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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

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Online Vocational Education Personalized Course Content Push Algorithm Based on Big data 3

Online Vocational Education Personalized Course Content Push...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Lan, Feipeng Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (纸本)9798350380989

A collaborative filtering based online teaching recommendation system for higher vocational colleges is proposed. UserCF system for user oriented personalized recommendation and object-oriented personalized recommendation system are established. The concept of multiprocessing is proposed to improve the learning performance of the network. By means of subject modeling, subject dictionary modeling, data preprocessing and behavior dictionary modeling, the dynamic information ontology is extracted. The dynamic ontology of network behavior is weighted by six factors, namely time, content, object, place, behavior, and number of reentries. This results in the decay model of user data in the network. Through the scoring matrix of user data, the degree of similarity between users and the filtering of data is to achieve the best recommendation. And the filtered data has less similarity and better identification, as well as less push delay. Practice has proved that this improved scheme is effective. © 2024 IEEE.

关键词： mathematical model personalized learning push algorithm Recommendation system search engine vocational education

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Advancing Bilingual SMS Spam Detection: A Comparative Exploration of machine learning and Deep Ensemble Techniques 3

Advancing Bilingual SMS Spam Detection: A Comparative Explor...

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3rd IEEE international conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Sultana, Hafsa Tanvin, Jamal Uddin Sharmin, Nusrat Arefin, Mohammad Shamsul Rahman, Md. Mahbubur Military Institute of Science and Technology Mirpur Cantonment Department of Computer Science and Engineering Dhaka1216 Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong4349 Bangladesh

ISBN: (纸本)9798331534400

Spam messages are increasingly becoming a major issue in digital communication, creating significant challenges for both users and organizations. With the rise in text-based interactions, especially in low-resource languages like Bangla-English SMS, there is a growing need for more effective spam detection systems. This paper presents BESpam, a unique bilingual dataset that contains 5,730 Bangla-English SMS messages, specifically designed to address this challenge. In the literature, various machine learning and deep learning techniques align with feature extraction techniques from natural language processing used for spam SMS detection. Still, no studies have been found that focus on comparing deep and machine learning approaches. As a further contribution, we propose an ensemble model that combines advanced transformer architectures with FastText to improve the accuracy of spam classification. Our results demonstrate that this ensemble approach outperforms both traditional machine learning models and individual transformer-based methods. The stacking ensemble achieved an accuracy of 99%, exceeding the performance of standalone models such as mBERT (98%) and XLM-RoBERTa (98%), as well as traditional classifiers such as logistic regression and SVM, both of which also scored 98%. This work provides insight analysis of machine learning and deep learning in terms of bilingual SMS spam detection, in addition to advancing spam detection methodologies. © 2024 IEEE.

关键词： Contrastive learning

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Global Self-Supervised Graph learning for Recommendation 3

Global Self-Supervised Graph Learning for Recommendation

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Liu, Xinyue Software Engineering Shandong University Jinan Shandong250000 China

ISBN: (纸本)9798350380989

In recent years, Comparative learning (CL) has driven a series of effective research in the recommendation domain, and it excels in dealing with the data sparsity problem in recommender systems since no data labeling is required in CL. The typical model flow of the approach consists of structurally perturbed augmentation of the user-item bipartite graph, and model optimization by maximizing the consistency of node representations across different graph augmentations. Although the model has been shown to be effective, we believe that existing contrastive learning research and applications are not yet sufficient. To enhance the global nature of contrast learning, we introduce global graph view self-supervised graph learning (GSGL). This method adds an auxiliary self-supervised task to the traditional recommendation supervision task to realize multi-view contrast learning. Specifically, each node builds enhanced views by three data enhancement methods (node loss, edge loss, and random noise) and performs two-by-two comparison learning on these views. Through empirical studies on three benchmark datasets, the effectiveness of the GSGL algorithm is demonstrated, which not only leads to a more uniform view embedding spatial distribution and improves the recommendation accuracy, but also exhibits strong robustness to interaction noise. The code and used datasets are released at https://***/LX-Moon/GSGL. © 2024 IEEE.

关键词： Comparative learning graph augmentations Recommending System Self-supervised learning

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Using an Extreme learning machine and an Extended Kalman Filter, State of Charge Estimation for Lithium-Ion Batteries 3

Using an Extreme Learning Machine and an Extended Kalman Fil...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Sasikumar, M. Kumar, C. Mahendran, R. Suryamanikandan, P. Department Of Mechatronics Engineering Tiruchengode India

ISBN: (纸本)9798350318609

When discussing battery management systems (BMSs), one of their most crucial tasks is undoubtedly detecting the state of charge (SoC) of the battery. Background of electrochemical properties of lithium-ion batteries (LIBs), accurately estimating the SoC can be challenging. This study proposes an approach that combines the use of a machine learning technique, the Extreme learning machine (ELM), with an extended Kalman filter (EKF) to improve the accuracy of SoC estimation. First, we develop an accurate model of LIBs using ELM. EKF approach with the trained ELM model to estimate the SoC. To validate our approach, compare it with a traditional EKF methodology, the Equivalent Circuit Model (ECM)-EKF, using the Federal Urban Driving Schedule (FUDS) movement over three warming cycles. Results achieved show that the ELM can more accurately monitor voltage compared to the ECM, and the ELM-EKF-based SoC estimation provides more precise voltage estimates compared to ECM-EKF. © 2024 IEEE.

关键词： Lithium-ion batteries

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The Design and Implementation of AI Based Smart Notice Board Using Advanced machine learning and Image Processing Techniques 3

The Design and Implementation of AI Based Smart Notice Board...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Md. Muhaiminul Hossain, Md. Rakib Akter, Farzana Barman, Bhakti Kusum Malakar, Shuvo Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

In contemporary society, conventional notice boards encounter challenges in terms of accessibility and engagement, especially within educational institutions where effective information dissemination is vital. By implementing a Smart Notice Board that is powered by cutting-edge technologies like artificial intelligence (AI), machine learning (ML), image processing, and effective frontend-backend communication, this research offers a game-changing solution. The system surpasses traditional analog methods by integrating digital capabilities, allowing for real-time updates and efficient communication within a global network. AI-based facial recognition is used by the Smart Notice Board to improve user experience and deliver tailored information to individuals or particular groups. When a person appears in front of the camera, the system recognizes them and pulls up relevant notifications based on their profile, which reduces the amount of work teachers, staff, and students have to do to stay informed. The system includes advanced image processing techniques to identify individuals, so notice delivery accuracy is guaranteed. In addition, the integration of socket technology guarantees smooth communication between the backend server and frontend user interface, facilitating effective data storage and transfer. By eliminating the time constraints linked to conventional notice boards, this method enables administrators to remotely oversee and distribute content. Additionally, the server stores individual images for facial recognition and customized notice delivery. By combining AI for facial recognition, ML and image processing for visual data analysis, and an advanced frontend-backend technology for system architecture, this smart notice board project not only streamlines the distribution of information but also greatly improves communication in educational setting as well as lessens burdens and produce a flexible platform that can adapt to changing user requirements. © 2

关键词： machine learning

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A Review on Oversampling Techniques for the Treatment of Class Imbalance Problem 3

A Review on Oversampling Techniques for the Treatment of Cla...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Anand, Apurv Biswas, Saroj Kr. Majumdar, Sounak Thounaojam, Dalton Meitei Department of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798331527549

The Class Imbalance Problem (CIP) is a critical challenge in machine learning, particularly in applications such as medical diagnosis and fraud detection, where minority classes are underrepresented but crucial. This research addresses the issue by introducing a novel classification of oversampling techniques into 11 distinct categories, with a unique distinction between duplication and derivation mechanisms. Through an in-depth review of each category, this study identifies specific challenges such as overfitting and noise in imbalanced datasets and demonstrates how these categorized strategies effectively enhance model performance. The outcomes highlight practical solutions to improving classification accuracy and robustness in imbalanced scenarios. © 2025 IEEE.

关键词： Contrastive learning

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Advanced Predictive Modeling for Pneumonia Diagnosis to Revolutionize Healthcare with Transfer learning 3

Advanced Predictive Modeling for Pneumonia Diagnosis to Revo...

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3rd IEEE North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Panda, Prasanta Sahoo, Debaryaan Sahoo, Debarjun Tcs Principal Data Scientist Bhubaneswar India Nit Jamshedpur Bhubaneswar India Bjem School Bhubaneswar India

ISBN: (纸本)9798350364569

A timely diagnosis is essential for the proper treatment of pneumonia, a serious respiratory infection. machine learning techniques offer the potential to improve pneumonia prediction by analysing medical imaging data (chest X-ray images) and patient information. This research explores the application of machine learning, specifically transfer learning (TL) using the ResNet50 architecture, to enhance healthcare outcomes through predictive modelling for disease diagnosis, with a focus on pneumonia. Leveraging a pre-trained ResNet50 Convolutional Neural Network (CNN) model, this research implemented transfer learning (TL) techniques to fine-tune the network at required level for the specific task of pneumonia detection from chest X-ray images. The proposed approach using ResNet50 model, achieved a remarkable accuracy (98.47%), F1 score (97.2%), and Precision (99.65%). This high level of accuracy demonstrates the capacity of cutting-edge machine learning techniques using the proposed algorithm to significantly improve diagnostic processes in healthcare, offering a promising tool for early and accurate pneumonia diagnosis, which is critical for timely and effective treatment. This research underscores the efficacy of transfer learning in medical imaging and pave the way for its broader application in various domains of disease diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Proceedings - 2024 3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

Proceedings - 2024 3rd International Conference on Artificia...

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3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

ISBN: (纸本)9798350376173

The proceedings contain 179 papers. The topics discussed include: signal detection and demodulation algorithm based on deep learning in communication network;design and implementation of intelligent control algorithm for energy power system combined with reinforcement learning;application of artificial intelligence technology in flood early warning system;implementation of a machine intelligence pronunciation system based on Mul Tran to improve the basic English speaking skills;design and optimization algorithm of interactive education system based on virtual reality technology;application of artificial intelligence technology in intelligent environment design system;intelligent anti-collision algorithm of electric bicycle helmet based on Arduino and sensor data;and neural network algorithm in automatic allocation system of water resources.

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Ocimum Sanctum Linn Plant Fertilizer and Insecticide Spraying System with Disease Prediction using machine learning 3

Ocimum Sanctum Linn Plant Fertilizer and Insecticide Sprayin...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Aagaash, K.R. Ramalakshmi, K. Venkatesan, R. Sundar, G Naveen Nancy, Golden Shirly, S. Karunya Institute of Technology and Sciences Department of Computer Science Engineering Coimbatore641114 India Alliance University Department of Cse Bangalore562106 India Institute of Technology and Sciences Division of Cse Karunya Coimbatore641114 India Karunya Institute of Technology and Sciences Division of Aiml Coimbatore641114 India

ISBN: (纸本)9798350375190

An integrated system for spraying pesticides and fertilizers on plants, coupled with machine learning capabilities for disease prediction, revolutionizes crop management, transforming crop management. This innovative solution administers precise doses of nutrients and pesticides to optimize crop health autonomously. Leveraging CNN algorithms and image processing, it predicts diseases proactively, mitigating risks. By integrating machine learning into traditional practices, it enhances efficiency and sustainability in terrace farming, tailoring care to each crop's needs. Continuous monitoring allows real-time adjustment of spraying, responding to changing conditions. The system learns from extensive plant image datasets to recognize disease patterns, empowering preemptive action. This proactive approach minimizes crop losses, promotes sustainability, and improves yields in terrace farming. © 2024 IEEE.

关键词： Forecasting

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machine learning Models for Advancing Heart Disease Prediction and Diagnosis 3

Machine Learning Models for Advancing Heart Disease Predicti...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Reddy, Santosh Shetty, Kruthi K. Apeksha, G.S. Department of Computer Science and Engineering B N M Institute of Technology Bangalore India

ISBN: (纸本)9798331527549

Cardiovascular diseases are still among the leading causes of deaths worldwide, thus calling for early detection and precise risk assessment. Traditional methods of diagnosing cardiovascular disease often rely on standardized risk factors, which might not always catch the condition in its early stages. machine learning and AI are revolutionizing CVD prediction by examining complex data in healthcare to find previously unknown patterns. The paper discusses advances in ML techniques such as supervised and unsupervised learning, deep learning, and ensemble methods. It emphasizes hyperparameter tuning, feature engineering, and model interpretability. AI models like Random Forest, SVM, and Neural Networks have shown better accuracy in predicting cardiovascular disease risk, enabling earlier and more personalized interventions. AI’s potential reaches from wearable devices to predictive cardiac event models, promising huge advances in cardiovascular care. Still, there are data privacy issues, model interpretability, and regulatory problems that will continue to pose significant challenges. Future advancements in multimodal imaging, precision medicine, and collaboration between clinicians and data scientists will be pivotal to the realization of AI’s full potential in transforming the prediction and prevention of CVD. © 2025 IEEE.

关键词： Artificial Intelligence Cardiovascular Diseases (CVDs) Early Detection Heart Disease Prediction machine learning Neural Networks Personalized Healthcare Random Forest Support Vector machines (SVMs)

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