Journal ArticleDOI
Echocardiography image segmentation using feed forward artificial neural network (FFANN) with fuzzy multi-scale edge detection (FMED)
TLDR
In this research, feed forward artificial neural network (FFANN) has been utilised and fuzzy multi-scale edge detection (FMED) process has been applied to detect the segmented edges to define the detected texture boundary with the help of FFANN weights.Abstract:
In the recent past Echocardiography image segmentation is one of the significant process describes about the segment out inner and outer walls or other parts of the organ boundaries. However, this kind of segmentation process is one of the difficult for physicians because of inexperience or subject specialists with the previous cases. To enhance the cardiac image segmentation accuracy and to minimise the segmentation time a machine learning method such as neural networks has been proposed in the segmentation process. In this research, feed forward artificial neural network (FFANN) has been utilised and fuzzy multi-scale edge detection (FMED) process has been applied to detect the segmented edges to define the detected texture boundary with the help of FFANN weights. An experimental result shows an efficient learning capacity of FFANN and this work deals with the segmentation of ultrasound images using MATLAB implementation.read more
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Wearable sensor-based fuzzy decision-making model for improving the prediction of human activities in rehabilitation
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An automatic detection system of diabetic retinopathy using a hybrid inductive machine learning algorithm
Mohamed H. Mahmoud,Salman Alamery,Hanan Fouad,Hanan Fouad,Amir Altinawi,Ahmed E. Youssef,Ahmed E. Youssef +6 more
TL;DR: In this article, a hybrid inductive machine learning algorithm (HIMLA) was proposed to detect diabetic retinopathy (DR) in colored fundus images, which comprises four stages: pre-processing, segmentation, feature extraction, and classification.
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College music education and teaching based on AI techniques
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Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks
TL;DR: An IoMT-based Intelligent Guided Particle Local Search with Optimized Neural Networks (IGPLONN) approach that improves the overall oral-linked neurological diseases detection rate and efficiently manages the forecast parameters that are used to predict the dental metastasis with minimum computational complexity.
Journal ArticleDOI
Mathematical modeling and fuzzy approach for disaster analysis on geo-spatial rock mass in open-pit mining
TL;DR: The fuzzy based disaster data management concept has been proposed for analyzing the disaster in open-pit mine area for the damaged rock due to disasters and the formulation of the Gauss–Legendre is a three-point technical illustration of damaged rock slope which has been used in the open pit.