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A modular supervised algorithm for vessel segmentation in red-free retinal images

COMPUTERS IN BIOLOGY AND MEDICINE 2008年第8期38卷 913-922页

作者： Anzalone, Andrea Bizzarri, Federico Parodi, Mauro Storace, Marco Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

In this paper, a supervised algorithm for vessel segmentation in red-free images of the human retina is proposed. The algorithm is modular and made up of two fundamental blocks. The optimal values of two algorithm parameters are found out by maximizing proper measures of performances (MOPS) able to evaluate from a quantitative point of view the results provided by the proposed algorithm. The choice of the MOP allows one to tailor the solution to the specific image features to be emphasized. The performances of the algorithm are compared with those of other methods described in the literature. The simulation results show a good trade-off between quality and processing speed times. For instance, in terms of the maximum average accuracy (MAA), K value, and specificity (SP), the best performance outcomes are 0.9587, 0.8069 and 0.9477, respectively. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： optimization blood vessel segmentation retinal vasculature mapping supervised algorithm retinal imaging

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Graphic Representation With Knowledge Structure of Mathematics and Clustering With supervised algorithm

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International Journal of Intelligent Technologies and Applied Statistics 2018年第4期11卷 271-281页

作者： Yuan-Horng Lin Jeng-Ming Yih

Gustafson-Kessel (GK) clustering algorithm needs added constraint of fuzzy covariance matrix, Gath-Geva (GG) clustering algorithm can only be used for the data with multivariate Gaussian distribution. In GK-algorithm, modified Mahalanobis distance with preserved volume was used. However, the added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. Improved normalized Mahalanobis clustering algorithm based on fuzzy c-means (FCM) by taking a new threshold value and a new convergent process is proposed. The experimental results of real data sets show that our proposed new algorithm has the best performance. Not only replacing the common covariance matrix with the correlation matrix in the objective function in the normalized Mahalanobis clustering algorithm The method of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. This method integrates fuzzy logic model of perception (FLMP) and interpretive structural modeling (ISM). The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. This integrated method should be feasible to represent concept structures. Besides, fuzzy clustering technique is adopted to provide features of concept structures based on homogeneity of sample. Applying the method of the cluster of FCM, we could distinguish characteristics of six groups. We analyzed the whole data and discuss the relationship between knowledge structures of the sample. The result and discoveries from the research can offer pupils' misconception of learning basic mathematics with reference of diagnosis.

关键词： Gustafson-Kessel algorithm Fuzzy c-means Interpretive structural modeling supervised algorithm

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RECENT ADVANCES ON DRUG DISCOVERY FOR ANTI-INFLAMMATORY ACTIVITY DETECTION: supervised AND UNsupervised algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2016年第5期12卷 1481-1503页

作者： Tian, Shengwei Gao, Shuangyin Yu, Long Shi, Xinyu Wang, Mei Li, Li Xinjiang Univ Coll Software 499 Xibei Rd Urumqi 830008 Peoples R China Xinjiang Univ Network Ctr 14 Shengli Rd Urumqi 830046 Peoples R China Xinjiang Med Univ Inst Med 8 Xinyi Rd Urumqi 830054 Peoples R China Xinjiang Med Univ Coll Med Engn Technol 393 Xinyi Rd Urumqi 830054 Peoples R China

Determining molecular activity from large and high-complexity drug molecules is a challenging task. Many methods have been tried to solve this difficult problem. However, there is not a comprehensive evaluation that covers the performance test of various molecular descriptors based on supervised and unsupervised methods. In practical application, unlabeled dataset is easier to get than labeled data, however, which cannot be a factor that has great influence on test results. Usually, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two common traditional supervised algorithms. Sparse AutoEncode (SAE) and Deep Belief Nets (DBN) are two typical unsupervised algorithms. In this paper, the two types of methods were adopted and extensive classification and evaluation strategies were performed to test and verify the IL-1B anti-inflammatory activity. The results reveal no matter what kind of descriptors, the unsupervised algorithm has more precise predictions than the supervised algorithm though the target output is the same. This research contributes to finding the best match algorithm to determine the reliability of drug activity discovery. It not only reduces the cost of research and improves the efficiency of anti-inflammatory drug discovery, but also has a great significance on promoting anti-inflammatory drug design.

关键词： Molecular activity supervised algorithm Unsupervised algorithm Classification IL-1B anti-inflammatory activity

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Computer-aided diagnostic system for breast cancer detection based on optimized segmentation scheme and supervised algorithm

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AUTOMATIKA 2023年第4期64卷 1244-1254页

作者： Balaji, S. Arunprasath, T. Rajasekaran, M. Pallikonda Vishnuvarthanan, G. Sindhuja, K. Kalasalingam Acad Res & Educ Dept Elect & Commun Engn Krishnankoil India VIT Bhopal Univ Sch Comp Sci & Engn Kothri Kalan Madhya Pradesh India Theni Kammavar Sangam Coll Technol Dept Elect & Commun Engn Theni India Kalasalingam Acad Res & Educ Dept Biomed Engn Krishnankoil 626126 India

Breast cancer is a serious threat to the womankind and it leads the susceptible kinds of cancer for women. The mortality rates due to breast cancer increases every single year and the World Health Organization (WHO) aims to reduce the occurrence of breast cancer by at least 2.5% per year. The occurrence of breast cancer can be minimized only when periodical screening is carried out. Mammography is one of the effective screening procedure, which can effectively locate earlier signs of breast cancer. As an aid, this work aims to present a system for the breast cancer detection and classification. This work is segregated into four phases and all these phases aim to enhance the classification performance. The efficiency of the proposed work is evaluated against the state-of-the-art approaches and the proposed contribution to the medical science. The computer-aided diagnostic system (CADS) proves 98.2% accuracy, with minimal false positive and false negative rates in a reasonable period of time.

关键词： Breast cancer detection classification CADS optimization supervised algorithm

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A Memristive Spiking Neural Network Circuit With Selective supervised Attention algorithm

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2023年第8期42卷 2604-2617页

作者： Deng, Zekun Wang, Chunhua Lin, Hairong Sun, Yichuang Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China

Spiking neural networks (SNNs) are biologically plausible and computationally powerful. The current computing systems based on the von Neumann architecture are almost the hardware basis for the implementation of SNNs. However, performance bottlenecks in computing speed, cost, and energy consumption hinder the hardware development of SNNs. Therefore, efficient non von Neumann hardware computing systems for SNNs remain to be explored. In this article, a selective supervised algorithm for spiking neurons (SNs) inspired by the selective attention mechanism is proposed, and a memristive SN circuit as well as a memristive SNN circuit based on the proposed algorithm are designed. The memristor realizes the learning and memory of the synaptic weight. The proposed algorithm includes a top-down (TD) selective supervision method and a bottom-up (BU) selective supervision method. Compared with other supervised algorithms, the proposed algorithm has excellent performance on sequence learning. Moreover, TD and BU attention encoding circuits are designed to provide the hardware foundation for encoding external stimuli into TD and BU attention spikes, respectively. The proposed memristive SNN circuit can perform classification on the MNIST dataset and the Fashion-MNIST dataset with superior accuracy after learning a small number of labeled samples, which greatly reduces the cost of manual annotation and improves the supervised learning efficiency of the memristive SNN circuit.

关键词： Circuit design image classification memristor selective attention sequence learning spiking neural network (SNN) supervised algorithm

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supervised Weighting-Online Learning algorithm for Short-Term Traffic Flow Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2013年第4期14卷 1700-1707页

作者： Jeong, Young-Seon Byon, Young-Ji Castro-Neto, Manoel Mendonca Easa, Said M. Khalifa Univ Sci Technol & Res Dept Ind & Syst Engn Abu Dhabi U Arab Emirates Khalifa Univ Sci Technol & Res Dept Civil Engn Abu Dhabi U Arab Emirates Univ Fed Ceara Dept Transportat Engn BR-60020181 Fortaleza Ceara Brazil Ryerson Univ Dept Civil Engn Toronto ON M5B 2K3 Canada

Prediction of short-term traffic flow has become one of the major research fields in intelligent transportation systems. Accurately estimated traffic flow forecasts are important for operating effective and proactive traffic management systems in the context of dynamic traffic assignment. For predicting short-term traffic flows, recent traffic information is clearly a more significant indicator of the near-future traffic flow. In other words, the relative significance depending on the time difference between traffic flow data should be considered. Although there have been several research works for short-term traffic flow predictions, they are offline methods. This paper presents a novel prediction model, called online learning weighted support-vector regression (OLWSVR), for short-term traffic flow predictions. The OLWSVR model is compared with several well-known prediction models, including artificial neural network models, locally weighted regression, conventional support-vector regression, and online learning support-vector regression. The results show that the performance of the proposed model is superior to that of existing models.

关键词： Intelligent transportation systems (ITSs) online learning weighted support-vector regression (OLWSVR) short-term traffic flow forecast supervised algorithm

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A supervised learning computer-based algorithm to derive the amplitude of oscillations of structures using noisy GPS and Robotic Theodolites (RTS) records

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COMPUTERS & STRUCTURES 2012年 92-93卷 337-348页

作者： Psimoulis, Panos A. Stiros, Stathis C. Univ Patras Dept Civil Engn Geodesy Lab Patras 26500 Greece

A computer-based algorithm computing the mean amplitude of small-scale, high frequency oscillations of civil engineering structures using GPS and Robotic Theodolites (RTS) is presented. This algorithm is based on a peak-picking filter derived from supervised learning through independent experiments. This filter is a function of the oscillation frequency, computed even from noisy, apparent displacement records. The algorithm minimizes the noise of high-frequency GPS and RTS recordings and calculates the mean amplitude of the oscillations with millimeter accuracy. The application of this method to the Structural Health Monitoring is demonstrated in the case of measurement of displacements of two short-span bridges. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： GPS RTS Amplitude Filter supervised algorithm

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RETRACTED: Review of ensemble classification over data streams based on supervised and semi-supervised (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 3859-3878页

作者： Han, Meng Li, Xiaojuan Wang, Le Zhang, Ni Cheng, Haodong North Minzu Univ Sch Comp Sci & Engn Yinchuan Ningxia Peoples R China

Most data stream ensemble classification algorithms use supervised learning. This method needs to use a large number of labeled data to train the classifier, and the cost of obtaining labeled data is very high. Therefore, the semi supervised learning algorithm using labeled data and unlabeled data to train the classifier becomes more and more popular. This article is the first to review data stream ensemble classification methods from the perspectives of supervised learning and semi-supervised learning. Firstly, basic classifiers such as decision trees, neural networks, and support vector machines are introduced from the perspective of supervised learning and semi-supervised learning. Secondly, the key technologies in data stream ensemble classification are explained from the two aspects of incremental and online. Finally, the majority voting and weight voting are explained in the ensemble strategies. The different ensemble methods are summarized and the classic algorithms are quantitatively analyzed. Further research directions are given, including the handling of concept drift under supervised and semi-supervised learning, the study of homogeneous ensemble and heterogeneous ensemble, and the classification of data stream ensemble under unsupervised learning.

关键词： Review ensemble learning supervised algorithm semi-supervised algorithm

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A General Approach for Achieving supervised Subspace Learning in Sparse Representation

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IEEE ACCESS 2019年 7卷 74017-74028页

作者： Sang, Jianshun Peng, Dezhong Sang, Yongsheng Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu 610065 Sichuan Peoples R China Chengdu Sobey Digital Technol Co Ltd Chengdu 610041 Sichuan Peoples R China Shenzhen Cyberspace Lab Shenzhen 518052 Peoples R China

Over the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature. Most of them are unsupervised algorithms that are applied to data without label scenarios. It is worth noting that the label information is available in some application scenarios such as face recognition where the above-mentioned dimensionality reduction techniques cannot employ the label information to improve their performance. Therefore, under these labeled scenarios, it is necessary to transform an unsupervised subspace learning algorithm into the corresponding supervised algorithm to improve the performance. In this paper, we propose an approach which can be used as a general way for developing a corresponding supervised algorithm based on any unsupervised subspace learning algorithm using sparse representation. Moreover, by utilizing the proposed approach, we achieve a new supervised subspace learning algorithm named supervised principal coefficients embedding (SPCE). We show that SPCE establishes the advantages over the state-of-the-art supervised subspace learning algorithm.

关键词： Subspace learning sparse representation supervised algorithm manifold learning dimensionality reduction

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Empirical Analysis of Application-Level Traffic Classification Using supervised Machine Learning

Empirical Analysis of Application-Level Traffic Classificati...

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11th Asia-Pacific Network Operations and Management Symposium

作者： Park, Byungchul Won, Young J. Choi, Mi-Jung Kim, Myung-Sup Hong, James W. POSTECH Dept Comp Sci & Engn Pohang South Korea Korea Univ Dept Comp & Informat Sci Jochiwon South Korea

ISBN: (纸本)9783540886228

Accurate application traffic classification and identification are important for network monitoring and analysis. The accuracy of traditional Internet application traffic classification approaches is rapidly decreasing due to the diversity of today's Internet application traffic, such as ephemeral port allocation, proprietary protocol, and traffic encryption. This paper presents an empirical evaluation of application-level traffic classification using supervised machine learning techniques. Our results indicate that we cannot achieve high accuracy with a simple feature set. Even if a simple feature set shows good performance in application category-level classification, more sophisticated feature selection methods and other techniques are necessary for performance enhancement.

关键词： Internet application traffic identification traffic measurement and analysis machine learning supervised algorithm

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