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检索条件"任意字段=14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013"

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intelligent data engineering and automated learning - 14th international conference, ideal 2013, Proceedings

Intelligent Data Engineering and Automated Learning - 14th I...

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14th international conference on intelligent data engineering and automated learning, ideal 2013

ISBN: (纸本)9783642412776

the proceedings contain 76 papers. the topics discussed include: a pruning algorithm for extreme learning machine;measuring stability and discrimination power of metrics in information retrieval evaluation;system for monitoring and optimization of micro- and nano-machining processes using intelligent voice and visual communication;racing for unbalanced methods selection;bilateral multi-issue parallel negotiation model based on reinforcement learning;learning to detect the subway station arrival for mobile users;vision based multi-pedestrian tracking using adaptive detection and clustering;drilling cost prediction based on self-adaptive differential evolution and support vector regression;web service evaluation method based on time-aware collaborative filtering;and an improved PBIL algorithm for path planning problem of mobile robots.

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A Deep Multi-Task learning Network for Activity Recognition and User Identification Using Smartphone Sensors 16

A Deep Multi-Task Learning Network for Activity Recognition ...

引用

16th international conference on ENTERprise Information Systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-than-ideal performance and restricted applicability. this research presents a novel deep multi-Task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-Term memory (BiLSTM) layers to extract features from raw sensor data. the CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. these features are processed by fully connected layers for both activity recognition and user identification. A multi-Task loss function is proposed to optimize both tasks based on their complexity and importance. the approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-the-Art methods. this demonstrates the effectiveness of the multi-Task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks. © 2025 the Author(s).

关键词： Convolutional neural networks

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Development Trends of Big data and Artificial Intelligence in Virtual Simulation Technology 14th

Development Trends of Big Data and Artificial Intelligence i...

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14th international conference on Frontier Computing, FC 2024

作者： Yang, Yidong Wang, Lanfeng Wei, Yuhua Guangzhou Huali Science and Technology Vocational College Guangzhou China

ISBN: (纸本)9789819627974

With the development of information technology, big data technology and artificial intelligence have become important directions for informationization in the world today. Big data technology can help people better process, manage, and utilize massive amounts of data, while artificial intelligence can make computers more intelligent, achieving automated and intelligent processing and decision-making. Combining the two can achieve more powerful information processing and application capabilities, with broad application prospects. the application of artificial intelligence in virtual simulation technology has also played a crucial role. Artificial intelligence technology can simulate human intelligent behavior and provide more intelligent support for virtual simulation technology. through artificial intelligence algorithms, virtual simulation can be more automated and intelligent for simulation and prediction, improving the efficiency and accuracy of virtual simulation. In addition, artificial intelligence can also help virtual simulation technology achieve autonomous learning and optimization, continuously improving its performance and functionality. Based on the above issues, this article explores a virtual gesture interaction perception simulation model based on CNN (Convolutional Neural Networks) algorithm, extracts gesture datasets using artificial intelligence algorithms, and estimates gesture coordinates. the experimental results indicate that the sample sizes are 100, 200, and 300. the time for virtual gesture interaction perception based on CNN algorithm is 7.39ms, 10.87ms, and 14.67ms, which are 4.37ms, 5.61ms, and 7.07ms less than RNN (Recurrent Neural Network) algorithm, respectively. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Application Research of intelligent Machinery Design Based on CAD System 14th

Application Research of Intelligent Machinery Design Based o...

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14th international conference on Frontier Computing, FC 2024

作者： Wang, Yinghui Guangdong Innovative Technical College Dongguan523960 China

ISBN: (纸本)9789819627974

With the development of artificial intelligence (AI) and machine learning (ML) technologies, CAD systems have evolved from simple drawing tools to complex design and analysis platforms. the system is now able to leverage AI algorithms to optimize the design process and assist designers through pattern recognition, automated design processes, and parameter optimization. Today's intelligent CAD systems can automatically identify design requirements and constraints to provide more accurate design solutions. Moreover, the intelligent CAD system can also carry out complex simulation and analysis, and the application research of intelligent mechanical design based on CAD system shows the great potential of this field, which not only improves the design quality and efficiency, but also opens new possibilities for the future development of the mechanical design industry. According to the experimental data, the success rate of intelligent mechanical design, the adaptation rate of material selection, the accuracy rate of manufacturing process and the cost control scheme of the CAD system combined with manual audit are all above 99.88%, and the error rate is below 0.12%. Moreover, with the continuous progress of technology, the application of intelligent CAD system in the field of mechanical design will be more extensive and in-depth in the near future. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Application Design Artificial Intelligence CAD Technology Cost Control Mechanical Design

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Face Emotion Recognition Using Deep Neural Network (DNN)

Face Emotion Recognition Using Deep Neural Network (DNN)

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international conference on Communication Systems and Network Technologies (CSNT)

作者： A Rajendra Babu Mohebbanaaz S. Chandrika G. Rajeswari U. Vidya Pavani Dept of ECE Ravindra College of Engineering for Women Kurnool Andhra Pradesh India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Facial expression recognition is an intelligent human-computer interaction technology that gives a great sense of communication of the expression of our emotions, understanding, and intent between ourselves. Many researchers have attempted to categorize facial expressions, and it has been proven that there are seven universal basic emotions: happiness, sadness, anger, fear, surprise, disgust, and neutrality. Real-time emotion detection is achievable using a webcam. the paper discusses an architecture based on CNN for emotion detection. this model was trained on the FER-2013 dataset after performing some pre-processing steps such as scaling images and color mode correction. A multi-layer CNN model is designed for the classification of emotions as happy, sad, angry, fearful, or surprised, as well as disgusted and neutral. In addition to this, it focuses on real-time analysis to present solutions based on emotions detected, such as automatically playing videos to address sad or angry or fearful emotions. Our developed algorithm attained an highest accuracy of 97.8%. the developed algorithms are very robust, effective and helps in automated seizure detection.

关键词： Human computer interaction Emotion recognition Accuracy Machine learning algorithms Face recognition Signal processing algorithms Artificial neural networks Real-time systems data models Classification algorithms

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Adapt a Text-Oriented Chunker for Oral data: How Much Manual Effort Is Necessary?

Adapt a Text-Oriented Chunker for Oral Data: How Much Manual...

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14th international conference on intelligent data engineering and automated learning (ideal)

作者： Tellier, Isabelle Dupont, Yoann Eshkol, Iris Wang, Ilaine Univ Paris 03 Lattice F-75230 Paris 05 France Univ Orleans LLL F-45067 Orleans France

In this paper, we try three distinct approaches to chunk transcribed oral data with labeling tools learnt from a corpus of written texts. the purpose is to reach the best possible results with the least possible manua... 详细信息

ISBN: (纸本)9783642412783;9783642412776

关键词： Artificial intelligence

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Anomaly Monitoring Framework Based on intelligent data Analysis

Anomaly Monitoring Framework Based on Intelligent Data Analy...

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14th international conference on intelligent data engineering and automated learning (ideal)

作者： Rattadilok, Prapa Petrovski, Andrei Petrovski, Sergei Robert Gordon Univ Sch Comp Sci & Digital Media Aberdeen AB9 1FR Scotland Samara State Tech Univ Sch Elect Stat Samara Russia

ISBN: (纸本)9783642412783;9783642412776

Real-time data processing has become an increasingly important challenge as the need for faster analysis of big data widely manifests itself. In this research, several Computational Intelligence methods have been applied for identifying possible anomalies in two real world sensor-based datasets. By achieving similar results to those of well respected methods, the proposed framework shows a promising potential for anomaly detection and its lightweight, real-time features make it applicable to a range of in-situ data analysis scenarios.

关键词： intelligent data analysis automated fault detection big data real-time K-Means

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Gradient Boosting-Based Negative Correlation learning

Gradient Boosting-Based Negative Correlation Learning

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14th international conference on intelligent data engineering and automated learning (ideal)

作者： Wan, Lunjun Tang, Ke Wang, Rui Univ Sci & Technol China Sch Comp Sci & Technol USTC Birmingham Joint Res Inst Intelligent Comput Hefei 230027 Peoples R China

ISBN: (纸本)9783642412783;9783642412776

Negative correlation learning (NCL) is a useful ensemble learning approach, and has been used for neural network ensembles. In this paper, a new NCL algorithm, NCL. GBM is proposed, which uses gradient boosting machine (GBM) as the base learner. First, the feasibility of combining NCL and GBM is analysed. then, we describe in detail how to apply negative correlation learning onto GMB. Empirical results show that NCL. GBM does have the ability to produce models with lower correlation and can usually result in lower generalization error than the original GBM.

关键词： Negative correlation learning gradient boosting machine

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Bilateral Multi-issue Parallel Negotiation Model Based on Reinforcement learning

Bilateral Multi-issue Parallel Negotiation Model Based on Re...

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14th international conference on intelligent data engineering and automated learning (ideal)

作者： Chen, Lihong Dong, Hongbin Han, Qilong Cui, Guangzhe Harbin Engn Univ Dept Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9783642412783;9783642412776

this paper proposes a bilateral multi-issue parallel negotiation model based on reinforcement learning. Considering the equality of both sides and that both negotiators refuse to give more information for their own interests, it introduces a mediator agent as the mediation mechanism. It considers the correlation between quantity and price simultaneously, and uses reinforcement learning to generate the optimal behavioral strategy. Comparing with 'A simultaneous multi-issue negotiation through autonomous agents', the experimental results show that the proposed method has decreased little in the negotiation joint utility, but has decreased significantly in the negotiation times, and has also improved the equality of both negotiators.

关键词： automated negotiation Reinforcement learning Mediator agent Related issues

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learning a Label-Noise Robust Logistic Regression: Analysis and Experiments

Learning a Label-Noise Robust Logistic Regression: Analysis ...

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14th international conference on intelligent data engineering and automated learning (ideal)

作者： Bootkrajang, Jakramate Kaban, Ata Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9783642412783;9783642412776

Label-noise robust logistic regression (rLR) is an extension of logistic regression that includes a model of random mislabelling. this paper attempts a theoretical analysis of rLR. By decomposing and interpreting the gradient of the likelihood objective of rLR as employed in gradient ascent optimisation, we get insights into the ability of the rLR learning algorithm to counteract the negative effect of mislabelling as a result of an intrinsic re-weighting mechanism. We also give an upper-bound on the error of rLR using Rademacher complexities.

关键词： label noise logistic regression robust learning gradient ascent optimisation generalisation error bounds

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