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Structured Process on FL for Big data Analysis 6

Structured Process on FL for Big Data Analysis

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Govindaram, Anitha Prasath, J.S. Jayasakthi, K. Rajkumar, N. Porkodi, G. Jose Anand, A. Thandalam Chennai602105 India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Karnataka Bengaluru560057 India Department of ECE KCG College of Technology Karapakkam Tamil Nadu Chennai600097 India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu Avadi600062 India Department of Computer Science and Business Systems Vel Tech Multi Tech Dr. Rangaranjan Dr.Sakunthala Engineering College Avadi Tamil Nadu Thiruvallur600062 India

ISBN: (纸本)9798331522667

the integration of the Internet of things (IoT) into various aspects of daily life is accelerated by the proliferation of cognitive capabilities and AI-driven applications. Traditional automated systems rely on centralized data storage and dispensation, which may be feasible in specific application scenarios. However, the increasing flexibility of recent IoT networks and rising concerns over data confidentiality are challenging this approach. Federated learning (FL), a decentralized collaborative AI technique, allows machine learning to occur directly on external IoT devices without the need for data to be centrally aggregated or shared. this paves the way for a wide variety of intelligent IoT applications to be developed. this article presents a thorough analysis of the along with a detailed analysis of the convergence of these two fields. After that, a full analysis of how FL is currently being utilized in several essential applications related to the IoT is provided. these include smart health, mobility, Unmanned Aerial Vehicles (UAVs), infrastructure improvements, and smart industries. In total, 49 publications from the years 2015-2022 were reviewed here. Finally, discuss future study challenges with issues and exposed questions about data with IoT. © 2025 IEEE.

关键词： Federated learning

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Regularized Multi-LLMs Collaboration for Enhanced Score-Based Causal Discovery 25th

Regularized Multi-LLMs Collaboration for Enhanced Score-Bas...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Li, Xiaoxuan Liu, Yao Wang, Ruoyu Yao, Lina University of New South Wales Sydney Australia School of Computer Science and Engineering Northeastern University Shenyang110169 China School of Computing Macquarie University Sydney Australia CSIRO’s Data61 Sydney Australia

ISBN: (纸本)9789819605729

As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from observational data has become a preferred and efficient approach over conducting randomized control trials. However, purely observational data could be insufficient to reconstruct the true causal graph. Consequently, many researchers tried to utilise some form of prior knowledge to improve causal discovery process. In this context, the impressive capabilities of large language models (LLMs) have emerged as a promising alternative to the costly acquisition of prior expert knowledge. In this work, we further explore the potential of using LLMs to enhance causal discovery approaches, particularly focusing on score-based methods, and we propose a general framework to utilise the capacity of not only one but multiple LLMs to augment the discovery process. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An intelligent Feature Extraction and Multiple learning-Based Classification Techniques for Diagnosis of Breast Cancer 8th

An Intelligent Feature Extraction and Multiple Learning-Base...

引用

8th international conference on Internet of things and Connected Technologies, ICIoTCT 2023

作者： Patro, Satyabrata Mishra, Jyotirmaya Panda, Bhavani Sankar Department of Computer Science and Engineering GIET University Odisha Gunupur765022 India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam531162 India

ISBN: (纸本)9789819757855

Breast cancer is often curable if discovered in its early stages. Machine learning algorithms are used to automate disease identification. To increase the likelihood of recognizing the condition at an early stage, an effective classifier for automated diagnosis of breast cancer is needed. this research attempts to introduce a mixed machine learning model for predicting breast cancer. To outperform other strategies, it requires enormous amounts of data. Moreover, the intricate data models increase the training cost. For enhancing the efficiency of basic classifiers, ensemble learning holds great promise. A rapid discrete wavelet transforms using the wrapping approach is then used to extract the characteristics from the Region of Interest (ROI) mammography pictures. In addition to being too high to be categorized, the extracted wavelet coefficients also have incredibly high run-time complexity. the modified whale optimization method, which employs swarm intelligence, has been presented to lower time complexity and select the significant characteristics. Ensemble learning has been applied for the requisite efficiency in the suggested technique. three machine learning (ML) classifiers—the extreme gradient boost classifier (XGB), support vector machine (SVM), and enhanced granular neural network (E-GNN)—form the ensemble voting system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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CL3: A Collaborative learning Framework for the Medical data Ensuring data Privacy in the Hyperconnected Environment 25th

CL3: A Collaborative Learning Framework for the Medical Da...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Parvez, Mohammad Zavid Islam, Rafiqul Islam, Md Zahidul School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW Australia

ISBN: (纸本)9789819605729

In a hyperconnected environment, medical institutions are particularly concerned with data privacy when sharing and transmitting sensitive patient information due to the risk of data breaches, where malicious actors could intercept sensitive information. A collaborative learning framework, including transfer, federated, and incremental learning, can generate efficient, secure, and scalable models while requiring less computation, maintaining patient data privacy, and ensuring an up-to-date model. this study aims to address the detection of COVID-19 using chest X-ray images through a proposed collaborative learning framework called CL3. Initially, transfer learning is employed, leveraging knowledge from a pre-trained model as the starting global model. Local models from different medical institutes are then integrated, and a new global model is constructed to adapt to any data drift observed in the local models. Additionally, incremental learning is considered, allowing continuous adaptation to new medical data without forgetting previously learned information. Experimental results demonstrate that the CL3 framework achieved a global accuracy of 89.99% when using Xception with a batch size of 16 after being trained for six federated communication rounds. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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FL-PPELA: Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated learning

FL-PPELA: Partial Parameter Enhancement and Local Adaptive A...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems engineering, WISE 2024

作者： Pan, Jinkun Liang, Xiaoyan Du, Ruizhong School of Cyber Security and Computer Hebei University Hebei070012 China Key Lab on High Trusted Information System of Hebei Province Hebei070012 China

ISBN: (纸本)9789819614820

A key challenge in federated learning is statistical heterogeneity, which affects the generalization ability of the global model on each client. To address this issue, we propose a method called Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated learning (FL-PPELA). the key component of FL-PPELA is the adaptive local aggregation module PPELA. In the PPELA, partial parameters in the global model are enhanced in the direction beneficial to the local client. Next, adaptive aggregation of the local model and the global model before local training. Finally, build different models for each client’s local dataset. To evaluate the effectiveness of FL-PPELA, we conducted experiments on four real datasets. FL-PPELA demonstrated superior test accuracy compared to 12 advanced baseline methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Model Lake : A New Alternative for Machine learning Models Management and Governance 25th

Model Lake : A New Alternative for Machine Learning Model...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Garouani, Moncef Ravat, Franck Valles-Parlangeau, Nathalie IRIT UMR 5505 CNRS Université Toulouse Capitole Toulouse France LIUPPA Université de Pau et des Pays de l’Adour Anglet France

ISBN: (纸本)9789819605729

the rise of artificial intelligence and data science across industries underscores the pressing need for effective management and governance of machine learning (ML) models. Traditional approaches to ML models management often involve disparate storage systems and lack standardized methodologies for versioning, audit, and re-use. Inspired by data lake concepts, this paper develops the concept of ML Model Lake as a centralized management framework for datasets, codes, and models within organizations environments. We provide an in-depth exploration of the Model Lake concept, delineating its architectural foundations, key components, operational benefits, and practical challenges. We discuss the transformative potential of adopting a Model Lake approach, such as enhanced model lifecycle management, discovery, audit, and reusability. Furthermore, we illustrate a real-world application of Model Lake and its transformative impact on data, code and model management practices. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Intent Identification Using Few-Shot and Active learning with User Feedback 25th

Intent Identification Using Few-Shot and Active Learning wi...

引用

25th international conference on Web Information Systems engineering, WISE 2024

作者： Yuvaraj, Senthil Ganesan Benatallah, Boualem Motahari-Nezhad, Hamid Reza Rabhi, Fethi School of Computer Science and Engineering University of New South Wales Sydney Australia Insight SFI Research Centre on Data Analytics Dublin City University Dublin Ireland UpBrains AI Inc. RestonVA United States Macquarie University Sydney Australia

ISBN: (纸本)9789819605729

Collaboration tools contain many intents in workplace conversation, and identifying these intents is important to increase workplace productivity. However, labelling these intents for a large collection of conversations is expensive and data availability for new intents is quite limited. the pre-trained models show outstanding results in text classification tasks and large language models have recently produced accurate models with few samples. We explored few-shot learning methods and active learning strategies for this problem. Our proposed method, "SetFit with AL" is a combination of Sentence Transformer Fine-tuning (SetFit) and active learning for intent identification. this method fine-tunes a sentence-transformer model to develop accurate models. the intent classification evaluation dataset was used to evaluate this method. the results show that our proposed method outperforms state-of-the-art large language model GPT-3.5 and is comparable to GPT-4. this method also can utilize user feedback to adapt to new data and develop personalized models. thus, the contribution of this paper is that fine-grained intents are identified using minimal data and the model is adaptable based on user feedback. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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7th international Russian Automation conference, RusAutoCon 2024

7th International Russian Automation Conference, RusAutoCon ...

引用

7th international Russian Automation conference, RusAutoCon 2024

ISBN: (纸本)9783031824937

the proceedings contain 37 papers. the special focus in this conference is on Russian Automation. the topics include: Mapping and Path Planning Methods for Highly automated Vehicles in Agriculture;morphological Analysis and Synthesis Features of Technological Processes;a Modern Method of Wireless Control of Unguarded Railway Crossings;duties and Obligations of the Railway Staff Concerned When the Microprocessorized Contactless Controlling Gauge Device Signal Is Triggered;integration of automated Management Systems for Enterprises’ Transport and Technical Services;development of an Automation Module for Planning Trajectories for Painting Aircraft Fuselage Elements;identification of Metal Sheets in the Flow, Based on the Marking Imprint, Using Neural Networks;study of the Performance of Adaptive Sensor Networks for Collecting and Processing thermoelectric data;development and Research of a Cartographic Model for Municipal Planning as the Basis of an intelligent Geoinformation System;BIM Visual Programming of Bionic Architecture Construction Using Dynamo and Revit;development of a Model and Algorithms for Trigger Control of Technological Processes of Resource Provision;system for Statistical Assessment of Means of Controlling Engineers’ Qualification for Information Support to Manage the Recruiting Process in Industry;smart Enumeration Technology;comparative Analysis of C-Band Conical Horn Antenna Sparse Structures Characteristics at Different Frequencies;software Implementation of Heuristic Methods of Optimization and Integration into a Cloud Service;computer System for Modeling Fluid Flow Around Bodies and Its Potential in Industry;the Effect of Regular and Irregular Segmentations on Characteristics and Charge Distribution Densities of Microstrip Lines;reinforcement learning with External Teacher for Building Energy Management.

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Application Research of intelligent Machinery Design Based on CAD System 14th

Application Research of Intelligent Machinery Design Based o...

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14th international conference on Frontier Computing, FC 2024

作者： Wang, Yinghui Guangdong Innovative Technical College Dongguan523960 China

ISBN: (纸本)9789819627974

With the development of artificial intelligence (AI) and machine learning (ML) technologies, CAD systems have evolved from simple drawing tools to complex design and analysis platforms. the system is now able to leverage AI algorithms to optimize the design process and assist designers through pattern recognition, automated design processes, and parameter optimization. Today's intelligent CAD systems can automatically identify design requirements and constraints to provide more accurate design solutions. Moreover, the intelligent CAD system can also carry out complex simulation and analysis, and the application research of intelligent mechanical design based on CAD system shows the great potential of this field, which not only improves the design quality and efficiency, but also opens new possibilities for the future development of the mechanical design industry. According to the experimental data, the success rate of intelligent mechanical design, the adaptation rate of material selection, the accuracy rate of manufacturing process and the cost control scheme of the CAD system combined with manual audit are all above 99.88%, and the error rate is below 0.12%. Moreover, with the continuous progress of technology, the application of intelligent CAD system in the field of mechanical design will be more extensive and in-depth in the near future. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Application Design Artificial Intelligence CAD Technology Cost Control Mechanical Design

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Prediction of Covid-19 Recovery Chances Using Supervised Machine learning Classification 5th

Prediction of Covid-19 Recovery Chances Using Supervised Mac...

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5th international conference on Innovative Product Design and intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Dhanalakshmi, R. Nivashini, A. Vijayaraghavan, N. Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering MNM Jain Engineering College Chennai India Department of Mathematics KCG College of Technology Chennai India

ISBN: (纸本)9789819767311

Predictive analytics if combined with machine learning approaches have the potential to play a significant role in forecasting the spread of respiratory infections. Machine learning approaches aid in the mining of data to better assess and forecast the presence or absence of covid-19 infection. In this paper, XGBoost Ensemble Classification using a fine-tuned approach is being used. Various machine learning algorithms are being analyzed and XGBoost Ensemble Classification algorithm is used to forecast and predict the covid-19 infected patient recovery chances that is the possible outcome of a covid-19 infected patient along with the prediction probability of recovery. the suggested categorization model is applied to a variety of situations. When compared to other ensemble models like random forest, hybrid ensemble and other individual classifiers such as decision tree, logistic regression, K-nearest neighbor, support vector machine and Naive Bayes, the XGBoost Ensemble Classification model gave the highest accuracy. Predicting the correct class is important. thus, the proposed system discusses the possibility of automated prediction for covid-19 infected patient recovery chances along with the probability of recovery and the effectiveness of such predictions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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