scholarly journals Detection of Tennis Activities with Wearable Sensors

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5004 ◽  
Author(s):  
Luis Benages Pardo ◽  
David Buldain Perez ◽  
Carlos Orrite Uruñuela
Keyword(s):  
Wearable Sensors ◽  
Low Energy ◽  
Sensor Nodes ◽  
Wireless Technology ◽  
Data Set ◽  
Supervised Training ◽  
Learning Techniques ◽  
Central Device ◽  
Tennis Match

This paper aims to design and implement a system capable of distinguishing between different activities carried out during a tennis match. The goal is to achieve the correct classification of a set of tennis strokes. The system must exhibit robustness to the variability of the height, age or sex of any subject that performs the actions. A new database is developed to meet this objective. The system is based on two sensor nodes using Bluetooth Low Energy (BLE) wireless technology to communicate with a PC that acts as a central device to collect the information received by the sensors. The data provided by these sensors are processed to calculate their spectrograms. Through the application of innovative deep learning techniques with semi-supervised training, it is possible to carry out the extraction of characteristics and the classification of activities. Preliminary results obtained with a data set of eight players, four women and four men have shown that our approach is able to address the problem of the diversity of human constitutions, weight and sex of different players, providing accuracy greater than 96.5% to recognize the tennis strokes of a new player never seen before by the system.

Classifying Images of Drought-Affected Area Using Deep Belief Network, kNN, and Random Forest Learning Techniques

2018 ◽  
pp. 102-119 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Pulkit Kulshrestha ◽  
Pijush Samui
Keyword(s):  
Random Forest ◽  
Performance Metrics ◽  
Deep Belief Network ◽  
Machine Learning Algorithms ◽  
Nearest Neighbour ◽  
Data Set ◽  
Belief Network ◽  
Learning Techniques ◽  
Severe Shortage

Drought is a condition of land in which the ground water faces a severe shortage. This condition affects the survival of plants and animals. Drought can impact ecosystem and agricultural productivity, severely. Hence, the economy also gets affected by this situation. This paper proposes Deep Belief Network (DBN) learning technique, which is one of the state of the art machine learning algorithms. This proposed work uses DBN, for classification of drought and non-drought images. Also, k nearest neighbour (kNN) and random forest learning methods have been proposed for the classification of the same drought images. The performance of the Deep Belief Network(DBN) has been compared with k nearest neighbour (kNN) and random forest. The data set has been split into 80:20, 70:30 and 60:40 as train and test. Finally, the effectiveness of the three proposed models have been measured by various performance metrics.

scholarly journals Multilink Internet-of-Things Sensor Communication Based on Bluetooth Low Energy Considering Scalability

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2335
Author(s):  
Dong-Suk Ryu ◽  
Yeung-Mo Yeon ◽  
Seung-Hee Kim
Keyword(s):  
Internet Of Things ◽  
Low Energy ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Central Processor ◽  
Bluetooth Low Energy ◽  
Switching Algorithm ◽  
Data Errors ◽  
The Internet Of Things ◽  
Central Device

As the growth rate of the internet-of-things (IoT) sensor market is expected to exceed 30%, a technology that can easily collect and processing a large number of various types of sensor data is gradually required. However, conventional multilink IoT sensor communication based on Bluetooth low energy (BLE) enables only the processing of up to 19 peripheral nodes per central device. This study suggested an alternative to increasing the number of IoT sensor nodes while minimizing the addition of a central processor by expanding the number of peripheral nodes that can be processed per central device through a new group-switching algorithm based on Bluetooth low energy (BLE). Furthermore, this involves verifying the relevancy of application to the industry field. This device environment lowered the possibility of data errors and equipment troubles due to communication interference between central processors, which is a critical advantage when applying it to industry. The scalability and various benefits of a group-switching algorithm are expected to help accelerate various services via the application of BLE 5 wireless communication by innovatively improving the constraint of accessing up to 19 nodes per central device in the conventional multilink IoT sensor communication.

scholarly journals Prediction and Classification of Low Birth Weight Data Using Machine Learning Techniques

2018 ◽  
Vol 3 (1) ◽  
pp. 18 ◽  
Author(s):  
Alfensi Faruk ◽  
Endro Setyo Cahyono
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Binary Logistic Regression ◽  
Machine Learning Techniques ◽  
Binary Logistic Regression Model ◽  
Data Set ◽  
Learning Techniques

Machine learning (ML) is a subject that focuses on the data analysis using various statistical tools and learning processes in order to gain more knowledge from the data. The objective of this research was to apply one of the ML techniques on the low birth weight (LBW) data in Indonesia. This research conducts two ML tasks; including prediction and classification. The binary logistic regression model was firstly employed on the train and the test data. Then; the random approach was also applied to the data set. The results showed that the binary logistic regression had a good performance for prediction; but it was a poor approach for classification. On the other hand; random forest approach has a very good performance for both prediction and classification of the LBW data set

scholarly journals Modeling cannabinoids from a large-scale sample of Cannabis sativa chemotypes

2020 ◽  
Author(s):  
Daniela Vergara ◽  
Reggie Gaudino ◽  
Thomas Blank ◽  
Brian Keegan
Keyword(s):  
Large Scale ◽  
Cannabis Sativa ◽  
Analytical Techniques ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Imputation Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Better Than

The accelerating legalization of Cannabis has opened the industry to using contemporary analytical techniques. The gene regulation and pharmacokinetics of dozens of cannabinoids remain poorly understood. Because retailers in many medical and recreational jurisdictions are required to report chemical concentrations of cannabinoids, commercial laboratories have growing chemotype datasets of diverse Cannabis cultivars. Using a data set of 17,600 cultivars tested by Steep Hill Inc., we apply machine learning techniques to interpolate missing chemotype observations and cluster cultivars together based on similarity. Our results show that cultivars cluster based on their chemotype, and that some imputation methods work better than others at grouping these cultivars based on chemotypic identity. However, due to the missing data for some of the cannabinoids their behavior could not be accurately predicted. These findings have implications for characterizing complex interactions in cannabinoid biosynthesis and improving phenotypical classification of Cannabis cultivars.

Machine Learning Approaches for Supernovae Classification

2017 ◽  
pp. 207-219 ◽  
Author(s):  
Surbhi Agrawal ◽  
Kakoli Bora ◽  
Swati Routh
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Type Ia Supernovae ◽  
Learning Approaches ◽  
Data Set ◽  
Learning Techniques ◽  
Type Ia ◽  
Ia Supernovae ◽  
The Universe

In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).

scholarly journals Classification of Autism Spectrum Disorder Data using Machine Learning Techniques

2019 ◽  
Vol 8 (6S) ◽  
pp. 565-569
Keyword(s):  
Machine Learning ◽  
Autism Spectrum ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Human Communication ◽  
Data Set ◽  
Complex Disorder ◽  
Learning Classifier ◽  
Learning Techniques

Autism is a neuro-developmental disability that affects human communication and behaviour. It is a condition that is associated with the complex disorder of the brain which can lead to significant changes in social interaction and behaviour of a human being.Machine learning techniques are being applied to autism data sets to discover useful hidden patterns and to construct predictive models for detecting its risk.This paper focuses on finding the best machine learning classifier on the UCI autism disorder data set for identifying the main factors associated with autism. The results obtained using Multilayer Perceptron, Naive Bayes Classifier and Bayesian Networkwere compared with J48 Decision tree algorithm. The superiority of MultilayerPerceptron over the well known classification algorithms in predicting the autism risk is established in this paper.

Machine Learning Approaches for Supernovae Classification

2020 ◽  
pp. 294-306
Author(s):  
Surbhi Agrawal ◽  
Kakoli Bora ◽  
Swati Routh
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Type Ia Supernovae ◽  
Learning Approaches ◽  
Data Set ◽  
Learning Techniques ◽  
Type Ia ◽  
Ia Supernovae ◽  
The Universe

In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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