In this paper, we consider an issue of hand posture classification. We improve a recently proposed signature, a matrix containing the distance of all contour pixels to an arbitrary reference point. Adequate pre-proces...
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ISBN:
(纸本)9781479903566
In this paper, we consider an issue of hand posture classification. We improve a recently proposed signature, a matrix containing the distance of all contour pixels to an arbitrary reference point. Adequate pre-processings ensure the invariance properties of the signature. Candidate postures are pre-selected with a surface criterion, and Principal Component Analysis (PCA) reduces the dimensionality of the data, which improves the classification process.
The ever-expanding volume of scientific literature necessitates innovative solutions for efficient information organization and retrieval. This final qualifying work focuses on the development of a robust algorithm fo...
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ISBN:
(纸本)9798350386813;9798350386820
The ever-expanding volume of scientific literature necessitates innovative solutions for efficient information organization and retrieval. This final qualifying work focuses on the development of a robust algorithm for the automated categorization of articles in a scientific journal. The primary objective is to streamline the process of classifying diverse research contributions, thereby enhancing accessibility and knowledge discovery within scholarly domains. The goal of the final qualifying work is to develop an algorithm for automated categorization of articles in a scientific journal. To achieve this goal, an application is developed for the administrator of a scientific journal, allowing for the preparation, classification and visualization of data.
In the current scenario, reliable recognition and classification of hand wrist gestures are gaining high demand for numerous applications including health care application, for Sign language recognition, and in roboti...
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The practical application of graph neural networks in online shopping malls is a significant area within real-life scenarios. As neural network technology continually advances, an increasing emphasis is being placed o...
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ISBN:
(纸本)9798350380989
The practical application of graph neural networks in online shopping malls is a significant area within real-life scenarios. As neural network technology continually advances, an increasing emphasis is being placed on how to efficiently leverage graph data for enhancing the functionality and performance of these e-commerce platforms. Nonetheless, prevailing methodologies commonly employ conventional adjacency matrices or basic attention mechanisms to consolidate and model data pertaining to items, users, and their interactions. To some extent, these approaches overlook the intricacy of graph structures and prove challenging to implement effectively on a grand scale. Running on graph data requires a large amount of computing resources, leading to performance issues. To address these issues, we propose a new method based on hierarchical transformation aggregation model to process graph data in online shopping malls more efficiently. This method first selects nodes at various levels and regions through random walks, and then uses graph convolutional neural networks (GCN) for information aggregation. Upon acquiring node features for each layer, a transformed attention mechanism is further introduced to more effectively consolidate the node features from every layer, thereby obtaining the ultimate representation of each node. This method better captures the local and global characteristics of graph data by considering node information at different levels, improving the performance and user experience of the mall. Ultimately, we substantiate the dependability of this model through multiple real-world dataset validations, thereby evidencing its practical application feasibility in online shopping malls. This method based on the hierarchical transformation aggregation model provides a more efficient way for malls to process and analyze graph data, and is expected to improve the performance of recommendation systems, personalized recommendations, and user shopping experienc
Most of prior hardware Trojan detection approaches require golden chips for references. A classification-based golden chips-free hardware Trojan detection technique has been proposed in the authors' previous work....
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Most of prior hardware Trojan detection approaches require golden chips for references. A classification-based golden chips-free hardware Trojan detection technique has been proposed in the authors' previous work. However, the algorithm in that work is trained by simulated ICs without considering a shift between the simulation and silicon fabrication. In this study, a co-training based hardware Trojan detection method by exploiting inaccurate simulation models and unlabeled fabricated ICs is proposed to provide reliable detection capability when facing fabricated ICs, which eliminates the need of golden chips. Two classification algorithms are trained using simulated ICs. These two algorithms can identify different patterns in the unlabelled ICs during test-time, and thus can label some of these ICs for the further training of the other algorithm. Moreover, a statistical examination is used to choose ICs labelling for the other algorithm. A statistical confidence interval based technique is also used to combine the hypotheses of the two classification algorithms. Furthermore, the partial least squares method is used to preprocess the raw data of ICs for feature selection. Both EDA experiment results and field programmable gate array (FPGA) experiment results show that the proposed technique can detect unknown Trojans with high accuracy and recall.
Driver fatigue is one of the causal factors for traffic accidents. Actions of facial units convey various information from our brains. This paper proposed a comprehensive approach to explore whether pattern of sequenc...
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Driver fatigue is one of the causal factors for traffic accidents. Actions of facial units convey various information from our brains. This paper proposed a comprehensive approach to explore whether pattern of sequences of the drivers facial landmarks changes from the alert start to the fatigue state. A driving-simulator-based experiment was designed and conducted. Videos of 21 participants faces were recorded during the experiment, together with subjective and others assessment of the level of alertness. Sequences of eye aspect ratio (EAR) and mouth aspect ratio (MAR) were calculated based on facial landmarks. Totally 21 feature candidates including Percent eye-closure over a fixed time window (PERCLOS), blink rate, statistics of blink duration, closing speed, reopening speed and number of yawns were extracted. A mental fatigue assessment model is proposed after feature selection. Four machine learning algorithms were used to build the assessment model of fatigue. The best performance was achieved by logistic regression, with cross-validation accuracies of 83.7 and 85.4. This study may contribute to the development of driver fatigue monitoring system for automotive ergonomics.
Digital media object classification plays an important role in digital media, but there is a problem of low classification accuracy. Deep learning algorithms cannot improve the accuracy of digital media object classif...
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Because there are lots of typical applications and urgent needs, the research on the efficient classification learning about accumulated big data in nonstationary environments has become one of the hot topics in the f...
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Because there are lots of typical applications and urgent needs, the research on the efficient classification learning about accumulated big data in nonstationary environments has become one of the hot topics in the field of data mining recently. The LearnNSE algorithm is an important research result in this field. For the long-term accumulated big data, the LearnNSE-Pruned-Age, a pruning version of LearnNSE, was given, which has received widespread attentions. However, it is found that the pruning mechanism of the LearnNSE-Pruned-Age algorithm is not perfect, which lost the core ability of the LearnNSE algorithm to reuse the learned classification knowledge. Therefore, the ensemble mechanism of LearnNSE is adjusted in this paper, and a novel ensemble mechanism is designed. The new mechanism uses the integration of the latest base-classifiers to track the changes of the data generation environment, and then selects the old base-classifiers that contribute to the current classification for forward supplementary integration. On this basis, a new pruned algorithm named FLearnNSE-Pruned-Age is proposed. The experiment results show that the FLearnNSE-Pruned-Age algorithm has the ability to reuse the learned classification knowledge and it can achieve the very close classification accuracy compared to LearnNSE, even better in some scenes. In addition, it improves the efficiency of ensemble learning and is suitable for the fast classification learning of accumulated big data.
The quality of remotely sensed land use and land cover (LULC) maps is affected by the accuracy of image data classifications. Various efforts have been made in advancing Supervised or unsupervised classification a met...
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The quality of remotely sensed land use and land cover (LULC) maps is affected by the accuracy of image data classifications. Various efforts have been made in advancing Supervised or unsupervised classification a methods to increase the repeatability and accuracy of LULC mapping. This study incorporates a data-assisted labeling approach (DALA) into the unsupervised classification of remotely sensed imagery. The DALA-unsupervised classification algorithm consists of three steps: (1) creation of N spectral-class maps using Iterative Self-Organizing Data Analysis Technique algorithm (ISODATA);(2) development of LULC maps with assistance of reference data;and (3) accuracy assessments of all the LULC maps using independent reference data and selection or one LULC map with the highest accuracy. classification experiments with a composite image of a Landsat Thematic Mapper (TM) image and tin Enhanced Thematic Mapper Plus (ETM +) image suggest that DALA was effective in making unsupervised classification process more objective, automatic, and accurate. A comparison between the DALA-unsupervised classifications and some conventional classifications suggests that the DALA-unsupervised classification algorithm yielded better classification accuracies compared to these conventional approaches. Such a simple, effective approach has not been systematically examined before but has great potential for many applications in the geosciences. (C) 2008 Elsevier Ltd. All rights reserved.
Due to the widespread use of mobile devices, services based on the users current indoor location are growing in significance. Such services are developed in the Machine Learning and Experst Systems realm, and ranges f...
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Due to the widespread use of mobile devices, services based on the users current indoor location are growing in significance. Such services are developed in the Machine Learning and Experst Systems realm, and ranges from guidance for blind people to mobile tourism and indoor shopping. One of the most used techniques for indoor positioning is WiFi fingerprinting, being its use of widespread WiFi signals one of the main reasons for its popularity, mostly on high populated urban areas. Most issues of this approach rely on the data acquisition phase;to manually sample WiFi RSSI signals in order to create a WiFi radio map is a high time consuming task, also subject to re-calibrations, because any change in the environment might affect the signal propagation, and therefore degrade the performance of the positioning system. The work presented in this paper aims at substituting the manual data acquisition phase by directly calculating the WiFi radio map by means of a radiosity signal propagation model. The time needed to acquire the WiFi radio map by means of the radiosity model dramatically reduces from hours to minutes when compared with manual acquisition. The proposed method is able to produce competitive results, in terms of accuracy, when compared with manual sampling, which can help domain experts develop services based on location faster. (C) 2018 Elsevier Ltd. All rights reserved.
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