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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Patch-Based 3D CNN-Transformer Approach for Non-Uniformly Compressed Voxel Classification 29

Patch-Based 3D CNN-Transformer Approach for Non-Uniformly Co...

引用

29th international conference on information technology, IT 2025

作者： Koba, Felix Marin, Aleksandr Čakić, Stevan University of Donja Gorica Faculty for Information Systems and Technologies Oktoih 1 Podgorica81000 Montenegro

ISBN: (纸本)9798331517649

Three-dimensional (3D) voxel models have wide applications in fields such as robotics, medical imaging, autonomous navigation, and augmented reality. To address challenges of spatial sparsity and computational efficiency, this research proposes a voxel-based 3D convolutional neural network (3D CNN) integrated with a Transformer encoder for object classification on the ModelNet10 dataset. The 3D models are non-uniformly compressed into a 16×16×16 voxel grid, with compression factors incorporated as inputs to reduce sparsity. An in-house augmentation tool further enhances the dataset size and diversity by performing real-time voxel editing and labeling. The proposed method achieves an accuracy of 92.15% while utilizing only 184k trainable parameters, demonstrating an efficient and lightweight approach to object classification. © 2025 IEEE.

关键词： Convolutional neural networks

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Very Short-Term Surplus Energy Forecasting for Peer-to-Peer Energy Trading in Microgrids 5

Very Short-Term Surplus Energy Forecasting for Peer-to-Peer ...

引用

5th international conference on Advanced Research in computing, ICARC 2025

作者： Tharumaraja, Sajarupan Liyanage, Madura Prabhani Pitigala Wijethunge, Akila Ekanayake, Janaka Rupasinghe, Akbo Faculty of Technology University of Sri Jayewardenepura Department of Information and Communication Technology Nugegoda Sri Lanka University of Sri Jayewardenepura Faculty of Technology Department of Material and Mechanical Technology Nugegoda Sri Lanka Cardiff University Institute of Energy Cardiff School of Engineering Cardiff United Kingdom Transgrid Solutions Inc Winnipeg Canada

ISBN: (纸本)9798331530983

Integrating renewable energy into microgrids facilitates peer-to-peer (P2P) energy trading between prosumers and consumers. However, variability in renewable power generation and dynamic consumer demand pose challenges to grid stability and trading mechanisms. This paper presents a forecasting method for available power from renewable-integrated households, focusing on Very short-term surplus power prediction for trading within a microgrid. The approach uses Internet of Things (IoT) monitoring, Long Short-Term Memory (LSTM) forecasting models, and the OpenDSS platform. The system includes an IoT-Photovoltaic (PV)-Grid Monitoring Unit to track power and indoor temperature, and an IoT-based Irradiance and Temperature Monitoring Unit for outdoor conditions. The monitored data is input into the LSTM model, and forecasted data is used in the OpenDSS DemandSide Management System (O-DSM) to predict PV generation and load behaviour in the microgrid at one-minute intervals over 15 minutes. The simulation provides insights into microgrid operations, energy flow, voltage profiles, and surplus power availability for trading, while the forecasting data supports trading decisions with peers. This research demonstrates the use of lightweight IoT devices and a cost-effective Raspberry Pi 5-based forecasting unit to enhance sustainable energy trading through improved forecasting and real-time data integration. © 2025 IEEE.

关键词： information management

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A Preliminary Study of the Stressed and Drowsy Driving Prediction Models using Semi-Supervised Learning 24

A Preliminary Study of the Stressed and Drowsy Driving Predi...

引用

8th international conference on Algorithms, computing and Systems, ICACS 2024

作者： Chui, Kwok Tai Liu, Jiaqi Hung, Kevin Wu, Ho Chun Zhao, Mingbo School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong School of Information Science and Technology Donghua University Shanghai China

ISBN: (纸本)9798400718304

Predicting the occurrence of stressed and drowsy driving provides sufficient time to alert drivers and prevent undesired driving behaviours that result in road traffic accidents. Typically, the predictive machine learning models for stress and drowsy driving are formulated using supervised learning with labelled data. Labelled data are available only in training datasets, where labelling new data becomes infeasible and costly. With the ever-growing quantity of new and unlabelled data, once the model is deployed, unlabelled data quickly dominate the whole data population, and bias toward unlabelled data may increase the error rate of the prediction model. A preliminary study was thus conducted to train the predictive model using semi-supervised learning. We analyze and compare the performance of four algorithms, namely extreme gradient boosting (XGBoost), autoregression (AR), autoregressive integrated moving average (ARIMA), and exponential smoothing (ES). Various portions of unlabeled data were introduced, and more window sizes (in advance intervals) were chosen for the analysis. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Rapidly Deployed Cyber Attack Bait Based on Software Defined Network Corresponding to Artificial Intelligence Strategy 27

Rapidly Deployed Cyber Attack Bait Based on Software Defined...

引用

27th international conference on Advanced Communications technology, ICACT 2025

作者： Jian, Ming-Shen Lee, Bo-Yi Liu, Xing-En Cai, Rui-Lun Cai, Zhong-Wei Liao, Pei-Cen Chou, Ta-Yuan Huang, Wun-Yuan CCIS Lab. Dept. of CSIE National Formosa University Yunlin County632 Taiwan 2National Center for High-Performance Computing Taiwan

ISBN: (纸本)9791188428137

To protect the services and data of the main servers, Rapidly Deployed Cyber Attack Bait is proposed for the unpredictable cybersecurity attack in this research. Based on Software Defined Network, the data packet and operation of cybersecurity attack could be redirected to the on demand deployed bait container. By suitable artificial intelligence strategy, the idle bait containers corresponding to the type of cybersecurity attack can be on demand deployed automatically and rapidly. Verification shows the feasibility of the virtualized bait container could be about 1.16 times to 2 times enough for the future coming attacker and also record the all operations of the cybersecurity attack individually. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Cyber attacks

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Discovering Optimal Subspaces in Deep Architectures for Offline Signature Verification 24

Discovering Optimal Subspaces in Deep Architectures for Offl...

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Zhu, Qifeng Zhang, Benzhuang Yuan, Limengzi Ren, Yuanyuan Zhao, Qingzhan Zheng, Yuchen College of Information Science and Technology Shihezi University Shihezi China Dalian University of Technology Dalian China

ISBN: (纸本)9798400717482

In offline signature verification systems, deep learning-based feature extractors often fix the feature space for feature extraction, which may not obtain the best features for subsequent verification tasks. To address this issue, this paper proposes a novel framework that controls the dimensionality of features by stretching and reducing deep features to obtain the best features. Specifically, deep features can be stretched through a higher-dimensional projection matrix whose elements are randomly sampled from a standard normal distribution to obtain a more discriminative representation, and further reduced by Linear Discriminant Analysis (LDA) to more effectively capture the subtle differences between different signers. In this way, the learned features can be scaled to any optimal subspace. Extensive experimental results and discussions show that the proposed method improves the performance of deep learning-based offline signature verification systems and surpasses existing state-of-the-art verification systems on three benchmark datasets. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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A Secure and Efficient Privacy Scheme for Location-Based Services in Cloud Environments 24

A Secure and Efficient Privacy Scheme for Location-Based Ser...

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9th international conference on Cloud computing and Internet of Things, CCIOT 2024

作者： Pu, Tongyu Zeng, Kai Bai, Fenhua Zhang, Chi Zhang, Xiaohui Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming China

ISBN: (纸本)9798400717161

Preserving privacy has become crucial in the field of location-based services (LBS) to leverage their full potential while protecting users from untrustworthy LBS providers. Given that LBS users typically use resource-constrained mobile devices, many existing privacy-preserving methods focus on anonymization. However, these methods have certain limitations regarding privacy protection and efficiency. This paper introduces a novel Privacy-preserving LBS scheme to address these issues. We develop an efficient query mechanism where users send encrypted distance queries to the server. The server then employs a PFR-tree and homomorphic encryption to filter Points of Interest (POIs) in a hierarchical manner. The PFR-tree quickly eliminates irrelevant regions based on their locations, minimizing unnecessary computations and enhancing query efficiency. Experimental results indicate that this PFR-tree-based scheme significantly reduces query time compared to traditional methods without a PFR tree, especially as the number of POIs increases, demonstrating superior performance and efficiency in scenarios with many POIs. This approach effectively meets privacy protection needs for indoor navigation in cloud environments while maintaining high efficiency and security. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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Artificial Intelligence Techniques for the Performance Analysis of Indian Mutual Fund Industry and Market Trends 6

Artificial Intelligence Techniques for the Performance Analy...

引用

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

作者： Priscila, S. Silvia Sakthivanitha, M. Yogeshwari, M. Devakirubai, T. Shirley Sirajudeen, M. Mohamed Department of Computer Science Bharath Institute of Higher Education and Research Tamil Nadu 600126 India Department of Information Technology VEL’S Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Data Analyst Saveetha Liberal Arts and Science College Tamil Nadu Chennai India Department of Commerce Madras Christian College Tamil Nadu Chennai India Thaloor Tamil Nadu The Nilgiris643239 India

ISBN: (纸本)9798331522667

A basic examination of a mutual fund's strategy (growth or value), median market cap, rolling returns, standard deviation, and maybe a portfolio breakdown by industry, geography, and other factors make up a typical mutual fund analysis. Mutual fund analysis typically involves calculating the amount of management contribution to the portfolio and ranking equities according to industry. Analyzing a mutual fund portfolio involves a few phases, one of which comprehends the fund's design. This study examine the basic mutual fund parameters and explore the use of artificial intelligence (AI) methods, including Data Mining, machine learning(ML), blockchain and predictive analytics in predicting market trends and assessing mutual fund performance. The purpose of this study is to give users a clear, concise, and professional overview of how to analyze the performance of mutual funds using AI Techniques. © 2025 IEEE.

关键词： Decentralized finance

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Question Pair Similarity Detection and Development of Question Answering Chatbot

Question Pair Similarity Detection and Development of Questi...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Gupta, Abhinay Tyagi, Praveen Kumar Siddiqui, Ebtasam Ahmad Poornima Institute of Engineering and Technology Rajasthan Jaipur302022 India Vit Bhopal University School of Computing Science and Engineering MP 466114 India

ISBN: (纸本)9798331529833

The development of a question-answer chatbot involves leveraging advanced embedding techniques to optimize language understanding and response generation. The process begins with curating a diverse dataset of questions and answers, followed by preprocessing steps such as tokenization, stemming, and stop-word removal. Various embedding techniques, including traditional methods like BoW and TF-IDF, as well as modern approaches such as Word2Vec, GloVe, and BERT, are explored to determine the most effective technique for chatbot optimization. Performance metrics such as accuracy, responsiveness, and scalability guide the evaluation process. The approach encompasses data collection, preprocessing, embedding, model training, and user interaction, aiming to enhance human-computer interactions and improve information retrieval. © 2025 IEEE.

关键词： Embeddings

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Fuzzy-AttnNet: A Deep Learning Framework for Image Retrieval Using Fuzzy Feature Fusion 16

Fuzzy-AttnNet: A Deep Learning Framework for Image Retrieval...

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2024 16th international conference on Graphics and Image Processing, ICGIP 2024

作者： Liu, Shengyu Pan, Jingshan Li, Na Hu, Tao Tian, Tangbin Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Image retrieval is the process of searching for similar images based on their visual content, which has significant applications in the field of medical imaging. Quickly retrieving similar images from a medical image database can assist doctors in rapid browsing and diagnosis. Although feature fusion-based descriptors have achieved great success in the field of image retrieval, there are relatively few cases of using fusion feature descriptors for image retrieval in the medical imaging field. To address this gap, we propose a novel feature extraction model, Fuzzy-AttnNet, aimed at enhancing the performance of medical image retrieval using fusion descriptors. The model simulates dilated convolutions and self-attention mechanisms, comprehensively modeling both local and global features of medical images and performing feature fusion. The fused descriptors are then applied to image retrieval. Additionally, Fuzzy-AttnNet employs a parallel optimization strategy using Arcface loss and Softmax loss, which increases intra-class spacing and significantly improves the model's recognition performance across different categories. In subclass classification tasks, Fuzzy-AttnNet demonstrates significant advantages, with an average precision improvement of approximately 10% and a recall increase of about 30%. The experimental results indicate that our proposed feature extraction model significantly enhances the accuracy of medical image retrieval. © 2025 SPIE.

关键词： Medical imaging

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