scholarly journals Real time Drowsy Driver Detection using Polynomial Kernel based Support Vector Machine

2021 ◽  
Vol 9 (9) ◽  
pp. 1389-1396
Author(s):  
Makhan Ahirwar
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Real Time ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Human Errors ◽  
Drowsiness Detection ◽  
Non Linear ◽  
Data Separation

Abstract: Casualty increases from road accidents day by day. There are so many reasons that accident causes and mostly due to human errors. Driver drowsiness is one of them. A small drowsiness may turn it into a big accident that resulted heavy casualties. If any of the system automatically detects the driver’s drowsiness and alert at real time may secure many lives. Drowsiness can be recognized by different situations such as by opening full mouth, by closing both the eyes and a combination of both. This may advised not to drive at drowsy state. There are various techniques through which drowsiness can be detected at real time but accuracy matters. OpenCV is a highly utilized open source computer vision library through which facial features can be recognized effectively. Polynomial kernel based support vector machine (SVM) is an advanced classification technique through which drowsiness can be classified from face. SVM is advanced machine learning approach through which linear and non-linear data can be classified with higher level of accuracy. System pertained 96.17 % of accuracy. Polynomial kernel is useful for non-linear data separation. Here system classifies the expressional features of face and result accordingly for drowsiness detection. Keywords: Support Vector Machine (SVM), OpenCV, Machine Learning, Non-Linear SVM Model, Drowsiness Detection, Face Detection, Computer Vision.

scholarly journals Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 667
Author(s):  
Wismaji Sadewo ◽  
Zuherman Rustam ◽  
Hamidah Hamidah ◽  
Alifah Roudhoh Chusmarsyah
Keyword(s):  
Machine Learning ◽  
Pancreatic Cancer ◽  
Support Vector Machine ◽  
Early Detection ◽  
Feature Space ◽  
Kernel Functions ◽  
Twin Support Vector Machine ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Rbf Kernel

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

scholarly journals Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca

2021 ◽  
Vol 8 (2) ◽  
pp. 311
Author(s):  
Mohammad Farid Naufal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbors

<p class="Abstrak">Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. <em>Computer vision</em> adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan <em>self autonomous machine</em> agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara <em>real time</em>. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari <em>Machine Learning</em> sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Weather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

2016 ◽  
Vol 16 (13) ◽  
pp. 8181-8191 ◽  
Author(s):  
Jani Huttunen ◽  
Harri Kokkola ◽  
Tero Mielonen ◽  
Mika Esa Juhani Mononen ◽  
Antti Lipponen ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Support Vector ◽  
Learning Methods ◽  
Surface Solar Radiation ◽  
Machine Learning Methods ◽  
Look Up Table ◽  
Non Linear

Abstract. In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the look-up table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during the observation period.

scholarly journals Intelligent Plant Disease Identification System Using Machine Learning

2020 ◽  
Vol 2 (1) ◽  
pp. 49
Author(s):  
Paramasivam Alagumariappan ◽  
Najumnissa Jamal Dewan ◽  
Gughan Narasimhan Muthukrishnan ◽  
Bhaskar K. Bojji Raju ◽  
Ramzan Ali Arshad Bilal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Plant Disease ◽  
Agricultural Sector ◽  
Plant Diseases ◽  
Support Vector ◽  
Identification System ◽  
Learning Machine

Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for identification of plant disease. Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed. Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier. It is also observed that the sensitivity of the support vector machine with a polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance, because the developed real-time hardware is capable of detecting different plant diseases.

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study

2021 ◽  
pp. neurintsurg-2021-017858
Author(s):  
Dee Zhen Lim ◽  
Melissa Yeo ◽  
Ariel Dahan ◽  
Bahman Tahayori ◽  
Hong Kuan Kok ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Acute Stroke ◽  
Real Time ◽  
Endovascular Intervention ◽  
Support Vector ◽  
Proof Of Concept ◽  
Location System ◽  
Real Time Location System

BackgroundDelivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention.MethodsWe conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24-hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43x×1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction.ResultsML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested.ConclusionsML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls.

scholarly journals Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Amjad Khan ◽  
Asfandyar Khan ◽  
Javed Iqbal Bangash ◽  
Fazli Subhan ◽  
Abdullah Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real Time ◽  
Cuckoo Search ◽  
Position Estimation ◽  
Support Vector ◽  
Hybrid Technique ◽  
Positioning Systems ◽  
Proposed Model ◽  
Radio Map

Internet of Things (IoT), an emerging technology, is becoming an essential part of today’s world. Machine learning (ML) algorithms play an important role in various applications of IoT. For decades, the location information has been extremely useful for humans to navigate both in outdoor and indoor environments. Wi-Fi access point-based indoor positioning systems get more popularity, as it avoids extra calibration expenses. The fingerprinting technique is preferred in an indoor environment as it does not require a signal’s Line of Sight (LoS). It consists of two phases: offline and online phase. In the offline phase, the Wi-Fi RSSI radio map of the site is stored in a database, and in the online phase, the object is localized using the offline database. To avoid the radio map construction which is expensive in terms of labor, time, and cost, machine learning techniques may be used. In this research work, we proposed a hybrid technique using Cuckoo Search-based Support Vector Machine (CS-SVM) for real-time position estimation. Cuckoo search is a nature-inspired optimization algorithm, which solves the problem of slow convergence rate and local minima of other similar algorithms. Wi-Fi RSSI fingerprint dataset of UCI repository having seven classes is used for simulation purposes. The dataset is preprocessed by min-max normalization to increase accuracy and reduce computational speed. The proposed model is simulated using MATLAB and evaluated in terms of accuracy, precision, and recall with K-nearest neighbor (KNN) and support vector machine (SVM). Moreover, the simulation results show that the proposed model achieves high accuracy of 99.87%.

scholarly journals Performance of Support Vector Machine Kernels (SVM-K) on Breast Cancer (BC) Dataset

2019 ◽  
Vol 8 (2S7) ◽  
pp. 412-417
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Basis Function ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Linear Kernel ◽  
Real Situation ◽  
Preventive Policy

Breast cancer (BC) most diagnosed invasive disorder and important cause of casualty for women worldwide. Indian contest BC most commonly spread disease among females. This problem is more alarming to economically developing country like India. Government of India made a lot of effort to make aware the women of the country, but despite of availability of diagnostic tool, prediction of disease in real situation is still a puzzle for researchers. Timely detection and categorization of BC using the evolving techniques like Machine Learning (ML) can show a significant role in BC identification and this could be a preventive policy which effectively reduces the risk of BC patients. Although there are four Kernels in ML, are widely in use but their performance varies with the kind of data available. In this study we, apply four different Kernels such as Linear Kernel (LK), Polynomial Kernel (PK), Sigmoid Kernel (SK) and Radial Basis Function Kernel (RBFK) on BC dataset. We estimated the performance of Support Vector Machine Kernels (SVM-K) on BC dataset .The basic idea is to check the exactness of SVM-K to classify WBCD in terms of effectiveness with respect to accuracy, runtime, specificity and precision. The investigations outcome displays that RBFK provides greater accuracy with minimal errors

scholarly journals PREDIKSI JUMLAH PENDERITA PENYAKIT TUBERKULOSIS DI KOTA BANDAR LAMPUNG MENGGUNAKAN METODE SVM (SUPPORT VECTOR MACHINE)

2020 ◽  
Vol 7 (3) ◽  
pp. 320
Author(s):  
Favorisen R. Lumbanraja ◽  
Ira Hariati Br Sitepu ◽  
Didik Kurniawan ◽  
Aristoteles Aristoteles
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Mycobacterium Tuberculosis ◽  
Gaussian Kernel ◽  
Weather Data ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Learning Support ◽  
Tuberculosis Patients ◽  
Long Time

<p><em>Tuberkulosis (TB atau TBC) merupakan salah satu penyakit infeksi yang disebabkan oleh Bakteri Mycobacterium tuberculosis. Bakteri tersebut merupakan bakteri yang sangat kuat sehingga dalam pengobatannya memerlukan waktu yang cukup lama. Pengobatan penyakit tuberkulosis dilakukan selama 6-9 bulan secara rutin dengan sedikitnya 3 macam jenis obat. Saat ini kebanyakan masyarakat menganggap batuk dalam jangka waktu berbulan-bulan merupakan batuk biasa, jika dicermati salah satu gejala yang ditimbulkan penyakit tuberkulosis, yaitu batuk dalam jangka waktu yang panjang. Pada penelitian ini digunakan data penderita tuberkulosis di Kota Bandar Lampung, data cuaca dan matrix jarak antara kejadian penderita tuberkulosis yang satu dengan kejadian yang lainnya dalam lingkup kecamatan. Jumlah dari keseluruhan data sebanyak 600 data dengan 44 variabel. Penelitian ini juga menggunakan 3 kernel yaitu, Linear, Gaussian, dan Polynomial dengan menggunakan Metode SVM dengan kernel Linear mendapatkan nilai rata-rata R<sup>2</sup> sebesar 51.43 %, pada percobaan dengan metode SVM dengan kernel Gaussian mendapatkan nilai rata-rata R<sup>2</sup> sebesar 58.53 % dan pada percobaan dengan metode SVM dengan kernel Polynomial mendapatkan nilai rata-rata R<sup>2</sup> sebesar 36.03 %.</em></p><p><strong><em>Kata Kunci</em></strong><em> : Prediksi penderita tuberculosis, tuberculosis, Machine Learning, Support Vector Machine.</em></p><p class="Abstrak"><em>Tuberculosis (TB / TBC) is one of infectious disease caused by Mycobacterium tuberculosis bacteria. These bacteria are very strong bacteria so for the treatment takes a long time. Tuberculosis treatment is carried out for 6-9 months regularly with at least 3 types of drugs. Currently, most of people consider a cough for months is a common cough, if looked by one of the symptoms caused by tuberculosis, which is a cough for a long time. In this research, data on tuberculosis patients in the city of Bandar Lampung were used, weather data and the distance matrix between the case of tuberculosis patients with other case within the district. The total number of data is 600 data with 44 variables. This research also uses 3 kernels</em><em> </em><em>namely, Linear, Gaussian, and Polynomial by using the SVM method with the Linear kernel getting an average R<sup>2</sup> value of 51.43%, in the experiment with the SVM method with a gaussian kernel getting an average R<sup>2</sup> value of 58.53% and at Experiments with the SVM method with the Polynomial kernel obtained an average value of R<sup>2</sup> of 36.03%</em><em> .</em></p><p class="Abstrak"><strong><em>Keywords</em></strong><em> : Prediction of tuberculosis sufferers, tuberculosis, Machine Learning, Support Vector Machine.</em></p>

scholarly journals Severity modeling of propionic acidemia using clinical and laboratory biomarkers

2021 ◽  
Author(s):  
Oleg A. Shchelochkov ◽  
Irini Manoli ◽  
Paul Juneau ◽  
Jennifer L. Sloan ◽  
Susan Ferry ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Dimensional Reduction ◽  
Expert Knowledge ◽  
Propionic Acidemia ◽  
Supervised Machine Learning ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Test Machine

Abstract Purpose To conduct a proof-of-principle study to identify subtypes of propionic acidemia (PA) and associated biomarkers. Methods Data from a clinically diverse PA patient population (https://clinicaltrials.gov/ct2/show/NCT02890342) were used to train and test machine learning models, identify PA-relevant biomarkers, and perform validation analysis using data from liver-transplanted participants. k-Means clustering was used to test for the existence of PA subtypes. Expert knowledge was used to define PA subtypes (mild and severe). Given expert classification, supervised machine learning (support vector machine with a polynomial kernel, svmPoly) performed dimensional reduction to define relevant features of each PA subtype. Results Forty participants enrolled in the study; five underwent liver transplant. Analysis with k-means clustering indicated that several PA subtypes may exist on the biochemical continuum. The conventional PA biomarkers, plasma total 2-methylctirate and propionylcarnitine, were not statistically significantly different between nontransplanted and transplanted participants motivating us to search for other biomarkers. Unbiased dimensional reduction using svmPoly revealed that plasma transthyretin, alanine:serine ratio, GDF15, FGF21, and in vivo 1-13C-propionate oxidation, play roles in defining PA subtypes. Conclusion Support vector machine prioritized biomarkers that helped classify propionic acidemia patients according to severity subtypes, with important ramifications for future clinical trials and management of PA.

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