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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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Proposing Big data Architecture for Addressing Dropout Problem in MOOC Platforms

Proposing Big Data Architecture for Addressing Dropout Probl...

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2024 international conference on Advanced Technologies for Communications, ATC 2024

作者： Nguyen, Anh-Khoa Nguyen, Dinh-Khoa Vo, Khoa Tan Nguyen, Thu Nguyen-Hoang, Tu-Anh Dinh, Ngoc-Thanh Nguyen, Hong-Tri University of Information Technology Faculty of Information Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam The Industrial University of Ho Chi Minh City Viet Nam Aalto University Finland

ISBN: (纸本)9798350353983

Accessing academic knowledge on Massive Open Online Courses (MOOCs) has made learning more convenient with flexible schedules and a vast array of course options. The common weakness of these platforms, however, lies in the difficulty of controlling learners' behaviour. Noone can know for certain whether the level of engagement during learning is sufficient for learners to fully grasp the knowledge, or whether learners may fail to complete the courses they have enrolled in, leading to dropout behaviour. In this study, we also ap-plied an artificial intelligence model to predict whether current students can complete the course, enabling quick detection of dropout behaviour and timely preventive measures. Based on the proposed architecture, a real-time monitoring, analysis, and management application system for learner behavior can be developed. This empowers course managers to detect which learners might drop out of which courses, enabling timely alerts to learners for adjusting their study plans or, on a broader scale, restructuring the organization of courses with excessively high dropout rates. To maximize scalability with the increasing volume of MOOC data and applicability across different MOOC platforms, our architecture will be built on the Microsoft Azure Cloud computing service, utilizing modern and renowned big data technologies to perform tasks ranging from streaming data collection, batch data processing, distributed processing of large-scale data, to storing data in any format with the latest data lakehouse storage architecture, and real-time data visualization and anomaly detection through integrated models. © 2024 IEEE.

关键词： Cloud computing architecture

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Users' Preference-Aware Music Recommendation with Contrastive learning 20th

Users' Preference-Aware Music Recommendation with Contrastiv...

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20th international conference on Intelligent computing (ICIC)

作者： Wang, Jian Ma, Huifang Northwest Normal Univ Conservatory Mus Lanzhou 730070 Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9789819756148;9789819756155

Music recommendation serves as a critical branch of recommender systems, which is pivotal to capture users' preference via their historical listening sequence for improving user experience. However, existing efforts in music domain acquire users' preference in a supervised manner, thus inevitably suffering from the problem of data sparsity due to rare interactions between users and music pieces and further failing to precisely model users' preference. Inspired by the recent success of self-supervised learning, in this paper, we propose a novel Users' Preference-aware Music recommendation with Contrastive learning (UPMCL) method to mitigate the above issues. To be specific, the proposed approach UPMCL first encodes the information of music pieces according to original and augmented listening sequences. Moreover, it employs the contrastive learning to maximize the agreement between mask- and permute-based augmented listening sequences to learn the representations of music pieces. Eventually, the attention mechanism is utilized to integrate different types of users' preferences to generate the comprehensive users' preference and further achieve accurate music recommendation. Extensive experimental results conducted on three real-world music datasets clearly demonstrate that UPMCL has the capability in effectively recommending appropriate music pieces to users.

关键词： Recommendation Systems Music Recommendation Users' Preference Contrastive learning

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RAPID: Integrating AI and Multi-Agent Systems for Enhanced Traffic Management Framework with YOLOv9

RAPID: Integrating AI and Multi-Agent Systems for Enhanced T...

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2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Baroud, Shadia Yahya Yahaya, Nor Adnan Department of Computing University Malaysia of Computer Science and Engineering Petaling Jaya Malaysia

ISBN: (纸本)9798331533137

Traffic accidents pose a major risk to human life and have a considerable economic impact worldwide. In this study, we present the RAPID framework, an innovative approach that integrates Artificial Intelligence (AI) and Multi-Agent Systems (MAS) to enhance traffic management. The core of the RAPID framework is the YOLOv9 model, a "state-of-the-art" deep learning (DL) algorithm for real-time object detection and classification, which we employ to detect and classify traffic accidents accurately. The RAPID framework addresses key challenges in modern traffic management, including data heterogeneity, scalability and real-time processing. Our system leverages a multi-layered architecture: data is acquired from various sources, including traffic cameras and CCTV and then transmitted to a cloud data center for storage and processing. Within the cloud infrastructure, the data undergoes pre-processing and is analyzed by the YOLOv9 model. The MAS coordinates various components of the framework, distributing tasks among specialized agents responsible for data acquisition, preprocessing, model training and real-time detection. We evaluated the performance of the RAPID framework using a dataset of 3000 labeled traffic accident images. The YOLOv9 model demonstrated 95.4% accuracy, 93.8% precision, 92.6% recall and 93.2% F1-score. The experimental results indicate that the RAPID framework significantly improves traffic accident detection and response times compared to traditional systems, highlighting its potential for real-world applications. The RAPID framework contributes to the field of intelligent transportation systems (ITS) by providing a robust, scalable and efficient solution for real-time traffic management. ©2024 IEEE.

关键词： Multi agent systems

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Exploratory data Analysis on Shopping Mall Customers’ dataset: A Case Study of Marketing Analysis 7th

Exploratory Data Analysis on Shopping Mall Customers’ Datas...

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7th international conference on Advanced computing and Intelligent Engineering, ICACIE 2022

作者： Swain, K.P. Misra, S. Barik, Shekharesh Samal, S.R. Sahoo, D. Department of Electronics and Communication Engineering GITA Autonomous College Bhubaneswar India Department of Computer Science and Engineering DRIEMS University Cuttack India Department of Electronics and Communication Engineering Silicon Institute of Technology Bhubaneswar India

ISBN: (纸本)9789819950140

Nowadays, data visualizations and data analysis are the most significant fact in providing a quick and clear understanding of the information. An exploratory data analysis of shopping mall customers’ data set is presented in this study to achieve the customers’ segmentation which will beneficial for making marketing strategies. To achieve this, deep data analysis is carried out by extracting specific items from the dataset to know the different groups of customers coming into a mall and their spending behaviors. Depending upon their spending behavior, a machine learning model is created using the K-Means clustering algorithm to serve different marketing strategies for different groups of customers. The developed model can make a business impact to solve similar scenario problems for improving the decision-making related to the best marketing strategies for the customers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data visualization

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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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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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