scholarly journals Emerging Aspects of Artificial Intelligence for Smart Life

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Kavitha A
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Cyber Security ◽  
Medical Image ◽  
Medical Image Processing ◽  
Robotics Education ◽  
Digital Era ◽  
Control Disease ◽  
Self Driving Cars ◽  
Development Network

Nowadays Artificial intelligence makes our life easy and comfortable that is hard to imagine that to survive our life without AI technology. We all know that Artificial Intelligence (AI) is a precious gift to human being. Recently it is used in robotics, education, agriculture, computer vision, cyber security, face recognition, speech recognition, self driving cars, medical image processing, biometrics, bioinformatics, satellite control, disease detection, drugs development, network developments, manufacturing, business, healthcare and medicine. In the digital era AI provides the best results in all most all the domains. This article helps to understand the emerging aspects of AI in various fields.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

scholarly journals A detailed survey on Prognostication of diabetes diagnosis on the basis of machine learning techniques and the detection approaches to diabetic retinopathy using Artificial Intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 874-887
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Medical Image ◽  
Data Science ◽  
Medical Image Processing ◽  
Machine Learning Techniques ◽  
Diabetes Diagnosis

The aim of the study is to compare, assess the optimum tools as well as the techniques and advanced features focused on prediction of diabetes diagnosis based on machine learning tactics and diabetic retinopathy using Artificial Intelligence. The literature on data science, Artificial Intelligence (AI) contains important knowledge and understanding of AI entities such as Data science, machine learning, deep learning, Medical image processing, feature extraction, classification techniques, etc. Diabetes diagnosis is a phenomenon that impacts individuals around the globe. Now, with diabetes impacting people from children to the elderly, the out-dated approaches to diabetes diagnosis should be replaced with new, time-saving technologies. There's several studies carried out by researchers to recognise and predict diabetes. Here plenty of classifiers in machine learning can be used, such as KNN, Random Tree, etc.They can save time and get more precise outcome when using these techniques to predict diabetes. Diabetic retinopathy (DR) is a typical disorder of diabetic disease that induces vision-impacting lesions in the retina. It also can turn to visual impairment if it is not addressed early. DR therapy only helps vision. Deep learning has in recent times being one of the most widely used approaches that has accomplished higher outcomes in so many fields, especially in the analysing and identification of medical image classification. In medical image processing, convolutional neural networks (CNN) using transfer learning are commonly used as a deep learning approach and they are incredibly beneficial. Key words: Diab

scholarly journals A Review for Artificial Intelligence Proving to Fight Against COVID-19 Pandemic and Prefatory Health Policy

2020 ◽  
Vol 8 (8) ◽  
pp. 494-506
Author(s):  
Hyunjo Kim ◽  
Gyoonhee Han ◽  
Jae-Hoon Song
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Drug Repurposing ◽  
Modern Technology ◽  
Substantial Impact ◽  
Infrastructure Systems ◽  
Health Infrastructure ◽  
Modern Technologies

The aim of the present study is to discuss the various aspects of modern technology used to fight against COVID-19 outbreak crisis at different scales, including medical image processing, disease tracking, prediction outcomes, computational biology and medicines. A progressive search of the database related to modern technology towards COVID-19 pandemic is made. Further, a brief review is done on the extracted information by assessing the various aspects of modern technologies for tackling COVID-19 pandemic. We provide a window of thoughts on review of the technology advances used to decrease and smother the substantial impact of the outburst. However, there are still constrained applications and contributions of technology in this fight although different studies relating to modern technology towards COVID-19 pandemic have come up yet. The modern technology on-going progress has contributed in improving people’s lives. Hence, there is a solid conviction that validated research plans including artificial intelligence will be of significant advantage in helping people to fight this infection.    Key words: COVID-19 pandemic, artificial intelligence (AI), molecular modeling, drug repurposing, vaccines, health infrastructure systems (HIS)

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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