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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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Risk Analysis of Labor Contracts Based on BCBR Model 24

Risk Analysis of Labor Contracts Based on BCBR Model

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8th international conference on computing and data Analysis, ICCDA 2024

作者： Chen, Fen Wang, Baosong Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798400710445

In recent years, with the rise of artificial intelligence technologies such as deep learning, natural language processing (NLP) has become a hot topic. However, research on contractual texts, particularly employment contracts, remains scarce due to challenges such as the limited availability of contract data. This paper pioneers a study on the classification and risk assessment of employment contract texts. Considering the unique characteristics of employment contract texts, we propose a fusion model that leverages the strengths of the BERT, CNN, and LSTM models, termed the BCBR model. This model integrates BERT’s self-attention mechanism to capture semantic relationships between words, CNN’s ability to extract local spatial features, and LSTM’s capacity to learn time-series dependencies. Initially, the self-attention mechanism in BERT is utilized to grasp the semantic relations among words. Subsequently, CNN is employed to extract textual features, while LSTM captures long-term dependencies. The classification results from BERT-CNN and BERT-LSTM are then linearly weighted. For comparative analysis, we benchmark the BCBR model against several others, including BERT-CNN, BERT-LSTM, TEXTCNN, TEXTRNN, and BERT. The results demonstrate that the BCBR model achieves the highest F1 scores, which is the harmonic mean of precision and recall, at 0.9168, 0.9143, and 0.9136, respectively. Based on these findings, we conduct a risk analysis of the contracts and present our conclusions along with future prospects. © 2024 Copyright held by the owner/author(s).

关键词： Risk analysis

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An Investigation of the Effect of Weather on Hand-Foot Mouth Disease Incidence in Sarawak 4th

An Investigation of the Effect of Weather on Hand-Foot Mouth...

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4th international conference on Advances in Computational science and Engineering, ICACSE 2023

作者： Ibrahim, Ashraf Osman Chen, Wong Yat Robert, Joel Zeolick Yi, Chiew Cheng Majid, Mazlina Abdul Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu88450 Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

ISBN: (纸本)9789819729760

Hand, foot, and mouth disease (HFMD) is consistently present in Malaysia, with a rising number of reported cases. Sabah has encountered multiple outbreaks of this disease. However, detailed information regarding the epidemiology and molecular traits of the viruses responsible for these outbreaks remains limited. This study used two data sets;one dataset was acquired from ***, containing HFMD across various Malaysian states. The second dataset was sourced from the National Center for Biotechnology Information (NCBI) and consists of HFMD statistics for Malaysia, including case counts and weather data. Both datasets were merged to create a comprehensive dataset that was specifically focused on examining the Malaysian state of Sarawak. This consolidated dataset contains a total of 20 at-tributes, forming the basis for the subsequent analyses conducted in this study. The study employed four distinct machine learning methods for regression analysis, comprising Random Forest, XGBoost, Decision Tree, and LASSO regression. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Plant diseases

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Enhancing Web Security with a Blockchain-Powered machine learning Model for Predicting Malicious Web Domains 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Yanqiu, Zeng Goyal, S.B. Rajawat, Anand Singh Solanki, Ram Kumar Chengyi University College Jimei University Xiamen361021 China Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computer Science & Engineering Sandip University Nashik422213 India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Introduction the purpose of this introductory section is to provide an overview of the topic at hand and set in the context of the dynamic and ever-changing digital era, the issue of web security continues to be of utmost importance, as rogue web domains pose substantial risks to both individuals and institutions. This study presents a novel methodology for addressing this issue by combining blockchain technology with machine learning techniques, notably employing the Support Vector machine (SVM) and Principal Component Analysis (PCA), for the purpose of forecasting and categorizing malevolent internet domains. The proposed architecture leverages the decentralized and immutable characteristics of blockchain technology to ensure the integrity and reliability of the acquired web domain data. Furthermore, the utilization of the blockchain architecture enables the seamless exchange of data in real-time, resulting in a perpetually updated and resilient model. Principal Component Analysis (PCA) is utilized in order to decrease the dimensionality of the information, hence enabling more efficient processing and emphasizing the most pertinent features. As a result, the Support Vector machine (SVM) is employed as the predominant predictive model owing to its established efficacy in addressing binary classification tasks. The findings from our experiment demonstrate that the SVM model, enhanced by blockchain technology and feature optimization using PCA, had greater performance in the identification of harmful domains when compared to conventional models. This was evident through a significant improvement in accuracy and a decrease in false positive rates. This technique not only enhances the robustness of web security infrastructure, but also showcases the possibility of integrating blockchain technology with machine learning algorithms to effectively tackle modern cyber threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Gamifying Language learning: Applying Augmented Reality and Gamification Strategies for Enhanced English Language Acquisition 3

Gamifying Language Learning: Applying Augmented Reality and ...

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3rd international conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Ravichandran, Kathiravan Anita Virgin, B. Patil, Surekha Fatma, Gulnaz Rengarajan, Manikandan Kiran Bala, B. Karunya Institute of Technology and Sciences Department of English Coimbatore India Nirma University Institute of Technology Humanities and Social Science Department Ahmedabad India University College Al Ardah Jazan University Language Instructor Jazan Saudi Arabia Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India K. Ramakrishnan College of Engineering Department of AI & DS Trichy India

ISBN: (纸本)9798350391565

In a time where interactive technology and digital innovations rule the roost, educators are always looking for new and creative ways to get students interested in learning and improve language acquisition. This study investigates how gamification techniques and augmented reality (AR) could be combined to transform English language instruction and create a captivating learning environment. With the growing ubiquity of digital technology, language learners may overcome obstacles and improve their English language skills by utilizing gamification and augmented reality. By establishing an interactive environment where students may interact with virtual components that are completely integrated into the actual world, the study explores the effects of augmented reality on language acquisition. Through the use of gamification concepts, including goal-setting, competition, and prizes, the study seeks to inspire students and cultivate a positive mindset toward language learning. Augmented reality and gamification work together to provide a personalized, dynamic learning environment that accommodates a range of learning preferences and styles. The study technique includes creating and deploying an augmented reality (AR) language learning application, then thoroughly evaluating its efficacy. data, both quantitative and qualitative, will be collected to assess how language proficiency, user involvement, and general satisfaction have improved. The results are intended to provide insightful information on the effectiveness of this novel methodology, with implications for teachers, curriculum developers, and curriculum designers who want to improve language learning techniques. In the end, our research aims to close the gap that exists between conventional language learning techniques and the digital age, laying the groundwork for language education's future by fusing cutting-edge tools with successful teaching techniques. © 2024 IEEE.

关键词： Contrastive learning

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Echoes of Truth: Unraveling Homophily in Attributed Networks for Rumor Detection 5

Echoes of Truth: Unraveling Homophily in Attributed Networks...

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5th international conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Rithish, S.V. Prabu, C.R. Anuush, M.B. Deepthi, L.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

The surge in information and misinformation during the COVID-19 pandemic, particularly regarding 5G-related rumors, was prominent on various social media platforms, notably Twitter. This study addresses the pervasive issue of rumor propagation on Twitter by employing diverse machine learning models, clustering techniques, and node detection algorithms to gain insights into the dynamics of rumor dissemination. The primary focus is identifying influential individuals who significantly impact the spread of these rumors and had an accuracy of 0.86 while classifying various labels. The study reveals network structures and key influencers within each cluster by categorizing users into clusters. While it is commonly observed that users with a more extensive follower base have a broader impact on rumor dissemination, our innovative approach integrates advanced community detection algorithms and methods, the Overlapnodedetect algorithm and Rumorsourcetrace algorithm for identifying influential characters to uncover latent homophily structures within these clusters. The objective is identifying crucial users responsible for initiating and perpetuating these rumors. © 2024 The Authors. Published by Elsevier B.V.

关键词： Complex networks

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Counterfactual Brain Graph Augmentation Guided Bi-Level Contrastive learning for Disorder Analysis 24

Counterfactual Brain Graph Augmentation Guided Bi-Level Cont...

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24th IEEE international conference on data Mining, ICDM 2024

作者： Dong, Guangwei Luo, Xuexiong Du, Jing Wu, Jia Xue, Shan Yang, Jian Beheshti, Amin School of Computing Macquarie University Australia

ISBN: (纸本)9798331506681

Deep learning is increasingly crucial in scientific discovery, accelerating research in various fields. Exploring brain science using deep learning has garnered significant interest, particularly in the recognition of brain disorders. However, existing methods face limitations in the discriminability of learned brain graph representations and the identification of neurological biomarkers associated with brain disorders. Moreover, the lack of model explainability leads to suboptimal brain graph analysis. To address these challenges, we propose a bi-level brain graph contrastive learning framework with an interpretable learning kernel for brain disorder analysis. Our framework diverges from traditional graph contrastive learning methods by augmenting meaningful brain graphs using the counterfactual thinking-based mechanism, ensuring reliable graph generation that preserves discriminative information. Secondly, we introduce bi-level contrastive loss with intra-and inter-class contrasts to enhance the brain graph representation learning. Most importantly, we design an interpretable brain graph learning kernel to highlight critical regions and connections, thereby facilitating the discovery of potential neurological biomarkers associated with brain disorders in subsequent analysis. The effectiveness of our method11https://***/JustinGie/Cf-BCL in brain graph representation learning and discriminative substructure detection is demonstrated through the evaluation of disorder prediction and pathogenic analysis on three real-world brain disorder datasets. Moreover, our framework may provide novel insights into brain science based on the results of comparison with medical research. © 2024 IEEE.

关键词： Contrastive learning

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A Sample-driven Selection Framework: Towards Graph Contrastive Networks with Reinforcement learning 24

A Sample-driven Selection Framework: Towards Graph Contrasti...

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32nd ACM international conference on Multimedia, MM 2024

作者： Zheng, Xiangping Hao, Xiuxin Wu, Bo Bao, Xigang Zhang, Xuan Li, Wei Liang, Xun College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Heilongjiang Harbin China Qingdao Innovation and Development Base of Harbin Engineering University Shandong Qindao China R&d and External Relations Department Xiangjiang Laboratory Hunan Changsha China School of Artificial Intelligence and Advanced Computing Hunan University of Technology and Business Hunan Changsha China Changsha Artificial Intelligence Social Laboratory Hunan Changsha China School of Information Renmin University of China Beijing China Guanghua School of Management Peking University Beijing China Harvest Fund Management Co. Ltd. Beijing China

ISBN: (纸本)9798400706868

Graph Contrastive learning (GCL) aims to address the issue of label scarcity by leveraging graph structures to propagate labels from a limited set of labeled data to a broader range of unlabeled data. However, recent GCL methods often rely on uniform negative sample selection schemes, such as random sampling, which results in suboptimal performance. To tackle this challenge, we present GraphSaSe, a tailored approach specifically designed for graph contrastive learning. Our method introduces an innovative reinforcement learning strategy that translates the divergence between positive pairs into a reinforcement reward mechanism. This mechanism generates selection probabilities to dynamically guide the selection of negative samples during training. We explore the impact of negative sample selection at different stages in graph contrastive learning and analyze how the discount factor affects the reward mechanism in reinforcement learning. These studies enhance the overall performance of the model. Comprehensive experimentation across diverse real-world datasets validates the effectiveness of our algorithm, positioning it favorably against contemporary state-of-the-art methodologies. © 2024 ACM.

关键词： Contrastive learning

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Federated learning for Enhanced ECG Signal Classification with Privacy Awareness 46

Federated Learning for Enhanced ECG Signal Classification wi...

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46th Annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Phan, Quoc Bao Nguyen, Linh Bui, Ngoc Thang Nguyen, Dinh C. Zhang, Lan Nguyen, Tuy Tan Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States Mayo Clinic Department of Radiology RochesterMN55905 United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States

ISBN: (纸本)9798350371499

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a high-performance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring. © 2024 IEEE.

关键词： Federated learning

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machine learning-Powered Intrusion Detection for Network Security 12th

Machine Learning-Powered Intrusion Detection for Network Sec...

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Nandish, H.D. Rahul, R. Dasari, Hari Charan Kumaran, U. Umah, B. Nagarajan, Senthil Murugan Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Mathematics Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-AlzetteL-4365 Luxembourg

ISBN: (纸本)9789819788354

In today's digital age, network security is paramount. This paper introduces a robust web application designed to swiftly identify network attacks and real-time anomalies, enabling users to protect their networks effectively. By leveraging cutting-edge technologies and machine learning, our system provides insights into network accuracy, F1-score, and precision, allowing users to gauge their network's safety. Our intrusion detection system classifies attacks into four categories: Denial of Service (DoS), Probe, Remote-to-User (R2L), and User-to-Root (U2R). Positioned strategically within the network, it monitors data traffic from all connected devices and promptly responds to suspicious activity, ensuring network security. Users receive instant alerts to take immediate action. Key technologies include data preprocessing (LabelEncoder and MinMaxScaler), user-friendly web interface (MERN stack), and enhanced security with Google Earth 2.0 for authentication and MongoDB for secure data storage. The Python-based backend operates on the NSL-KDD dataset, facilitating effective intrusion detection evaluation. To enhance accuracy, our system employs four machine learning algorithms: K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Random Forest, and Long Short-Term Memory (LSTM), offering comprehensive protection against network threats. In summary, this paper presents a potent network intrusion detection system for real-time threat identification and security assessment. By combining advanced technologies and machine learning, it ensures accuracy and reliability, serving as a valuable tool for network administrators and security professionals in safeguarding digital networks from evolving threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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MultiVFL: A Training-Efficient Framework for Multiple Vertical Federated learning 10

MultiVFL: A Training-Efficient Framework for Multiple Vertic...

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10th international conference on Big data computing and Communications, BIGCOM 2024

作者： Huang, Ju Zhang, Lan Ding, Cheng Huang, Dongbo Xu, Lan School of Computer Science and Technology University of Science and Technology of China Hefei China Tencent Shanghai China

ISBN: (纸本)9798331509538

Vertical Federated learning (VFL) is an effective paradigm which matches the enterprises' demands of leveraging more valuable features to achieve better model performance while keeping their data decentralized. Training costs, especially communication costs among parties have always been one of the major bottlenecks in VFL. Almost all existing approaches is aimed at optimizing the overhead of a single VFL task. However, there are situations where a passive party cooperates with multiple active parties to jointly train VFL models task by task, which causing the passive party incurring significant duplication of training costs. In this scenario, because these methods tailored for single VFL task cannot leverage the inter-task correlations, they might be suboptimal in terms of optimization and the model's performance may also be less satisfactory. In this article, we focus on reducing the training overhead of the passive party in multiple VFL collaborations. We utilize all local data of the passive party, including data discarded in the VFL task, for unsupervised training to accelerate the convergence speed of VFL training. Some new problems arise due to the data heterogeneity between tasks and the issue of catastrophic forgetting in models. In order to solve these issues, we propose a training-efficient algorithm named MultiVFL for multiple VFL collaborations. MultiVFL adopt parameter-level knowledge retention and fine-tuning algorithm, motivated by the sparsity of the bottom model in VFL. Through this approach, MultiVFL is able to maintain efficient training across various scenarios. We conducted experiments on two datasets containing five tasks each, and the results indicate that MultiVFL can continuously and efficiently learn tasks in multiple rounds of VFL collaboration. Compared to traditional VFL methods, MultiVFL saves about 70% of training overhead on tabular datasets and 21% on image datasets with excellent model accuracy. © 2024 IEEE.

关键词： Federated learning

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