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The role of artificial neural network and machine learning in utilizing spatial information
Akash Goel,Amit Kumar Goel,Adesh Kumar 대한공간정보학회 2023 Spatial Information Research Vol.31 No.3
In this age of the fourth industrial revolution 4.0, the digital world has a plethora of data, including the internet of things, mobile, cybersecurity, social media, forecasts, health data, and so on. The expertise of machine learning and artificial intelligence (AI) is required to soundly evaluate the data and develop related smart and automated applications, These fields use a variety of machine learning techniques including supervised, unsupervised, and reinforcement learning. The objective of the study is to present the role of artificial neural networks and machine learning in utilizing spatial information. Machine learning and AI play an increasingly important role in disaster risk reduction from hazard mapping and forecasting severe occurrences to real-time event detection, situational awareness, and decision assistance. Some of the applications employed in the study to analyze the various ANN domains included weather forecasting, medical diagnosis, aerospace, facial recognition, stock market, social media, signature verification, forensics, robotics, electronics hardware, defense, and seismic data gathering. Machine learning determines the many prediction models for problems involving classification, regression, and clustering using known variables and locations from the training dataset, spatial data that is based on tabular data creates different observations that are geographically related to one another for unknown factors and places. The study presents that the Recurrent neural network and convolutional neural network are the best method in spatial information processing, healthcare, and weather forecasting with greater than 90% accuracy.
The Risk Factors for Acute Pancreatitis after Endoscopic Ultrasound Guided Biopsy
( Afonso Ribeiro ),( Akash Goel ) 대한소화기학회 2018 대한소화기학회지 Vol.72 No.3
Background/Aims: The risk of developing pancreatitis induced by endoscopic ultrasound-guided fine needle aspiration (EUS FNA) is relatively small. However, patients undergoing sampling through the normal pancreatic parenchyma or the pancreatic duct may have a higher rate of pancreatitis. Here, we determine the factors associated with increased risk of acute pancreatitis in patients undergoing FNA through normal pancreatic parenchyma/pancreatic duct. Methods: In this prospective study at a tertiary cancer center, patients undergoing sampling through the pancreatic duct or ≥5 mm of the normal parenchyma between December 2013 and September 2017 were included. Post-EUS induced pancreatitis was diagnosed by the presence of abdominal pain with an amylase or lipase level higher than three times normal value. Results: A total of 712 patients underwent pancreatic EUS FNA. A total of 163 patients were included in the high-risk group. Mean age was 63 years, 82 females, mean number of needle-passes was 3.3 (range, 1-7). Fifteen patients (15/163, 9.2%) developed pancreatitis after EUS FNA through the pancreatic parenchyma compared with only one case among the control group (<5 mm of normal parenchyma) (0.18%, 1/549, p<0.0001). Several factors appeared to be associated with pancreatitis, including young age, solid lesion, and a recent history of acute pancreatitis. By logistic regression, a prior history of recent pancreatitis was the only statistically significant factor associated with post-EUS-guided biopsy pancreatitis (p=0.008). Conclusions: Patients with a recent history of acute pancreatitis undergoing EUS FNA through 5 mm or more of the normal pancreatic parenchyma are at a much greater risk of acute pancreatitis. (Korean J Gastroenterol 2018;72:135-140)