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International Conference on Signal Processing Systems (ICSPS 2009)

作者： Sun Xia Dong Lehong NW Univ Xian Dept Comp Sci & Technol Xian 710127 Peoples R China

ISBN: (纸本)9780769536545

In this paper, we propose a feature-based Chinese term relation extraction approach that combined the advantages of both naive bayes algorithm and perceptron algorithm. A subset of the features was estimated in training data;another subset of the features was trained by discriminative function. The results demonstrate that the proposed hybrid algorithm almost always outperforms the naive bayes algorithms and perceptron algorithms whether the training set is small or not. On the other hand, a novel feature representation was proposed, which included term sequence feature, term appearance features and context information features. Comparing the previous method, long-range dependence was considered in the proposed feature representation, which add the position of feature into vector space model (VSM) and promotes the capability of feature representation. Further, punctuation feature is the important character for terms relation extraction.

关键词： term relation extraction classification algorithm feature representation

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The Application of Negative Selection algorithm in Multi-angle Infrared Vehicle Images Recognition 38

The Application of Negative Selection Algorithm in Multi-ang...

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38th International Conference on Telecommunications and Signal Processing (TSP)

作者： Yu, Xi Fu, Dongmei Yang, Tao Riha, Kamil Univ Sci & Technol Beijing Sch Infommt & Engeering Xueyuan Rd Beijing 100083 Peoples R China Brno Univ Technol Dept Telecommun Tech 12 Brno 61600 Czech Republic

ISBN: (纸本)9781479984985

At present, the objects of the vehicle recognition based on image processing are mostly visible images with rich details and limited camera angles. Few studies have been done on the recognition of multi-angle infrared vehicle images with less detail. However, it has important implications for transportation and especially public security. This paper extracts object contour of the vehicle from infrared vehicle images. After image correction and normalization, the geometric features and moment invariants are extracted. Based on the tolerance process of biological immune cells, this paper proposes a new Multi-classification Artificial Immunity Negative Selection (MAINS) algorithm. This algorithm can recognize vehicle types by the objects' geometric features and moment invariants. It has a stable performance and global search ability, also a high correct recognition rate in infrared vehicle image recognition.

关键词： classification algorithm Infrared Images Negative Selection Vehicle Recognition

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Hand posture recognition using jointly optical flow and dimensionality reduction

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2013年第1期2013卷 1-22页

作者： Boughnim, Nabil Marot, Julien Fossati, Caroline Bourennane, Salah Aix Marseille Univ CNRS UMR 7249 Fresnel InstEcole Cent MarseilleDU St Jerome F-13397 Marseille 20 France

Hand posture recognition is generally addressed by using either YCbCr (luminance and chrominance components) or HSV (hue, saturation, value) mappings which assume that a hand can be distinguished from the background from some colorfulness and luminance properties. This can hardly be used when a dark hand, or a hand of any color, is under study. In addition, existing recognition processes rely on descriptors or geometric shapes which can be reliable;this comes at the expense of an increased computational complexity. To cope with these drawbacks, this paper proposes a four-step method recognition technique consisting of (i) a pyramidal optical flow for the detection of large movements and hence determine the region of interest containing the expected hand, (ii) a preprocessing step to compute the hand contour while ensuring geometric and illumination invariance, (iii) an image scanning method providing a signature which characterizes non-star-shaped contours with a one-pixel precision, and (iv) a posture classification method where a sphericity criterion preselects a set of candidate postures, principal component analysis reduces the dimensionality of the data, and Mahalanobis distance is used as a criterion to identify the hand posture in any test image. The proposed technique has been assessed in terms of its performances including the computational complexity using both visual and statistical results.

关键词： Hand posture Contour signature classification algorithm Optical flow Principal component analysis

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Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification

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DIGITAL HEALTH 2023年 9卷

作者： Choi, Junggu Kwon, Seohyun Park, Sohyun Han, Sanghoon Yonsei Univ Yonsei Grad Program Cognit Sci Seoul South Korea Munice Inc Vienna VA USA Yonsei Univ Underwood Int Coll Econ Seoul South Korea Yonsei Univ Dept Psychol Seoul South Korea Yonsei Univ Dept Psychol Seoul 03722 South Korea Yonsei Univ Yonsei Grad Program Cognit Sci Seoul 03722 South Korea

BackgroundSleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification. MethodsTo compare as many conditions as possible, 414 experimental conditions were applied, considering the combination of different biosignals, biosignal length, and window length. Five biosignals in polysomnography (i.e. electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram left, and electrooculogram right) were used to identify optimal signal combinations for classification. In addition, three different signal-length conditions and six different window-length conditions were applied. The validity of each condition was examined via classification performance from the XGBoost classifiers trained using 10-fold cross-validation. Furthermore, results considering feature importance were examined to validate the experimental results in terms of model explanation. ResultsThe combination of EEG + EMG + ECG with a 40 s window and 120 s signal length resulted in the best classification performance (precision: 0.853, recall: 0.855, F1-score: 0.853, and accuracy: 0.853). Compared to other conditions and feature importance results, EEG signals showed a relatively higher importance for classification in the present study. ConclusionWe determined the optimal biosignal and window conditions for the feature-extraction process in machine learning algorithm-based sleep stage classification. Our experimental results inform researchers in the future conduct of related studies. To generalize our results, more diverse methodologies and conditions should be applied in future studies.

关键词： Sleep stage classification polysomnography biosignal machine learning classification algorithm

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Alternative Ensemble Classifier Based on Penalty Strategy for Improving Prediction Accuracy 1st

Alternative Ensemble Classifier Based on Penalty Strategy fo...

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1st International Conference on Human Systems Engineering and Design (IHSED) - Future Trends and Applications

作者： Lopez, Cindy-Pamela Tenemaza, Maritzol Loza-Aguirre, Edison Escuela Politec Nacl Dept Informat & Ciencias Comp Quito Ecuador

ISBN: (纸本)9783030020538;9783030020521

The Increasing demand for accurate classifier systems for user's service has called the application of machine learning techniques. One of the most used techniques consist in grouping classifiers into an ensemble classifier. The resulting classifier is generally more accurate than any individual classifier. In this work, we propose an alternative ensemble classification system based on combining three classifiers: Naive Bayes, Random Forest and Multilayer Perceptron. To increase robustness of prediction, we organized the algorithms used by penalty calculations instead of a score-based voting system. We have compared the results of our proposed penalty factor system with the most popular classification algorithms and an ensemble classifier that uses the voting technique. Our results show that our algorithm improves the accuracy in prediction of classification in exchange of a reasonable response time.

关键词： Ensemble classification Machine learning classification algorithm classification

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Subject Identification Using Walking Posture 10

Subject Identification Using Walking Posture

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10th International Conference on Speech Technology and Human-Computer Dialogue (SpeD)

作者： Hnatiuc, Mihaela Paun, Mirel Lafeuille, Ambroise Constanta Maritime Univ Dept Elect & Telecommun Constanta Romania Natl Inst Appl Sci Dept Ind Syst Engn Blois Loir & Cher France

ISBN: (纸本)9781728109848

Human posture can provide important information through non-verbal communication. In recent years, studies of human posture recognition are becoming more and more numerous in the areas of personal healthcare, environmental awareness, human-computer interaction and surveillance systems. This paper proposes a simple method of posture recognition during walking using the leg inclination. The developed system uses mobile phone tilt sensors and a deflection sensor. Mobile phones are positioned on the back of the foot above and below the knee and the Flex sensor is positioned on the foot. The purpose of this system is to identify the subject after the walking posture. The test analysis is conducted on four subjects of different height and mass. The results are compared using MATLAB processing algorithms.

关键词： mobile phone inclinometer classification algorithm posture recognition walking cycle

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NETWORK INTRUSION DETECTION USING CLUSTERING AND GRADIENT BOOSTING 9

NETWORK INTRUSION DETECTION USING CLUSTERING AND GRADIENT BO...

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9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Verma, Parag Anwar, Shayan Khan, Shadab Mane, Sunil B. Coll Engn Dept Comp Engn & IT Pune Maharashtra India

ISBN: (纸本)9781538644300

An unauthorized activity on the network is called network intrusion and device or software application which monitors the network parameters in order to detect such an intrusion is called network intrusion detection system (NIDS). With high rise in malicious activities on the internet, it is extremely important for NIDS to quickly and correctly identify any kind of malicious activity on the network. Moreover, the system must refrain from raising false alarms in case of normal usage detected as malicious. This paper proposes use of machine learning classification algorithms - XGBoost and AdaBoost with and without clustering to train a model for NIDS. The models are trained and tested using NSL KDD dataset and the results are an improvement over the previous works related to intrusion detection on the same dataset.

关键词： Intrusion detection XGBoost AdaBoost Machine learning decision trees classification algorithm clustering algorithm gradient boosting

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Automatic Image selection from Images with Similar Contents 25

Automatic Image selection from Images with Similar Contents

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25th Signal Processing and Communications Applications Conference (SIU)

作者： Bardak, Batuhan Demirci, M. Fatih TOBB Ekon & Teknol Univ Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781509064946

With the increased use of smart devices, digital cameras and abundance of memory in the devices, the pictures of the same scenes have been taken several times, resulting in a number of images consisting of the same or very similar content. in memory. Manually selecting the good ones is time-consuming as well as error prone. In this paper, the features of the images in the data sets were extracted and machine learning algorithms were applied to successfully classify the images as "good" or "bad" with %97.6 precision and %89.3 recall score.

关键词： feature extraction classification algorithm ensemble learning

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Automatic differentiation of melanoma and clark nevus skin lesions

Automatic differentiation of melanoma and clark nevus skin l...

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Medical Imaging 2007 Conference

作者： LeAnder, R. W. Kasture, A. Pandey, A. Umbaugh, S. E. So Illinois Univ Edwardsville Dept Elect & Comp Engn Edwardsville IL 62026 USA

ISBN: (纸本)9780819466327

Skin cancer is the most common form of cancer in the United States. Although melanoma accounts for just 11% of all types of skin cancer, it is responsible for most of the deaths, claiming more than 7910 lives annually. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are benign. The application of pattern recognition techniques to these lesions may be useful as an educational tool for teaching physicians to differentiate lesions, as well as for contributing information about the essential optical characteristics that identify them. Purpose: This study sought to find the most effective features to extract from melanoma, melanoma in situ and Clark nevus lesions, and to find the most effective pattern-classification criteria and algorithms for differentiating those lesions, using the Computer Vision and Image Processing Tools (CVIPtools) software package. Methods: Due to changes in ambient lighting during the photographic process, color differences between images can occur. These differences were minimized by capturing dermoscopic images instead of photographic images. Differences in skin color between patients were minimized via image color normalization, by converting original color images to relative-color images. Relative-color images also helped minimize changes in color that occur due to changes in the photographic and digitization processes. Tumors in the relative-color images were segmented and morphologically filtered. Filtered, relative-color, tumor features were then extracted and various pattern-classification schemes were applied. Results: Experimentation resulted in four useful pattern classification methods, the best of which was an overall classification rate of 100% for melanoma and melanoma in situ (grouped) and 60% for Clark nevus. Conclusion: Melanoma and melanoma in situ have feature parameters and feature values that are similar enough to be considered one class of tumor that significantly differs from

关键词： skin cancer dermoscopic images preprocessing color normalization relative-color images quantitative image analysis tumor texture features tumor identification tumor differentiation classification algorithm

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Semorph: A Morphology Semantic Enhanced Pre-trained Model for Chinese Spam Text Detection 22

Semorph: A Morphology Semantic Enhanced Pre-trained Model fo...

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31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Lai, Kaiting Long, Yinong Wu, Bowen Li, Ying Wang, Baoxun Peking Univ Beijing Peoples R China Tencent Platform & Content Grp Beijing Peoples R China Peking Univ Natl Res Ctr Software Engn Beijing Peoples R China

ISBN: (纸本)9781450392365

Chinese spam text detection is essential for social media since these texts affect the user experience of Chinese speakers and pollute the community. The underlying text classification method is employed to explore the unique combinations of characters that represent clues of spam information from annotated or further augmented data. However, based on the diversity of Chinese characters in glyphs, the spammers frequently wrap the spam content in another visually close text to fool the model but make sure people understand1. This paper proposes to adopt the essence of human cognition of these adversarial texts into spam text detection models, by designing a pre-trained model to learn the morphology semantics of Chinese characters and represent their contextual meanings from scratch. The model pre-trains on self-supervised Chinese corpus and fine-tunes on spam-annotated community texts. Besides, cooperating with the pre-trained model that can capture the morphological features of Chinese, a new data perturbation method is introduced to guide the optimization towards the direction of recognizing the actual meaning of a text after spammers tamper with partial characters by visually close ones. The experimental results have shown that our proposed methodology can notably improve the performance of spam text detection as well as maintain robustness against adversarial samples.

关键词： Spam Text Detection Chinese Character Representation Pre-trained Language Model classification algorithm

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