scholarly journals Role of Restored Underwater Images in Underwater Imaging Applications

2021 ◽  
Vol 4 (4) ◽  
pp. 96
Author(s):  
Jarina Raihan A ◽  
Pg Emeroylariffion Abas ◽  
Liyanage C De Silva
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Feature Detection ◽  
Sea Water ◽  
Learning Algorithm ◽  
Underwater Imaging ◽  
Underwater Image ◽  
Common Problems ◽  
Restoration Method

Underwater images are extremely sensitive to distortion occurring in an aquatic underwater environment, with absorption, scattering, polarization, diffraction and low natural light penetration representing common problems caused by sea water. Because of these degradation of quality, effectiveness of the acquired images for underwater applications may be limited. An effective method of restoring underwater images has been demonstrated, by considering the wavelengths of red, blue, and green lights, attenuation and backscattering coefficients. The results from the underwater restoration method have been applied to various underwater applications; particularly, edge detection, Speeded Up Robust Feature detection, and image classification that uses machine learning. It has been shown that more edges and more SURF points can be detected as a result of using the method. Applying the method to restore underwater images in image classification tasks on underwater image datasets gives accuracy of up to 89% using a simple machine-learning algorithm. These results are significant as it demonstrates that the restoration method can be implemented on underwater system for various purposes.

scholarly journals Image Classification using CNN and Machine Learning

2019 ◽  
pp. 575-580
Author(s):  
G. Keerthi Devipriya ◽  
E. Chandana ◽  
B. Prathyusha ◽  
T. Seshu Chakravarthy
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Models ◽  
Classification Of Images ◽  
Respective Category

Here by in this paper we are interested for classification of Images and Recognition. We expose the performance of training models by using a classifier algorithm and an API that contains set of images where we need to compare the uploaded image with the set of images available in the data set that we have taken. After identifying its respective category the image need to be placed in it. In order to classify images we are using a machine learning algorithm that comparing and placing the images.

Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network

2020 ◽  
Vol 12 (1) ◽  
pp. 36-48
Author(s):  
Vijayaprabakaran K. ◽  
Sathiyamurthy K. ◽  
Ponniamma M.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Elderly People ◽  
Activity Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Human Vision

A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.

scholarly journals A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2105 ◽  
Author(s):  
Shrinathan Esakimuthu Pandarakone ◽  
Yukio Mizuno ◽  
Hisahide Nakamura
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Fault Detection ◽  
Induction Motors ◽  
Learning Algorithm ◽  
Detection Techniques ◽  
Bearing Fault ◽  
Fast Fourier Transform Analysis ◽  
The Difference

Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use.

scholarly journals Acute Diseases Prognosis using Chatbot

2021 ◽  
Vol 9 (VII) ◽  
Author(s):  
Shivanand Tiwari
Keyword(s):  
Machine Learning ◽  
Healthcare Professionals ◽  
Learning Algorithm ◽  
Early Stage ◽  
Machine Learning Algorithm ◽  
Patient Needs ◽  
Healthcare Organizations ◽  
Physician Time ◽  
Consultant Doctor

The role of chatbots in healthcare is to help free-up valuable physician-time by reducing or eliminating unnecessary doctor’s appointments. As the increase in cost, various healthcare organizations are looking for different ways to manage cost while improving the user’s experience. As we know there is shortage of healthcare professionals that makes it increasingly necessary for us to augment technology with health facilities in order to allow doctors to focus on more critical patient needs. Keeping this in Mind we are aiming to develop a Project that will basically ask for Symptoms from the Patient and perform the Prognosis on the basis of already developed dataset. The Machine Learning Algorithm will work on that dataset of symptoms and their prognosis to tell exactly what has happened to the Patient and will help to Reach the Desired Consultant/Doctor with respect to the Prognosis. It will also help the Patients to get Useful Information regarding different diseases that may help to deal with some Chronic Diseases at an early Stage!’

scholarly journals The Prediction of Application for Loan using Machine Learning Technique

2020 ◽  
Vol 9 (10) ◽  
pp. 265-268
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Third Party ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Internet Platform ◽  
The Many ◽  
Prediction Problems

Machine learning techniques are used to verify the many kinds of loan prediction problems. This study pursueS two major goals. Firstly, this paper is to understand the role of variables in loan prediction modeling better. Secondly, the study evaluates the predictive performance of the decision trees. The corresponding variable information is drawn from a third-party website, international challenge on the popular internet platform Kaggle (www.kaggle.com), which provides data in the title of ‘Loan Prediction’ that was uploaded by Amit Parajapet. We used decision tree which is a powerful and popular machine learning algorithm to this date for predicting and classifying big data. Based on these results, first, women seem to be more likely to get to loan than men. credit history, self-employed, property area, and applicant income also show significance with loan prediction. This study contributes to the literature regarding loan prediction by providing a global model summarizing the loan prediction determinants of customers’ factors.

scholarly journals MRI Image Classification of Brain Tumor Using Deep Neural Network and Deployment using Web Framework

2021 ◽  
Author(s):  
Sumithra M ◽  
Shruthi S ◽  
SmithiRam ◽  
Swathi S ◽  
Deepika T
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Image Classification ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Extraction Methods ◽  
Tumor Diagnostics

A brain tumor is a mass or growth of abnormal cells in our brain. Many different types of brain tumors exist. Some brain tumors are noncancerous (benign), and some brain tumors are cancerous (malignant). Brain tumors can begin in your brain (primary brain tumors), or cancer can begin in other parts of your body and spread to your brain (secondary, or metastatic, brain tumors). Brain tumor treatment options depend on the type of brain tumor you have, as well as its size and location. The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image classification is the convolution neural network (CNN). It is predicted that the success of the obtained results will increase if the CNN method is supported by adding extra feature extraction methods and classify successfully brain tumor normal and abnormal image.

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