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Proceedings of the 4th international conference on data Science, machine learning and Applications 1

丛书名： Lecture Notes in Electrical engineering

1000年

作者： Amit Kumar Vinit Kumar Gunjan Yu-Chen Hu Sabrina Senatore

ISBN: (数字)9789819920587

ISBN: (纸本)9789819920570;9789819920600

This book includes peer reviewed articles from the 4th international conference on data Science, machine learning and Applications, 2022, held at the Hyderabad Institute of Technology & Management on 26-27th December, India. ICDSMLA is one of the most prestigious conferences conceptualized in the field of data Science & machine learning offering in-depth information on the latest developments in Artificial Intelligence, machine learning, Soft Computing, Human Computer Interaction, and various data science & machine learning applications. It provides a platform for academicians, scientists, researchers and professionals around the world to showcase broad range of perspectives, practices, and technical expertise in these fields. It offers participants the opportunity to stay informed about the latest developments in data science and machine learning.

关键词： Artificial Intelligence Computational Intelligence IT in Business Mathematical Models of Cognitive Processes and Neural Networks Cyber-physical systems, IoT Professional Computing

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Knowledge Graphs (KG) Assisted Variational Autoencoder (VAE) for Large-Scale Anomaly and Event Detection 16th

Knowledge Graphs (KG) Assisted Variational Autoencoder (VAE)...

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16th international conference on Social Networks Analysis and Mining

作者： Zhao, Ying Naval Postgrad Sch Monterey CA 93943 USA

ISBN: (纸本)9783031785535;9783031785542

This work focuses on general ML/AI assisted analytic processes for monitoring, detection, and classification of anomaly signals from multi-modality sensor data. Specifically, I will show series of variational autoencoders (VAE) including unsupervised AI transformers and related workflow pipelines applied to maritime surveillance in the different time scales of hours, minutes, and seconds. I show these developments using the distributed acoustic sensing (DAS) data set. The DAS data set is from the Sandia National Laboratories. DAS is a special type of fiber optic seafloor communications cables to interrogate the submarine environment at Arctic Alaska. Acoustic heatmaps can be generated to detect waves, ships, marine mammals, and other events. There are 18000 channels and sampled at 1kHZ for the data in 2022. The data set is used to demonstrate the VAE methodology for detecting anomaly, event, and classify objects. The results can enable processing and data analytical capabilities critical to actionable intelligence for mission planning and emerging behavior detection.

关键词： anomaly detection event detection machine learning ML artificial intelligence AI generative AI AI transformer variational autoencoder VAE distributed acoustic sensing DAS heatmap latent variables knowledge graph KG

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FedVC: Virtual Clients for Federated learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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On Jumping Graph Grammars

On Jumping Graph Grammars

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international conference on Mathematics of Intelligent Computing and data Science, ICMICDS 2022

作者： Vijayakumar, Jayakrishna Mathew, Lisa Mathew, Juby Amal Jyothi College of Engineering Kerala Kottayam Kanjirappally India APJ Abdul Kalam Technological University Kerala Thiruvananthapuram India Amal Jyothi College of Engineering Kerala Kottayam Kanjirappally India

ISBN: (纸本)9789819615049

This paper introduces the notion of jumping graph grammars. In particular it focuses on the non-confluent version. As a graphical counterpart of general jumping grammars, jumping graph grammars have a greater generative power compared to existing graph grammars. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Research on Travel Mode Identification Based on Trajectory data 11th

Research on Travel Mode Identification Based on Trajectory D...

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11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Zhang, Ruonan Li, Dewei Huang, Yue School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796434

Understanding users’ travel behaviors is an important subject in traffic science, which helps traffic management departments to formulate appropriate traffic control strategies. Travel mode identification is a critical aspect of analyzing users’ travel behaviors, aiming to precisely determine the travel modes of users and promote the sustainable development of urban intelligent transportation systems. In this article, we focus on identification methods for travel modes and propose a multi-scale convolutional neural network model based on channel attention mechanism. Our model efficiently extracts multi-scale sub-features and prioritizes key features. Additionally, to deeply extract effective information from the GPS data, we design 24 time-domain features and frequency-domain features and simplify them using principal component analysis and random forest algorithm. To verify the effectiveness of our method, we design five traditional machine learning models and two shallow deep learning models as comparative models, which are trained and tested on the Geolife dataset. Experimental results show that our approach is superior to the baseline models, achieving a maximum accuracy of 83.09%. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Convolutional neural networks

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Pathologic Complete Response Prediction with machine learning Using Hierarchical Attention Feature Extraction 27th

Pathologic Complete Response Prediction with Machine Learni...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Paolo, Domenico Russo, Ciro Russo, Giulio Greco, Carlo Cortellini, Alessio Russano, Marco Ramella, Sara Soda, Paolo Marrocco, Claudio Bria, Alessandro Sicilia, Rosa Unit of Computer Systems and Bioinformatics Department of Engineering Universita‘ Campus Bio-Medico di Roma Selcetta Italy Department of Electrical and Information Engineering University of Cassino and Lazio Meridionale FR Cassino03043 Italy Research Unit of Radiation Oncology Department of Medicine and Surgery University Campus Bio-Medico di Roma Selcetta Italy Operative Research Unit of Medical Oncology Fondazione Policlinico Universitario Campus Bio-Medico Portillo Italy Department of Diagnostics and Intervention Umeå University Umeå Sweden

ISBN: (纸本)9783031876592

Predicting pathologic complete response in non-small cell lung cancer is crucial for tailoring effective treatment strategies and to improve patient outcomes. With the increasing application of artificial intelligence in cancer research, machine learning is poised to play a significant role in prognostication and decision-making. This paper presents a novel approach that utilizes named entity recognition and attention mechanisms applied to electronic health records to predict the pathologic complete response. We first employ named entity recognition to extract relevant biomedical entities from unstructured clinical notes within reports. These entities, combined with structured data, are then processed using a hierarchical attention mechanism to generate comprehensive patient representations. This approach captures complex relationships and contextual information within electronic health records compared to traditional methods. The results highlight the potential of advanced natural language processing techniques to enhance clinical decision-making and support personalized treatment planning in oncology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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A Byzantine Client Detection Method for Federated learning Based on Encrypted Repeated Median Linear Regression 24

A Byzantine Client Detection Method for Federated Learning B...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Guo, Jinlong Yang, Ming Li, Huijuan Wu, Xiaoming He, Yunpeng Wang, Xin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Shandong Jinan China Shandong Evayinfo Technology Co. Ltd. Shandong Jinan China

ISBN: (纸本)9798400709999

Federated learning is an emerging distributed machine learning technology that allows participants to jointly train machine learning models locally without the need for large-scale data transmission and sharing, thereby greatly improving data security. However, some Byzantine clients may intentionally tamper with local model parameter updates with the intention of manipulating the global model's update direction, which can lead to a decline in the overall performance of federated learning. Besides, existing Byzantine client detection research is conducted in plain text, which will lead to the leakage of private information. To solve the above issues, we propose a Byzantine client detection method for federated learning based on encrypted repeated median linear regression. Specifically, it combines the repeated median linear regression algorithm with homomorphic encryption, which can detect and eliminate clients with Byzantine behavior in the ciphertext state, ensuring the security and reliability of the parameters in the entire federated learning process. Additionally, we also designed an adaptive gradient clipping method based on historical iteration to clip the gradient to a reasonable range, which can not only reduce communication and computing overhead, but also improve the convergence speed and stability of the model. Experiments show that the proposed method can maintain high accuracy under the influence of malicious behavior of Byzantine clients. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Prediction of CO2 emissions to achieve net-zero objectives in the iron and steel sectors of North America

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international JOURNAL OF MANAGEMENT SCIENCE AND engineering MANAGEMENT 2025年第2期20卷 183-196页

作者： Roman, Angel Francisco Galaviz Kabir, Golam Azim, M. Bakhtiar Univ Regina Ind Syst Engn Regina SK S4S 0A2 Canada

Predictive models are widely used to create effective plans for reducing CO2 emissions in manufacturing. The Iron and Steel (I&S) industries play a crucial role in meeting international commitments to achieve Net-zero emissions by 2050. The objective of this study is to forecast carbon dioxide emissions from the I&S industries in North America through the utilization of a Multi-Objective Mathematical model. The proposed data-driven approach is integrated with various machine learning algorithms capable of accurately predicting future values with a small dataset. Additionally, sensitivity analyses under different scenarios are conducted to evaluate the impact of implementing proposed solutions by the research community. Results show a significant improvement in accuracy through the employment of the Whale Optimization Algorithm (WOA). Forecasts reveal a sustained increment of 0.7 MtCO2 every year spanning between 2022 and 2050. This study provides valuable information for stakeholders and policymakers as it allows a more precise evaluation to integrate new technologies to abate forthcoming CO2 emissions.

关键词： Carbon emission iron and steel forecasting machine learning algorithm North America C53 C61 L61

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Federated learning on Virtual Reality Environments: Performance Analysis on Standalone Devices 4th

Federated Learning on Virtual Reality Environments: Performa...

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24th international conference Web engineering (ICWE)

作者： Flores-Martin, Daniel Diaz-Barrancas, Francisco Pardo, Pedro J. Berrocal, Javier Murillo, Juan M. COMPUTAEX Extremadura Supercomp Ctr Caceres Spain Univ Extremadura Badajoz Spain

ISBN: (纸本)9783031751097;9783031751103

Training algorithms through Federated learning has emerged as a promising strategy to safeguard data privacy in distributed environments. This training can be performed on several devices, ranging from high-capacity servers to devices with limited capabilities. However, handling numerous data sources can overload these devices, especially low-power ones, increasing response time. A particular scenario is Virtual Reality, as it requires connection to multiple data sources where latency is critical. Virtual Reality devices have traditionally required a continuous connection to computer equipment, limiting their versatility and the advantages of wireless devices. Recent technological advancements in these devices have increased their computational capabilities, enabling them to perform certain tasks independently. This work addresses the challenge of training a neural network on Virtual Reality devices through a federated system, to develop an enriched collaborative model stored and aggregated in the Cloud. The objective is to evaluate the computational costs and discern the possibilities and limitations of Virtual Reality in Artificial Intelligence.

关键词： Virtual Reality Federated learning Neural Networks Training Cloud Computing

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Collaborative learning as a Service – a Blueprint for a Cloud Based Rural IoTs Deployment Facility 15th

Collaborative Learning as a Service – a Blueprint for a Clo...

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15th international conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Krawczyk, Henryk Wiszniewski, Bogdan Faculty of ETI Gdańsk University of Technology Gdańsk Poland

ISBN: (纸本)9783031856969

Vast spaces with inadequate telecommunications infrastructure pose a challenge to deploy IoT systems. A tech stack is proposed and implemented on TASKcloud at Gdansk Tech, based on widely available open-source technology components, making it possible to deploy various machine learning models developed on the cloud to constrained end devices, to make them capable of intelligently cleaning measurement data and optimizing their volume to save available bandwidth. Thus processed data are transferred from end devices via a nomadic edge UAV based gateway to the cloud instance for further processing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Gateways (computer networks)

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