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Evaluation of spectral built-up indices for impervious surface extraction using Sentinel-2A MSI imageries: A case of Addis Ababa city, Ethiopia

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Environmental Challenges 2022年 8卷

作者： Kebede, Tsion Ayalew Hailu, Binyam Tesfaw Suryabhagavan, Karuturi Venkata Department of Land Administration and Surveying Dilla University Dilla Ethiopia School of Earth Sciences Addis Ababa University P.O. Box 1176 Addis Ababa Ethiopia

Impervious surface extraction with high accuracy is important for monitoring urban expansion to sustainably manage the land resources and save the environment. In this context, use of spectral built-up indices has been extensively explored. This study examines the performance of Built-up Area Extraction Index (BAEI), Band Ratio for Built-up Area (BRBA), Modified Built-up Index (MBI), Normalized Built-up Area Index (NBAI), New Built-up Index (NBI), Normalized Difference Built-up Index (NDBI), and Urban Index (UI) in the classification and change detection of impervious surfaces using sentinel-2A MSI imagery. All these maps were classified into built-up and non-built-up areas, and evaluated based on histogram overlap and spectral discrimination index (SDI). Simultaneously, support vector machine (SVM) algorithm was employed to classify the imageries into land-use and land-cover (LULC) classes, viz., bare land, built-up, forest, vegetation, and water bodies. The findings indicate that NBAI, NBI, and NDBI have the highest SDI values of 1.24, 1.23 and 1.54;1.06, 1.08, and 1.23;1.16, 1.1 and 1.26 for 2016, 2018, and 2020, respectively. However, other indices show unsatisfactory results. The LULC change between 2016 and 2020 showed that the built-up area, bare land, and water bodies have increased by 9,084.5 ha, 813 ha, and 2 ha, respectively whereas vegetation and forest areas declined by 9,279.3 ha and 620.2 ha, respectively. The classification overall accuracy was 81%, 86%, and 82% for 2016, 2018, and 2020 images, respectively thereby affirming that spectral built-up indices in urban environments can deduce impervious surface extraction quickly and accurately. © 2022 The Authors

关键词： Land-use and land-cover Spectral built-up indices Spectral discrimination index support vector machine algorithm Urban impervious surface

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A DDoS attack detection method based on SVM and K-nearest neighbour in SDN environment

A DDoS attack detection method based on SVM and K-nearest ne...

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International Journal of Computational Science and Engineering

作者： Ma, Zhaohui Li, Bohong School of Computer Science South China Normal University Guangzhou510006 China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510420 China Volgenau School of Engineering George Mason University 4400 University Dr FairfaxVA22030 United States

This paper presents a detection method for DDoS attack in SDN based on K-nearest neighbour (KNN) algorithm and support vector machine (SVM) algorithm. This method makes use of the characteristics of SDN centralised control, collects flow characteristic information efficiently, classifies the flow, screens out the attack flow, and determines whether the system is attacked or not. Experiments show that the resource consumption rate of this model is only 11% when detecting DDOS attack. Meanwhile, the accuracy rate exceeded 99%. © 2020 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Denial-of-service attack Nearest neighbor search support vector machines Accuracy rate Attack flow Detection methods Flow charac teristics K nearest neighbours (k NN) K nearest neighbours Resource consumption support vector machine algorithm

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Investigating effects of force and pressure centre signals on stabilogram analysis

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2019年第9期13卷 1305-1310页

作者： Cetin, Egehan Bilgin, Suleyman Akdeniz Univ Inst Nat Sci Dept Elect & Elect Engn Antalya Turkey Akdeniz Univ Fac Engn Dept Elect & Elect Engn Antalya Turkey

In this study, the centre of pressure (COPx) and force change (F-x) in the anterior-posterior (AP) plane, the centre of pressure (COPy) and force change (F-y) signals in medial-lateral (ML) plane were compared. In order to make comparisons in this context, the time-dependent norms of the COPx-COPy, and F-x-F-y signals (F-B, COPB) of 163 subjects were calculated separately. Subsequently, the mean standard deviation values (F-STD, COPSTD) of these values generated by time-based were obtained. The scatter plots in the AP plane $\lpar F_{x_{{\rm STD}}}\comma \;\, {\rm CO}{\rm P}_{x_{{\rm STD}}}\rpar $(FxSTD,COPxSTD) and the ML plane $\lpar F_{y_{{\rm STD}}}\comma \;\, {\rm CO}{\rm P}_{y_{{\rm STD}}}\rpar $(FySTD,COPySTD) are presented in different categories, which depend on the standard deviation values, young-elderly of the measurement group, the firm-soft of the ground and open-close of the eyes. Finally, the separation successes of $F_{x_{{\rm STD}}} - F_{y_{{\rm STD}}}$FxSTD-FySTD and ${\rm CO}{\rm P}_{x_{{\rm STD}}} - {\rm CO}{\rm P}_{y_{{\rm STD}}}$COPxSTD-COPySTD were calculated by means of basic classification algorithms. According to the results obtained, it is observed that the force signals generally provide more successful results than COP signals. In the study, the highest accuracy was performed with force signals and the support vector machine algorithm separated the young-aged group with an accuracy of 81.67%.

关键词： support vector machines biomechanics signal classification medical signal processing pressure centre signals anterior-posterior plane medial-lateral plane mean standard deviation values force signals COP signals force effects stabilogram analysis centre of pressure force change signals time-dependent norms scatter plots standard deviation values c classification algorithms support vector machine algorithm

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Fused spectral-decomposition seismic attributes and forward seismic modelling to predict sand bodies in meandering fluvial reservoirs

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MARINE AND PETROLEUM GEOLOGY 2019年 99卷 27-44页

作者： Yue, Dali Li, Wei Wang, Wurong Hu, Guangyi Qiao, Huili Hu, Jiajing Zhang, Manling Wang, Wenfeng China Univ Petr State Key Lab Petr Resources & Prospecting Beijing 102249 Peoples R China China Univ Petr Coll Geosci Beijing 102249 Peoples R China Res Inst CNOOC Beijing 100028 Peoples R China

Understanding the hierarchical architectural elements of fluvial sand bodies is important for planning their development strategy and to enhance oil recovery. Red-Green-Blue (RGB) blending of multiple seismic attributes and forwarding seismic modelling are commonly used in the analysis of compound sand bodies. However, RGB blending of multiple seismic attributes can only qualitatively describe the boundaries and thickness of sand bodies. The forward seismic modelling techniques previously documented in the literature are not effective when depicting the geometry of, and stacking relationships between, sand bodies (i.e., reservoir compartmentalisation). Hence, we propose in this work a new workflow that combines fused spectral-decomposition seismic attributes (SDSAs) and forwarding seismic modelling to quantitatively predict sand thickness, and to characterise stacking relationships between sand bodies. First, we employ a support vector machine (SVM) algorithm to fuse high, middle, and low frequency components (attributes) of seismic data so as to quantitatively predict the thickness of sand bodies. Second, we define the seismic waveform response patterns corresponding to the typical conceptual stacked sand bodies. With the constraints of waveform patterns and predicted sand thickness (fused SDSAs), the geometry and stacking relationships of the sand bodies are characterised by forward seismic modelling. To illustrate the effectiveness of our proposed workflow, we apply it to the Neogene Minghuazhen Formation (Nm) of the QHD 32-6 oil field, Bohai Bay Basin, China. We define five architectural elements of a meandering fluvial reservoir by analysing the hierarchy of sand bodies using our workflow. The predicted sand bodies in this workflow were further proven by horizontal drilling and production data.

关键词： Fused spectral-decomposition seismic attributes support vector machine algorithm Forward seismic modelling Stacked sand body models Reservoir compartmentalisation Bozhong sub-basin

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Mapping distribution of water hyacinth (Eichhornia crassipes) in Rwanda using multispectral remote sensing imagery

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AFRICAN JOURNAL OF AQUATIC SCIENCE 2019年第4期44卷 339-348页

作者： Mukarugwiro, J. A. Newete, S. W. Adam, E. Nsanganwimana, F. Abutaleb, K. A. Byrne, M. J. Univ Witwatersrand Sch Anim Plant & Environm Sci APES Johannesburg South Africa Univ Witwatersrand Ctr Invas Biol Sch APES Johannesburg South Africa Agr Res Council Inst Soil Climate & Water Pretoria South Africa Univ Witwatersrand Sch Geog Archaeol & Environm Studies GAES Johannesburg South Africa Univ Rwanda African Ctr Excellence Innovat Teaching & Learnin Kigali Rwanda Natl Author Remote Sensing & Space Sci NARSS Cairo Egypt

Water hyacinth, Eichhornia crassipes (C. Mart) Solms (Pontederiaceae), is an invasive aquatic macrophyte with major negative economic and ecological impacts in Rwanda and other East African countries since its establishment in the region in the 1960s. Reliable estimates of water hyacinth distribution are required to determine the severity of the problem and identify waterbodies requiring management. Remote sensing techniques, based on the Landsat 8 sensor, offer promising alternatives to accurately detect, map and monitor the extent of the water hyacinth invasion in Rwandan waterbodies. The aim of the current study was to investigate the utility of multispectral remote sensed imagery using Random Forest and support vector machine algorithms to detect and map water hyacinth in Rwandan waterbodies. Random Forest had a high overall accuracy of 85%, compared with support vector machine (65%). These algorithms confirmed different levels of water hyacinth infestations in three main Rwandan rivers. Many of the wetlands along these riparian systems and most of the lakes, particularly those from the Eastern Province of the country were found to be invaded by water hyacinth. These findings would, therefore, assist government partners and policy makers to put in place sustainable methods, such as biological control, along with integrated pest management, to control the of water hyacinth invasion in Rwanda.

关键词： invasive aquatic macrophytes Landsat 8 Random Forest algorithm spatial distribution support vector machine algorithm

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Determination of sodium alginate in algae by near-infrared spectroscopy

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DESALINATION AND WATER TREATMENT 2019年第0期168卷 117-122页

作者： Xu, Peilong Na, Na Gao, Shouwu Geng, Cunzhen Qingdao Univ State Key Lab Biofibers & Ecotext Qingdao 266071 Shandong Peoples R China Qingdao Municipal Hlth Commiss Qingdao 266071 Shandong Peoples R China

In this study, the components of natural seaweed fiber were determined by near-infrared spectroscopy, and the qualitative analysis model of natural seaweed was established by using support vector machine algorithm based on principal component analysis. For the natural algae containing sodium alginate, a near-infrared spectrum analysis model for the content of sodium alginate was established by using multi-model method. The model has very good analytical and predictive ability. The model can be used to distinguish the composition and active component contents of natural algae quickly, and it also has the advantages of fast, nondestructive, convenient and so on.

关键词： Near-infrared spectrum Natural algae support vector machine algorithm Multi-model method

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Automatic detection of acute lymphoblastic leukaemia based on extending the multifractal features (vol 14, pg 132, 2020)

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IET IMAGE PROCESSING 2020年第5期14卷 995-995页

作者： Abbasi, M. Kermani, S. Talebi, Ardeshir Isfahan University of Medical Sciences P.O. Box 319 Hezar-Jerib Ave. Isfahan 81746 73461 Iran Isfahan University of Medical Sciences Faculty of advanced Technologies P.O. Box 319 Hezar-Jerib Ave. Isfahan 81746 73461 Iran School of Medicine Isfahan University of Medical Sciences P.O. Box 319 Hezar-Jerib Ave. Isfahan 81746 73461 Iran School of Medicine Tehran University of Medical Sciences (TUMS) Department of Medical Physics and Biomedical Engineering Tehran Iran Islamic Azad University North Tehran Branch Vafadar Blvd. Shahid Sadoughi St. Hakimiyeh Exit Shahid Babaee Highway Tehran *** Iran

The main purpose of this study is to introduce a new species of features to improve the diagnosis efficiency of acute lymphoblastic leukaemia from microscopic images. First, the authors segmented nuclei by the \nk\n-means and watershed algorithms. They extracted three sets of geometrical, statistical, and chaotic features from nuclei images. Six chaotic features were extracted by calculating the fractal dimension from five sub-images driven from the nuclei images, with their grey levels being modified. The authors classified the images into binary and multiclass types via the support vector machine algorithm. They conducted principal component analysis for dimensional reduction of feature space and then evaluated the proposed algorithm for the overfitting problem. The obtained overall results represent 99% accuracy, 99% specificity, and 97% sensitivity values in the classification of six-cell groups. The difference between the train and test errors was <;3%, which proves that the classification performance had improved by using the multifractal features.

关键词： cancer image colour analysis blood pattern classification medical image processing image denoising feature extraction principal component analysis fractals image segmentation image enhancement image classification support vector machines automatic detection acute lymphoblastic leukaemia multifractal features diagnosis efficiency microscopic images watershed algorithms chaotic features nuclei images fractal dimension binary types multiclass types support vector machine algorithm principal component analysis feature space

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Hypertensive Disorders of Pregnancy: Kurtosis-Based Classification of Fetal Doppler Ultrasound Signals 5

Hypertensive Disorders of Pregnancy: Kurtosis-Based Classifi...

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5th International Conference on Advances in Biomedical Engineering (ICABME)

作者： Chaaban, Rayan Issa, Walaa Bouakaz, Ayache Zaylaa, Amira J. Beirut Int Univ Dept Biomed Engn Beirut Lebanon Univ Franoise Rabelais Tours Imaging & Brain Lab Tours France Lebanese Univ Fac Med Sci Neorosci Res Ctr Beirut Lebanon Lebanese Univ Fac Publ Hlth 5 Beirut Lebanon

ISBN: (纸本)9781728123141

Hypertensive Disorders of Pregnancy (HDP), a group of medical conditions occurring during pregnancy, have wide-reaching implications on the normal progress of 17% of pregnancies, leading to maternal and perinatal morbidity and mortality. One of the HDP complications is the Intrauterine Growth Restriction (IUGR). IUGR changes the behavior of any feature extracted from Fetal Heart Rates (FHRs). These features if well-selected improve the classification of IUGR. However, the choice of features was reliant on whether it's linear or nonlinear. Also, the classification algorithms such as, K-Means and support vector machine (SVM) used to predict and classify biomedical signals were not optimal, and the best classification algorithm was not yet set. Our aim is to propose a new kurtosis-based combinations of features and explore their effect on HDP and IUGR classification from Doppler Ultrasound FHRs. Features extracted from FHRs were fed into K-means and SVM classification algorithms. The database comprised 50 normal and 50 IUGR FHRs. Results showed that the best extracted features were those based on kurtosis, and the best classification method was the SVM. The best combination result was 67% sensitive, 100% specific and 100% precise to the classification and detection of IUGR and thus HDP. A further future study could test additional combination of features and other classification-based methods to predict IUGR and thus HDP.

关键词： Hypertensive Disorders of Pregnancy Intrauterine Growth Restriction Entropy Features Kurtosis K-means clustering support vector machine algorithm Statistical Analysis

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Computer-Aided Liver Cirrhosis Diagnosis via Automatic Liver Segmentation and machine Learning algorithm

Computer-Aided Liver Cirrhosis Diagnosis via Automatic Liver...

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International Forum on Medical Imaging in Asia

作者： Su, Ting-Yu Yang, Wei-Tse Cheng, Tsu-Chi He, Yi-Fei Yang, Ching-Juei Fang, Yu-Hua Natl Cheng Kung Univ Tainan Dept Biomed Engn Tainan Taiwan Natl Cheng Kung Univ Hosp Dept Diagnost Radiol Tainan Taiwan

ISBN: (数字)9781510627765

ISBN: (纸本)9781510627765

In this paper, a new computer-aided diagnosis system is proposed to automatically diagnose liver cirrhosis based on four-phases CT images, which included non-contrast phase, arterial phase, delay phase and portal venous phase. It is developed for the purpose of discriminating the cirrhosis into mild or severe level by automatic liver segmentation method and classification method using machine learning algorithm. First, the gradient-inverse map of CT images are calculated to derive the relative-smooth features in local area. Then we compared the centroid and area of each binary labeled groups through each slice to quantitatively extract the volume of interest (VOI) of liver automatically. In classification step, some first-order features and texture features are calculated to describe the intensity representation of liver parenchyma. Some parameters are also used to quantify the distribution of intensity in VOI. By the way, we also quantified the shape of VOI and derived some structural features. Finally, the trained support vector machine (SVM) and Neural Network (NN) classifier is applied to classify the subjects into clinical stages of the liver cirrhosis.

关键词： Liver segmentation non-contrast phase arterial phase delay phase and portal venous phase of CT images support vector machine algorithm

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support vectors machine-based model for diagnosis of multiple sclerosis using the plasma levels of selenium, vitamin B₁₂, and vitamin D₃

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Informatics in Medicine Unlocked 2020年 20卷

作者： Sharifmousavi, Seyed Sajjad Borhani, Matia Sadat Biomedical Engineering Department Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Biology Department Faculty of Sciences Gonbad Kavous University Golestan Iran

Background: It has been reported that some nutritional factors can play a significant role in immune system regulation and the etiology of immune-mediated diseases such as multiple sclerosis (MS). However, there have been no studies on the relationship between nutritional factors and MS as a primary diagnostic method. Methods: Plasma samples of 99 MS patients and 81 healthy people as the control group were assayed using atomic absorption spectroscopy and the chemical autoanalyzer method to determine the amount of selenium and vitamins (B₁₂, D₃), respectively. To find a diagnostic model, three types of supervised machine learning methods, i.e., support vector machine algorithm (SVM), decision tree (DT), and K-nearest neighbor (KNN) were used. Results: The diagnostic model based on the support vector machine method had high accuracy (98.89%), sensitivity (98.98%), and positive predictive value (98.98%). Moreover, the SVM had a true positive rate of 99.9%. Conclusion: The results of this study showed that the SVM algorithm with Radial Basis Function Kernel could be effective in obtaining a good MS diagnostic method based on the plasma levels of selenium, vitamin B₁₂, and vitamin D₃ regarding DT and KNN. This method is non-invasive and cost-effective and can be used as a primary screening method for the diagnosis of MS. © 2020

关键词： Multiple sclerosis Selenium support vector machine algorithm Vitamin B12 Vitamin D3

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