Classification of Outlier’s Detection Methods Based on Quantitative or Semantic Learning

2020 ◽  
pp. 45-59 ◽  
Author(s):  
Rasha Kashef ◽  
Michael Gencarelli ◽  
Ahmed Ibrahim
Keyword(s):  
Detection Methods ◽  
Semantic Learning

Community Detection Approaches in Real World Networks

2014 ◽  
Vol 6 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Pooja Wadhwa ◽  
M.P.S Bhatia
Keyword(s):  
Community Detection ◽  
Online Social Networks ◽  
Temporal Dynamics ◽  
Detection Methods ◽  
Unified Approach ◽  
Detection Algorithms ◽  
Dynamic Community Detection ◽  
Dynamic Communities ◽  
Dynamic Community

Online social networks have been continuously evolving and one of their prominent features is the evolution of communities which can be characterized as a group of people who share a common relationship among themselves. Earlier studies on social network analysis focused on static network structures rather than dynamic processes, however, with the passage of time, the networks have also evolved and the researchers have started to focus on the aspect of studying dynamic behavior of networks. This paper aims to present an overview of community detection approaches graduating from static community detection methods towards the methods to identify dynamic communities in networks. The authors also present a classification of the existing dynamic community detection algorithms along the dimension of studying the evolution as either a two-step approach comprising of community detection via static methods and then applying temporal dynamics or a unified approach which comprises of dynamic detection of communities along with their evolutionary characteristics.

scholarly journals A comparative study of motion detection with FMG and sEMG methods for assistive applications

2020 ◽  
Vol 7 ◽  
pp. 205566832093858
Author(s):  
Muhammad Raza Ul Islam ◽  
Asim Waris ◽  
Ernest Nlandu Kamavuako ◽  
Shaoping Bai
Keyword(s):  
Motion Detection ◽  
Classification Accuracy ◽  
Detection Method ◽  
Daily Basis ◽  
Detection Methods ◽  
Feed Forward Neural Network ◽  
Training Scheme ◽  
Training Schemes ◽  
And Training

Introduction While surface-electromyography (sEMG) has been widely used in limb motion detection for the control of exoskeleton, there is an increasing interest to use forcemyography (FMG) method to detect motion. In this paper, we review the applications of two types of motion detection methods. Their performances were experimentally compared in day-to-day classification of forearm motions. The objective is to select a detection method suitable for motion assistance on a daily basis. Methods Comparisons of motion detection with FMG and sEMG were carried out considering classification accuracy (CA), repeatability and training scheme. For both methods, classification of motions was achieved through feed-forward neural network. Repeatability was evaluated on the basis of change in CA between days and also training schemes. Results The experiments shows that day-to-day CA with FMG can reach 84.9%, compared with a CA of 77.8% with sEMG, when the classifiers were trained only on the first day. Moreover, the CA with FMG can reach to 86.5%, comparable to CA of 84.1% with sEMG, if classifiers were trained daily. Conclusions Results suggest that data recorded from FMG is more repeatable in day-to-day testing and therefore FMG-based methods can be more useful than sEMG-based methods for motion detection in applications where exoskeletons are used as needed on a daily basis.

Object-oriented classification of QuickBird data for mapping seagrass spatial structure

2009 ◽  
Vol 38 (1) ◽  
Author(s):  
Jacek Urbański ◽  
Aleksandra Mazur ◽  
Urszula Janas
Keyword(s):  
Spatial Structure ◽  
Nearest Neighbor ◽  
Ecological Model ◽  
Spatial Scales ◽  
Object Oriented ◽  
Feature Space ◽  
Mean Value ◽  
Detection Methods ◽  
Object Domain

Object-oriented classification of QuickBird data for mapping seagrass spatial structureQuickBird satellite images were processed using object-based analysis to map the spatial structure of seagrass in sandy shoal habitat in the southern Baltic Sea. A three-level ecological model of seagrass landscape, composed of meadows, beds and patches/gaps, was implemented in the multi-scale object domain. Image segmentation was performed at different spatial scales. In order to determine representative scales for bed level and patch/gap level objects, histograms of delineated objects were analyzed. Using object-oriented classification methods, two hierarchically nested maps of seagrass spatial structure were created. The map of patches/gaps was created using the nearest neighbor classification method in the feature space defined by the mean value of band 2 and the value of the proposed seagrass index. Overall map accuracy was 83%. The second map, which depicted the cover density of seagrass beds, was created on the basis of hierarchical relationships between objects at two chosen spatial scale levels. Both maps were exported as vector objects to GIS. Vector-based mapping of seagrass landscape structures at two scales simultaneously provides new possibilities for using landscape metrics and time change detection methods.

Immunohistochemistry in the era of personalised medicine

2012 ◽  
Vol 66 (1) ◽  
pp. 58-61 ◽  
Author(s):  
Clare Margaret McCourt ◽  
David Boyle ◽  
Jacqueline James ◽  
Manuel Salto-Tellez
Keyword(s):  
Clinical Significance ◽  
Immunohistochemical Staining ◽  
Patient Management ◽  
Personalised Medicine ◽  
Detection Methods ◽  
Diagnostic Pathology ◽  
Laboratory Environment ◽  
Histopathological Classification ◽  
Classification Of Diseases

BackgroundImmunohistochemistry (IHC) plays a central role in the histopathological classification of diseases, including cancer. More recently, the importance of immunohistochemical staining is increasing. IHC usage in diagnostics is invaluable; however, the genetic and therapeutic significance of biomarker immunostaining has become equally relevant.ContentIn this article, we would like to analyse the three distinct roles of IHC and review their individual impacts on modern diagnostic pathology: (1) diagnostic IHC; (2) genetic IHC and (3) therapeutic IHC.SummaryThus, we will characterise the different analytical processes that are required in the three approaches to IHC usage stated above, as well as the clinical significance and overall importance in patient management. This will allow us to hypothesise on the most appropriate laboratory environment and detection methods for the future.

scholarly journals Benefit of Neural Network for the Optimization of Defect Detection on Composite Material Using Ultrasonic Non Destructive Testing

2021 ◽  
Author(s):  
P. Trouvé-Peloux ◽  
B. Abeloos ◽  
A. Ben Fekih ◽  
C. Trottier ◽  
J.-M. Roche
Keyword(s):  
Defect Detection ◽  
Semantic Segmentation ◽  
Detection Methods ◽  
Learning Approaches ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Experimental Database ◽  
Out Of Plane ◽  
Non Destructive

Abstract This paper is dedicated to out-of-plane waviness defect detection within composite materials by ultrasonic testing. We present here an in-house experimental database of ultrasonic data built on composite pieces with/without elaborated defects. Using this dataset, we have developed several defect detection methods using the C-scan representation, where the defect is clearly observable. We compare here the defect detection performance of unsupervised, classical machine learning methods and deep learning approaches. In particular, we have investigated the use of semantic segmentation networks that provides a classification of the data at the “pixel level”, hence at each C-scan measure. This technique is used to classify if a defect is detected, and to produce a precise localization of the defect within the material. The results we obtained with the various detection methods are compared, and we discuss the drawbacks and advantages of each method.

scholarly journals Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2444
Author(s):  
Mazhar Javed Awan ◽  
Osama Ahmed Masood ◽  
Mazin Abed Mohammed ◽  
Awais Yasin ◽  
Azlan Mohd Zain ◽  
...  
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
Malware Detection ◽  
Detection Methods ◽  
Learning Framework ◽  
Malware Classification ◽  
Proposed Model ◽  
Communication Devices ◽  
Digital Assets

In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state-of-the-art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions.

scholarly journals Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Shengqi Yang ◽  
Ran Li ◽  
Jiliang Chen ◽  
Zhen Li ◽  
Zhangqin Huang ◽  
...  
Keyword(s):  
Binary Classification ◽  
Detection Algorithm ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Detection Methods ◽  
Cardiac Pathology ◽  
Powerful Approach ◽  
Whole Heart ◽  
Event Based

Ca2+ sparks are the elementary Ca2+ release events in cardiomyocytes, altered properties of which lead to impaired Ca2+ handling and finally contribute to cardiac pathology under various diseases. Despite increasing use of machine-learning algorithms in deciphering the content of biological and medical data, Ca2+ spark images and data are yet to be deeply learnt and analyzed. In the present study, we developed a deep residual convolutional neural network method to detect Ca2+ sparks. Compared to traditional detection methods with arbitrarily defined thresholds to distinguish signals from noises, our new method detected more Ca2+ sparks with lower amplitudes but similar spatiotemporal distributions, thereby indicating that our new algorithm detected many very weak events that are usually omitted when using traditional detection methods. Furthermore, we proposed an event-based logistic regression and binary classification model to classify single cardiomyocytes using Ca2+ spark characteristics, which to date have generally been used only for simple statistical analyses and comparison between normal and diseased groups. Using this new detection algorithm and classification model, we succeeded in distinguishing wild type (WT) vs RyR2-R2474S± cardiomyocytes with 100% accuracy, and vehicle vs isoprenaline-insulted WT cardiomyocytes with 95.6% accuracy. The model can be extended to judge whether a small number of cardiomyocytes (and so the whole heart) are under a specific cardiac disease. Thus, this study provides a novel and powerful approach for the research and application of calcium signaling in cardiac diseases.

An assessment of reference method selective broths and plating media using 19 Listeria spp. highlights the importance of including diverse species in Listeria spp. method evaluations

2021 ◽  
Author(s):  
Catharine R Carlin ◽  
Sherry Roof ◽  
Martin Wiedmann
Keyword(s):  
Bacterial Growth ◽  
Reference Method ◽  
Sensu Stricto ◽  
Detection Methods ◽  
Department Of Agriculture ◽  
Method Performance ◽  
The Media ◽  
The U.S ◽  
Selection Of

Reference methods developed for L. monocytogenes are commonly used for Listeria spp. detection. Improved method performance data are needed, since the genus Listeria has expanded from 6 to 26 species and now includes several Listeria sensu lato species, which can show phenotypes distinct from Listeria sensu stricto . Here, we evaluated growth of 19 Listeria spp., including 12 recently described sensu lato species, using the media specified by (i) the U.S. Food and Drug Administration (FDA) Bacteriological Analytical Manual , (ii) the U.S. Department of Agriculture (USDA) Microbiology Laboratory Guidebook , and (iii) the International Organization for Standardization (ISO). The FDA enrichment procedure allowed all species to grow to detectable levels (≥ 4 log 10 ), yielded the highest mean growth (7.58 log 10 ), and was the only procedure where no sensu lato species yielded significantly higher bacterial growth than a sensu stricto species. With the USDA or ISO enrichment procedures several sensu lato species yielded significantly higher bacterial growth than either L. seeligeri or L. ivanovii , suggesting that these two sensu stricto species could be outgrown by sensu lato species. On selective and differential agars, L. seeligeri, L. ivanovii, and L. grayi yielded atypical colony morphologies and/or showed inhibited growth (which may lead to incorrect classification of a sample as negative), while several newly described sensu lato species grew well and showed typical morphologies. Overall, our study shows that the ability to detect different Listeria spp. can be impacted by the specific broth and selective and differential agars used. Our data will aid with selection of media and detection methods for environmental Listeria monitoring programs and facilitate selection of methods that are most likely to detect the targeted Listeria groups (e.g., Listeria sensu stricto, which appear to be the most appropriate index organisms for the pathogen L. monocytogenes ).

scholarly journals Design of Accurate Classification of COVID-19 Disease in X-Ray Images Using Deep Learning Approach

2021 ◽  
Vol 2 (2) ◽  
pp. 132-148
Author(s):  
Joy Iong-Zong Chen
Keyword(s):  
Transfer Learning ◽  
Research Work ◽  
Well Being ◽  
Classification Model ◽  
Classification Method ◽  
World Health ◽  
Detection Methods ◽  
X Rays ◽  
X Ray

COVID-19 appears to be having a devastating influence on world health and well-being. Moreover, the COVID-19 confirmed cases have recently increased to over 10 million worldwide. As the number of verified cases increase, it is more important to monitor and classify healthy and infected people in a timely and accurate manner. Many existing detection methods have failed to detect viral patterns. Henceforth, by using COVID-19 thoracic x-rays and the histogram-oriented gradients (HOG) feature extraction methodology; this research work has created an accurate classification method for performing a reliable detection of COVID-19 viral patterns. Further, the proposed classification model provides good results by leveraging accurate classification of COVID-19 disease based on the medical images. Besides, the performance of our proposed CNN classification method for medical imaging has been assessed based on different edge-based neural networks. Whenever there is an increasing number of a class in the training network, the accuracy of tertiary classification with CNN will be decreasing. Moreover, the analysis of 10 fold cross-validation with confusion metrics can also take place in our research work to detect various diseases caused due to lung infection such as Pneumonia corona virus-positive or negative. The proposed CNN model has been trained and tested with a public X-ray dataset, which is recently published for tertiary and normal classification purposes. For the instance transfer learning, the proposed model has achieved 85% accuracy of tertiary classification that includes normal, COVID-19 positive and Pneumonia. The proposed algorithm obtains good classification accuracy during binary classification procedure integrated with the transfer learning method.

scholarly journals Identification and classification of honey's authenticity by attenuated total reflectance Fourier-transform infrared spectroscopy and chemometric method

2019 ◽  
Vol 12 (8) ◽  
pp. 1304-1310 ◽  
Author(s):  
Muhamad Sahlan ◽  
Seffiani Karwita ◽  
Misri Gozan ◽  
Heri Hermansyah ◽  
Masafumi Yohda ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Wavelength Region ◽  
Fourier Transform Infrared ◽  
Detection Methods ◽  
Attenuated Total Reflectance ◽  
Nuclear Magnetic Resonance Analysis ◽  
Alternative Analysis ◽  
Total Reflectance ◽  
13C Nuclear Magnetic Resonance

Background and Aim: The authentication of honey is important to protect industry and consumers from such adulterated honey. However, until now, there has been no guarantee of honey's authenticity, especially in Indonesia. The classification of honey is based on the bee species (spp.) that produces it. The study used honey from sting bee Apis spp. and stingless bee Tetragonula spp. based on the fact that the content off honey produced between them has differences. Authenticating honey with currently available rapid detection methods, such as 13C nuclear magnetic resonance analysis, is costly. This study aimed to develop an inexpensive, fast, precise, and accurate classification method for authenticating honey. Materials and Methods: In this study, we use attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy with wavelengths ranging between 550 and 4000 cm-1 as an alternative analysis method, which is relatively less expensive. The spectra of authentic and fake honey samples were obtained using ATR-FTIR and plotted using chemometric discriminant analysis. The authentic honey samples were acquired from a local Indonesian breeder of honey bees, while the fake honey samples were made from a mixture of water, sugar, sodium bicarbonate, and authentic honey. Data were collected using Thermo Scientific's OMNIC FTIR software and processed using Thermo Scientific's TQ Analyst software. Results: Our method effectively classified the honey as authentic or fraudulent based on the FTIR spectra. To authenticate the honey, we formed two classes: Real honey and fake honey. The wavelengths that can best differentiate between these two classes correspond to four regions: 1600-1700 cm-1; 1175-1540 cm-1; 940-1175 cm-1; and 700-940 cm-1. Similarly, for classification purpose, we formed two classes: Apis spp. and Tetragonula spp. The wavelength region that can best classify the samples as belonging to the Apis spp. or Tetragonula spp. class is explicitly within the range of 1600-1700 cm-1. Conclusion: This study successfully demonstrated a method to rapidly and accurately classify and authenticate honey. ATR-FTIR is a useful tool to test the authenticity of honey. Keywords: Apis spp., attenuated total reflectance Fourier transform infrared, discriminant, spectrum, Tetragonula spp.

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