scholarly journals Improving Quality Inspection of Textiles by an Augmented RGB-IR-HS-AI Approach

2021 ◽  
Vol 8 (1) ◽  
pp. 21
Author(s):  
Ritchie Heirmans ◽  
Olivier De Moor ◽  
Simon Verspeek ◽  
Sander De Vrieze ◽  
Bart Ribbens ◽  
...  
Keyword(s):  
Textile Industry ◽  
Learning Algorithm ◽  
Imaging Techniques ◽  
Open Data ◽  
Research Topic ◽  
Machine Learning Algorithm ◽  
Quality Inspection ◽  
Active Thermography ◽  
Open Datasets ◽  
Later Phase

The aim of this research topic and paper is to investigate the application possibilities of vision technology in the textile industry. These include RGB, active thermography and hyperspectral imaging techniques. In the future, this approach will be supplemented by a machine learning algorithm (e.g., in Matlab or Python) to enable the detection of defects in textiles and to correctly categorize these defects. In the first place, the various options for building such a convolutional neural network are discussed. The focus was on the models used in the literature. Based on the effectiveness of these ML models and the feasibility to build them, choices can be made to determine the most suitable models. Sufficient samples are an important link to properly train a model. Because there is a shortage of open data, it is also discussed how samples obtained from the textile industry, were measured in the lab. At first, we will limit ourselves to the five most common defects. In a later phase of research, the results with this dataset and the open datasets are benchmarked against the results from the literature.

An automated lung cancer detection system based on machine learning algorithm

2021 ◽  
Vol 40 (4) ◽  
pp. 6355-6364
Author(s):  
S. Lalitha
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Cancer Detection ◽  
Learning Algorithm ◽  
Detection System ◽  
Imaging Techniques ◽  
Medical Science ◽  
Lung Cancer Mortality ◽  
Machine Learning Algorithm ◽  
Lung Cancer Detection

Cancer has been one of the most serious health challenges to the human kind for a long period of time. Lung cancer is the most prevalent type of cancer which shows higher death rates. However, lung cancer mortality rates can be tracked by periodic screening. With the advanced medical science, the society has reaped numerous benefits with respect to screening equipments. Computed Tomography (CT) is one of the popular imaging techniques and this work utilizes the CT images for lung cancer detection. An early detection of lung cancer could prolong the lifetime of the patient and is made effortless by the latest screening technology. Additionally, the accuracy of disease detection can be enhanced with the help of the automated systems, which could support the healthcare experts in effective diagnosis. This article presents an automated lung cancer detection system equipped with machine learning algorithm, which can differentiate between the benign, malignant and normal classes of lung cancer. The accuracy of the proposed lung cancer detection method is around 98.7%, which is superior to the compared approaches.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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