scholarly journals A Fast Online Classification Method of Solid Wood Floors Based on Stochastic Sampling and Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2899
Author(s):  
Tingting Zhu ◽  
Kun Ding ◽  
Zhenye Li ◽  
Xianxu Zhan ◽  
Rong Du ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decisive Role ◽  
Solid Wood ◽  
Great Accuracy ◽  
Classification Method ◽  
Color Classification ◽  
Feature Extraction Method ◽  
Stochastic Sampling ◽  
Online Classification ◽  
Execution Speed

Solid wood floors are widely used as an interior decoration material, and the color of solid wood surfaces plays a decisive role in the final decoration effect. Therefore, the color classification of solid wood floors is the final and most important step before laying. However, research on floor classification usually focuses on recognizing complex and diverse features but ignores execution speed, which causes common methods to not meet the requirements of online classification in practical production. In this paper, a new online classification method of solid wood floors was proposed by combining probability theory and machine learning. Firstly, a probability-based feature extraction method (stochastic sampling feature extractor) was developed to obtain rapid key features regardless of the disturbance of wood grain. The stochastic features were determined by a genetic algorithm. Then, an extreme learning machine—as a fast classification neural network—was selected and trained with the selected stochastic features to classify solid wood floors. Several experiments were carried out to evaluate the performance of the proposed method, and the results showed that the proposed method achieved a classification accuracy of 97.78% and less than 1 ms for each solid wood floor. The proposed method has advantages including a high execution speed, great accuracy, and flexible adaptability. Overall, it is suitable for online industry production.

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

A machine learning-based underwater noise classification method

2021 ◽  
Vol 184 ◽  
pp. 108333
Author(s):  
Guoli Song ◽  
Xinyi Guo ◽  
Wenbo Wang ◽  
Qunyan Ren ◽  
Jun Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Method ◽  
Underwater Noise ◽  
Noise Classification

scholarly journals Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Go-Eun Yu ◽  
Younhee Shin ◽  
Sathiyamoorthy Subramaniyam ◽  
Sang-Ho Kang ◽  
Si-Myung Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Feature Selection Method ◽  
Flower Color ◽  
Classification Performance ◽  
Snp Markers ◽  
Rna Seq ◽  
Color Classification ◽  
Dna Pcr ◽  
Selection Of

AbstractBellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. Six machine learning methods were deployed to explore the classification potential of the selected SNPs as features in two datasets, namely training (60 RNA-Seq samples) and validation (480 Fluidigm chip samples). SNP selection was performed in sequential order. Firstly, 96 SNPs were selected from the transcriptome-wide SNPs using the principal compound analysis (PCA). Then, 9 among 96 SNPs were later identified using the Random forest based feature selection method from the Fluidigm chip dataset. Among six machines, the random forest (RF) model produced higher classification performance than the other models. The 9 SNP marker candidates selected for classifying the flower color classification were verified using the genomic DNA PCR with Sanger sequencing. Our results suggest that this methodology could be used for future selection of breeding traits even though the plant accessions are highly heterogeneous.

scholarly journals A Research Paper on Traffic Sign Recognition with Machine Learning and IOT

2021 ◽  
Vol 9 (VI) ◽  
pp. 1774-1778
Author(s):  
Mahesh Singh
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Real World ◽  
Research Paper ◽  
Raspberry Pi ◽  
Detection Accuracy ◽  
Great Success ◽  
The Real ◽  
Road Traffic Safety ◽  
Execution Speed

This paper will help to bring out some amazing findings about autonomous prediction and performing action by establishing a connection between the real world with machine learning and Internet Of thing. The purpose of this research paper is to perform our machine to analyze different signs in the real world and act accordingly. We have explored and found detection of several features in our model which helped us to establish a better interaction of our model with the surroundings. Our algorithms give very optimized predictions performing the right action .Nowadays, autonomous vehicles are a great area of research where we can make it more optimized and more multi - performing .This paper contributes to a huge survey of varied object detection and feature extraction techniques. At the moment, there are loads of object classification and recognition techniques and algorithms found and developed around the world. TSD research is of great significance for improving road traffic safety. In recent years, CNN (Convolutional Neural Networks) have achieved great success in object detection tasks. It shows better accuracy or faster execution speed than traditional methods. However, the execution speed and the detection accuracy of the existing CNN methods cannot be obtained at the same time. What's more, the hardware requirements are also higher than before, resulting in a larger detection cost. In order to solve these problems, this paper proposes an improved algorithm based on convolutional model A classic robot which uses this algorithm which is installed through raspberry pi and performs dedicated action.

scholarly journals Analysis of EEG signals using Machine Learning for the Detection and Diagnosis of Epilepsy

2020 ◽  
Vol 10 (1) ◽  
pp. 89-93
Keyword(s):  
Machine Learning ◽  
Epileptic Seizures ◽  
Severe Damage ◽  
Tree Model ◽  
Data Set ◽  
Feature Extraction Method ◽  
Precautionary Measures ◽  
Detection And Diagnosis ◽  
Electroencephalogram Eeg ◽  
Mean Variance

Electroencephalogram (EEG) is one of the most commonly used tools for epilepsy detection. In this paper we have presented two methods for the diagnosis of epilepsy using machine learning techniques.EEG waveforms have five different kinds of frequency bands. Out of which only two namely theta and gamma bands carry epileptic seizure information. Our model determines the statistical features like mean, variance, maximum, minimum, kurtosis, and skewness from the raw data set. This reduces the mathematical complexities and time consumption of the feature extraction method. It then uses a Logistic regression model and decision tree model to classify whether a person is epileptic or not. After the implementation of the machine learning models, parameters like accuracy, sensitivity, and recall have been found. The results for the same are analyzed in detail in this paper. Epileptic seizures cause severe damage to the brain which affects the health of a person. Our key objective from this paper is to help in the early prediction and detection of epilepsy so that preventive interventions can be provided and precautionary measures are taken to prevent the patient from suffering any severe damage

A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

2021 ◽  
Author(s):  
Julia Kaltenborn ◽  
Viviane Clay ◽  
Amy R. Macfarlane ◽  
Joshua Michael Lloyd King ◽  
Martin Schneebeli
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Snow Layer ◽  
Arctic Sea ◽  
Execution Speed

<p>Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.</p><p>Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.</p><p>We will test and compare the different algorithms and evaluate the algorithms’ effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.</p><p>The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms’ comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation. </p><p> </p><p>King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. <em>The Cryosphere</em>, <em>14</em>(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.</p>

scholarly journals Earthquake Detection in a Static and Dynamic Environment Using Supervised Machine Learning and a Novel Feature Extraction Method

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 800 ◽  
Author(s):  
Irshad Khan ◽  
Seonhwa Choi ◽  
Young-Woo Kwon
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Dynamic Environment ◽  
Supervised Machine Learning ◽  
Experimental Result ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
Unseen Data ◽  
Iot Devices ◽  
Hard Real Time

Detecting earthquakes using smartphones or IoT devices in real-time is an arduous and challenging task, not only because it is constrained with the hard real-time issue but also due to the similarity of earthquake signals and the non-earthquake signals (i.e., noise or other activities). Moreover, the variety of human activities also makes it more difficult when a smartphone is used as an earthquake detecting sensor. To that end, in this article, we leverage a machine learning technique with earthquake features rather than traditional seismic methods. First, we split the detection task into two categories including static environment and dynamic environment. Then, we experimentally evaluate different features and propose the most appropriate machine learning model and features for the static environment to tackle the issue of noisy components and detect earthquakes in real-time with less false alarm rates. The experimental result of the proposed model shows promising results not only on the given dataset but also on the unseen data pointing to the generalization characteristics of the model. Finally, we demonstrate that the proposed model can be also used in the dynamic environment if it is trained with different dataset.

scholarly journals Bot Detection on Social Networks Using Persistent Homology

2020 ◽  
Vol 25 (3) ◽  
pp. 58
Author(s):  
Minh Nguyen ◽  
Mehmet Aktas ◽  
Esra Akbas
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Persistent Homology ◽  
Structural Features ◽  
Higher Order ◽  
Ego Networks ◽  
Feature Extraction Method ◽  
Topological Features ◽  
Bot Detection

The growth of social media in recent years has contributed to an ever-increasing network of user data in every aspect of life. This volume of generated data is becoming a vital asset for the growth of companies and organizations as a powerful tool to gain insights and make crucial decisions. However, data is not always reliable, since primarily, it can be manipulated and disseminated from unreliable sources. In the field of social network analysis, this problem can be tackled by implementing machine learning models that can learn to classify between humans and bots, which are mostly harmful computer programs exploited to shape public opinions and circulate false information on social media. In this paper, we propose a novel topological feature extraction method for bot detection on social networks. We first create weighted ego networks of each user. We then encode the higher-order topological features of ego networks using persistent homology. Finally, we use these extracted features to train a machine learning model and use that model to classify users as bot vs. human. Our experimental results suggest that using the higher-order topological features coming from persistent homology is promising in bot detection and more effective than using classical graph-theoretic structural features.

scholarly journals Online classification of user activities using machine learning on network traffic

2020 ◽  
Vol 181 ◽  
pp. 107557
Author(s):  
Víctor Labayen ◽  
Eduardo Magaña ◽  
Daniel Morató ◽  
Mikel Izal
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Online Classification ◽  
User Activities
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