Application of Machine Learning Techniques for the Analysis of National Bridge Inventory and Bridge Element Data

2019 ◽  
Vol 2673 (7) ◽  
pp. 99-110 ◽  
Author(s):  
Graziano Fiorillo ◽  
Hani Nassif
Keyword(s):  
Machine Learning ◽  
Asset Management ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
The Past ◽  
Precise Evaluation ◽  
Learning Techniques ◽  
Good State ◽  
Wide Range ◽  
National Bridge Inventory

The MAP-21 Act requires information on bridge assets to be at the element level for management operations in the U.S.A. This approach has the objective of improving future predictions of the performance of bridge assets for a more precise evaluation of condition and correct allocation of management funds to keep bridges in a good state of repair. Although bridge element conditions were introduced in the 1990s, the application of such data had never been mandatory for bridge asset management until 2014, therefore, the amount of historical data on bridge element (BE) condition is still limited. On the other hand, National Bridge Inventory (NBI) ratings have been collected since the 1970s and a wide range of data are available. Therefore, it is natural to ask whether BE condition can be predicted using NBI data. In the past, researchers statistically related BE and NBI data, but little has been done to revert NBI to BE. This paper addresses both challenges of mapping BE–NBI condition data using several machine learning techniques. The results of the analysis of these techniques applied to a sample of about 9,000 bridges from northeastern states of the U.S.A. shows that between 79.8% and 100% of the NBI ratings for deck, superstructure, and substructure can be predicted within a rating error of ± 1. The back-mapping operation of NBI time-dependent ratings to BE deterioration profiles for deck, superstructure, and substructure can also be predicted accurately with a probability greater than 50% at the 95% confidence level.

scholarly journals Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Majid Amirfakhrian ◽  
Mahboub Parhizkar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Machine Vision ◽  
Traffic Accidents ◽  
Fuzzy Theory ◽  
Machine Learning Techniques ◽  
Industrial Setting ◽  
Technological Advances ◽  
Learning Techniques ◽  
Wide Range

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

Imaging and machine learning techniques for diagnosis of Alzheimer’s disease

2016 ◽  
Vol 27 (8) ◽  
pp. 857-870 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Anahita Adeli ◽  
Hojjat Adeli
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Probabilistic Neural Network ◽  
Machine Learning Techniques ◽  
Detection Techniques ◽  
The Past ◽  
Learning Techniques ◽  
Diagnosis Accuracy ◽  
Rate Research

AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

scholarly journals Predictive Analysis of IPL Match Winner using Machine Learning Techniques

2019 ◽  
Vol 9 (2S) ◽  
pp. 430-435
Keyword(s):  
Machine Learning ◽  
Multivariate Regression ◽  
Time Lapse ◽  
Machine Learning Techniques ◽  
Past Performance ◽  
The Past ◽  
Premier League ◽  
Learning Techniques ◽  
Time Problem ◽  
Machine Learning Models

Artificial intelligence (AI) can be implemented using Machine Learning which allows the computing to potentially robotically study and improve from its previous experiences without being manually typed. Data can be accessed and used by the computer programs developed using Machine learning. This paper mainly focused on implementation of machine learning in the arena of sports to predict the captivating team of an IPL match. Cricket is a popular uncertain sport, particularly the T-20 format, there’s a possibility of the complete game play to change with the effect of any single over. Millions of spectators watch the Indian Premier League (IPL) every year, hence it becomes a real-time problem to compose a technique that will forecast the conclusion of matches. Many aspects and features determine the result of a cricket match each of which has a weighted impact on the result of a T20 cricket match. This paper describes all those features in detail. A multivariate regression-based approach is proposed to measure the team's points in the league. The past performance of every team determines its probability of winning a match against a particular opponent. Finally, a set of seven factors or attributes is identified that can be used for predicting the IPL match winner. Various machine learning models were trained and used to perform within the time lapse between the toss and initiation of the match, to predict the winner. The performance of the model developed are evaluated with various classification techniques where Random Forest and Decision Tree have given good results.

scholarly journals Prediction and Recommendation of Precision Medicine for Cancer using Machine Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 792-795
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Genetic Model ◽  
Machine Learning Techniques ◽  
Genetic Profile ◽  
Gene Expressions ◽  
Genetic Changes ◽  
The Past ◽  
Learning Techniques ◽  
Near Future

Cancer is one of the major causes of death by disease and treatment of cancer is one of the most crucial phases of oncology. Precision medicine for cancer treatment is an approach that uses the genetic profile of individual patients. Researchers have not yet discovered all the genetic changes that causes cancer to develop, grow and spread. The Neuro-Genetic model is proposed here for the prediction and recommendation of precision medicine. The proposed work attempts to recommend precision medicine to cancer patients based upon the past genomic data of patient’s survival. The work will employ machine learning (ML) approaches to provide recommendations for different gene expressions. This work can be used in caner hospitals, research institutions for providing personalized treatment to the patient using precision medicine. Precision medicine can even be used to treat other complex diseases like diabetes, dentistry, cardiovascular diseases etc. Precision medicine is the kind of treatment to be offered in the near future.

scholarly journals Ecg Heartbeat Classification: Conceptual Understanding through Cnn & Rnn – A Machine Learning Approach

2020 ◽  
Vol 10 (2) ◽  
pp. 143-147
Author(s):  
P. Rama Santosh Naidu ◽  
K.Venkata Ramana ◽  
G. Lavanya Devi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Dominant Role ◽  
Machine Learning Techniques ◽  
Heartbeat Classification ◽  
Ecg Signals ◽  
The Past ◽  
Learning Techniques ◽  
Machine Learning Approach

In recent days Machine Learning has become major study aspect in various applications that includes medical care where convenient discovery of anomalies in ECG signals plays an important role in monitoring patient's condition regularly. This study concentrates on various MachineLearning techniques applied for classification of ECG signals which include CNN and RNN. In the past few years, it is being observed that CNN is playing a dominant role in feature extraction from which we can infer that machine learning techniques have been showing accuracy and progress in classification of ECG signals. Therefore, this paper includes Convolutional Neural Network and Recurrent Neural Network which is being classified into two types for better results from considerably increased depth.

scholarly journals Fraud Detection: A Study of AdaBoost Classifier and K-Means Clustering

2021 ◽  
Author(s):  
Anwesha Mishra
Keyword(s):  
Machine Learning ◽  
Research Collaboration ◽  
Credit Cards ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Learning Group ◽  
The Past ◽  
Big Data Mining ◽  
Learning Techniques ◽  
Adaboost Classifier

Abstract Fraud is a problem which can affect the economy greatly. Billions of dollars are lost because of fraud cases. These problems can occur through credit cards, insurance and bank accounts. Currently there have been many studies for preventing fraud. Machine learning techniques have helped in analysing fraud detection. These include many supervised and unsupervised models. Neural networks can be used for fraud detection. The dataset for the present work was collected from a research collaboration between Worldline and the Machine Learning Group of Université Libre de Bruxelles on the topic of big data mining and fraud detection. It consists of the time and amount of various transactions of European card holders during the month of September in 2013. This paper gives an analysis of the past and the present models used for fraud detection and presents a study of using K-Means Clustering and AdaBoost Classifier by comparing their accuracies.

scholarly journals On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

2020 ◽  
Vol 12 (6) ◽  
pp. 2544
Author(s):  
Alice Consilvio ◽  
José Solís-Hernández ◽  
Noemi Jiménez-Redondo ◽  
Paolo Sanetti ◽  
Federico Papa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Case Studies ◽  
Real World ◽  
Asset Management ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
First Case ◽  
Learning Techniques ◽  
Track Circuits

The objective of this study is to show the applicability of machine learning and simulative approaches to the development of decision support systems for railway asset management. These techniques are applied within the generic framework developed and tested within the In2Smart project. The framework is composed by different building blocks, in order to show the complete process from data collection and knowledge extraction to the real-world decisions. The application of the framework to two different real-world case studies is described: the first case study deals with strategic earthworks asset management, while the second case study considers the tactical and operational planning of track circuits’ maintenance. Although different methodologies are applied and different planning levels are considered, both the case studies follow the same general framework, demonstrating the generality of the approach. The potentiality of combining machine learning techniques with simulative approaches to replicate real processes is shown, evaluating the key performance indicators employed within the considered asset management process. Finally, the results of the validation are reported as well as the developed human–machine interfaces for output visualization.

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