scholarly journals Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future

2021 ◽  
Vol 13 ◽  
pp. 175628722110448
Author(s):  
B.M. Zeeshan Hameed ◽  
Gayathri Prerepa ◽  
Vathsala Patil ◽  
Pranav Shekhar ◽  
Syed Zahid Raza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Error ◽  
Data Science ◽  
Image Interpretation ◽  
X Ray ◽  
X Ray Imaging ◽  
Potential Applications ◽  
Efficiency And Productivity

Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.

scholarly journals Opportunities of Artificial Intelligence and Machine Learning in the Food Industry

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Indrajeet Kumar ◽  
Jyoti Rawat ◽  
Noor Mohd ◽  
Shahnawaz Husain
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supply Chain ◽  
Food Safety ◽  
Food Industry ◽  
Human Error ◽  
Data Science ◽  
Food Industries ◽  
Sales Prediction ◽  
Supply Chain Management System

The food processing and handling industry is the most significant business among the various manufacturing industries in the entire world that subsidize the highest employability. The human workforce plays an essential role in the smooth execution of the production and packaging of food products. Due to the involvement of humans, the food industries are failing to maintain the demand-supply chain and also lacking in food safety. To overcome these issues of food industries, industrial automation is the best possible solution. Automation is completely based on artificial intelligence (AI) or machine learning (ML) or deep learning (DL) algorithms. By using the AI-based system, food production and delivery processes can be efficiently handled and also enhance the operational competence. This article is going to explain the AI applications in the food industry which recommends a huge amount of capital saving with maximizing resource utilization by reducing human error. Artificial intelligence with data science can improve the quality of restaurants, cafes, online delivery food chains, hotels, and food outlets by increasing production utilizing different fitting algorithms for sales prediction. AI could significantly improve packaging, increasing shelf life, a combination of the menu by using AI algorithms, and food safety by making a more transparent supply chain management system. With the help of AI and ML, the future of food industries is completely based on smart farming, robotic farming, and drones.

scholarly journals Artificial intelligence and echocardiography

2018 ◽  
pp. R115-R125 ◽  
Author(s):  
M Alsharqi ◽  
W J Woodward ◽  
J A Mumith ◽  
D C Markham ◽  
R Upton ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Error ◽  
Image Interpretation ◽  
Future Directions ◽  
Wide Range ◽  
Automated Interpretation ◽  
Artery Disease ◽  
Consistent Assessment ◽  
Ischaemic Burden

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.

Ionic homeostasis and subcellular element compartmentation

1990 ◽  
Vol 48 (2) ◽  
pp. 150-151
Author(s):  
Ann LeFurgey ◽  
Peter Ingram ◽  
J.J. Blum ◽  
M.C. Carney ◽  
L.A. Hawkey ◽  
...  
Keyword(s):  
Leishmania Major ◽  
Ionic Homeostasis ◽  
X Ray ◽  
Functional Studies ◽  
Cell Compartments ◽  
X Ray Imaging ◽  
Resolution Electron ◽  
Multiple Cell ◽  
Role Of Zn

Subcellular compartments commonly identified and analyzed by high resolution electron probe x-ray microanalysis (EPXMA) include mitochondria, cytoplasm and endoplasmic or sarcoplasmic reticulum. These organelles and cell regions are of primary importance in regulation of cell ionic homeostasis. Correlative structural-functional studies, based on the static probe method of EPXMA combined with biochemical and electrophysiological techniques, have focused on the role of these organelles, for example, in maintaining cell calcium homeostasis or in control of excitation-contraction coupling. New methods of real time quantitative x-ray imaging permit simultaneous examination of multiple cell compartments, especially those areas for which both membrane transport properties and element content are less well defined, e.g. nuclei including euchromatin and heterochromatin, lysosomes, mucous granules, storage vacuoles, microvilli. Investigations currently in progress have examined the role of Zn-containing polyphosphate vacuoles in the metabolism of Leishmania major, the distribution of Na, K, S and other elements during anoxia in kidney cell nuclel and lysosomes; the content and distribution of S and Ca in mucous granules of cystic fibrosis (CF) nasal epithelia; the uptake of cationic probes by mltochondria in cultured heart ceils; and the junctional sarcoplasmic retlculum (JSR) in frog skeletal muscle.

A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

2020 ◽  
Vol 64 (1) ◽  
pp. 129-133
Author(s):  
Katherine Darveau ◽  
Daniel Hannon ◽  
Chad Foster
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Human Error ◽  
Data Science ◽  
Aircraft Engine ◽  
Rule Based ◽  
Root Cause ◽  
Textual Data ◽  
Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

Role of X-Ray Imaging in the Development of Drugs

1999 ◽  
Vol 33 (2) ◽  
pp. 635-640
Author(s):  
Sanjay Saini ◽  
Raju Sharma
Keyword(s):  
X Ray ◽  
X Ray Imaging

scholarly journals Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

2021 ◽  
Vol 11 (2) ◽  
pp. 411-424 ◽  
Author(s):  
José Daniel López-Cabrera ◽  
Rubén Orozco-Morales ◽  
Jorge Armando Portal-Diaz ◽  
Orlando Lovelle-Enríquez ◽  
Marlén Pérez-Díaz
Keyword(s):  
Artificial Intelligence ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

Sign in / Sign up

Export Citation Format

Share Document