Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps

2013 ◽  
Author(s):  
Diego F. Silva ◽  
Vinicius M.A. De Souza ◽  
Gustavo E.A.P.A. Batista ◽  
Eamonn Keogh ◽  
Daniel P.W. Ellis
Keyword(s):  
Machine Learning ◽  
Audio Analysis ◽  
Analysis Techniques

scholarly journals AN AUTOMATED FRAMEWORK FOR CORONARY ANALYSIS FROM CORONARY CINE ANGIOGRAMS USING MACHINE LEARNING AND IMAGE ANALYSIS TECHNIQUES

2021 ◽  
Vol 9 (1) ◽  
pp. 1406-1412
Author(s):  
K. Santhi, A. Rama Mohan Reddy
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Image Analysis ◽  
Visual Assessment ◽  
Disease Diagnosis ◽  
Healthy Life ◽  
Common Cause ◽  
Analysis Techniques ◽  
Image Modality ◽  
Image Analysis Techniques

Cardiovascular disease (CVD) is one of the critical diseases and the most common cause of morbidity and mortality worldwide. Therefore, early detection and prediction of such a disease is extremely essential for a healthy life. Cardiac imaging plays an important role in the diagnosis of cardiovascular disease but its role has been limited to visual assessment of heart structure and its function. However, with the advanced techniques and tools of big data and machine learning, it become easier to clinician to diagnose the CVD. Stenosis with in the Coronary Arteries (CA) are often determined by using the Coronary Cine Angiogram (CCA). It comes under the invasive image modality. CCA is the effective method to detect and predict the stenosis. In this paper a coronary analysis automation method is proposed in disease diagnosis. The proposed method includes pre-processing, segmentation, identifying vessel path and statistical analysis.

A Bayesian Based Machine Learning Application to Task Analysis

2012 ◽  
pp. 234-242
Author(s):  
Shu-Chiang Lin
Keyword(s):  
Machine Learning ◽  
Task Analysis ◽  
Analysis Tool ◽  
Analysis Techniques ◽  
Bayesian Methodology ◽  
The Past ◽  
Expensive Process

Many task analysis techniques and methods have been developed over the past decades, but identifying and decomposing a user’s task into small task components remains a difficult, impractically time-consuming, and expensive process that involves extensive manual effort (Sheridan, 1997; Liu, 1997; Gramopadhye and Thaker, 1999; Annett and Stanton, 2000; Bridger, 2003; Stammers and Shephard, 2005; Hollnagel, 2006; Luczak et al., 2006; Morgeson et al., 2006). A practical need exists for developing automated task analysis techniques to help practitioners perform task analysis efficiently and effectively (Lin, 2007). This chapter summarizes a Bayesian methodology for task analysis tool to help identify and predict the agents’ subtasks from the call center’s naturalistic decision making’s environment.

scholarly journals Machine Learning for the Evolutionary Analysis of Breast Cancer

2018 ◽  
Vol 3 (CITT2017) ◽  
pp. 44-49
Author(s):  
Alexander Mackenzie Rivero ◽  
Alberto Rodríguez Rodríguez ◽  
Edwin Joao Merchán Carreño ◽  
Rodrigo Martínez Béjar
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Model ◽  
Missing Values ◽  
Medical Center ◽  
Classification Algorithms ◽  
Evolutionary Analysis ◽  
Data Set ◽  
Analysis Techniques ◽  
The University

The use of machine learning allows the creation of a predictive data model, as a result of the analysis in a data set with 286 instances and nine attributes belonging to the Institute of Oncology of the University Medical Center. Ljubljana. Based on this situation, the data are preprocessed by applying intelligent data analysis techniques to eliminate missing values as well as the evaluation of each attribute that allows the optimization of results. We used several classification algorithms including J48 trees, random forest, bayes net, naive bayes, decision table, in order to obtain one that given the characteristics of the data, would allow the best classification percentage and therefore a better matrix of confusion, Using 66 % of the data for learning and 33 % for validating the model. Using this model, a predictor with a 71,134 % e effectiveness is obtained to estimate or not the recurrence of breast cancer.

scholarly journals Malware: The Never-Ending Arm Race

2021 ◽  
pp. 1-25
Author(s):  
Hector David Menendez
Keyword(s):  
Machine Learning ◽  
Dynamic Analysis ◽  
Detection System ◽  
Vice President ◽  
Historical Background ◽  
Malicious Software ◽  
Static And Dynamic Analysis ◽  
Analysis Techniques ◽  
Single Strategy ◽  
New Strategies

"Antivirus is death"' and probably every detection system that focuses on a single strategy for indicators of compromise. This famous quote that Brian Dye --Symantec's senior vice president-- stated in 2014 is the best representation of the current situation with malware detection and mitigation. Concealment strategies evolved significantly during the last years, not just like the classical ones based on polimorphic and metamorphic methodologies, which killed the signature-based detection that antiviruses use, but also the capabilities to fileless malware, i.e. malware only resident in volatile memory that makes every disk analysis senseless. This review provides a historical background of different concealment strategies introduced to protect malicious --and not necessarily malicious-- software from different detection or analysis techniques. It will cover binary, static and dynamic analysis, and also new strategies based on machine learning from both perspectives, the attackers and the defenders.

Intelligent Log Analysis Using Machine and Deep Learning

2019 ◽  
pp. 154-189 ◽  
Author(s):  
Steven Yen ◽  
Melody Moh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Volume ◽  
Comparative Effectiveness ◽  
Rich Source ◽  
Log Analysis ◽  
Statistical Machine Learning ◽  
Analysis Techniques ◽  
Learning Techniques ◽  
Source Of Information

Computers generate a large volume of logs recording various events of interest. These logs are a rich source of information and can be analyzed to extract various insights about the system. However, due to its overwhelmingly large volume, logs are often mismanaged and not utilized effectively. The goal of this chapter is to help researchers and industrial professionals make more informed decisions about their logging solutions. It first lays the foundation by describing log sources and format. Then it describes all the components involved in logging. The remainder of the chapter provides a survey of different log analysis techniques and their applications, consisting of conventional techniques using rules and event correlators that can detect known issues, plus more advanced techniques such as statistical, machine learning, and deep learning techniques that can also detect unknown issues. The chapter concludes describing the underlying concepts of the techniques, their application to log analysis, and their comparative effectiveness.

Intelligent Log Analysis Using Machine and Deep Learning

2021 ◽  
pp. 1154-1182
Author(s):  
Steven Yen ◽  
Melody Moh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Volume ◽  
Comparative Effectiveness ◽  
Rich Source ◽  
Log Analysis ◽  
Statistical Machine Learning ◽  
Analysis Techniques ◽  
Learning Techniques ◽  
Source Of Information

Computers generate a large volume of logs recording various events of interest. These logs are a rich source of information and can be analyzed to extract various insights about the system. However, due to its overwhelmingly large volume, logs are often mismanaged and not utilized effectively. The goal of this chapter is to help researchers and industrial professionals make more informed decisions about their logging solutions. It first lays the foundation by describing log sources and format. Then it describes all the components involved in logging. The remainder of the chapter provides a survey of different log analysis techniques and their applications, consisting of conventional techniques using rules and event correlators that can detect known issues, plus more advanced techniques such as statistical, machine learning, and deep learning techniques that can also detect unknown issues. The chapter concludes describing the underlying concepts of the techniques, their application to log analysis, and their comparative effectiveness.

Introduction to Machine Learning in Digital Healthcare Epidemiology

2018 ◽  
Vol 39 (12) ◽  
pp. 1457-1462 ◽  
Author(s):  
Jan A. Roth ◽  
Manuel Battegay ◽  
Fabrice Juchler ◽  
Julia E. Vogt ◽  
Andreas F. Widmer
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Routine Data ◽  
Full Potential ◽  
Broad Area ◽  
Analysis Techniques ◽  
Large Scale Analysis ◽  
Healthcare Epidemiology ◽  
Technology Specialists ◽  
Digital Healthcare

AbstractTo exploit the full potential of big routine data in healthcare and to efficiently communicate and collaborate with information technology specialists and data analysts, healthcare epidemiologists should have some knowledge of large-scale analysis techniques, particularly about machine learning. This review focuses on the broad area of machine learning and its first applications in the emerging field of digital healthcare epidemiology.

scholarly journals Machine Learning for Identifying Medication-Associated Acute Kidney Injury

Informatics ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 18
Author(s):  
Sheikh S. Abdullah ◽  
Neda Rostamzadeh ◽  
Kamran Sedig ◽  
Daniel J. Lizotte ◽  
Amit X. Garg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Clinical Medicine ◽  
Kidney Injury ◽  
Population Based ◽  
Frequent Itemset ◽  
Machine Learning Techniques ◽  
Analysis Techniques ◽  
Stratified Analysis ◽  
The Impact

One of the prominent problems in clinical medicine is medication-induced acute kidney injury (AKI). Avoiding this problem can prevent patient harm and reduce healthcare expenditures. Several researches have been conducted to identify AKI-associated medications using statistical, data mining, and machine learning techniques. However, these studies are limited to assessing the impact of known nephrotoxic medications and do not comprehensively explore the relationship between medication combinations and AKI. In this paper, we present a population-based retrospective cohort study that employs automated data analysis techniques to identify medications and medication combinations that are associated with a higher risk of AKI. By integrating multivariable logistic regression, frequent itemset mining, and stratified analysis, this study is designed to explore the complex relationships between medications and AKI in such a way that has never been attempted before. Through an analysis of prescription records of one million older patients stored in the healthcare administrative dataset at ICES (an independent, non-profit, world-leading research organization that uses population-based health and social data to produce knowledge on a broad range of healthcare issues), we identified 55 AKI-associated medications among 595 distinct medications and 78 AKI-associated medication combinations among 7748 frequent medication combinations. In addition, through a stratified analysis, we identified 37 cases where a particular medication was associated with increasing the risk of AKI when used with another medication. We have shown that our results are consistent with previous studies through consultation with a nephrologist and an electronic literature search. This research demonstrates how automated analysis techniques can be used to accomplish data-driven tasks using massive clinical datasets.

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