scholarly journals A Fall and Near-Fall Assessment and Evaluation System

2009 ◽  
Vol 3 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Anh Dinh ◽  
Yang Shi ◽  
Daniel Teng ◽  
Amitoz Ralhan ◽  
Li Chen ◽  
...  
Keyword(s):  
Evaluation System ◽  
Model Building ◽  
Fall Detection ◽  
High Accuracy ◽  
Ease Of Use ◽  
Machine Learning Algorithms ◽  
Wireless Sensor ◽  
Assessment And Evaluation ◽  
Near Falls ◽  
Bayes Algorithm

The FANFARE (Falls And Near Falls Assessment Research and Evaluation) project has developed a system to fulfill the need for a wearable device to collect data for fall and near-falls analysis. The system consists of a computer and a wireless sensor network to measure, display, and store fall related parameters such as postural activities and heart rate variability. Ease of use and low power are considered in the design. The system was built and tested successfully. Different machine learning algorithms were applied to the stored data for fall and near-fall evaluation. Results indicate that the Naïve Bayes algorithm is the best choice, due to its fast model building and high accuracy in fall detection.

scholarly journals Construction of Music Teaching Evaluation Model Based on Weighted Naïve Bayes

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiongjun Xia ◽  
Jin Yan
Keyword(s):  
Evaluation System ◽  
Naive Bayes ◽  
Evaluation Model ◽  
Audio Signal ◽  
Naïve Bayes ◽  
Teaching Evaluation ◽  
Machine Learning Algorithms ◽  
Music Teaching ◽  
Model Based ◽  
Bayes Algorithm

Evaluation of music teaching is a highly subjective task often depending upon experts to assess both the technical and artistic characteristics of performance from the audio signal. This article explores the task of building computational models for evaluating music teaching using machine learning algorithms. As one of the widely used methods to build classifiers, the Naïve Bayes algorithm has become one of the most popular music teaching evaluation methods because of its strong prior knowledge, learning features, and high classification performance. In this article, we propose a music teaching evaluation model based on the weighted Naïve Bayes algorithm. Moreover, a weighted Bayesian classification incremental learning approach is employed to improve the efficiency of the music teaching evaluation system. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in the context of music teaching evaluation.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

scholarly journals Financial Context News Sentiment Analysis for the Lithuanian Language

2021 ◽  
Vol 11 (10) ◽  
pp. 4443
Author(s):  
Rokas Štrimaitis ◽  
Pavel Stefanovič ◽  
Simona Ramanauskaitė ◽  
Asta Slotkienė
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Experimental Investigations ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Bayes Algorithm ◽  
Website Content

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

scholarly journals Prediction of Heart Disease Using Machine Learning Algorithms

2018 ◽  
Vol 7 (2.32) ◽  
pp. 363 ◽  
Author(s):  
N Rajesh ◽  
Maneesha T ◽  
Shaik Hafeez ◽  
Hari Krishna
Keyword(s):  
Risk Factors ◽  
Heart Disease ◽  
Decision Trees ◽  
Naive Bayes ◽  
Heart Diseases ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Common Disease ◽  
Bayes Algorithm ◽  
The One

Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the accurate results.  

Multi-Source Hybrid Energy Harvesting Wireless Sensor Network Framework for Addressing Energy Unpredictability Issues

2021 ◽  
Vol 57 ◽  
pp. 225-241
Author(s):  
Aditi Paul ◽  
Indu Pandey
Keyword(s):  
Wireless Sensor Network ◽  
Energy Harvesting ◽  
Sensor Network ◽  
Complex Structure ◽  
Research Direction ◽  
Ease Of Use ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Functional Efficiency ◽  
Design Paradigm

Energy harvesting wireless sensor network (EH-WSN) harvests energy from the environment to supply power to the sensor nodes which apparently enhances their lifetime. However, the unpredictable nature of the resources throws challenges to the sustainability of energy supply for the continuous network operation. This creates a gap between unstable energy harvesting rates & energy requirements of the nodes of the network. The state-of-the-art algorithms proposed so far to address this problem domain are not able to bridge the gap fully to standardize the framework. Hence there is considerable scope of research to create a trade-off between EH techniques and specially designed protocols for in EH-WSN. Current study evaluates the performance and efficiency of some futuristic techniques which incorporate advanced tools and algorithms. The study aims to identify the strength and weaknesses of the proposed techniques which can emerge specific research requirement in this field. Finally, we propose a research direction towards Multi-source Hybrid EH-WSN (MHEHWSN) which is able to maximize energy availability and functional efficiency. The scope of this study is to develop a notion of a framework which eliminates the limitations of very recent techniques of EH-WSN by including multiple energy resources to extract required energy even in presence of unpredictability. However, keeping in mind the ease of use and less complex structure Multi-source hybrid EH technique requires a careful design paradigm.

Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

2022 ◽  
pp. 27-50
Author(s):  
Rajalaxmi Prabhu B. ◽  
Seema S.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Model Building ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Large Data Sets ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Important Challenge

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.

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