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Classifying for interval and applying for image based on the extracted texture feature

GRANULAR COMPUTING 2024年第2期9卷 1-16页

作者： Nguyen-Thihong, Dan Vo-Van, Tai Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol HCMUT Fac Appl Sci Ho Chi Minh City Vietnam Can Tho Univ Coll Nat Sci Can Tho Vietnam

This study develops a classification algorithm designed to handle interval data and to apply effectively in image processing. The proposed algorithm utilizes an innovative measure called overlap distance to assess the similarity between two intervals within multidimensional space. In addition, it integrates an improved method for determining prior probabilities by employing a fuzzy clustering technique. Furthermore, the study introduces a classification rule based on the quasi-Bayes method specifically tailored for interval data. According to this rule, an interval is assigned to a particular group if it holds the highest prior probability and the minimum distance to that group. The proposed algorithm is systematically presented in a step-by-step manner, elucidated by a numerical example, and executed using a well-established Matlab procedure. Another significant contribution of this study is its application to images, wherein texture features are extracted and represented as two-dimensional intervals. The effectiveness and superiority of the proposed algorithm are demonstrated through its application to various sets of medical images.

关键词： Interval data classification algorithm Prior probability Texture feature of image

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Binary Hermitian Lattices over Number Fields

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EXPERIMENTAL MATHEMATICS 2022年第1期31卷 280-301页

作者： Kirschmer, Markus Nebe, Gabriele Rhein Westfal TH Aachen Math D D-52056 Aachen Germany

In a previous paper the authors developed an algorithm to classify certain quaternary quadratic lattices over totally real fields. The present article applies this algorithm to the classification of binary Hermitian lattices over totally imaginary fields. We use it in particular to classify the 48-dimensional extremal even unimodular lattices over the integers that admit a semilarge automorphism.

关键词： binary Hermitian forms Hermitian lattices over CM-fields genera of lattices orders in quaternion algebras class numbers classification algorithm

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EMR-Based Phenotyping of Ischemic Stroke Using Supervised Machine Learning and Text Mining Techniques

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第10期24卷 2922-2931页

作者： Sung, Sheng-Feng Lin, Chia-Yi Hu, Ya-Han Chiayi Christian Hosp Ditmanson Med Fdn Dept Internal Med Div Neurol Chiayi 600 Taiwan Natl Chung Cheng Univ Inst Healthcare Informat Management Dept Informat Management Chiayi 62102 Taiwan Min Hwei Jr Coll Hlth Care Management Dept Nursing Tainan 736 Taiwan Natl Cent Univ Dept Informat Management Taoyuan 320 Taiwan Natl Chung Cheng Univ Ctr Innovat Res Aging Soc CIRAS Chiayi 62102 Taiwan Natl Cheng Kung Univ MOST AI Biomed Res Ctr Tainan 701 Taiwan

Ischemic stroke is a major cause of death and disability in adulthood worldwide. Because it has highly heterogeneous phenotypes, phenotyping of ischemic stroke is an essential task for medical research and clinical prognostication. However, this task is not a trivial one when the study population is large. Phenotyping of ischemic stroke depends primarily on manual annotation of medical records in previous studies. This article evaluated various strategies for automated phenotyping of ischemic stroke into the four subtypes of the Oxfordshire Community Stroke Project classification based on structured and unstructured data from electronical medical records (EMRs). A total of 4640 adult patients who were hospitalized for acute ischemic stroke in a teaching hospital were included. In addition to the structured items in the National Institutes of Health Stroke Scale, unstructured clinical narratives were preprocessed using MetaMap to identify medical concepts, which were then encoded into feature vectors. Various supervised machine learning algorithms were used to build classifiers. The study results indicate that textual information from EMRs could facilitate phenotyping of ischemic stroke when this information was combined with structured information. Furthermore, decomposition of this multi-class problem into binary classification tasks followed by aggregation of classification results could improve the performance.

关键词： Natural language processing Tools Hospitals Machine learning algorithms Task analysis Information management classification algorithm clinical diagnosis electronic medical records machine learning natural language processing text mining

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Validating effectiveness of subgroup identification for longitudinal data

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STATISTICS IN MEDICINE 2018年第1期37卷 98-106页

作者： Andrews, Nichole Cho, Hyunkeun Western Michigan Univ Dept Stat Kalamazoo MI 49008 USA Univ Iowa Dept Biostat Iowa City IA 52242 USA

In clinical trials and biomedical studies, treatments are compared to determine which one is effective against illness;however, individuals can react to the same treatment very differently. We propose a complete process for longitudinal data that identifies subgroups of the population that would benefit from a specific treatment. A random effects linear model is used to evaluate individual treatment effects longitudinally where the random effects identify a positive or negative reaction to the treatment over time. With the individual treatment effects and characteristics of the patients, various classification algorithms are applied to build prediction models for subgrouping. While many subgrouping approaches have been developed recently, most of them do not check its validity. In this paper, we further propose a simple validation approach which not only determines if the subgroups used are appropriate and beneficial but also compares methods to predict individual treatment effects. This entire procedure is readily implemented by existing packages in statistical software. The effectiveness of the proposed method is confirmed with simulation studies and analysis of data from the Women Entering Care study on depression.

关键词： classification algorithm effectiveness of subgrouping personalized treatment random effects linear model

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Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment Detected by Neurite Orientation Dispersion and Density Imaging (NODDI) Combined with Machine Learning

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CLINICAL NEURORADIOLOGY 2023年第2期33卷 445-453页

作者： Fu, Xiuwei Wang, Xiaonan Zhang, Yu Li, Tongtong Tan, Zixuan Chen, Yuanyuan Zhang, Xianchang Ni, Hongyan Tianjin Med Univ Gen Hosp Dept Radiol Tianjin Peoples R China Tianjin Med Univ Cent Clin Inst 1 Dept Radiol Tianjin Peoples R China Tianjin Med Univ Inst Med Imaging Tianjin Peoples R China Tianjin Univ Inst Med Engn & Translat Med Tianjin Peoples R China Siemens Healthineers Ltd MR Collaborat Beijing Peoples R China Tianjin First Cent Hosp Dept Radiol 24 Fukang Rd Tianjin 300192 Peoples R China

Purpose This study investigated brain microstructural changes in patients with amnestic mild cognitive impairment (aMCI) by retrospectively analyzing neurite orientation dispersion and density imaging (NODDI) data with machine learning *** A total of 26 aMCI patients and 24 healthy controls (HC) underwent NODDI magnetic resonance imaging (MRI) examinations. The NODDI parameters including neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were estimated. Machine learning algorithms such as K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to evaluate the diagnostic efficacy of NODDI parameters in predicting aMCI. The differences in the NODDI parameter values between the aMCI and HC groups were analyzed using the independent sample t-test, False discovery rate (FDR) correction was used for multiple testing. After adjusting for age, sex, and educational years, partial correlation analysis was used to evaluate the relationship between NODDI parameters and clinical cognitive status of aMCI *** The NDI, ODI, and Viso values of white matter (WM) and gray matter (GM) structure templates combined with the KNN, LR, RF and SVM machine learning algorithms accomplished the discrimination between aMCI and HC groups. The NDI and ODI values decreased (p value range, < 0.001-0.042) and Viso values increased (p value range, < 0.001-0.043) in the aMCI group compared to the HCs. The NDI, ODI, and Viso values of the WM and GM structure templates with significant differences were significantly correlated with mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) *** NODDI combined with machine learning algorithms is a promising strategy for early diagnosis of aMCI. Moreover, NODDI parameters correlated with the clinical cognitive status of aMCI patients.

关键词： Cognitive impairment classification algorithm Diffusion weighted imaging Neurite density Orientation dispersion

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Machine learning approaches for prediction of linear B-cell epitopes on proteins

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JOURNAL OF MOLECULAR RECOGNITION 2006年第3期19卷 200-208页

作者： Söllner, Johannes Mayer, Bernd Emergentec Biodev GmbH A-1010 Vienna Austria Intercell AG A-1030 Vienna Austria Univ Vienna Inst Theoret Chem A-1090 Vienna Austria

Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural as well as physicochemical parameters have been utilized for predicting the antigenic propensity of protein sites. However, the performance of computational routines has been low when compared to experimental alternatives. Here we describe the construction of machine learning based classifiers to enhance the prediction quality for identifying linear B-cell epitopes on proteins. Our approach combines several parameters previously associated with antigenicity, and includes novel parameters based on frequencies of amino acids and amino acid neighborhood propensities. We utilized machine learning algorithms for deriving antigenicity classification functions assigning antigenic propensities to each amino acid of a given protein sequence. We compared the prediction quality of the novel classifiers with respect to established routines for epitope scoring, and tested prediction accuracy on experimental data available for HIV proteins. The major finding is that machine learning classifiers clearly outperform the reference classification systems on the HIV epitope validation set. Copyright (c) 2006 John Wiley & Sons, Ltd.

关键词： machine learning epitope classification algorithm peptide descriptor

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Towards Real-Time Web Tracking Detection with *** - The Transparency EXtension 7th

Towards Real-Time Web Tracking Detection with *** - The Tran...

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7th Annual Privacy Forum (APF)

作者： Raschke, Philip Zickau, Sebastian Kroeger, Jacob Leon Kuepper, Axel Tech Univ Berlin Serv Ctr Networking Weizenbaum Inst Telekom Innovat Labs Berlin Germany

ISBN: (纸本)9783030217525;9783030217518

Targeted advertising is an inherent part of the modern Web as we know it. For this purpose, personal data is collected at large scale to optimize and personalize displayed advertisements to increase the probability that we click them. Anonymity and privacy are also important aspects of the World Wide Web since its beginning. Activists and developers relentlessly release tools that promise to protect us from Web tracking. Besides extensive blacklists to block Web trackers, researchers used machine learning techniques in the past years to automatically detect Web trackers. However, for this purpose often artificial data is used, which lacks in quality. Due to its sensitivity and the manual effort to collect it, real user data is avoided. Therefore, we present *** - The Transparency EXtension, which aims to record a browsing session in a secure and privacy-preserving manner. We define requirements and objectives, which are used for the design of the tool. An implementation is presented, which is evaluated for its performance. The evaluation shows that our implementation can be used for the collection of data to feed machine learning algorithms.

关键词： Web tracking Browsing behavior Data privacy Browser extension Data quality Machine-learning classification algorithm

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Identification of a Hidden movement by Using LLCPT algorithm

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NATIONAL ACADEMY SCIENCE LETTERS-INDIA 2024年 1-3页

作者： Sharma, Pharindra Kumar Sahu, Neeraj Sahu, Swatantra Kumar Jain, Neha Res Scientist PRAA Robot Res & Dev Labs Gwalior Madhya Pradesh India G H Raisoni Univ Dept Comp Sci & Engn Amravati Maharashtra India Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyala Dept Phys Sci Satna Madhya Pradesh India

In this paper, proposed an algorithm with the name LLCPT, that will trace out the offender/offenders related to a crime. The idea used in this algorithm takes into consideration that, almost everybody uses mobile phones now a days be it basic phone or smart phone. This leads us to notice that, offender/s too may use or carry a mobile phone at or near the site of crime. The LLCPT algorithm works on the principle of earth position and calculate path of mobile.

关键词： Latitude Longitude GPS Mobile technology classification algorithm

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A comparison of data-driven fault detection methods with application to aerospace electro-mechanical actuators

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IFAC-PapersOnLine 2017年第1期50卷 12797-12802页

作者： Mazzoleni, M. Maccarana, Y. Previdi, F. Department of Management Information and Production engineering University of Bergamo via Galvani 2 Dalmine [BG] 24044 Italy

In this paper, a model-free framework is proposed in order to equip electromechanical actuators, deployed in aerospace applications, with health-monitoring capabilities. A large experimental activity has been carried out to perform acquisitions with both healthy and faulty components, taking into consideration the standard regulations for environmental testing of avionics hardware. The injected faults followed a Fault Tree Analysis and Failure Mode and Effect Analysis. Features, belonging to different domains, have been extracted from the measured signals. These indexes are based largely on the motor driving currents, in order to avoid the installation of new sensors. Finally, a Gradient Tree Boosting algorithm has been chosen to detect the system status: the choice has been dictated by a comparison with other known classification algorithms. Furthermore, the most promising features for a classification point of view are reported. © 2017

关键词： Fault detection Aerospace applications Electromechanical actuators Electromechanical devices Environmental regulations Environmental testing Fault tree analysis Learning systems Mechanical actuators Trees (mathematics) classification algorithm Detection methods Different domains Experimental activities Failure mode and effect analysis Gradient tree boosting Health monitoring Measured signals

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Fuzzy vector quantization with a step-optimizer to improve pattern classification

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 188卷 115941-115941页

作者： Saha, Abhinaba Dewan, Dipayan Ghosh, Lidia Konar, Amit Jadavpur Univ Elect & Tele Commun Engn Dept 188 Raja SC Mallick Rd Kolkata 700032 W Bengal India

The paper proposes a novel supervised algorithm for data classification based on fuzzy vector quantization. The novelty here lies in including an optimization procedure to determine the step size of the quantization process. Adoption of fuzzy space for data exploration eliminates the problem of class-overlapping, while dealing with high dimensional non-linear data. In the proposed approach, the input data are first been transformed from the Euclidean space to the fuzzy space with three membership grades. Next, we have used uniform manifold approximation algorithm (UMAP), for projecting the data in visual space by selection of the most contrasting features from the fuzzy vectors. Then these features are passed through a quantization step with a novel step -optimization technique. Optimizing the quantization step and making it independent of data sets significantly speeds up the process. As the class information of all the feature vectors obtained by UMAP is known, majority voting principle has been used to locate the class-centroids in the subsequent step which in turn represent the class labels of the test samples. In test phase, after obtaining the test vectors, the Hyperspherical Direction Cosines (HDC) between the test vectors and the previously obtained class-centroids are evaluated. The test sample is finally assigned that class label where the sum of absolute differences (SAD) of these direction cosines is minimum. We have validated our classifier on various benchmark data sets and achieved higher accuracy with significantly low computation time than the existing state-of-the-art algorithms.

关键词： Fuzzy logic Vector quantization Step size optimization Uniform manifold approximation and projection UMAP classification algorithm MNIST BCI Linear and non-linear classification Multi-class high dimensional features

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