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Unleashing the power of federated learning and reinforcement learning in the intelligent evolution of Web3.0 4

Unleashing the power of federated learning and reinforcement...

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4th International conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： thomas, Aby K Laxmikanth Reddy, C. Kumar Suman, Sanjay Tamilselvi, S. Devarasu, Neelamegam Arulananth, T.S. Alliance University Alliance College of Engineering and Design Dept. of Ece Karnataka Bengaluru India Malla Reddy Engineering College Dept. of Ece Telangana Secunderabad India Dean R&d St. Martin's Engineering College Dept. of Ece Secunderabad India Kongu Engineering College Dept. of Computer Technology TN Erode India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Ece TN Chennai India Mlr Institute of Technology Dept. of Ece Telangana Hyderabad India

ISBN: (纸本)9798350364729

Web 3.0's dynamic environment calls for an intelligent transition toward decentralized, privacy-aware, and ethical AI. We have provided a detailed analysis of how the suggested technique, which brings together federated learning and reinforcement learning, stacks up against more conventional approaches to machine learning. We examined these solutions across critical aspects, including data protection, customization, flexibility, ethical compliance, and scalability. the findings have been successfully communicated via the use of visualizations. Our research shows that the suggested approach, which utilizes distributed learning, is superior in many important respects. By putting sensitive data where it belongs - on users' devices - it ensures the highest levels of privacy possible while yet allowing for customization. Reinforcement learning's incorporation guarantees Web 3.0 intelligent systems can adapt to user behavior. In addition, the process is ethically compliant, meeting the requirements of the modern internet age. the capacity to scale up as the number of users increases is also fully supported. While conventional approaches do provide some useful resources, they fall short when compared to the new way. they are less suited to the requirements of Web 3.0 because they typically fail to achieve the optimal balance between data protection, customization, flexibility, and ethical compliance. © 2024 IEEE.

关键词： Federated learning

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

2024 4th International Conference on Neural Networks, Inform...

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

ISBN: (纸本)9798350394375

the proceedings contain 346 papers. the topics discussed include: dimensionality reduction methods comparison in the application of power prediction for a hybrid electrical vehicle;a deep neural networks-based significant wave height inversion method for GNSS-R signals;uncertainty-aware robot control via model-based reinforcement learning;research on image pre-processing techniques for multimode data metrology detection;characterization of signal transmission in bidirectional power frequency communication;joint trajectory and power optimization for UAV relay network over time-varying Rician channel;research and simulation of satellite communication channel for ship rolling in sea waves;an enhancement method with autoencoder for deep learning based hybrid beamforming;resource allocation method for 5G communication system based on ICHOA;and cross-domain recommendation based on meta-networks and attention transfer.

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Accessing the Power of Machine learning to Detect Fake News 4

Accessing the Power of Machine Learning to Detect Fake News

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4th intelligent Cybersecurity conference, ICSC 2024

作者： Ahmed, Imen Ben Magalhaes, Joao Paulo University of Sousse Sousse Tunisia ESTG Polytechnic of Porto CIICESI Rua do Curral Felgueiras Portugal

ISBN: (纸本)9798350354775

Global studies indicate that fake news spreads up to 70% faster than true news on social media, with significant consequences for society, including misinformation and political polarization. the problem of fake news can be addressed through a multifaceted approach (e.g. digital education and literacy, fact-checking, promotion of quality journalism, use of Artificial Intelligence, awareness, and public engagement). this paper explores the application of Machine learning algorithms to detect fake news. A dataset composed of 33,800 records and 216 features, created from fake and true news, is analyzed using several Machine learning models, such as neural networks, XGB, SVM, and Random Forest, comparing their effectiveness in classifying true and fake news. Feature selection techniques, such as SelectKBest and RandomForestClassifier, are used to optimize the performance of the models. the study also addresses the use of Explainable AI (XAI) methods, such as SHAP and LIME, to interpret model decisions, ensuring greater transparency and confidence in the proposed solutions. the results indicate that integrating Machine learning with explanatory approaches can provide powerful and reliable tools for identifying fake news. Within the scope of the same, accuracy rates of 99% were obtained, reinforcing the added value of the dataset and ML algorithms to provide reliable results for the identification of fake news, significantly contributing to the preservation of the integrity of the information. © 2024 IEEE.

关键词： Explainable Artificial Intelligence Fake News Machine learning

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Proceedings - 4th IEEE 2023 International conference on Computing, Communication, and intelligent Systems, ICCCIS 2023

Proceedings - 4th IEEE 2023 International Conference on Comp...

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4th IEEE International conference on Computing, Communication, and intelligent Systems, ICCCIS 2023

ISBN: (纸本)9798350306118

the proceedings contain 194 papers. the topics discussed include: comparative study of DDoS detection and mitigation techniques;a machine learning-based blockchain model for the storage of maternal health records and safety prediction;enhancing digital investigation: leveraging ChatGPT for evidence identification and analysis in digital forensics;managing metadata in data warehouse for data quality and data stewardship in telecom industry – a compact survey;a review on detection and prevention of the DDoS attacks in the blockchain;analysis of face recognition technique: plastic surgery altered face;image classification using federated averaging algorithm;navigating the gray area: a three-label framework for uncovering uncertainty in fake news;and improvement in validation score with loss function for breast cancer detection using deep learning.

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Research on the Construction of Smart Grid Digital System Based on Big Data Cloud Computing Model optimization Algorithm 4

Research on the Construction of Smart Grid Digital System Ba...

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4th IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Li, Zhipeng Liu, Shaobo Zhang, Jianfeng Wang, Jun Shen, Shuo State Grid Gansu Electric Power Company Lanzhou China State Grid Gansu Electric Power Company Digital Division Lanzhou China

ISBN: (纸本)9798350359589

An intelligent power business development method based on digital service information is proposed. At the same time, a complete data mining framework is constructed. Parameters such as distance of adjacent pixels, similarity degree of carrier components and average amplitude were determined by coding and parsing. then the digital business information resources are extracted. the mining method of data cloud in power system based on time series is established. the correlation degree of each information characteristic of middle station is analyzed based on classification and characteristic identification of power system middle station. Experiments show that the algorithm can effectively improve the distribution of data in the middle station in every minute. the standard deviation of the algorithm is reduced and the mining effect is improved. © 2024 IEEE.

关键词： artificial intelligence digital system optimization algorithm smart grid

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Research on Storage Performance optimization of New Power Materials Based on MGF 4th

Research on Storage Performance Optimization of New Power Ma...

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4th International conference on Advanced intelligent Technologies, ICAIT 2023

作者： Zhou, Rencai Xu, Yu Ningbo Yongyao Power Investment Co. Ltd Ningbo China

ISBN: (纸本)9789819732098

the performance optimization of new power materials is of great significance for improving the efficiency of traditional power equipment and reducing energy consumption and environmental pollution. New power materials are widely used in the field of manufacturing, transportation, storage, and utilization of power, such as high-temperature superconductors, photovoltaic cells, and lithium-ion batteries. the performance of these materials has a direct impact on the development of the entire industry and the sustainable development of human society. Optimizing the performance of new electric power materials can improve its electrical conductivity, heat transfer, mechanical strength, corrosion resistance, and other characteristics, so as to improve the efficiency and safety of electric power equipment and reduce resource waste and environmental pollution. However, existing research on storage performance optimization of equipment is mostly based on traditional methods. Nowadays, with the rapid development of deep learning technology, traditional methods can no longer meet the needs of the Times. therefore, this paper proposes a neural network-based performance optimization research method of MGF new power material equipment. First, we use deep learning method to complete the end-to-end power prediction. In the power-upgrading task, we propose a correction model based on multi-grain cascade forest and fuzzy control rules to correct the prediction results. Finally, the reliability of our model is proved by experiments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： High temperature superconductors

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4th International conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

4th International Conference on Smart IoT Systems: Innovatio...

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4th International conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

ISBN: (纸本)9789819751990

the proceedings contain 40 papers. the special focus in this conference is on Smart IoT Systems: Innovations and Computing. the topics include: Systematic Literature Review on Role of AI in IoT-Based Healthcare Solutions;a Hybrid Machine learning Model for Lung Disease Prediction;analyzing Student Feedback with Natural Language Processing for Enhancing Student Perception;enabling Technology for Smart and intelligent Transportation: Analyse the Uses and Impact of Blockchain Technology and Artificial intelligent on Trucking Industry for Better Results;Detecting Parkinson Disease Using Various AI Enabled Methods: Recent Advances, Requirements, and Open Challenges;synergistic Fusion of Steganography and Cryptography in Medical Imaging: A Comparative Analysis to Enhance Image Security;detecting Inattentive and Aggressive Driver Behavior Using Deep learning: Recent Advances, Challenges with Performance Evaluation;Improved Brain Tumor Classification Accuracy Using CNN Architecture with Efficient Net on Magnetic Resonance Imaging;noise Prediction: A Case Study of Gjilan City;economic Crimes in Current Era: World and Indian Context;analyzing the Citation Networks Using Community Detection Approaches: A Review;A Hybrid Correlation Feature Selection Based Support Vector Machine (CFS-SVM) for Effective Software Defect Prediction;mitigating Risks in Vehicle Fires: An Integrated Approach for Early Detection and Real-Time Alert System;multimodal Data Fusion: Combining Big Data and Deep learning for Enhanced Predictive Models;Analyzing Artificial Intelligence Based Intrusion Detection System in Detecting DDoS Attack;preserving Our Heritage: Buildings Deep learning Solutions for Monitoring Cultural Heritage Structures Using Automated Crack Detection;Insurance_4_YOU: MCDM Equipped User-Friendly intelligent System for Personalized Life Insurance Recommendations;apriori Analysis of Deep learning Models on a Multi-class Data Set.

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Research on intelligent correction system of 3D image reconstruction error based on deep neural network 4

Research on intelligent correction system of 3D image recons...

引用

4th IEEE International conference on Data Science and Computer Application, ICDSCA 2024

作者： Xu, Zhihua School of Design and Art Jingdezhen Ceramic University Jingdezhen China

ISBN: (纸本)9798350368239

In order to improve the accuracy and stability of 3D image reconstruction, an intelligent correction system based on generative adversarial network (GAN), convolutional neural network (CNN) and Bat algorithm (BA) is proposed. As a powerful generative model, generative adversarial network can effectively generate high-quality images through adversarial training mechanism, while convolutional neural network is excellent in feature extraction. Combining these two advantages, we can build a generator and discriminator with excellent performance for achieving high-quality 3D image reconstruction. By simulating the echolocation behavior of bats in the process of hunting, the bat algorithm can find the optimal parameters in the complex solution space, thus accelerating the convergence of the model and improving the reconstruction accuracy. In the process of model training, the Bat algorithm constantly adjusts the learning rate, batch size and other hyperparameters to adapt to different reconstruction scenarios and improve the overall performance. through experimental verification, the proposed model shows superior performance in 3D image reconstruction tasks, which is significantly superior to traditional methods and other deep learning models. the experimental results show that the intelligent correction system can effectively reduce the reconstruction error and improve the image quality, which provides a new idea and method for the research of 3D image reconstruction. ©2024 IEEE.

关键词： Generative adversarial networks

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DeepSipred: A deep-learning-based approach on siRNA inhibition prediction 4

DeepSipred: A deep-learning-based approach on siRNA inhibiti...

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4th International conference on Bioinformatics and intelligent Computing (BIC)

作者： Liu, Bin Huang, Huiya Liao, Weixi Pan, Xiaoyong Jin, Cheng Yuan, Ye Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Syngentech Inc Zhongguancun Life Sci Pk Beijing 102206 Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Med Robot Res Inst Shanghai Peoples R China

ISBN: (纸本)9798400716645

Using exogenous small interfering RNAs (siRNAs) for gene silencing has become a widespread molecular tool for gene function study and new drug identification. Although the pathway of RNAi to mediate gene expression has been widely investigated, the selection of hyper-functional siRNA with high inhibition remains challenging. In this study, we build a deep-learning-based approach on siRNA inhibition prediction, named DeepSipred. It combines features from sequence context, thermodynamic property, and other expert knowledge together to predict the inhibition more accurately than existing methods. the sequence features from siRNA and local target mRNA are generated via one-hot encoding and pretrained RNA-FM encoding. the convolutions with multiple kernels can detect various decisive motifs in sequence embedding, which may determine the actual inhibition. the thermodynamic features are calculated from Gibbs Free Energy. In addition, the expert knowledge includes those design criteria from previous studies. Benchmarked on large available public datasets, the 10-fold cross-validation results indicate that our predictor achieves the state-of-the-art performance.

关键词： siRNA design inhibition prediction deep neural network

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Image recognition of soybean leaf disease based on bilateral branching network BBN 4

Image recognition of soybean leaf disease based on bilateral...

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4th International conference on Bioinformatics and intelligent Computing (BIC)

作者： Sun, Jianming Hao, Xuyao Zhao, Mengxin Harbin Univ Commerce Sch Comp Informat & Engn Harbin Peoples R China

ISBN: (纸本)9798400716645

In recent years, with the rapid growth of China's economy, environmental pollution has become more and more serious. Fog and haze weather, water pollution and other factors have led to the occurrence of soybean diseases, seriously affecting the quality and yield of soybean. With the development of computer vision technology and deep learning and gradually applied to the development of agriculture, it is possible to use computers to intelligently diagnose crop diseases. Due to the different incidence of different types of soybean diseases in different regions and at different times, the long tail distribution problem of soybean leaf disease datasets is caused, which leads to the overall performance of disease recognition models. To solve this problem, BBN_IC_MobileNetV3 network model is proposed. the new network is based on MobileNetV3. Firstly, the multi-scale feature extraction module is used to replace the first 3 x 3 convolution layer of the original network to improve the feature extraction ability of the network for different areas of disease spots. Secondly, the CA attention mechanism is used to replace the SE attention mechanism of the original network to distinguish between the target and background. Finally, the improved MobileNetV3 network is combined with the BBN network to improve the recognition accuracy of the tail data by modeling the tail data, so as to improve the overall performance of the model. Experiments show that the overall disease recognition rate of the BBN_IC_MobileNetV3 network model reaches 95.882%, and the recognition accuracy of the tail data is close to 90%, which has been improved compared with the original network.

关键词： Deep learning Long-tail distribution Disease identification Multiscale feature extraction Attention mechanism

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