scholarly journals Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Choudhary Sobhan Shakeel ◽  
Saad Jawaid Khan ◽  
Beenish Chaudhry ◽  
Syeda Fatima Aijaz ◽  
Umer Hassan
Keyword(s):  
Machine Learning ◽  
Alopecia Areata ◽  
Nearest Neighbor ◽  
Autoimmune Disorder ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Framework ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Image Set

Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrated potential in different areas of dermatology and may play a significant role in classifying alopecia areata for better prediction and diagnosis. We propose a framework pertaining to the classification of healthy hairs and alopecia areata. We used 200 images of healthy hairs from the Figaro1k dataset and 68 hair images of alopecia areata from the Dermnet dataset to undergo image preprocessing including enhancement and segmentation. This was followed by feature extraction including texture, shape, and color. Two classification techniques, i.e., support vector machine (SVM) and k -nearest neighbor (KNN), are then applied to train a machine learning model with 70% of the images. The remaining image set was used for the testing phase. With a 10-fold cross-validation, the reported accuracies of SVM and KNN are 91.4% and 88.9%, respectively. Paired sample T -test showed significant differences between the two accuracies with a p < 0.001 . SVM generated higher accuracy (91.4%) as compared to KNN (88.9%). The findings of our study demonstrate potential for better prediction in the field of dermatology.

Classification Framework for Healthy Hairs and Alopecia Areata using Machine Learning (Preprint)

2021 ◽  
Author(s):  
Choudhary Sobhan Shakeel ◽  
Saad Jawaid Khan ◽  
Syeda Fatima Aijaz ◽  
Umer Hassan ◽  
Beenish Chaudhry
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Alopecia Areata ◽  
Nearest Neighbor ◽  
Financial Burden ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Classification Framework ◽  
Learning Techniques

BACKGROUND Alopecia areata is an auto-immune disorder that involves non-scarring hair loss in well-defined patches as well as affecting the entire scalp region and ultimately leads to baldness. The latest worldwide statistics have exhibited that Alopecia areata affects millions of people. Furthermore, the use of conventional methods often leads to poor diagnosis of Alopecia ultimately increasing the medical financial burden on the population. It has been reported that 85% of the individuals suffering from Alopecia areata complain about significant financial burden along with associated costs that are beyond cosmetic concerns. Many individuals adhere to treatment discontinuation owing to enhanced expenses and poor diagnosis. OBJECTIVE The objectives of the study comprise of utilizing datasets of healthy hairs and Alopecia areata, extracting color, texture and shape features from the images and applying machine learning algorithms including support vector machine (SVM) and k-nearest neighbor (KNN). METHODS Two datasets with images of healthy hairs and Alopecia areata have been utilized. A total of 200 healthy hair images were retrieved from Figaro1k dataset. A total of 68 images of Alopecia areata were retrieved from a dataset known as Dermnet. The images initially go through pre-processing steps including enhancement and segmentation. Following image segmentation, three features of color, texture and shape are extracted. Following feature extraction, machine learning algorithms including support vector machine (SVM) and k-nearest neighbor (KNN) are applied that aid in classifying Alopecia areata and healthy hairs. RESULTS A total of 81 images are tested with support vector machine (SVM) and k- nearest neighbor (KNN) yielding an accuracy of 91.4% and 88.9% respectively. The results of the paired sample T-test via SPSS analysis demonstrate a p < 0.001 and exhibits that the accuracies acquired from the two machine learning techniques are significantly different. The accuracies reported will enable a hair expert in recommending a suitable diagnosis and hair treatment regimen to a patient. CONCLUSIONS The application of support vector machine (SVM) presented an accuracy of 91.4% and that of k-nearest neighbor (KNN) presented an accuracy of 88.9%. These accuracies exhibit that the proposed classification framework is found to be successful and robust. However, future work with deep learning techniques such as convolutional neural networks (CNN) can be also be carried out and integrated with the existing system.

scholarly journals A Comparative Study on University Admission Predictions Using Machine Learning Techniques

2021 ◽  
pp. 537-548
Author(s):  
Prince Golden ◽  
Kasturi Mojesh ◽  
Lakshmi Madhavi Devarapalli ◽  
Pabbidi Naga Suba Reddy ◽  
Srigiri Rajesh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Education Systems ◽  
University Admissions ◽  
The Troubles ◽  
Learning Techniques ◽  
Cloud Servers

In this era of Cloud Computing and Machine Learning where every kind of work is getting automated through machine learning techniques running off of cloud servers to complete them more efficiently and quickly, what needs to be addressed is how we are changing our education systems and minimizing the troubles related to our education systems with all the advancements in technology. One of the the prominent issues in front of students has always been their graduate admissions and the colleges they should apply to. It has always been difficult to decide as to which university or college should they apply according to their marks obtained during their undergrad as not only it’s a tedious and time consuming thing to apply for number of universities at a single time but also expensive. Thus many machine learning solutions have emerged in the recent years to tackle this problem and provide various predictions, estimations and consultancies so that students can easily make their decisions about applying to the universities with higher chances of admission. In this paper, we review the machine learning techniques which are prevalent and provide accurate predictions regarding university admissions. We compare different regression models and machine learning methodologies such as, Random Forest, Linear Regression, Stacked Ensemble Learning, Support Vector Regression, Decision Trees, KNN(K-Nearest Neighbor) etc, used by other authors in their works and try to reach on a conclusion as to which technique will provide better accuracy.

scholarly journals The Role of Medication Data to Enhance the Prediction of Alzheimer’s Progression Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Bader Alouffi ◽  
Radhya Sahal ◽  
Naglaa Abdelhade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Multimodal Data ◽  
Learning Techniques ◽  
Early Progression ◽  
Neuroimaging Data

Early detection of Alzheimer’s disease (AD) progression is crucial for proper disease management. Most studies concentrate on neuroimaging data analysis of baseline visits only. They ignore the fact that AD is a chronic disease and patient’s data are naturally longitudinal. In addition, there are no studies that examine the effect of dementia medicines on the behavior of the disease. In this paper, we propose a machine learning-based architecture for early progression detection of AD based on multimodal data of AD drugs and cognitive scores data. We compare the performance of five popular machine learning techniques including support vector machine, random forest, logistic regression, decision tree, and K-nearest neighbor to predict AD progression after 2.5 years. Extensive experiments are performed using an ADNI dataset of 1036 subjects. The cross-validation performance of most algorithms has been improved by fusing the drugs and cognitive scores data. The results indicate the important role of patient’s taken drugs on the progression of AD disease.

Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

2020 ◽  
pp. 1577-1597
Author(s):  
Kusuma Mohanchandra ◽  
Snehanshu Saha
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Support Vector ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Broad Perspective ◽  
Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

On the Use of Artificial Intelligence Techniques in Crop Monitoring and Disease Identification

2021 ◽  
pp. 166-186
Author(s):  
Muzaffer Kanaan ◽  
Rüştü Akay ◽  
Canset Koçer Baykara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Crop Yields ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Disease Identification ◽  
Use Of Technology ◽  
Learning Techniques

The use of technology for the purpose of improving crop yields, quality and quantity of the harvest, as well as maintaining the quality of the crop against adverse environmental elements (such as rodent or insect infestation, as well as microbial disease agents) is becoming more critical for farming practice worldwide. One of the technology areas that is proving to be most promising in this area is artificial intelligence, or more specifically, machine learning techniques. This chapter aims to give the reader an overview of how machine learning techniques can help solve the problem of monitoring crop quality and disease identification. The fundamental principles are illustrated through two different case studies, one involving the use of artificial neural networks for harvested grain condition monitoring and the other concerning crop disease identification using support vector machines and k-nearest neighbor algorithm.

Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

2017 ◽  
pp. 186-206
Author(s):  
Kusuma Mohanchandra ◽  
Snehanshu Saha
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Support Vector ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Broad Perspective ◽  
Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

Human Computer Interface Using Electroencephalography for Driver Behavior Classification

2019 ◽  
Author(s):  
Vamsi K. Manchala ◽  
Alvaro V. Clara ◽  
Susheelkumar C. Subramanian ◽  
Sangram Redkar ◽  
Thomas Sugar
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Driver Behavior ◽  
Driving Behavior ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Affective States ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Artificial Neural Network Ann

Abstract It is important to know and be able to classify the drivers’ behavior as good, bad, keen or aggressive, which would aid in driver assist systems to avoid vehicle crashes. This research attempts to develop, test, and compare the performance of machine learning methods for classifying human driving behavior. It also proposes to correlate driver affective states with the driving behavior. The major contributions of this work are to classify the driver behavior using Electroencephalograph (EEG) while driving simulated vehicle and compare them with the behavior classified using vehicle parameters and affective states. The study involved both classical machine learning techniques such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and latest “unsupervised” Hybrid Deep Learning techniques, and compared the accuracy of classification across subjects, various driving scenarios and affective states.

scholarly journals Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 79-89
Author(s):  
Ashfaq Ali Kashif ◽  
Birra Bakhtawar ◽  
Asma Akhtar ◽  
Samia Akhtar ◽  
Nauman Aziz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatitis C ◽  
Treatment Response ◽  
Nearest Neighbor ◽  
Response Prediction ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Treatment Response Prediction

The proper prognosis of treatment response is crucial in any medical therapy to reduce the effects of the disease and of the medication as well. The mortality rate due to hepatitis c virus (HCV) is high in Pakistan as well as all over the world. During the treatment of any disease, prediction of treatment response against any particular medicine is difficult. This paper focuses on predicting the treatment response of a drug: “L-ornithine L-Aspartate (LOLA)” in hepatitis c patients. We have used various machine learning techniques for the prediction of treatment response, including: “K Nearest Neighbor, kStar, Naive Bayes, Random Forest, Radial Basis Function, PART, Decision Tree, OneR, Support Vector Machine and Multi-Layer Perceptron”. Performance measures used to analyze the performance of used machine learning techniques include, “Accuracy, Recall, Precision, and F-Measure”.

scholarly journals Comparative study of Image Classification Algorithms

2021 ◽  
Vol 7 (01) ◽  
pp. 88-92
Author(s):  
Utkarsh Pandey, Himanshu Aneja Deepanshu Jindal and Ajay Tiwari
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Binary Decision Tree ◽  
Learning Techniques ◽  
Vector Machines ◽  
Execution Models

This investigation analyzed five common machine learning techniques for performing image classification included Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Binary Decision Tree (BDT) and Discriminant Analysis (DA). AlexNet deep learning model was utilized to fabricate these machine learning classifiers. The structure classifiers were executed and assessed by standard execution models of Accuracy (ACC), Precision (P), Sensitivity (S), Specificity (Spe) and Area Under the ROC Curve (AUC). The five strategies were assessed utilizing 2608 histopathological pictures for head and neck cancer. The examination was directed utilizing multiple times 10-overlay cross validation. For every strategy, the pre-trained AlexNet network was utilized to separate highlights from the activation layer. The outcomes outlined that, there was no contrast between the consequences of SVM and KNN. Both have the equivalent and the higher accuracy than others were 99.98 %, though 99.81%, 97.32% and 93.68% for DA, BDT and NB, separately. The current examination shows that the SVM, KNN and DA are the best techniques for classifying our dataset images.

Introduction to Machine Learning

2017 ◽  
pp. 1-14 ◽  
Author(s):  
Arvind Kumar Tiwari
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Hybrid Methods ◽  
Performance Measure ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Subspace Method ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Feature Selection Techniques

Machine learning refers to the changes in systems that perform tasks associated with artificial intelligence. This chapter presents introduction types and application of machine learning. This chapter also presents the basic concepts related to feature selection techniques such as filter, wrapper and hybrid methods and various machine learning techniques such as artificial neural network, Naive Bayes classifier, support vector machine, k-nearest-neighbor, decision trees, bagging, boosting, random subspace method, random forests, k-means clustering and deep learning. In the last the performance measure of the classifier is presented.

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