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fusion-Based Machine Learning Architecture for Heart Disease Prediction

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Computers, Materials & Continua 2021年第5期67卷 2481-2496页

作者： Muhammad Waqas Nadeem Hock Guan Goh Muhammad Adnan Khan Muzammil Hussain Muhammad Faheem Mushtaq Vasaki a/p Ponnusamy Faculty of Information and Communication Technology(FICT) Universiti Tunku Abdul Rahman(UTAR)KamparPerak31900Malaysia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan

The contemporary evolution in healthcare technologies plays a considerable and signicant role to improve medical services and save human lives.Heart disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes,as numerous people have been suffering from this disease globally.Heart attacks occur when the ranges of vital signs such as blood pressure,pulse rate,and body temperature exceed their normal values.The efcient diagnosis of heart diseases could play a substantial role in the eld of cardiology,while diagnostic time could be reduced.It has been a key challenge for researchers and medical experts to diagnose heart diseases accurately and timely.Therefore,machine learning-based techniques are used for the diagnosis with higher accuracy,using datasets compiled from former medical patients’reports.In recent years,numerous studies have been presented in the literature propose machine learning techniques for diagnosing heart diseases.However,the existing techniques have some limitations in terms of their accuracy.In this paper,a novel Support Vector Machine(SVM)based architecture for heart disease prediction,empowered with a fuzzy based decision level fusion,is presented.The SVMbased architecture has improved the accuracy signicantly as compared to existing solutions,where 96.23%accuracy has been achieved.

关键词： Heart disease machine learning support vector machine fuzzy logic fusion cardiovascular

fusion Analysis of Resting-State Networks and Its Application to Alzheimer's Disease

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Tsinghua Science and Technology 2019年第4期24卷 456-467页

作者： Shengbing Pei Jihong Guan Shuigeng Zhou the Department of Computer Science and Technology Tongji University Shanghai Key Lab of Intelligent Information Processing School of Computer ScienceFudan University

Functional networks are extracted from resting-state functional magnetic resonance imaging data to explore the biomarkers for distinguishing brain disorders in disease diagnosis. Previous works have primarily focused on using a single Resting-State Network(RSN) with various techniques. Here, we apply fusion analysis of RSNs to capturing biomarkers that can combine the complementary information among the RSNs. Experiments are carried out on three groups of subjects, i.e., Cognition Normal(CN), Early Mild Cognitive Impairment(EMCI), and Alzheimer's Disease(AD) groups, which correspond to the three progressing stages of AD; each group contains18 subjects. First, we apply group Independent Component Analysis(ICA) to extracting the Default Mode Network(DMN) and Dorsal Attention Network(DAN) for each subject group. Then, by obtaining the common DMN and DAN as templates for each group, we employ the individual ICA to extract the DMN and DAN for each subject.Finally, we fuse the DMNs and DANs to explore the biomarkers. The results show that(1) the templates generated by group ICA can extract the RSN for each subject by individual ICA effectively;(2) the RSNs combined with the fusion analysis can obtain more informative biomarkers than without fusion analysis;(3) the most different regions of DMN and DAN are found between CN and EMCI and between EMCI and AD, which show differences. For the DMN, the difference in the medial prefrontal cortex between the EMCI and AD is smaller than that between CN and EMCI, whereas that in the posterior cingulate between EMCI and AD is larger. As for the DAN, the difference in the intraparietal sulcus is smaller than that between CN and EMCI;(4) extracting DMN and DAN for each subject via the back reconstruction of group ICA is invalid.

关键词： Independent Component Analysis(ICA) group analysis fusion analysis Alzheimer’s Disease(AD)

fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China

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Journal of Meteorological Research 2020年第6期34卷 1335-1346页

作者： Lipeng JIANG Chunxiang SHI Shuai SUN Xiao LIANG School of Geographic Sciences Nanjing University of Information Science&TechnologyNanjing 210044 National Meteorological Information Center China Meteorological AdministrationBeijing 100081

Land data assimilation(DA)is an effective method to provide high-quality spatially and temporally continuous soil moisture datasets that are crucial in weather,climate,hydrological,and agricultural research.However,most existing land DA applications have used remote sensing observations,and are based on one-dimensional(1 D)analysis,which cannot be directly employed to reasonably assimilate the recently expanded in-situ soil moisture observations in China.In this paper,a two-dimensional(2 D)localized ensemble-based optimum interpolation(En OI)scheme for assimilating in-situ soil moisture observations from over 2200 stations into land surface models(LSMs)is introduced.This scheme uses historical LSM simulations as ensemble samples to provide soil moisture background error covariance,allowing the in-situ observation information to be propagated to surrounding pixels.It is also computationally efficient because no additional ensemble simulations are needed.A set of ensemble sampling and localization length scale sensitivity experiments are performed.The En OI performs best for in-situ soil moisture fusion over China with an ensemble sampling of hourly soil moisture from the previous 7 days and a localization length scale of 100 km.Following the evaluation,simulations for in-situ soil moisture fusion are also performed from May 2016 to September 2016.The En OI analysis is notably better than that without in-situ observation fusion,as the wet bias of 0.02 m3 m-3 is removed,the root-mean-square error(RMSE)is reduced by about 37%,and the correlation coefficient is increased by about 25%.Independent evaluation shows that the En OI analysis performs considerably better than that without fusion in terms of bias,and marginally better in terms of RMSE and correlation.

关键词： soil moisture fusion land surface model(LSM) ensemble optimum interpolation localization

fusion Systems over M_p(1, 1, 1) × A-groups

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Acta Mathematica Sinica,English Series 2019年第8期35卷 1311-1320页

作者： Jun LIAO He Guo LIU Ji Ping ZHANG School of Mathematics and Statistics Hubei University Wuhan 430062 P. R. China School of Mathematical Sciences Peking University Beijing 100871 P. R. China

In this paper we study the saturated fusion systems over a direct product of the extraspecial group of order p^3 of exponent p and a finite abelian p-group. The result provides some new exotic fusion systems, whose un... 详细信息

关键词： Finite groups fusion systems generalized Fitting subgroups exotic fusion systems

Maize(Zea mays L.)seedling detection based on the fusion of a modified deep learning model and a novel Lidar points projecting strategy

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International Journal of Agricultural and Biological Engineering 2022年第5期15卷 172-180页

作者： Gang Wang Dongyan Huang Deyi Zhou Huili Liu Minghao Qu Zhongyang Ma College of Biological and Agricultural Engineering Jilin UniversityChangchun 130022China Key Laboratory of Bionic Engineering Ministry of EducationJilin UniversityChangchun 130022China

Accurate crop detection is the prerequisite for the operation of intelligent agricultural machinery.Image recognition usually lacks accurate orientation information,and Lidar point clouds are not easy to distinguish different objects.Fortunately,the fusion of images and Lidar points can complement each other.This research aimed to detect maize(Zea mays L.)seedlings by fusing Lidar data with images.By applying coordinate transformation and time stamps,the images and Lidar points were realized homogeneous in spatial as well as temporal dimensions.Deep learning was used to develop a maize seedling recognition model,then the model recognized maize seedlings by labeling them with bounding boxes.Meanwhile,Lidar points were mapped to the bounding boxes.Only one-third of points that fell into the right middle of bounding boxes were selected for clustering operation,the calculated center of the cluster provided spatial information for target maize seedlings.This study modified the classical single shot multi-box detector(SSD)by merely linking the last feature map to the final output layer,owing to the higher feature maps having the unique advantages of detecting relatively larger objects.In images,maize seedlings were just the largest objects owing to be shot on purpose.This modification enabled the recognition model to finish recognizing an image by only consuming around 60 ms,which saved about 10 ms/image compared with the classical SSD model.The experiment was conducted in a maize field,and the maize was during the elongation stage.Experimental results demonstrated that the standard deviations for maximum distance error and maximum angle error were 1.4 cm and 1.1°,respectively,which can be tolerated under current technical requirements.Since agricultural fields are subject to staple crop-orientated and changeable ambient environment,the fusion of images and Lidar points can derive more precision information,and make agricultural machinery smarter.This study can act as an upstream technology for other researches on inte

关键词： maize seedling detection fusion deep learning Lidar

Optimized functional and structural design of dual-target LMRAP,a bifunctional fusion protein with a 25-amino-acid antitumor peptide and GnRH Fc fragment

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Acta Pharmaceutica Sinica B 2020年第2期10卷 262-275页

作者： Meng Li Hanmei Xu Junzhi Wang Shenyang Pharmaceutical University Shenyang 110016China Key Laboratory of the Ministry of Health for Research on Quality and Standardization of Biotech Products National Institutes for Food and Drug ControlBeijing 102629China State Key Laboratory of Natural Medicines Ministry of Educationthe Engineering Research Center of Synthetic Polypeptide Drug Discovery and Evaluation of Jiangsu ProvinceDepartment of Marine PharmacyChina Pharmaceutical UniversityNanjing 211198China

To develop fusion protein of a GnRH Fc fragment and the integrin targeting AP25 antitumor peptide for GnRH receptor-expressing cancer therapy.The LMRAP fusion protein was constructed.A transwell invasion assay was performed.The gene mRNA and protein levels of GnRHR-I,α5β1,andαvβ3 in different cancer cell lines were assessed.Cell proliferation was measured using a cell counting kit-8.An antagonist assay was performed on GnRH receptors.Anti-tumor activity was evaluated with a mouse xenograft tumor model.Immunohistochemistry(IHC)was applied to detect CD31 and CD34 expressions.Pharmacokinetic characteristics were determined with an indirect competition ELISA.The developed bifunctional fusion protein LMRAP not only inhibited HUVEC invasion,but also inhibited proliferation of GnRHR-I,α5β1,and high expression cancer cells.The IC50 for LMRAP in the GnRH receptor was 6.235 x 10-4 mol/L.LMRAP significantly inhibited human prostate cancer cell line22 RV1 proliferation in vivo and in vitro.LMRAP significantly inhibited CD31 and CD34 expressions.The elimination half-life of the fusion protein LMRAP was 33 h in rats.The fusion protein made of a GnRH Fc fragment and the integrin targeting AP25 peptide retained the bifunctional biological activity of GnRHR blocking,angiogenesis inhibition,prolonged half-life and good tolerance.

关键词： fusion protein GnRH Integrin Angiogenesis Prostate cancer

Short-segment decompression/fusion versus long-segment decompression/fusion and osteotomy for Lenke-Silva type Ⅵ adult degenerative scoliosis

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Chinese Medical Journal 2019年第21期132卷 2543-2549页

作者： Hao-Cong Zhang Hai-Long Yu Hui-Feng Yang Peng-Fei Sun Hao-Tian Wu Yang Zhan Zheng Wang Liang-Bi Xiang Department of Orthopaedics The General Hospital of Northern Theater CommandShenyangLiaoning 110016China Department of Orthopaedics Chinese PLA General HospitalBeijing 100853China.

Background:The effect of short-segment decompression/fusion versus long-segment decompression/fusion and osteotomy for Lenke-Silva type Ⅵ adult degenerative scoliosis(ADS)has not been clarified.This study aimed to compare the clinical and radiographic results of short-segment fusion vs.long-segment fusion and osteotomy for patients with Lenke-Silva type Ⅵ ADS.Methods:Data of 28 patients who underwent spinal surgery for ADS from January 2012 to January 2014 in the General Hospital of Northern Theater Command were reviewed.Of the 28 patients,12 received long-segment fusion and osteotomy and 16 received short-segment fusion.Radiographic imaging parameters and clinical outcomes,including the sagittal vertical axis(SVA),lumbar lordosis(LL)angle,pelvic tilt(PT),sacral slope(SS),the visual analog scale(VAS),Japanese Orthopedic Association(JOA),Oswestry disability index(ODI),and lumbar stiffness disability index(LSDI)scores,were recorded.The difference between groups was compared using the dependent t test or Chi-squared test.Results:The Cobb and LL angles and SVA improved in both groups;however,PT and SS angles did not improve following short fusion.There were significant differences in the post-operative SVA(26.8±5.4mm vs.47.5±7.6 mm,t=–8.066,Pfusion groups.All patients had improved ODI,JOA,and VAS scores post-operatively(all P0.05).The post-operative LSDI score was 3.5±0.5 in the long fusion group,which was significantly higher than that of the short fusion group(1.4±0.7;Pfusion were comparable to those of patients who underwent long-segment decompression/fusion and osteotomy despite poor correction of sagittal imbalance.Moreover,short-segment decompression/fusion showed a short operation time and reduced surgic

关键词： Degenerative scoliosis Short fusion Long fusion Spine surgery Lenke-Silva type Ⅵ

fusion of spatiotemporal and thematic features of textual data for animal disease surveillance

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Information Processing in Agriculture 2023年第3期10卷 347-360页

作者： Sarah Valentin Renaud Lancelot Mathieu Roche CIRAD F-34398 MontpellierFrance ASTRE Univ MontpellierCIRADINRAEMontpellierFrance TETIS Univ MontpellierAgroParisTechCIRADCNRSINRAEMontpellierFrance Departement de Biologie Universite de SherbrookeSherbrookeQuebecCanada

Several internet-based surveillance systems have been created to monitor the web for animal health surveillance.These systems collect a large amount of news dealing with outbreaks related to animal diseases.Automatically identifying news articles that describe the same outbreak event is a key step to quickly detect relevant epidemiological information while alleviating manual curation of news content.This paper addresses the task of retrieving news articles that are related in epidemiological terms.We tackle this issue using text mining and feature fusion methods.The main objective of this paper is to identify a textual representation in which two articles that share the same epidemiological content are close.We compared two types of representations(i.e.,features)to represent the documents:(i)morphosyntactic features(i.e.,selection and transformation of all terms from the news,based on classical textual processing steps)and(ii)lexicosemantic features(i.e.,selection,transformation and fusion of epidemiological terms including diseases,hosts,locations and dates).We compared two types of term weighing(i.e.,Boolean and TF-IDF)for both representations.To combine and transform lexicosemantic features,we compared two data fusion techniques(i.e.,early fusion and late fusion)and the effect of features generalisation,while evaluating the relative importance of each type of feature.We conducted our analysis using a corpus composed of a subset of news articles in English related to animal disease outbreaks.Our results showed that the combination of relevant lexicosemantic(epidemiological)features using fusion methods improves classical morphosyntactic representation in the context of disease-related news retrieval.The lexicosemantic representation based on TF-IDF and feature generalisation(F-measure=0.92,r-precision=0.58)outperformed the morphosyntactic representation(F-measure=0.89,r-precision=0.45),while reducing the features space.Converting the features into lower granular features(i.e.,generalisation)contributed to impr

关键词： Animal disease surveillance Text mining Ranking fusion

A Fractal Dimension Based Framework for Night Vision fusion

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 220-227页

作者： Gaurav Bhatnagar Q.M.Jonathan Wu IEEE the Department of Mathematics Indian Institute of Technology Jodhpur the Department of Electrical and Computer Engineering University of Windsor

In this paper, a novel fusion framework is proposed for night-vision applications such as pedestrian recognition,vehicle navigation and surveillance. The underlying concept is to combine low-light visible and infrared imagery into a single output to enhance visual perception. The proposed framework is computationally simple since it is only realized in the spatial domain. The core idea is to obtain an initial fused image by averaging all the source images. The initial fused image is then enhanced by selecting the most salient features guided from the root mean square error(RMSE) and fractal dimension of the visual and infrared images to obtain the final fused image.Extensive experiments on different scene imaginary demonstrate that it is consistently superior to the conventional image fusion methods in terms of visual and quantitative evaluations.

关键词： Fractal dimension image fusion navigation and surveillance night vision