Using Machine Learning to Perform Proximity Detection - Classifying Bluetooth Beacon RSSI V alues

Proximity Detection ◽

Tree Classifier ◽

Testing Accuracy

This project focuses on using machine learning classification algorithms to determine whether two people are 6 feet apart or not. Two Raspberry Pis were used simulate smart phones. RSSI values of the Bluetooth beacons transmitted between the Raspberry Pis were collected and recorded to train the classifier. The Gaussian Support Vector Machine Classifer yielded the highest testing accuracy of 79.670 and the Decision Tree Classifier yielded the highest AUC of 0.80.

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

Sentiment Analysis on Social Media Big Data With Multiple Tweet Words

10.35940/ijitee.j9684.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 3429-3434 ◽

Cited By ~ 2

Keyword(s):

Machine Learning ◽

Social Media ◽

Big Data ◽

Sentiment Analysis ◽

Language Processing ◽

Sentiment Classification ◽

Support Vector ◽

Tree Classifier

The main objective of this paper is Analyze the reviews of Social Media Big Data of E-Commerce product’s. And provides helpful result to online shopping customers about the product quality and also provides helpful decision making idea to the business about the customer’s mostly liking and buying products. This covers all features or opinion words, like capitalized words, sequence of repeated letters, emoji, slang words, exclamatory words, intensifiers, modifiers, conjunction words and negation words etc available in tweets. The existing work has considered only two or three features to perform Sentiment Analysis with the machine learning technique Natural Language Processing (NLP). In this proposed work familiar Machine Learning classification models namely Multinomial Naïve Bayes, Support Vector Machine, Decision Tree Classifier, and, Random Forest Classifier are used for sentiment classification. The sentiment classification is used as a decision support system for the customers and also for the business.

Diabetes Prediction Using Machine Learning Techniques

Journal of Intelligent Systems with Applications ◽

10.54856/10.54856/jiswa.202112183 ◽

2021 ◽

pp. 150-152

Author(s):

Seyma Kiziltas Koc ◽

Mustafa Yeniad

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

High Performance ◽

Nearest Neighbor ◽

Classification Performance ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbor ◽

Machine Learning Classification

Technologies which are used in the healthcare industry are changing rapidly because the technology is evolving to improve people's lifestyles constantly. For instance, different technological devices are used for the diagnosis and treatment of diseases. It has been revealed that diagnosis of disease can be made by computer systems with developing technology.Machine learning algorithms are frequently used tools because of their high performance in the field of health as well as many field. The aim of this study is to investigate different machine learning classification algorithms that can be used in the diagnosis of diabetes and to make comparative analyzes according to the metrics in the literature. In the study, seven classification algorithms were used in the literature. These algorithms are Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Random Forest, Decision Trees, Support Vector Machine and Naive Bayes. Firstly, classification performance of algorithms are compared. These comparisons are based on accuracy, sensitivity, precision, and F1-score. The results obtained showed that support vector machine algorithm had the highest accuracy with 78.65%.

Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2021/v40i3831588 ◽

2021 ◽

pp. 29-37

Author(s):

Premkumar Borugadda ◽

R. Lakshmi ◽

Surla Govindu

Keyword(s):

Machine Learning ◽

Precision Agriculture ◽

Performance Model ◽

Gradient Boosting ◽

Support Vector ◽

Cotton Leaf ◽

Tree Classifier ◽

Fully Connected

Computer vision has been demonstrated as state-of-the-art technology in precision agriculture in recent years. In this paper, an Alex net model was implemented to identify and classify cotton leaf diseases. Cotton Dataset consists of 2275 images, in which 1952 images were used for training and 324 images were used for validation. Five convolutional layers of the AlexNet deep learning technique is applied for features extraction from raw data. They were remaining three fully connected layers of AlexNet and machine learning classification algorithms such as Ada Boost Classifier (ABC), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC). K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Classifier (SVC) are used for classification. Three fully connected layers of Alex Net provided the best performance model with a 94.92% F1_score at the training time of about 51min.

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

An Ontology Driven System to Predict Diabetes with Machine Learning Techniques

10.35940/ijitee.b7586.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 4005-4011

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Decision Tree ◽

Early Stage ◽

Machine Learning Techniques ◽

Support Vector ◽

Diagnostic Center ◽

Mental Trauma

Diabetes Mellitus is considered one of the chronic diseases of humankind which causes an increase in blood sugar. Many complications are reported if DM remains untreated and unidentified. Identification of this disease requires a lot of physical and mental trauma and effort which involves visiting a doctor, blood and urine test at the diagnostic center which consumes more time. Difficulties can be over crossed using the trending technology of Machine learning. The idea of the model is to prognosticate the occurrence of a diabetic with high accuracy. Therefore, two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage. Therefore two machine learning classification algorithms namely Fine Decision Tree and Support Vector Machine are used in this experiment to detect diabetes at an early stage.

Afaan Oromo News Text Categorization using Decision Tree Classifier and Support Vector Machine: A Machine Learning Approach

International Journal of Computer Trends and Technology ◽

10.14445/22312803/ijctt-v47p104 ◽

2017 ◽

Vol 47 (1) ◽

pp. 29-41

Author(s):

Kamal Mohammed Jimalo ◽

◽

Ramesh Babu P ◽

Yaregal Assabie

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Decision Tree ◽

Text Categorization ◽

Support Vector ◽

Learning Approach ◽

Tree Classifier ◽

Machine Learning Approach

Afaan Oromo News Text Categorization using Decision Tree Classifier and Support Vector Machine: A Machine Learning Approach

International Journal of Computer Trends and Technology ◽

10.14445/22312803/ijctt-v47p101 ◽

2017 ◽

Vol 47 (1) ◽

pp. 1-12

Author(s):

Kamal Mohammed Jimalo ◽

◽

Ramesh Babu P ◽

Yaregal Assabie

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Decision Tree ◽

Text Categorization ◽

Support Vector ◽

Learning Approach ◽

Tree Classifier ◽

Machine Learning Approach

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

Exploration of Neighbor Kernels and Feature Estimators for Heart Disease Prediction using Machine Learning

10.35940/ijitee.l3472.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 599-605

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Random Forest ◽

Random Forest Classifier ◽

Support Vector ◽

Disease Prediction ◽

Support Vector Classifier ◽

Tree Classifier

In the growing era of technological world, the people are suffered with various diseases. The common disease faced by the population irrespective of the age is the heart disease. Though the world is blooming in technological aspects, the prediction and the identification of the heart disease still remains a challenging issue. Due to the deficiency of the availability of patient symptoms, the prediction of heart disease is a disputed charge. With this overview, we have used Heart Disease Prediction dataset extorted from UCI Machine Learning Repository for the analysis and comparison of various parameters in the classification algorithms. The parameter analysis of various classification algorithms of heart disease classes are done in five ways. Firstly, the analysis of dataset is done by exploiting the correlation matrix, feature importance analysis, Target distribution of the dataset and Disease probability based on the density distribution of age and sex. Secondly, the dataset is fitted to K-Nearest Neighbor classifier to analyze the performance for the various combinations of neighbors with and without PCA. Thirdly, the dataset is fitted to Support Vector classifier to analyze the performance for the various combinations of kernels with and without PCA. Fourth, the dataset is fitted to Decision Tree classifier to analyze the performance for the various combinations of features with and without PCA. Fifth, the dataset is fitted to Random Forest classifier to analyze the performance for the various levels of estimators with and without PCA. The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that for KNN classifier, the performance for 12 neighbours is found to be effective with 0.52 before applying PCA and 0.53 after applying PCA. For Support Vector classifier, the rbf kernel is found to be effective with the score of 0.519 with and without PCA. For Decision Tree classifier, before applying PCA, the score is 0.47 for 7 features and after applying PCA, the score is 0.49 for 4 features. For, Random Forest Classifier, before applying PCA, the score is 0.53 for 500 estimators and after applying PCA, the score is 0.52 for 500 estimators.

Diabetes Prediction Using Machine Learning Techniques

Journal of Intelligent Systems with Applications ◽

10.54856/jiswa.202112183 ◽

2021 ◽

pp. 150-152

Author(s):

Seyma Kiziltas Koc ◽

Mustafa Yeniad

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

High Performance ◽

Nearest Neighbor ◽

Classification Performance ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbor ◽

Machine Learning Classification

Technologies which are used in the healthcare industry are changing rapidly because the technology is evolving to improve people's lifestyles constantly. For instance, different technological devices are used for the diagnosis and treatment of diseases. It has been revealed that diagnosis of disease can be made by computer systems with developing technology.Machine learning algorithms are frequently used tools because of their high performance in the field of health as well as many field. The aim of this study is to investigate different machine learning classification algorithms that can be used in the diagnosis of diabetes and to make comparative analyzes according to the metrics in the literature. In the study, seven classification algorithms were used in the literature. These algorithms are Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Random Forest, Decision Trees, Support Vector Machine and Naive Bayes. Firstly, classification performance of algorithms are compared. These comparisons are based on accuracy, sensitivity, precision, and F1-score. The results obtained showed that support vector machine algorithm had the highest accuracy with 78.65%.

Prototype Classification: Insights from Machine Learning

Neural Computation ◽

10.1162/neco.2009.01-07-443 ◽

2009 ◽

Vol 21 (1) ◽

pp. 272-300 ◽

Cited By ~ 8

Author(s):

Arnulf B. A. Graf ◽

Olivier Bousquet ◽

Gunnar Rätsch ◽

Bernhard Schölkopf

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Relevance Vector Machine ◽

Support Vector ◽

Normal Vector ◽

Formal Framework ◽

Soft Margin ◽

Prototype Classification

We shed light on the discrimination between patterns belonging to two different classes by casting this decoding problem into a generalized prototype framework. The discrimination process is then separated into two stages: a projection stage that reduces the dimensionality of the data by projecting it on a line and a threshold stage where the distributions of the projected patterns of both classes are separated. For this, we extend the popular mean-of-class prototype classification using algorithms from machine learning that satisfy a set of invariance properties. We report a simple yet general approach to express different types of linear classification algorithms in an identical and easy-to-visualize formal framework using generalized prototypes where these prototypes are used to express the normal vector and offset of the hyperplane. We investigate non-margin classifiers such as the classical prototype classifier, the Fisher classifier, and the relevance vector machine. We then study hard and soft margin classifiers such as the support vector machine and a boosted version of the prototype classifier. Subsequently, we relate mean-of-class prototype classification to other classification algorithms by showing that the prototype classifier is a limit of any soft margin classifier and that boosting a prototype classifier yields the support vector machine. While giving novel insights into classification per se by presenting a common and unified formalism, our generalized prototype framework also provides an efficient visualization and a principled comparison of machine learning classification.