The Machine Learning Method: Analysis of Experimental Results

2004 ◽  
Vol 11 (3) ◽  
pp. 215-232
Author(s):  
Jaroslav E. Poliscuk
Keyword(s):  
Machine Learning ◽  
Experimental Results ◽  
Machine Learning Method ◽  
Learning Method ◽  
Method Analysis

scholarly journals Application of Sensors in Intelligent Clothing Design

2018 ◽  
Vol 14 (06) ◽  
pp. 4
Author(s):  
Shali Jiang ◽  
Qiong Ren
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Experimental Results ◽  
Machine Learning Method ◽  
Learning Method ◽  
Clothing Design ◽  
Artificial Intelligence Technology ◽  
Contact Sensor ◽  
Intelligent Clothing ◽  
Contact Sensors

<p class="0abstract"><span lang="EN-US">In order to study the application of sensors in intelligent clothing design, the artificially intelligent cutting-edge technology -machine learning method was proposed to combine a variety of signals of non-contact sensors in several different positions. Higher accuracy was achieved, while maintaining the comfort brought by a non-contact sensor. The experimental results showed that the proposed strategy focused on the combination of clothing design technology and artificial intelligence technology. As a result, without changing the sensor materials, it enhances the comfort and precision of clothing, eliminates the comfort reduced by sensor close to the skin, and transforms inaccurate measurement into accurate measurement. </span></p>

scholarly journals Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2981
Author(s):  
Haotai Sun ◽  
Xiaodong Zhu ◽  
Yuanning Liu ◽  
Wentao Liu
Keyword(s):  
Machine Learning ◽  
Radio Frequency ◽  
Indoor Navigation ◽  
Experimental Results ◽  
Machine Learning Method ◽  
Learning Method ◽  
Dual Frequency ◽  
Evaluation Algorithm ◽  
Precision And Accuracy ◽  
Navigation Service

Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone.

Appraisal of Two Arabic Opinion Summarization Methods: Statistical Versus Machine Learning

2020 ◽  
Author(s):  
Imen Touati ◽  
Mariem Ellouze ◽  
Marwa Graja ◽  
Lamia Hadrich Belguith
Keyword(s):  
Machine Learning ◽  
Arabic Language ◽  
Experimental Results ◽  
Main Topic ◽  
News Article ◽  
Machine Learning Method ◽  
Learning Method ◽  
Opinion Summarization ◽  
Sentence Extraction ◽  
Many Sources

Abstract In this paper, we propose to overcome the challenge of digesting opinions in a news article. Our objective is to provide a summary of opinions delivered by many sources about a main topic in an Arabic news article. In literature, several studies addressed issues related to opinion summarization. However, we noticed a lack of studies that address this problem in Arabic language. So, we have proposed two different methods: multi-criteria and machine learning-based methods. We proceed by comparing the results provided by the proposed methods for opinionated sentence extraction. The proposed methods were evaluated using two feature types: text-based features and opinion-specific features. Experimental results show the robustness of machine learning method to extract opinionated sentences with consideration of two sets of features.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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