： Advanced Machine learning Energy optimization Healthcare Facilities Neural Networks Reinforcement learning Renewable Energy Systems

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Design and implementation of network security defense system based on deep learning 4

Design and implementation of network security defense system...

引用

4th international conference on Network Communication and Information Security, ICNCIS 2024

作者： Gao, Yang Lu, Xuezhong Abulikenmu, Gulixiati Su, Xuefei State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Xinjiang Urumqi China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

With the growing popularity of the Internet and digital technology, network security threats are increasing, and people's demand for advanced security defense means is rising. By combining advanced deep learning algorithms, AI based network security defense systems can provide real-time, automated threat detection and response. they can not only detect known threat patterns, but also learn to identify new and unknown attack techniques. Based on the requirements of computer network security, this article designs a computer network security protection system. the system applies an artificial intelligence analysis engine and combines hardware and software design optimization to achieve multi-level security protection measures. After testing, the system has high capabilities in data capture rate, restoration rate, data encryption and protection, and can provide an effective solution for the security protection of computer networks. © 2025 SPIE.

关键词： Software testing

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Social Group optimization-Based Neural Networks for Classification of Dataset 1

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8th international conference on Information System Design and Intelligent applications, ISDIA 2024

作者： Sagiraju, Sudhirvarma Mohanty, Jnyana Ranjan School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India CSE Department Raghu Engineering College Visakhapatnam India

ISBN: (数字)9789819786664

ISBN: (纸本)9789819786657

Evolutionary computation is a collection of algorithms based on the evolution of a population toward a solution to a certain problem. these algorithms have demonstrated their effectiveness in various optimization tasks. Notably, they have been extensively utilized to enhance the learning capabilities of classifiers, especially in the context of artificial neural network (ANN) Classifiers. Slow convergence and constant trapping at the local minima are two major issues with ANN classifiers. To address this issue, social group optimization (SGO) has been employed to identify the optimal parameters for the learning mechanism. In this work, a feedforward neural network (FNN) was fitted with SGO to improve learning and enable lower error rates and more precise categorization of high-dimensional datasets. three distinct programs were developed: differential evolution with NN (DENN), particle swarm optimization with NN (PSONN), and teaching learning-based optimization with NN (TLBONN). these programs were employed to investigate the impact of these optimization methods on social group optimization with neural network (SGONN) learning across a variety of datasets. the results of this investigation revealed that SGONN has exhibited highly promising outcomes and manifested notably smaller errors when compared to the results achieved by PSONN, DENN, and TLBONN. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive learning

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Library User Behavior and Service optimization Using Artificial Intelligence 4

Library User Behavior and Service Optimization Using Artific...

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4th IEEE international conference on Electronic Communications, Internet of things and Big Data, ICEIB 2024

作者： Wang, Ping Shandong Institute of Commerce and Technology Jinan China

ISBN: (纸本)9798350360721

Library user behavior was investigated using artificial intelligence (AI) technology to propose corresponding service optimization strategies. through data collection and analysis, the behavioral characteristics of users' borrowing, querying, and reading were determined. Machine learning and data mining were used to identify and predict the patterns of user behavior and provide personalized recommendations and services. the effectiveness and feasibility of the proposed strategies were verified in case studies and field research. © 2024 IEEE.

关键词： Behavioral research

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