Machine learning and digital image processing for non-contact modal parameters identification of structures

2018 ◽  
Author(s):  
Marco Torbol ◽  
Kyeong Taek Park
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Parameters Identification ◽  
Modal Parameters ◽  
Modal Parameters Identification

scholarly journals A Review on Automatic Soil Classification in Digital Image Processing

2021 ◽  
Vol 9 (VIII) ◽  
pp. 449-455
Author(s):  
Shraddha Shivhare
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
False Alarm ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing Time ◽  
Soil Classification ◽  
Research Papers ◽  
Digital World ◽  
Precision Rate

Soil classification is an essential piece of geology. However, many examinations have assessed the precision and consistency of the soil classification using various techniques. This examination starts by evaluating the verifiable advancement of soil classification science. The verifiable audit contextualizes the wordings and the speculations of soil development factors, which supported soil classification frameworks. This paper is intended to review some research papers on soil classification and analyze the limitations of implemented techniques by their parameters. In the age of digital world, it is beneficial to obtain the information from image without any hassle. Machine learning is an approach through which we can obtain the better level of accuracy and minimize the false alarm rate. But machine learning requires so many samples through which we can observe the correct precision that also requires much storage that may takes much processing time that reduces the feasibility of the system. We have to train a system with limited number of samples with high iterations that produces higher precision rate with minimal errors.

scholarly journals Machine Learning Techniques for Prediction of Lung Cancer

2020 ◽  
Vol 8 (6) ◽  
pp. 241-249
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Data Mining ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Machine Learning Techniques ◽  
Image Dataset

Lung cancer has been one of the deadliest diseases in today’s decades. It has become one of the causes of death in both man and woman. There are various reasons for which lung cancer occurs but classification of tumor and predicting it in the right stage is the most important part. This paper focused on the numerous approaches has been derived for lung cancer detection from different literature survey to advance the ability of detection of cancer. Digital image processing and data mining both are equally important because for prediction either image dataset or statistical dataset is used so for pre-processing the image dataset digital image processing is applied for statistical dataset data mining is applied. After pre-processing, segmentation and feature extraction we apply various machine learning algorithm for the prediction of lung cancer. So first we have provided a sketch of Machine learning and then various fields like in image data or statistical data where machine learning has been used for classification. Once the classification is done confusion matrix is generated for calculating accuracy, sensitivity, precision, these method is used to measure the rate of accuracy of the proposed model.

scholarly journals Digital Image Processing Using Machine Learning

2019 ◽  
pp. 125-128
Author(s):  
Priya Bansal ◽  
Mrs. Mamta
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Daily Life ◽  
Pictorial Information ◽  
Important Data ◽  
Information Description

Main aim of Digital Image Processing Using Machine Learning is to extract important data from images. Using this extracted information description, interpretation and understanding of the scene can be provided by the machine. Main point of image processing is to modify images in to desired manner. Image processing is called as altering and analyzing pictorial information of images. In our daily life we come across different type of image processing best example of image processing in our daily life is our brain sensing lot of images when we see images with eyes and processing is done is very less time.

scholarly journals HEURISTIC ALGORITHM FOR PET IMAGES’ SEGMENTATION USING ARTIFICIAL INTELIGENCE TECHNIQUES

Anales AFA ◽  
2021 ◽  
Vol 31 (4) ◽  
pp. 165-171
Author(s):  
I. E. Scarinci ◽  
◽  
P. Pérez ◽  
M. Valente ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Nuclear Medicine ◽  
Digital Image Processing ◽  
Heuristic Algorithm ◽  
Digital Image ◽  
Data Science ◽  
Machine Learning Techniques ◽  
Patient Specific ◽  
Unsupervised Segmentation

The overall quantity of nuclear medicine procedures has increased remarkably in recent years, making them a daily tool capable of reaching wide sectors of the population. Regarding the nuclear medicine therapeutic applications, it is worth noting that there is an increasing demand of novel techniques and greater variety of radioisotopes requiring accurate patient-specific dosimetry aimed at evaluating lethal damage to the tumor while maintaining acceptable dose levels in healthy tissues. Image-guided internal dosimetry appears as particularly suitable for theranostics procedures, which allow the joint implementation of diagnose and treatment. In this case, the correct segmentation of the images is critical for the identification of different tissues and organs. On the other hand, modern tools based on data science and artificial intelligence have spread in several fields, particularly in the digital image processing. The use of machine learning models for digital image processing appears as a promising opportunity to complement clinical analysis by experts. This paper reports about an unsupervised segmentation heuristic algorithm using clustering and machine learning techniques together, based on the use of two algorithms: K-Means and HDBSCAN. The results obtained highlight the capacity of automatic segmentation by means of clustering algorithms, becoming a useful tool to assist clinician experts and shorten the segmentation times.

scholarly journals MACHINE LEARNING BASED CLOUD MUSIC APPLICATION WITH FACIAL RECOGNITION USING ANDROID STUDIO (MUSYNC)

2021 ◽  
pp. 36-52
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Mobile Phone ◽  
Digital Image Processing ◽  
Digital Image ◽  
Unique Feature ◽  
Facial Recognition ◽  
Music Player

This paper output is a music player application but when it comes to its features it will be way more than a simple music player. It is developed on Android Studio and other tools like: Firebase is used as database, Android phone camera, Music library of Android Phone are used in the development of application. When user changes his phone or reset his phone then all of his data is lost or user has to put all the data in his computer and then back to his mobile phone except data that is backed up online. Message data, photos and contacts are that things that users backed up online. But music files normally don’t get backed up and user troubles in re downloading the files or moving files in computer and back to phone. In this purposed work the targeted problem is resolved as MUSYNC application is be able to automatically backup all the mp3 data from the phone and user will get all of his data by just signing in the application in his new phone. The purposed application has a feature of sync music. Users can sync music with another one and that person will able to listen to same music instantly. Application also provides a unique feature of mood detection using digital image processing DIP. This feature is able to check your face emotion and play music according to it. User just has to take a picture and that is it, this music player plays the music according to your mood. This feature is useful when user having tough time what to listen.

scholarly journals Bottle Line Detection using Digital Image Processing with Machine Learning

2021 ◽  
Vol 1998 (1) ◽  
pp. 012033
Author(s):  
Ch Anush ◽  
K. Yashwanth ◽  
S. Shashank ◽  
M. Venkat Reddy ◽  
Ashwani Kumar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Line Detection
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