Machine Learning using retarget data to improve accuracy of fast lithographic hotspot detection

2020 ◽  
Author(s):  
Aliaa Kabeel ◽  
Wael ElManhawy ◽  
Joe Kwan ◽  
Asmaa Rabie ◽  
Mohamed Ismail ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hotspot Detection ◽  
Improve Accuracy

Machine learning to improve accuracy of fast lithographic hotspot detection

2019 ◽  
Author(s):  
Namjae Kim ◽  
KiHeung Park ◽  
Jiwon Oh ◽  
Sangwoo Jung ◽  
Sangah Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hotspot Detection ◽  
Improve Accuracy

Machine learning for mask/wafer hotspot detection and mask synthesis

2017 ◽  
Author(s):  
David Z. Pan ◽  
Yibo Lin ◽  
Xiaoqing Xu ◽  
Jiaojiao Ou
Keyword(s):  
Machine Learning ◽  
Hotspot Detection

Early Intervention Systems: Predicting Adverse Interactions Between Police and the Public

2017 ◽  
Vol 29 (2) ◽  
pp. 190-209 ◽  
Author(s):  
Jennifer Helsby ◽  
Samuel Carton ◽  
Kenneth Joseph ◽  
Ayesha Mahmud ◽  
Youngsoo Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Intervention ◽  
Police Department ◽  
Data Sources ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Police Legitimacy ◽  
The Public ◽  
Learning Techniques ◽  
Improve Accuracy

Adverse interactions between police and the public hurt police legitimacy, cause harm to both officers and the public, and result in costly litigation. Early intervention systems (EISs) that flag officers considered most likely to be involved in one of these adverse events are an important tool for police supervision and for targeting interventions such as counseling or training. However, the EISs that exist are not data-driven and based on supervisor intuition. We have developed a data-driven EIS that uses a diverse set of data sources from the Charlotte-Mecklenburg Police Department and machine learning techniques to more accurately predict the officers who will have an adverse event. Our approach is able to significantly improve accuracy compared with their existing EIS: Preliminary results indicate a 20% reduction in false positives and a 75% increase in true positives.

Litho-Aware Machine Learning for Hotspot Detection

2018 ◽  
Vol 37 (7) ◽  
pp. 1510-1514 ◽  
Author(s):  
Jea Woo Park ◽  
Andres Torres ◽  
Xiaoyu Song
Keyword(s):  
Machine Learning ◽  
Hotspot Detection

scholarly journals Multi Class Data Classification to Improve Accuracy in Sentiment Analysis using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 1457-1461
Author(s):  
Daram Vishnu
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Confusion Matrix ◽  
Training Data ◽  
Natural Languages ◽  
Parts Of Speech ◽  
Testing Data ◽  
Improve Accuracy ◽  
Textual Form ◽  
Speech Tagging

Sentiment analysis means classifying a text into different emotional classes. These days most of the sentiment analysis techniques divide the text into either binary or ternary classification in this paper we are classifying the movie reviews into 5 classes. Multi class sentiment analysis is a technique which can be used to know the exact sentiment of a review not just polarity of a given textual statement from positive to negative. So that one can know the precise sentiment of a review . Multi class sentiment analysis has always been a challenging task as natural languages are difficult to represent mathematically. The number of features are also generally large which requires huge computational power so to reduce the number of features we will use parts-of-speech tagging using textblob to extract the important features. Sentiment analysis is done using machine learning, where it requires training data and testing data to train a model. Various kinds of models are trained and tested at last one model is selected based on its accuracy and confusion matrix. It is important to analyze the reviews in textual form because large amount of reviews is present all over the web. Analyzing textual reviews can help the firms that are trying to find out the response of their products in the market. In this paper sentiment analysis is demonstrated by analyzing the movie reviews, reviews are taken from IMDB website.

scholarly journals Analytics, Machine Learning & NLP – use in BioSurveillance and Public Health practice

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Mujitha B. K B ◽  
Ajil Jalal ◽  
Vishnuprasad V ◽  
Nishad K A
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Language Processing ◽  
News Media ◽  
Public Health Practice ◽  
Simulation Environment ◽  
Operating Environment ◽  
Improve Accuracy ◽  
Clinical Records ◽  
Social News

This presentation summarizes ways in which Analytics, Machine Learning (ML) and Natural Language Processing (NLP) can improve accuracy and efficiency in bio surveillance and public health practices. Currently, there is an abundance of data coming from most of the surveillance environments and applications. Identification and filtering of responsive messages from this big data ocean and then processing these informative datasets to gain knowledge are the two real challenges in today's applications. Details of a Simulation environment consisting of Devices/Sensors, Web/Mobile, Clinical Records, Internet queries, Social/News media, in which this ML platform was evaluated is also discussed. Infrastructure needs for this operating environment is also covered.

scholarly journals Improving preterm newborn identification in low-resource settings with machine learning

2018 ◽  
Author(s):  
Katelyn J. Rittenhouse ◽  
Bellington Vwalika ◽  
Alex Keil ◽  
Jennifer Winston ◽  
Marie Stoner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Birth Weight ◽  
Preterm Birth ◽  
Preterm Infants ◽  
Preterm Newborn ◽  
Maternal Factors ◽  
Factors Associated ◽  
Improve Accuracy ◽  
Hiv Serostatus ◽  
Twin Delivery

AbstractBackgroundGlobally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low‐ and middle‐income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings.Methods and FindingsThis study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live‐born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3 ‐ 40.3). Among assessed newborns with complete data included in this analysis (n = 458), the median GA by ultrasound was 39.6 weeks (IQR: 38.4 ‐ 40.3). Using machine learning, we identified a combination of six accessible parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and HIV serostatus) that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796.ConclusionsWe identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess GA.

scholarly journals Monitoring bias and fairness in machine learning models: A review

2021 ◽  
Author(s):  
Marley Bacelar
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Economic Sectors ◽  
Improve Accuracy ◽  
Private And Public ◽  
Decision Making Processes ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Machine Learning Models

Introduction Machine learning algorithms are quickly gaining traction in both the private and public sectors for their ability to automate both simple and complex decision-making processes. The vast majority of economic sectors, including transportation, retail, advertisement, and energy, are being disrupted by widespread data digitization and the emerging technologies that leverage it. Computerized systems are being introduced in government operations to improve accuracy and objectivity, and AI is having an impact on democracy and governance [1]. Numerous businesses are using machine learning to analyze massive quantities of data, from calculating credit for loan applications to scanning legal contracts for errors to analyzing employee interactions with customers to detect inappropriate behavior. New tools make it easier than ever for developers to design and deploy machine-learning algorithms [2] [3].

scholarly journals Botnet Forensic Analysis Using Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Anchit Bijalwan
Keyword(s):  
Machine Learning ◽  
Ensemble Classifier ◽  
Forensic Analysis ◽  
Modus Operandi ◽  
Botnet Detection ◽  
Quality Of Results ◽  
Proposed Model ◽  
Improve Accuracy ◽  
Rapid Pace

Botnet forensic analysis helps in understanding the nature of attacks and the modus operandi used by the attackers. Botnet attacks are difficult to trace because of their rapid pace, epidemic nature, and smaller size. Machine learning works as a panacea for botnet attack related issues. It not only facilitates detection but also helps in prevention from bot attack. The proposed inquisition model endeavors improved quality of results by comprehensive botnet detection and forensic analysis. This scenario has been applied in eight different combinations of ensemble classifier technique to detect botnet evidence. The study is also compared to the ensemble-based classifiers with the single classifier using different parameters. The results exhibit that the proposed model can improve accuracy over a single classifier.

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