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classification of human gingival sulcus using swept-source optical coherence tomography: In vivo imaging

INFRARED PHYSICS & TECHNOLOGY 2019年第0期98卷 155-160页

作者： Lee, Jaeyul Park, Jaeseok Shirazi, Muhammad Faizan Jo, Hosung Kim, Pilun Wijesinghe, Ruchire Eranga Jeon, Mansik Kim, Jeehyun Kyungpook Natl Univ Coll IT Engn Sch Elect Engn 80 Daehak Ro Daegu 41566 South Korea Med Univ Vienna Ctr Med Phys & Biomed Engn Waehringer Guertel 18-20 A-1090 Vienna Austria Kyungpook Natl Univ Inst Biomed Engn Sch Med 680 Gukchaebosang Ro Daegu 41944 South Korea Kyungil Univ Dept Biomed Engn Coll Engn 50 Gamasil Gil Gyongsan 38428 Gyeongsangbuk D South Korea

We demonstrated a preliminary research to investigate the feasible in vivo utilization of swept-source optical coherence tomography (SS-OCT) system with 1310 nm wavelength band to obtain morphological visualizations and human gingival sulcus depth measurements. Apart from the cross-sectional analysis, pixel intensity based OCT image classification algorithm is developed to identify the depth of gingival sulcus quantitatively. A total of 43 sites from the periodontal tissues of five healthy individuals were imaged in vivo by using the OCT system. Two periodontal tissues were right and left maxillary central incisors, while the other four periodontal tissues were left and right mandibular central incisors and later incisors. The developed classification algorithm could measure the gingival sulcus depths, which are 1.15 +/- 0.21 mm of the maxilla and 1.06 +/- 0.27 mm of the mandible. The averaged total depths obtained by the system was 1.10 +/- 0.26 mm. Hence, the gingival sulcus depth could be quantitatively measured by using the swept-source OCT system with the developed image classification algorithm as well as revealing a structural visualization, which ultimately confirmed the potential applicability for gingival sulcus depth real-time assessment.

关键词： Optical coherence tomography Swept-source optical coherence tomography classification algorithm Gingival sulcus Periodontal tissue

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RBNN application and simulation in big data set classification

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第4期37卷 4467-4475页

作者： Zhang, Qin Wilson, Fred Xinxiang Univ Sch Math & Informat Sci Xinxiang 453000 Henan Peoples R China

Text classification technology, an important basis for text mining and information retrieval, is mainly to determine the text category according to the text content under a predetermined set of categories. Traditional manual text categorization has gradually failed to meet the needs, while automatic text categorization based on artificial intelligence has become an important research direction in the field of natural language processing. To this end, this paper introduced the RBNN-based classification algorithm by considering the high dimensionality, non-linearity and complex correlation between feature items, and the theoretical and feasibility analysis were carried out so as to apply it to text feature dimension reduction. Also, the effects of the distribution density of the radial basis function in the radial basis neural network and the normalized form of the input data on the classification results were studied. Through the computer simulation experiment, the influence rule of distribution density of the radial basis function in the radial basis neural network and the normalized form of the input data on the training precision and test accuracy of the classification process were demonstrated in the form of curves, which provides guidance for the application of RBNN in pattern recognition.

关键词： Application and simulation radial basis function neural network big data set classification RBNN classification algorithm

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A Text Mining Based Supervised Learning algorithm for classification of Manufacturing Suppliers 6th

A Text Mining Based Supervised Learning Algorithm for Classi...

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World Conference on Information Systems and Technologies (WorldCIST)

作者： Manupati, V. K. Akhtar, M. D. Varela, M. L. R. Putnik, G. D. Trojanowska, J. Machado, J. Vellore Inst Technol Vellore Tamil Nadu India Univ Minho Guimaraes Portugal Poznan Univ Tech Poznan Poland

ISBN: (纸本)9783319776996;9783319777009

With the expeditious growth of unstructured massive data on the World Wide Web (WWW), more advanced tools, techniques, methods, and approaches for information organization and retrieval are desired. Text mining is one such approach to achieve the above mentioned demand. One of the main text mining applications is how to classify data presented by different industries into groups. In this paper, the classification of data into various groups based on the choice of the users at any given point of time is proposed. Here, a support vector machine (SVM) based classification algorithm is established to classify the text data into two broad categories of Manufacturing and Non-Manufacturing suppliers. Later, the performance of the proposed classifier was tested experimentally using most commonly used accuracy measures such as precision, recall, and F-measure. Results proved the efficiency of the proposed approach for classification of the texts.

关键词： Text mining Support vector machine classification algorithm

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COMPUTER HYBRID SYSTEM OF HEMORRHAGE (HES) DETECTION USED FOR AIDED DIAGNOSIS OF DIABETIC RETINOPATHY

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JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY 2021年第6期21卷 2150044-2150044页

作者： Feroui, A. Messadi, M. Lazouni, A. Bessaid, A. Univ Tlemcen Fac Technol Dept Elect & Elect Biomed Lab Tilimsen 13000 Algeria

Diabetes cause's metabolic and physiological abnormalities in the retina and the changes suggest a role for inflammation in the development of diabetic retinopathy. Abnormal blood vessels can form in the back of the eye of a person with diabetes. These new blood vessels are weaker and prone to breaking and causing hemorrhage (HEs). Diabetic retinopathy (DR) accounts for 31.5-54% of all cases of vitreous hemorrhage in adults in the world. Therefore, detection of HEs is still a challenging factor task for computer-aided diagnostics of DR. Many researchers have developed advanced algorithms of hemorrhages detection using fundus images. In this paper, a robust and computationally efficient approach for HEs with different shape and size detection and classification is presented. First, brightness correction and contrast enhancement are applied to fundus images. Second, candidate hemorrhages are extracted by using an unsupervised classification algorithm. Third, an approach based on mathematical morphology is carried out for vascular network and macula segmentation. Finally, a total of 13 HEs features are considered in this study and selected for classification. The proposed method is evaluated on 419 fundus images of DIARETDB0, DIARETDB1 and MESSIDOR databases. Experimental results show that overall average sensitivity, specificity, predictive value and accuracy for hemorrhage in lesion level are 98.90%, 99.66%, 97.63% and 99.56%, respectively. The results show that the proposed method outperforms other state-of-the-art methods in detection of hemorrhages. These results indicate that this new method may improve the performance of diagnosis of DR system.

关键词： Diabetes diabetic retinopathy fundus image red lesion hemorrhage image processing segmentation mathematical morphology classification algorithm FP reduction

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Prediction model for recurrence of hepatocellular carcinoma after resection by using neighbor2vec based algorithms

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WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY 2021年第2期11卷 e1390-e1390页

作者： Cao, Yuankui Fan, Junqing Cao, Hong Chen, Yunliang Li, Jie Li, Jianxin Zhang, Simin China Univ Geosci Sch Comp Sci Wuhan Peoples R China Shandong Univ Shandong Prov Hosp Affiliated Jinan Shandong Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic Australia

Liver cancer has become the third cause that leads to the cancer death. For hepatocellular carcinoma (HCC), as the highly malignant type of liver cancer, its recurrence rate after operation is still very high because there is no reliable clinical data to provide better advice for patients after operation. To solve the challenging issue, in this work, we design a novel prediction model for recurrence of HCC using neighbor2vec based algorithm. It consists of three stages: (a) In the preparation stage, the Pearson correlation coefficient was used to explore the independent predictors of HCC recurrence, (b) due to the low correlation between individual dimension and prediction target, K-nearest neighbors (KNN) were found as a K-vectors list for each patient (neighbor2vec), (c) all vectors lists were applied as the input of machine learning methods such as logistic regression, KNN, decision tree, naive Bayes (NB), and deep neural network to establish the neighbor2vec based prediction model. From the experimental results on the real data from Shandong Provincial Hospital in China, the proposed neighbor2vec based prediction model outperforms all the other models. Especially, the NB model with neighbor2vec achieves up to 83.02, 82.86, 77.6%, in terms of accuracy, recall rates, and precision. This article is categorized under: Technologies > Data Preprocessing Technologies > classification Technologies > Machine Learning

关键词： classification algorithm hepatocellular carcinoma prediction model neighbor2vec

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Machine Learning classification algorithms for Phishing Detection: A Comparative Appraisal and Analysis

Machine Learning Classification Algorithms for Phishing Dete...

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International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf)

作者： Noah Ndakotsu Gana Shafi’I Muhammad Abdulhamid Department of Cyber Security Science Federal University of Technology Minna Nigeria

ISBN: (纸本)9781728107141

Exponential growth experienced in Internet usage have pave way to exploit users of the Internet, phishing attack is one of the means that can be used to obtained victim confidential details unwittingly across the Internet. A high false positive rate and low accuracy has been a setback in phishing detection. In this research RandomForest, SysFor, SPAARC, RepTree, RandomTree, LMT, ForestPA, JRip, PART, NNge, OneR, AdaBoostM1, RotationForest, LogitBoost, RseslibKnn, LibSVM, and BayesNet were employed to achieve the comparative analysis of machine classifier. The performance of the classifier algorithms were rated using Accuracy, Precision, Recall, F-Measure, Root Mean Squared Error, Receiver Operation Characteristics Area, Root Relative Squared Error False Positive Rate and True Positive Rate using WEKA data mining tool. The research revealed that quit a number of classifiers also exist which if properly explored will yield more accurate results for phishing detection. RondomForest was found to be an excellent classifier that gives the best accuracy of 0.9838 and a false positive rate of 0.017. The comparative analysis result indicates the achievement of low false positive rate for phishing classification which suggest that anti-phishing application developer can implement the machine learning classification algorithm that was discovered to be the best in this study to enhance the feature of phishing attack detection and classification.

关键词： Phishing attack classification algorithm Accuracy Performance metric RandomForest spear phishing Sorting algorithm phishing computer information security False-positives Classifiers Performance metrics application developer quadratic errors

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A machine learning algorithm for stock picking built on information based outliers

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115497-115497页

作者： Barucci, Emilio Bonollo, Michele Poli, Federico Rroji, Edit Politecn Milan Dept Math Milan Italy Univ Milano Bicocca Dept Stat & Quantitat Methods Milan Italy Lason Italy SRL Milan Italy

Stock picking based on regularities in time series is one of the most studied topics in the financial industry. Various machine learning techniques have been employed for this task. We build a trading strategy algorithm that receives as input indicators defined through outliers in the time series of stocks (return, volume, volatility, bid-ask spread). The procedure identifies the most relevant subset of indicators for the prediction of stock returns by combining an heuristic search strategy, guided from the Information Gain Criterium, with the Naive-Bayes classification algorithm. We apply the methodology to two sets of stocks belonging respectively to the EUROSTOXX50 and the DOW JONES index. Performance is smoother than in the Buy&Hold strategy and yields a higher risk-adjusted return, in particular in a turbulent period. However, outperformance vanishes when transaction costs (5-15 basis points) are inserted. Asset return and return/volume serial correlation turn out to be the most relevant indicators to build the trading algorithm.

关键词： Finance Stock picking Technical analysis Private information classification algorithm

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A Pruning Optimized Fast Learn plus plus NSE algorithm

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IEEE ACCESS 2021年 9卷 150733-150743页

作者： Chen, Yong Zhu, Yuquan Chen, Haifeng Shen, Yan Xu, Zhao Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China China Unicom Internet Things Co Ltd Nanjing 210014 Jiangsu Peoples R China Jiangsu Univ Dept Informat Management & Informat Syst Zhenjiang 212013 Jiangsu Peoples R China Nanjing Longyuan Microelect Technol Co Ltd Nanjing 211106 Jiangsu Peoples R China

Due to the large number of typical applications, it is very important and urgent to study the fast classification learning of accumulated big data in nonstationary environments. The newly proposed algorithm, named Learn++.NSE, is one of the important research results in this research field. And a pruning version, named Learn++.NSE-Error-based, was given for accumulated big data to improve the learning efficiency. However, the studies have found that the Learn++.NSE-Error-based algorithm often encounters a situation that the newly generated base classifier is pruned in the next integration, which reduces the accuracy of the ensemble classifier. The newly generated base classifier is very important in the next ensemble learning and should be retained. Therefore, the two latest base classifiers are reserved without being pruned, and a new pruning algorithm named NewLearn++.NSE-Error-based was proposed. The experimental results on the generated dataset and the real-world dataset show that NewLearn++.NSE-Error-based can further improve the accuracy of the ensemble classifier under the premise of obtaining the same time complexity as Learn++.NSE algorithm. It is suitable for fast classification learning of long-term accumulated big data.

关键词： classification algorithms Big Data Error analysis Detection algorithms Data models Batch production systems Probability distribution Ensemble learning nonstationary environment classification algorithm big data mining

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Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem

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EUROPEAN JOURNAL OF EPIDEMIOLOGY 2019年第2期34卷 153-162页

作者： Zhong, Qiu-Yue Mittal, Leena P. Nathan, Margo D. Brown, Kara M. Gonzalez, Deborah Knudson Cai, Tianrun Finan, Sean Gelaye, Bizu Avillach, Paul Smoller, Jordan W. Karlson, Elizabeth W. Cai, Tianxi Williams, Michelle A. Harvard TH Chan Sch Publ Hlth Dept Epidemiol 677 Huntington Ave Boston MA 02115 USA Brigham & Womens Hosp Div Womens Mental Hlth Dept Psychiat 75 Francis St Boston MA 02115 USA Univ S Florida Morsani Coll Med Dept Psychiat & Behav Neurosci Tampa FL USA Brigham & Womens Hosp Dept Med Div Rheumatol Immunol & Allergy 75 Francis St Boston MA 02115 USA Boston Childrens Hosp Childrens Hosp Informat Program Boston MA USA Harvard Med Sch Dept Biomed Informat Boston MA USA Massachusetts Gen Hosp Psychiat & Neurodev Genet Unit Boston MA 02114 USA Harvard TH Chan Sch Publ Hlth Dept Biostat Boston MA USA

We developed algorithms to identify pregnant women with suicidal behavior using information extracted from clinical notes by natural language processing (NLP) in electronic medical records. Using both codified data and NLP applied to unstructured clinical notes, we first screened pregnant women in Partners HealthCare for suicidal behavior. Psychiatrists manually reviewed clinical charts to identify relevant features for suicidal behavior and to obtain gold-standard labels. Using the adaptive elastic net, we developed algorithms to classify suicidal behavior. We then validated algorithms in an independent validation dataset. From 275,843 women with codes related to pregnancy or delivery, 9331 women screened positive for suicidal behavior by either codified data (N=196) or NLP (N=9,145). Using expert-curated features, our algorithm achieved an area under the curve of 0.83. By setting a positive predictive value comparable to that of diagnostic codes related to suicidal behavior (0.71), we obtained a sensitivity of 0.34, specificity of 0.96, and negative predictive value of 0.83. The algorithm identified 1423 pregnant women with suicidal behavior among 9331 women screened positive. Mining unstructured clinical notes using NLP resulted in a 11-fold increase in the number of pregnant women identified with suicidal behavior, as compared to solely reliance on diagnostic codes.

关键词： Natural language processing Suicidal behavior Pregnant women Electronic medical Records classification algorithm

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A Review of classification algorithms for Data Mining

A Review of Classification Algorithms for Data Mining

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2019 2nd International Conference on Mechanical,Electronic and Engineering Technology(MEET 2019)

作者： Li Mindong Chen Qingwei Huang Panling Zhou Jun Gong Weike Shandong University

classification algorithm is one of the important algorithms in data mining. Common classification algorithms such as decision tree, Bayesian network, support vector machine, association rules based classification algorithm and K-nearest neighbor algorithm have been widely used. This paper introduces the classical classification algorithm, compares the advantages and disadvantages of each algorithm and the latest research progress of each algorithm.

关键词： data mining classification algorithm review

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