scholarly journals Novel Image Processing Method for Detecting Strep Throat (Streptococcal Pharyngitis) Using Smartphone

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3307 ◽  
Author(s):  
Behnam Askarian ◽  
Seung-Chul Yoo ◽  
Jo Woon Chong
Keyword(s):  
Detection Method ◽  
Binary Classification ◽  
Streptococcal Pharyngitis ◽  
Machine Learning Techniques ◽  
Decision Boundary ◽  
Learning Techniques ◽  
Color Transformation ◽  
Test Sets ◽  
Validation Technique ◽  
Strep Throat

In this paper, we propose a novel strep throat detection method using a smartphone with an add-on gadget. Our smartphone-based strep throat detection method is based on the use of camera and flashlight embedded in a smartphone. The proposed algorithm acquires throat image using a smartphone with a gadget, processes the acquired images using color transformation and color correction algorithms, and finally classifies streptococcal pharyngitis (or strep) throat from healthy throat using machine learning techniques. Our developed gadget was designed to minimize the reflection of light entering the camera sensor. The scope of this paper is confined to binary classification between strep and healthy throats. Specifically, we adopted k-fold validation technique for classification, which finds the best decision boundary from training and validation sets and applies the acquired best decision boundary to the test sets. Experimental results show that our proposed detection method detects strep throats with 93.75% accuracy, 88% specificity, and 87.5% sensitivity on average.

scholarly journals DeepMQ: A Deep Learning Approach Based Myelin Quantification in Microscopic Fluorescence Images

2018 ◽  
Author(s):  
Sibel Çimen ◽  
Abdulkerim Çapar ◽  
Dursun Ali Ekinci ◽  
Umut Engin Ayten ◽  
Bilal Ersen Kerman ◽  
...  
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Deep Learning ◽  
Neurological Diseases ◽  
Binary Classification ◽  
Signal Transmission ◽  
Machine Learning Techniques ◽  
Neuronal Communication ◽  
Learning Classifier ◽  
Learning Techniques

AbstractOligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors’ knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.

scholarly journals Predicting Takeover Success Using Machine Learning Techniques

2012 ◽  
Vol 10 (10) ◽  
pp. 547
Author(s):  
Mei Zhang ◽  
Gregory Johnson ◽  
Jia Wang
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Binary Classification ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Success Prediction ◽  
Support Vector ◽  
Font Size ◽  
Network Support ◽  
Learning Techniques

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none; mso-layout-grid-align: none;" class="MsoNormal"><span style="color: black; font-size: 10pt; mso-themecolor: text1;"><span style="font-family: Times New Roman;">A takeover success prediction model aims at predicting the probability that a takeover attempt will succeed by using publicly available information at the time of the announcement.<span style="mso-spacerun: yes;"> </span>We perform a thorough study using machine learning techniques to predict takeover success.<span style="mso-spacerun: yes;"> </span>Specifically, we model takeover success prediction as a binary classification problem, which has been widely studied in the machine learning community.<span style="mso-spacerun: yes;"> </span>Motivated by the recent advance in machine learning, we empirically evaluate and analyze many state-of-the-art classifiers, including logistic regression, artificial neural network, support vector machines with different kernels, decision trees, random forest, and Adaboost.<span style="mso-spacerun: yes;"> </span>The experiments validate the effectiveness of applying machine learning in takeover success prediction, and we found that the support vector machine with linear kernel and the Adaboost with stump weak classifiers perform the best for the task.<span style="mso-spacerun: yes;"> </span>The result is consistent with the general observations of these two approaches.</span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>

scholarly journals Automated Detection Methods for Solar Activities and an Application for Statistic Analysis of Solar Filament

2018 ◽  
Vol 13 (S340) ◽  
pp. 101-107 ◽  
Author(s):  
Q. Hao ◽  
P. F. Chen ◽  
C. Fang
Keyword(s):  
Detection Method ◽  
Rapid Development ◽  
Automated Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Solar Cycles ◽  
Learning Techniques ◽  
Solar Filaments ◽  
Solar Filament ◽  
Full Disk

AbstractWith the rapid development of telescopes, both temporal cadence and the spatial resolution of observations are increasing. This in turn generates vast amount of data, which can be efficiently searched only with automated detections in order to derive the features of interest in the observations. A number of automated detection methods and algorithms have been developed for solar activities, based on the image processing and machine learning techniques. In this paper, after briefly reviewing some automated detection methods, we describe our efficient and versatile automated detection method for solar filaments. It is able not only to recognize filaments, determine the features such as the position, area, spine, and other relevant parameters, but also to trace the daily evolution of the filaments. It is applied to process the full disk Hα data observed in nearly three solar cycles, and some statistic results are presented.

Defect Classification Using Machine Learning Techniques for Flat Display Panels

2013 ◽  
Vol 365-366 ◽  
pp. 720-724
Author(s):  
Du Hyung Cho ◽  
Seok Lyong Lee
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Defect Classification ◽  
Gaussian Filter ◽  
Classification Methods ◽  
Production Environment ◽  
Comparison Result ◽  
Learning Techniques ◽  
Different Types ◽  
Test Sets

Defect classification for a flat display panel (FDP) is the crucial process that identifies and classifies defects automatically during the final step of its manufacturing process. It plays an important role since it prevents possible malfunction by inspecting defects timely and reduces time for identifying inferior products. In this paper, we propose the defect classification methods for FDP using various machine learning techniques and provide the comparison among them for practical use in production environment. First, we identify defects through Gaussian filter and threshold technique. Then, those defects are classified into different types based on geometric characteristics of them using four machine learning techniques that are widely used. The experimental results using training and test sets of FDP images show considerable effectiveness in classifying defect types. We also believe that the comparison result might be quite useful when engineers determine methods for defect classification during FDP manufacturing.

scholarly journals SAMbinder: A web server for predicting SAM binding residues of a protein from its amino acid sequence

2019 ◽  
Author(s):  
Piyush Agrawal ◽  
Gaurav Mishra ◽  
Gajendra P. S. Raghava
Keyword(s):  
Binding Sites ◽  
Protein Sequence ◽  
Cross Validation ◽  
Sequence Similarity ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Evolutionary Information ◽  
Learning Techniques ◽  
Binding Residues ◽  
Validation Technique

AbstractMotivationS-adenosyl-L-methionine (SAM) is one of the important cofactor present in the biological system and play a key role in many diseases. There is a need to develop a method for predicting SAM binding sites in a protein for designing drugs against SAM associated disease. Best of our knowledge, there is no method that can predict the binding site of SAM in a given protein sequence.ResultThis manuscript describes a method SAMbinder, developed for predicting SAM binding sites in a protein from its primary sequence. All models were trained, tested and evaluated on 145 SAM binding protein chains where no two chains have more than 40% sequence similarity. Firstly, models were developed using different machine learning techniques on a balanced dataset contain 2188 SAM interacting and an equal number of non-interacting residues. Our Random Forest based model developed using binary profile feature got maximum MCC 0.42 with AUROC 0.79 on the validation dataset. The performance of our models improved significantly from MCC 0.42 to 0.61, when evolutionary information in the form of PSSM profile is used as a feature. We also developed models on realistic dataset contains 2188 SAM interacting and 40029 non-interacting residues and got maximum MCC 0.61 with AUROC of 0.89. In order to evaluate the performance of our models, we used internal as well as external cross-validation technique.Availability and implementationhttps://webs.iiitd.edu.in/raghava/sambinder/.

scholarly journals Model-Agnostic Adversarial Detection by Random Perturbations

2019 ◽  
Author(s):  
Bo Huang ◽  
Yi Wang ◽  
Wei Wang
Keyword(s):  
Machine Learning ◽  
Detection Method ◽  
Theoretical Framework ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Random Perturbations ◽  
Model Classification ◽  
Learning Techniques ◽  
Classification Errors ◽  
Adversarial Examples

Adversarial examples induce model classification errors on purpose, which has raised concerns on the security aspect of machine learning techniques. Many existing countermeasures are compromised by adaptive adversaries and transferred examples. We propose a model-agnostic approach to resolve the problem by analysing the model responses to an input under random perturbations, and study the robustness of detecting norm-bounded adversarial distortions in a theoretical framework. Extensive evaluations are performed on the MNIST, CIFAR-10 and ImageNet datasets. The results demonstrate that our detection method is effective and resilient against various attacks including black-box attacks and the powerful CW attack with four adversarial adaptations.

scholarly journals Predictive Models of Student College Commitment Decisions Using Machine Learning

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 65 ◽  
Author(s):  
Kanadpriya Basu ◽  
Treena Basu ◽  
Ron Buckmire ◽  
Nishu Lal
Keyword(s):  
Machine Learning ◽  
Liberal Arts ◽  
Optimal Allocation ◽  
Binary Classification ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Student College

Every year, academic institutions invest considerable effort and substantial resources to influence, predict and understand the decision-making choices of applicants who have been offered admission. In this study, we applied several supervised machine learning techniques to four years of data on 11,001 students, each with 35 associated features, admitted to a small liberal arts college in California to predict student college commitment decisions. By treating the question of whether a student offered admission will accept it as a binary classification problem, we implemented a number of different classifiers and then evaluated the performance of these algorithms using the metrics of accuracy, precision, recall, F-measure and area under the receiver operator curve. The results from this study indicate that the logistic regression classifier performed best in modeling the student college commitment decision problem, i.e., predicting whether a student will accept an admission offer, with an AUC score of 79.6%. The significance of this research is that it demonstrates that many institutions could use machine learning algorithms to improve the accuracy of their estimates of entering class sizes, thus allowing more optimal allocation of resources and better control over net tuition revenue.

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