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 ...
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.
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 ...
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Withthe 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 pr...
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Withthe 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 lever...
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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 rese...
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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-2013dataset 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.
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 withthe least possible manua...
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ISBN:
(纸本)9783642412783;9783642412776
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 withthe least possible manual correction or re-learning effort.
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 appl...
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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.
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 machin...
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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.
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 in...
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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.
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 ...
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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.
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