scholarly journals Alzheimer Disease Classification using Machine Learning

2020 ◽  
Vol 9 (6) ◽  
pp. 6-9
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Developed Countries ◽  
Disease Classification ◽  
Support Vector ◽  
Data Set ◽  
Classification Technique ◽  
Routine Tasks ◽  
Memory Power ◽  
To Come

Alzheimer's disease is the most popular and persuading dementia that affects our memory power, reasoning and deportment. Symptoms rise up slowly and worsen with time, becoming an obstacle in doing our routine tasks. Alzheimer is not conventional wedge of aging. The substantial and known risk factor is up surging age. The prevalence of AD is depicted to be around 5% after an age of 65 years and took a leap of 30% for people of 85 years old in developed countries [1]. In this project we proposed a detection and classification technique using Random Forest(RF) and Support Vector Machine(SVM) algorithms on the oasis longitudinal data set and compare their respective accuracies to come to a conclusion that which algorithm best suits for this detection and classification. paper Setup must be in A4 size with Margin: Top 0.7”,

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals Prediction of Collapsibility of Loess of Construction Sites in Xining Based on Machine Learning Methods

2021 ◽  
Author(s):  
Qifei Zhao ◽  
Xiaojun Li ◽  
Yunning Cao ◽  
Zhikun Li ◽  
Jixin Fan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Training Data ◽  
Support Vector ◽  
Engineering Practice ◽  
Burial Depth ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods ◽  
North East

Abstract Collapsibility of loess is a significant factor affecting engineering construction in loess area, and testing the collapsibility of loess is costly. In this study, A total of 4,256 loess samples are collected from the north, east, west and middle regions of Xining. 70% of the samples are used to generate training data set, and the rest are used to generate verification data set, so as to construct and validate the machine learning models. The most important six factors are selected from thirteen factors by using Grey Relational analysis and multicollinearity analysis: burial depth、water content、specific gravity of soil particles、void rate、geostatic stress and plasticity limit. In order to predict the collapsibility of loess, four machine learning methods: Support Vector Machine (SVM), Random Subspace Based Support Vector Machine (RSSVM), Random Forest (RF) and Naïve Bayes Tree (NBTree), are studied and compared. The receiver operating characteristic (ROC) curve indicators, standard error (SD) and 95% confidence interval (CI) are used to verify and compare the models in different research areas. The results show that: RF model is the most efficient in predicting the collapsibility of loess in Xining, and its AUC average is above 80%, which can be used in engineering practice.

scholarly journals Artificial neural networks as a tool for pattern recognition and electrofacies analysis in Polish palaeozoic shale gas formations

2019 ◽  
Vol 67 (6) ◽  
pp. 1991-2003 ◽  
Author(s):  
Edyta Puskarczyk
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
P Wave ◽  
Well Logs ◽  
Support Vector ◽  
Western Slope ◽  
Data Set ◽  
Artificial Neural ◽  
Gas Bearing

Abstract Unconventional oil and gas reservoirs from the lower Palaeozoic basin at the western slope of the East European Craton were taken into account in this study. The aim was to supply and improve standard well logs interpretation based on machine learning methods, especially ANNs. ANNs were used on standard well logging data, e.g. P-wave velocity, density, resistivity, neutron porosity, radioactivity and photoelectric factor. During the calculations, information about lithology or stratigraphy was not taken into account. We apply different methods of classification: cluster analysis, support vector machine and artificial neural network—Kohonen algorithm. We compare the results and analyse obtained electrofacies. Machine learning method–support vector machine SVM was used for classification. For the same data set, SVM algorithm application results were compared to the results of the Kohonen algorithm. The results were very similar. We obtained very good agreement of results. Kohonen algorithm (ANN) was used for pattern recognition and identification of electrofacies. Kohonen algorithm was also used for geological interpretation of well logs data. As a result of Kohonen algorithm application, groups corresponding to the gas-bearing intervals were found. Analysis showed diversification between gas-bearing formations and surrounding beds. It is also shown that internal diversification in gas-saturated beds is present. It is concluded that ANN appeared to be a useful and quick tool for preliminary classification of members and gas-saturated identification.

scholarly journals Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jianwei Liu ◽  
Shuang Cheng Li ◽  
Xionglin Luo
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Adaptive Learning ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Support Vector ◽  
Data Set ◽  
Cancer Data ◽  
Public Data

Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data.L2regularization has been commonly used. If the training dataset contains many noise variables,L1regularization SVM will provide a better performance. However, bothL1andL2are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweightedp-norm regularization support vector machine for 0 <p≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that apvalue of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using apvalue less thanL1norm. Moreover, we observe that the proposedLppenalty is more robust to noise variables than theL1andL2penalties.

scholarly journals A Machine Learning Approach to Determine Maturity Stages of Tomatoes

2017 ◽  
Vol 10 (3) ◽  
pp. 683-690 ◽  
Author(s):  
Kamalpreet Kaur ◽  
O.P. Guptata
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Fruits And Vegetables ◽  
Support Vector ◽  
Large Set ◽  
Accuracy Score ◽  
Data Set ◽  
Maturity Stages ◽  
Total Data

Maturity checking has become mandatory for the food industries as well as for the farmers so as to ensure that the fruits and vegetables are not diseased and are ripe. However, manual inspection leads to human error, unripe fruits and vegetables may decrease the production [3]. Thus, this study proposes a Tomato Classification system for determining maturity stages of tomato through Machine Learning which involves training of different algorithms like Decision Tree, Logistic Regression, Gradient Boosting, Random Forest, Support Vector Machine, K-NN and XG Boost. This system consists of image collection, feature extraction and training the classifiers on 80% of the total data. Rest 20% of the total data is used for the testing purpose. It is concluded from the results that the performance of the classifier depends on the size and kind of features extracted from the data set. The results are obtained in the form of Learning Curve, Confusion Matrix and Accuracy Score. It is observed that out of seven classifiers, Random Forest is successful with 92.49% accuracy due to its high capability of handling large set of data. Support Vector Machine has shown the least accuracy due to its inability to train large data set.

scholarly journals Using Support Vector Machine Detection of Breast Cancer in Early stage

2020 ◽  
pp. 213-216
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Sensitivity Specificity

The Breast Cancer is disease which tremendously increased in women’s nowadays. Mammography is technique of low-powered X-ray diagnosis approach for detection and diagnosis of cancer diseases at early stage. The proposed system shows the solution of two problems. First shows to detect tumors as suspicious regions with a weak contrast to their background and second shows way to extract features which categorize tumors. Hence this classification can be done with SVM, a great method of statistical learning has made significant achievement in various field. Discovered in the early 90’s, which led to an interest in machine learning? Here the different types of tumor like Benign, Malignant, or Normal image are classified using the SVM classifier. This techniques shows how easily we can detect region of tumor is present in mammogram images with more than 80% of accuracy rates for linear classification using SVM. The 10-fold cross validation to get an accurate outcome is been used by proposed system. The Wisconsin breast cancer diagnosis data set is referred from UCI machine learning repository. The considering accuracy, sensitivity, specificity, false discovery rate, false omission rate and Matthews’s correlation coefficient is appraised in the proposed system. This Provides good result for both training and testing phase. The techniques also shows accuracy of 98.57% and 97.14% by use of Support Vector Machine and K-Nearest Neighbors

scholarly journals Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM)

2021 ◽  
Vol 8 (1) ◽  
pp. 147
Author(s):  
Primandani Arsi ◽  
Retno Waluyo
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Public Discourse ◽  
Capital City ◽  
Support Vector ◽  
The Public ◽  
Machine Learning Methods ◽  
Classification Technique

<p class="Abstrak">Dewasa ini, media sosial berkembang pesat di internet, salah satu yang banyak digemari adalah Twitter. Berbagai topik ramai diperbincangkan di Twitter mulai dari ekonomi, politik, sosial, budaya, hukum dan lain-lain. Salah satu topik yang ramai diperbincangkan di Twitter adalah terkait isu pemindahan ibu kota Indonesia. Namun dibalik hal tersebut terdapat kontroversi dari  pihak yang merasa  pro dan kontra, masing-masing memiiki sudut pandang yang berbeda.  Hal ini menyebabkan munculnya fenomena perdebatan khususnya di Twitter yang sebenarnya menunjukkan perhatian kolektif mengenai wacana publik tersebut. Analisis sentimen adalah proses mengekstraksi, memahami dan mengolah data berupa teks yang tidak terstruktur secara otomatis guna mendapatkan informasi sentimen yang terdapat pada sebuah kalimat pendapat atau opini. Dalam penerapan analisis sentimen menggunakan metode <em>machine learning</em> terdapat beberapa metode yang sering digunakan. Dalam penelitian ini diusulkan metode <em>Support Vector Machine</em> (SVM) untuk diterapkan pada <em>tweets</em> topik pemindahan ibu kota Indonesia untuk tujuan klasifikasi kelas sentimen pada media sosial <em>twitter</em>. Teknis klasifikasi  dilakukan dengan cara mengklasifikasikan menjadi 2 kelas yakni positif dan negatif. Berdasarkan hasil pengujian yang dilakukan terhadap <em>tweets</em> sentimen pemindahan ibu kota dari media sosial twitter sebanyak 1.236 <em>tweets</em> (404 positif dan 832 negatif) menggunakan SVM diperoleh akurasi =96,68%, <em>precision=</em>95.82%, <em>recall</em>=94.04% dan AUC = 0,979.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><em>Today, social media is growing fast on the internet<span lang="EN-GB">.</span><span lang="EN-GB">On</span>e of the most popular<span lang="EN-GB"> social media</span> is Twitter. Many topics are discussed on Twitter such as economic, politic, socia<span lang="EN-GB">l</span>, cultur<span lang="EN-GB">e</span>, <span lang="EN-GB">and l</span>aw<span lang="EN-GB">.</span> One of the hot topics discussed on Twitter is the issue of relocating Indonesia's capital city. However<span lang="EN-GB">, </span>there is controversy from supporters and opponents<span lang="EN-GB">. They</span> have different views. <span lang="EN-GB">This issue leads to</span> a phenomenon of debate on Twitter <span lang="EN-GB">that </span>actually show<span lang="EN-GB">s a </span>collective concern about the public discourse. Sentiment analysis is a process of extracting, understand<span lang="EN-GB">ing </span>and process<span lang="EN-GB">ing</span> unstructured data to get sentiment information which is<span lang="EN-GB"> found</span> in an opinion sentence. Application of sentiment analysis using machine learning methods<span lang="EN-GB"> shows that</span> there are several methods that are often used. In this study, the Support Vector Machine (SVM) method is proposed to be applied to tweets on the topic of relocating Indonesia's capital city for sentiment classification on social media twitter. The classification technique is carried out into 2 classes, namely positive and negative. Based on testing on the sentiment of relocating Indonesia's capital city from social media twitter from 1,116 tweets (404 positive and 832 negative) using SVM obtained accuracy = 96.68%, precision = 95.82%, recall = 94.04% and AUC = 0.979.</em></em></p>

Detection of Breast Cancer Using Machine Learning Support Vector Machine Algorithm

2019 ◽  
Vol 16 (2) ◽  
pp. 441-444
Author(s):  
D. V. Soundari ◽  
R. Padmapriya ◽  
C. Thirumariselvi ◽  
N. Nanthini ◽  
K. Priyadharsini
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Learning Support ◽  
Support Vector Machine Algorithm ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data ◽  
Hormone Imbalance

A woman majorly suffers due to breast cancer which is due to hormone imbalance. It leads to huge death in recent years. Early detection of the breast cancer is more important to prevent human lives. Image Processing plays an important to classify and detect the same. So this paper proposes machine learning based cancer classification using support vector machine with Wisconsin breast cancer data set.

scholarly journals Heart Disease Prediction and Performance Assessment through Attribute Element Diminution using Machine Learning

2019 ◽  
Vol 8 (11) ◽  
pp. 604-609
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Random Forest ◽  
Heart Diseases ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Set ◽  
Raw Data ◽  
Reduced Data

In today’s modern world, the human beings are affected with heart disease irrespective of the age. With the advancement of technological growth, predicting the availability of Heart diseases still remains a challenging issue. The difficulty of predicting the heart disease prevails due to the lack of availability of the symptoms. According to World Health Organization, 33% of population died due to heart diseases. For this, the diagnosis of heart diseases is made by complex combination of clinical data. With this overview, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for predicting the level of heart disease. The prediction of heart disease classes are achieved in four ways. Firstly, the data set is preprocessed with Feature Scaling and Missing Values. Secondly, the raw data set is fitted to classifiers like logistic regression, KNN classifier, Support Vector Machine, Kernel Support Vector Machine, Naive Bayes, Random Forest and Decision Tree classifiers. Third, the raw data set is subjected to dimensionality reduction using Principal Component Analysis to project the dataset with important components. The dimensionality PCA reduced data set is fitted to the above-mentioned classifiers. Fourth, the performance comparison of raw data set and PCA reduced data set is done by analyzing the performance metrics like Precision, Recall, Accuracy and F-score. The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that Random forest is found to be effective with the accuracy of 89% without applying PCA, 85% with five component PCA and 86% with seven component PCA.

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