Physical Activity Classification with Wearable Sensor Data in an eCoach Recommendation System (Preprint)

UNSTRUCTURED Leading a sedentary lifestyle may cause numerous health problems. Therefore, sedentary lifestyle changes should be given priority to avoid severe damage. Research in eHealth can provide methods to enrich personal healthcare with Information and Communication Technologies (ICTs). An eCoach system may allow people to manage a healthy lifestyle with health state monitoring and personalized recommendations. Using machine learning (ML) techniques, this study investigated the possibility of classifying daily physical activity for adults into the following classes - sedentary, low active, active, active, highly active, and rigorous active. The daily total step count, total daily minutes of sedentary time, low physical activity (LPA), medium physical activity (MPA), and vigorous physical activity (VPA) served as input for the classification models. We first used publicly available Fitbit data to build the classification models. Second, using the transfer learning approach, we re-used the top five best-performing models on a real dataset as collected from the MOX2-5 wearable medical-grade activity sensor. We found that ensemble ExtraTreesClassifier with an estimator value of 150 outperformed other classifiers with a mean accuracy score of 99.72% for single feature and support vector classifier (SVC) with “linear” kernel outpaced other classifiers with a mean accuracy score of 99.14% for five features, for the public Fitbit datasets. To demonstrate the practical usefulness of the classifiers, we conceptualized how the classifier model can be used in an eCoach prototype system to attain personalized activity goals (e.g., stay active for the entire week). After transfer learning, K-Nearest-Neighbor (KNN) outpaced the other four classifiers for a single feature, and SVC with a “linear” kernel outdid the other four classifiers for multiple features.

Multi-class classification for large datasets with optimized SVM by non-linear kernel function

Most important part of Support Vector Machines(SVM) are the kernels. Although there are several widely used kernel functions, a carefully designed kernel will help improve the accuracy of SVM. The proposed work aims to develop a new kernel function for a multi-class support vector machine, perform experiments on various data sets, and compare them with other classification methods. Directly it is not possible multiclass classification with SVM. In this proposed work first designed a model for binary class then extended with the one-verses-all approach. Experimental results have proved the efficiency of the new kernel function. The proposed kernel reduces misclassification and time. Other classification methods observed better results for some data sets collected from the UCI repository.

Orchid types classification using supervised learning algorithm based on feature and color extraction

Orchid flower as ornamental plants with a variety of types where one type of orchid has various characteristics in the form of different shapes and colors. Here, we chosen support vector machine (SVM), Naïve Bayes, and k-nearest neighbor algorithm which generates text input. This system aims to assist the community in recognizing orchid plants based on their type. We used more than 2250 and 1500 images for training and testing respectively which consists of 15 types. Testing result shown impact analysis of comparison of three supervised algorithm using extraction or not and several variety distance. Here, we used SVM in Linear, Polynomial, and Gaussian kernel while k-nearest neighbor operated in distance starting from K1 until K11. Based on experimental results provide Linear kernel as best classifier and extraction process had been increase accuracy. Compared with Naïve Bayes in 66%, and a highest KNN in K=1 and d=1 is 98%, SVM had a better accuracy. SVM-GLCM-HSV better than SVM-HSV only that achieved 98.13% and 93.06% respectively both in Linear kernel. On the other side, a combination of SVM-KNN yield highest accuracy better than selected algorithm here.

Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

The strength of the company's competitiveness is needed because the current industrial development is very rapid. It is necessary to maintain the quality and quantity of the products produced according to company standards. One of the companies that must maintain the quality and quantity is PT. XYZ is a clay tile company. The classification of products used by this company to maintain good quality is three classes: good tile, white stone tile, and cracked tile. However, quality control based on classification still uses the traditional way by relying on sight. It can increase errors and slow down the process. It can be overcome with artificial visual detectors. It is a result of the rapid development of automation. So to detect defects, this research can use image preprocessing, supervised learning algorithms, and measurement methods. Support Vector Machine (SVM) is used in this study to perform classification, while feature extraction on clay tiles used the Local Binary Pattern (LBP) method. The algorithm is made using python, while for image retrieval, raspberry pi is used. The linear kernel on the SVM algorithm is used in this study. The conclusion in this study obtained 86.95% is the highest accuracy with a linear kernel. It takes 10.625 seconds to classify.

PREDICTION OF OZONE (O3) VALUES USING SUPPORT VECTOR REGRESSION METHOD

A large number of motor vehicles that cause congestion is a major factor in the poor air quality in big cities. Ozone (O3) is one of the main indicators in measuring the level of air pollution in the city of Surabaya to find out how air quality. Prediction of Ozone (O3) value is important as a support for the community and government in efforts to improve the air quality. This study aims to predict the value of Ozone (O3) in the form of time series data using the Support Vector Regression (SVR) method with the Linear, Polynomial, RBF, and ANOVA kernels. The data used in this study are 549 primary data from the daily average of ozone (O3) value of Surabaya in the period 1 July 2017 - 31 December 2018. The data will be used in the training and testing process until prediction results are obtained. The results obtained from this study are the Linear kernel produces the best prediction model with a MAPE value of 21.78% with a parameter value 𝜆 = 0.3; 𝜀 = 0.00001; cLR = 0.005; and C = 0.5. The results of the Polynomial kernel are not much different from the Linear kernel which has a MAPE value of 21.83%. While the RBF and ANOVA kernels each produce a model with MAPE value of 24.49% and 22.0%. These results indicate that the SVR method with the kernels used can predict Ozone values quite well.

Perbandingan Performa Model Data Mining untuk Prediksi Dropout Mahasiwa

Penentuan teknik/model data mining yang tepat pada sebuah kasus sangat penting untuk mendapatkan model yang baik (tingkat akurat tinggi dan kesesuaiannya dengan masalah yang dipecahkan). Penelitian ini bertujuan untuk membandingkan performa teknik data mining untuk diterapkan pada kasus prediksi dropout mahasiswa. Perbandingan performa dilakukan menggunakan library PyCaret pada Python untuk melakukan pemodelan menggunakan 14 model / teknik data mining yaitu: Extreme Gradient Boosting, Ada Boost Classifier, Light Gradient Boosting Machine, Random Forest Classifier, Gradient Boosting Classifier, Extra Trees Classifier, Decision Tree Classifier, K Neighbors Classifier, Naive Bayes, Ridge Classifier, Linear Discriminant Analysis, Logistic Regression, SVM - Linear Kernel, dan Quadratic Discriminant Analysis. Metrik evaluasi performa model yang digunakan yaitu Accuracy, AUC, Recall, Precision, F1, Kappa, dan MCC (Matthews correlation coefficient). Hasil eksperimen menunjukkan bahwa kasus prediksi dropout mahasiswa lebih tepat jika dimodelkan dengan model berbasis ensemble learner dan pohon keputusan dengan akurasi mencapai 99%. Pohon keputusan memiliki keunggulan dibandingkan model lain seperti SVM - Linear Kernel dan Quadratic Discriminant Analysis karena ia dapat dengan lebih detil dalam memisahkan data ke dalam kedua kelas target. Setelah dilakukan penyesuaian atribut, pembuangan data dengan missing values, dan parameter tuning, didapatkan hasil akurasi yang mirip dari berbagai model yaitu sebesar 87%. Perbedaan akurasi antar model menjadi sangat kecil di saat atribut data yang digunakan sedikit.

Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter

Sentiment analysis is a technique to extract information of one’s perception, called sentiment, on an issue or event. This study employs sentiment analysis to classify society’s response on covid-19 virus posted at twitter into 4 polars, namely happy, sad, angry, and scared. Classification technique used is support vector machine (SVM) method which compares the classification performance figure of 2 linear kernel functions, linear and polynomial. There were 400 tweet data used where each sentiment class consists of 100 data. Using the testing method of k-fold cross validation, the result shows the accuracy value of linear kernel function is 0.28 for unigram feature and 0.36 for trigram feature. These figures are lower compared to accuracy value of kernel polynomial with 0.34 and 0.48 for unigram and trigram feature respectively. On the other hand, testing method of confusion matrix suggests the highest performance is obtained by using kernel polynomial with accuracy value of 0.51, precision of 0.43, recall of 0.45, and f-measure of 0.51.

Toxic Comment Classification on Social Media Using Support Vector Machine and Chi Square Feature Selection

The use of social media in society continues to increase over time and the ease of access and familiarity of social media then make it easier for an irresponsible user to do unethical things such as spreading hatred, defamation, radicalism, pornography so on. Although there are regulations that govern all the activities on social media. However, the regulations are still not working effectively. In this study, we conducted a classification of toxic comments containing unethical matters using the SVM method with TF-IDF as the feature extraction and Chi Square as the feature selection. The best performance result based on the experiment that has been carried out is by using the SVM model with a linear kernel, without implementing Chi Square, and using stemming and stopwords removal with the F1 − Score equal to 76.57%.