Designing of smart chair for monitoring of sitting posture using convolutional neural networks

2019 ◽  
Vol 53 (2) ◽  
pp. 142-155 ◽  
Author(s):  
Wonjoon Kim ◽  
Byungki Jin ◽  
Sanghyun Choo ◽  
Chang S. Nam ◽  
Myung Hwan Yun
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Content Type ◽  
Sitting Posture ◽  
Posture Classification

Purpose Sitting in a chair is a typical act of modern people. Prolonged sitting and sitting with improper postures can lead to musculoskeletal disorders. Thus, there is a need for a sitting posture classification monitoring system that can predict a sitting posture. The purpose of this paper is to develop a system for classifying children’s sitting postures for the formation of correct postural habits. Design/methodology/approach For the data analysis, a pressure sensor of film type was installed on the seat of the chair, and image data of the postu.re were collected. A total of 26 children participated in the experiment and collected image data for a total of seven postures. The authors used convolutional neural networks (CNN) algorithm consisting of seven layers. In addition, to compare the accuracy of classification, artificial neural networks (ANN) technique, one of the machine learning techniques, was used. Findings The CNN algorithm was used for the sitting position classification and the average accuracy obtained by tenfold cross validation was 97.5 percent. The authors confirmed that classification accuracy through CNN algorithm is superior to conventional machine learning algorithms such as ANN and DNN. Through this study, we confirmed the applicability of the CNN-based algorithm that can be applied to the smart chair to support the correct posture in children. Originality/value This study successfully performed the posture classification of children using CNN technique, which has not been used in related studies. In addition, by focusing on children, we have expanded the scope of the related research area and expected to contribute to the early postural habits of children.

Rapid Defect Detection and Classification in Images Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Peter Warren ◽  
Hessein Ali ◽  
Hossein Ebrahimi ◽  
Ranajay Ghosh
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Defect Detection ◽  
Visual Inspection ◽  
Surface Defects ◽  
Image Data ◽  
Rapid Identification ◽  
Machine Learning Techniques ◽  
Great Success

Abstract Several image processing methods have been implemented over recent years to assist and partially replace on-site technician visual inspection of both manufactured parts and operational equipments. Convolutional neural networks (CNNs) have seen great success in their ability to both identify and classify anomalies within images, in some cases they do this to a higher degree of accuracy than an expert human. Several parts that are manufactured for various aspects of turbomachinery operation must undergo a visual inspection prior to qualification. Machine learning techniques can streamline these visual inspection processes and increase both efficiency and accuracy of defect detection and classification. The adoption of CNNs to manufactured part inspection can also help to improve manufacturing methods by rapidly retrieving data for overall system improvement. In this work a dataset of images with a variety of surface defects and some without defects will be fed through varying CNN set-ups for the rapid identification and classification of the flaws within the images. This work will examine the techniques used to create CNNs and how they can best be applied to part surface image data, and determine the most accurate and efficient techniques that should be implemented. By combining machine learning with non-destructive evaluation methods component health can be rapidly determined and create a more robust system for manufactured parts and operational equipment evaluation.

scholarly journals Vehicle Price Prediction using SVM Techniques

2020 ◽  
Vol 9 (8) ◽  
pp. 398-401
Keyword(s):  
Machine Learning ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Network Support ◽  
Java Application ◽  
Learning Techniques ◽  
The Individual

The prediction of price for a vehicle has been more popular in research area, and it needs predominant effort and information about the experts of this particular field. The number of different attributes is measured and also it has been considerable to predict the result in more reliable and accurate. To find the price of used vehicles a well defined model has been developed with the help of three machine learning techniques such as Artificial Neural Network, Support Vector Machine and Random Forest. These techniques were used not on the individual items but for the whole group of data items. This data group has been taken from some web portal and that same has been used for the prediction. The data must be collected using web scraper that was written in PHP programming language. Distinct machine learning algorithms of varying performances had been compared to get the best result of the given data set. The final prediction model was integrated into Java application

scholarly journals Application of Machine Learning for Prediction of Lung Cancer using Omics Data

2020 ◽  
Vol 9 (6) ◽  
pp. 230-236
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Data Mining ◽  
Survival Analysis ◽  
Early Stage ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Omics Data

Cancer is one of the deadly diseases across many countries. However, cancer can be cured, if detected at an early stage. Researchers are working on healthcare for early detection and prevention of cancer. Medical data has reached its utmost potential by providing researchers with huge data sets collected from all over the globe. In the present scenario, Machine Learning has been widely used in the area of cancer diagnosis and prognosis. Survival analysis may help in the prediction of the early onset of disease, relapse, re-occurrence of diseases and biomarker identification. Applications of machine learning and data mining methods in medical field are currently the most widespread in cancer detection and survival analysis. In this survey, different ways to detect and predict lung cancer using latest Machine learning algorithms combined with data mining has been analyzed. Comparative study of various machine learning techniques and technologies has been done over different types of data such as clinical data, omics data, image data etc.

scholarly journals Efficient Machine Learning Techniques to Diagnose and Predict Alzheimer’s disease

2020 ◽  
Vol 9 (3) ◽  
pp. 3953-3960
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Mechanisms ◽  
Learning Techniques

Recent research in computational engineering have evidenced the design and development numerous intelligent models to analyze medical data and derive inferences related to early diagnosis and prediction of disease severity. In this context, prediction and diagnosis of fatal neurodegenerative diseases that comes under the class of dementia from medical image data is considered as the challenging area of research for many researchers. Recently Alzheimer’s disease is considered as major category of dementia that affects major population. Despite of the development of numerous machine learning models for early diagnosis of Alzheimer’s disease, it is observed that there is a lot more scope of research. Addressing the same, this article presents a systematic literature review of machine learning techniques developed for early diagnosis of Alzheimer’s disease. Furthermore this article includes major categories of machine learning algorithms that include artificial neural networks, Support vector machines and Deep learning based ensemble models that helps the budding researchers to explore the scope of research in predicting Alzheimer’s disease. Implementation results depict the comparative analysis of state of art machine learning mechanisms.

Transformer network-based word embeddings approach for autonomous cyberbullying detection

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Subbaraju Pericherla ◽  
E. Ilavarasan
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Convolutional Neural Networks ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Word Embeddings ◽  
Content Type ◽  
Light Gradient ◽  
Cyberbullying Detection

PurposeNowadays people are connected by social media like Facebook, Instagram, Twitter, YouTube and much more. Bullies take advantage of these social networks to share their comments. Cyberbullying is one typical kind of harassment by making aggressive comments, abuses to hurt the netizens. Social media is one of the areas where bullying happens extensively. Hence, it is necessary to develop an efficient and autonomous cyberbullying detection technique.Design/methodology/approachIn this paper, the authors proposed a transformer network-based word embeddings approach for cyberbullying detection. RoBERTa is used to generate word embeddings and Light Gradient Boosting Machine is used as a classifier.FindingsThe proposed approach outperforms machine learning algorithms such as logistic regression, support vector machine and deep learning models such as word-level convolutional neural networks (word CNN) and character convolutional neural networks with short cuts (char CNNS) in terms of precision, recall, F1-score.Originality/valueOne of the limitations of traditional word embeddings methods is context-independent. In this work, only text data are utilized to identify cyberbullying. This work can be extended to predict cyberbullying activities in multimedia environment like image, audio and video.

scholarly journals Steganalysis Using Yedrodj-net net's Convolutional Neural Networks (CNN) Method on Steganography Tools

2020 ◽  
Vol 3 ◽  
pp. 207-211
Author(s):  
Nurmi Hidayasari ◽  
Imam Riadi ◽  
Yudi Prayudi
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Learning Performance ◽  
Machine Learning Techniques ◽  
Detection Model ◽  
Learning Techniques ◽  
Net Method ◽  
Blind Steganalysis ◽  
Better Than

Steganalysis method is used to detect the presence or absence of steganography files or can be referred to anti-steganography. Steganalysis can be used for positive purposes, which is to know the weaknesses of a steganography method, so that improvements can be made. One category of steganalysis is blind steganalysis, which is a way to detect secret files without knowing what steganography method is used. Blind steganalysis is difficult to implement, but then machine learning techniques emerged that could be used to create a detection model using experimental data, one of which is Convolutional Neural Networks (CNN). A study proposes that the CNN method can detect steganography files using the latest method with a low error probability value compared to other methods, CNN Yedroudj-net. As one of the steganalysis methods with the latest machine learning steganalysis techniques, an experiment is needed to find out whether Yedroudj-net can be a steganalysis for the output of many tools commonly used for steganography applications. Knowing the performance of CNN Yedroudj-net on several steganography tools is very important, to measure the level of ability in terms of steganalysis of some of these tools. Especially so far, machine learning performance is still doubtful in blind steganalysis. Plus some previous research only focused on certain methods to prove the performance of the proposed technique, including Yedroudj-net. This study will use five tools that are Hide In Picture (HIP), OpenStego, SilentEye, Steg and S-Tools, which are not known exactly what steganography methods are used on the tools. Yedroudj-net method will be implemented in the steganography file from the output of the five tools. Then a comparison with the popular steganalysis tool is used, StegSpy. The results show that Yedroudj-net is quite capable of detecting the presence of steganography files, slightly better than StegSpy.

scholarly journals Feature Selection for Breast Cancer Detection using Machine Learning Algorithms

2019 ◽  
Vol 8 (9) ◽  
pp. 2080-2083
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Neural Networks ◽  
Cancer Detection ◽  
Predictive Models ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Cancer Type ◽  
Wide Range ◽  
Logistic Regression Algorithm

Cancer has been portrayed as a heterogeneous disease comprising of a wide range of subtypes. The early diagnosis of a cancer type is very important to determine the course of medical treatment required by the patient. The significance of classifying cancerous cells into benign or malignant has driven many research studies, in the biomedical and the bioinformatics field. In the past years researchers have been encouraged to use different machine learning (ML) techniques for cancer detection, as well as prediction of survivability and recurrence. What's more, ML instruments can be used to distinguish key highlights from complex datasets and uncover their significance. An assortment of these procedures, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Random Forest Methods (RVMs) and Decision Trees (DTs) has been usually used in cancer research for the development of predictive models, resulting in successful and exact decision making. Although it is obvious that the usage of machine learning techniques can enhance our comprehension of cancer detection, progression, recurrence and survivability, a proper level of accuracy is required for these strategies to be considered in the ordinary clinical practice. The predictive models talked about here depend on different administered ML strategies and on various input features and data samples. We have used Naïve-Bayes classifier, Neural Networks method, Decision Tree and Logistic Regression algorithm to detect the type of breast cancer (Benign or Malignant) and selection of features which are more relevant for prediction. We have made a comparative study to find out the best algorithm of the above four, for prediction of cancer type. With a high level of accuracy, any of these methods can be used to predict the type of breast cancer of any particular patient

Recognition on Images From Internet Street View Based on Hierarchical Features Learning With CNNs

2019 ◽  
pp. 1411-1424
Author(s):  
Jian-min Liu ◽  
Min-hua Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Unsupervised Learning ◽  
Convolutional Neural Networks ◽  
Error Rates ◽  
Machine Learning Techniques ◽  
Street View ◽  
Learning Techniques ◽  
Similarities And Differences ◽  
Hierarchical Features

This article describes hierarchical features with unsupervised learning on images from internet street view images. This is due to the time spent by trained researchers on feature construction steps with traditional methods. This article focuses on the activation of each layer of with convolutional neural networks (CNNs) on Internet street view images detection and compared similarities and differences among them on each layer. The experiment results achieved error rates of 21% on recognition which work went relatively well than the traditional machine learning techniques, such as Parallel SVM.

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