scholarly journals Exploring the self-service model to visualize the results of the ATLAS Machine Learning analysis jobs in BigPanDA with Openshift OKD3

2021 ◽  
Vol 251 ◽  
pp. 02009
Author(s):  
Ioan-Mihail Stan ◽  
Siarhei Padolski ◽  
Christopher Jon Lee ◽  
Keyword(s):  
Machine Learning ◽  
Data Visualization ◽  
Hadron Collider ◽  
Machine Learning Algorithms ◽  
Main Role ◽  
Scientific Experiments ◽  
Self Service ◽  
Analysis System ◽  
Learning Analysis ◽  
Computing Infrastructure

A large scientific computing infrastructure must offer versatility to host any kind of experiment that can lead to innovative ideas. The ATLAS experiment offers wide access possibilities to perform intelligent algorithms and analyze the massive amount of data produced in the Large Hadron Collider at CERN. The BigPanDA monitoring is a component of the PanDA (Production ANd Distributed Analysis) system, and its main role is to monitor the entire lifecycle of a job/task running in the ATLAS Distributed Computing infrastructure. Because many scientific experiments now rely upon Machine Learning algorithms, the BigPanDA community desires to expand the platform’s capabilities and fill the gap between Machine Learning processing and data visualization. In this regard, BigPanDA partially adopts the cloud-native paradigm and entrusts the data presentation to MLFlow services running on Openshift OKD. Thus, BigPanDA interacts with the OKD API and instructs the containers orchestrator how to locate and expose the results of the Machine Learning analysis. The proposed architecture also introduces various DevOps-specific patterns, including continuous integration for MLFlow middleware configuration and continuous deployment pipelines that implement rolling upgrades. The Machine Learning data visualization services operate on demand and run for a limited time, thus optimizing the resource consumption.

scholarly journals Kunstmatige intelligentie voor klinische toepassing van beeldvorming in de psychiatrie

2020 ◽  
Author(s):  
Guido van Wingen
Keyword(s):  
Machine Learning ◽  
Psychological Symptoms ◽  
Predictive Biomarkers ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Clinical Applications ◽  
Machine Learning Algorithms ◽  
Brain Scans ◽  
Learning Analysis ◽  
The Brain

The clinical application of neuroimaging for psychological complaints has so far been limited to the exclusion of somatic pathology. Radiological assessment of brain scans usually does not explain the psychological symptoms. However, that does not mean that psychological symptoms have no neurobiological basis. Hope has therefore been placed on functional MRI, which measures the activity of the brain. However, this has not yet resulted in clinical applications. A multivariate approach using machine learning analysis now appears to be changing this. Machine learning algorithms can already automate various tasks in radiology. Recent studies show that machine learning analysis of MRI images can also provide diagnostic, prognostic and predictive biomarkers for psychiatry. Larger studies are needed to develop clinical applications, such as clinical decision support systems to support personalized treatment choices.

scholarly journals Data mining methods of healthy indoor climate coefficients for comfortable well-being

2018 ◽  
Vol 29 (3) ◽  
pp. 7-12
Author(s):  
Grit Behrens ◽  
Klaus Schlender ◽  
Florian Fehring
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Indoor Air ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Indoor Climate ◽  
Measurement And Analysis ◽  
Scientific Project ◽  
Mining Methods ◽  
Analysis System

Abstract This article provides information about a currently developed measurement and analysis system ‘Smart Monitoring’, which is used on scientific project in terms of healthy indoor air coefficients, as well as the processing of the collected data for machine learning algorithms. The target is to reduce CO2 emissions caused by wrong ventilation habits in building sector after renovation process in older buildings.

iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data

2019 ◽  
Vol 21 (3) ◽  
pp. 1047-1057 ◽  
Author(s):  
Zhen Chen ◽  
Pei Zhao ◽  
Fuyi Li ◽  
Tatiana T Marquez-Lago ◽  
André Leier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dimensionality Reduction ◽  
Sequence Data ◽  
Machine Learning Algorithms ◽  
User Friendliness ◽  
Data Set ◽  
Protein Sequence Data ◽  
Learning Analysis ◽  
High Throughput Manner ◽  
Online Web

Abstract With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date; however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.

Predicting Anticoagulation Need for Otogenic Intracranial Sinus Thrombosis: A Machine Learning Approach

2020 ◽  
Author(s):  
Matthew R. Kaufmann ◽  
Philip Ryan Camilon ◽  
Jessica R. Levi ◽  
Anand K. Devaiah
Keyword(s):  
Machine Learning ◽  
Sinus Thrombosis ◽  
Cerebral Venous Sinus Thrombosis ◽  
Machine Learning Algorithms ◽  
Baseline Risk ◽  
Cochrane Library ◽  
Intracranial Abscess ◽  
Machine Learning Approach ◽  
The Cochrane Library ◽  
Learning Analysis

Abstract Objective The role of anticoagulation (AC) in the management of otogenic cerebral venous sinus thrombosis (OCVST) remains controversial. Our study aims to better define when AC is used in OCVST. Methods MEDLINE, EMBASE, and The Cochrane Library were searched from inception to February 14, 2019 for English and English-translated articles. References cited in publications meeting search criteria were searched. Titles and abstracts were screened and identified in the literature search, assessing baseline risk of bias on extracted data with the methodological index for nonrandomized studies (MINORS) scale. Random effects meta-regression followed by random forest machine learning analysis across 16 moderator variables between AC and nonanticoagulated (NAC) cohorts was conducted. Results A total of 92% of treated patients were free of neurologic symptoms at the last follow-up (mean 29.64 months). Four percent of AC and 14% of NAC patients remained symptomatic (mean 18.72 and 47.10 months). 3.5% of AC patients experienced postoperative wound hematomas. AC and NAC recanalization rates were 81% (34/42) and 63% (five-eights), respectively. OCVST was correlated with cholesteatoma and intracranial abscess. Among the analyzed covariates, intracranial abscess was most predictive of AC and cholesteatoma was most predictive of NAC. Comorbid intracranial abscess and cholesteatoma were predictive of AC. Conclusion The present study is the first to utilize machine learning algorithms in approaching OCVST. Our findings support the therapeutic use of AC in the management of OCVST when complicated by thrombophilia, intracranial abscess, and cholesteatoma. Patients with intracranial abscess and cholesteatoma may benefit from AC and surgery. Patients with cholesteatoma can be managed with NAC and surgery.

scholarly journals MAPPING GLACIER CHANGES USING CLUSTERING TECHNIQUES ON CLOUD COMPUTING INFRASTRUCTURE

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 29-34
Author(s):  
V. Ayma ◽  
C. Beltrán ◽  
P. N. Happ ◽  
G. A. O. P. Costa ◽  
R. Q. Feitosa
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Machine Learning Algorithms ◽  
Local Mode ◽  
Ground Truth Data ◽  
Computational Performance ◽  
Thematic Accuracy ◽  
Computing Infrastructure

<p><strong>Abstract.</strong> Climate change and its effects are taking more importance nowadays; and glaciers are one of the most affected ecosystems by that, considering that the energy of Earth’s surface and its temperature may be directly related to glacier temporal changes. Then, the comprehension of glaciers behaviour, by its retreating or melting critical conditions, can be achieved by the analysis of Remote Sensing data, but considering the unprecedented volumes of information currently provided by satellites sensors, we can refer to this analysis as a big data problem. Machine learning techniques have the potential to improve the analysis of this type of data; however, most current machine learning algorithms are unable to properly process such huge volumes of data. In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means and Expectation Maximization algorithms, as distributed clustering solutions, exploiting the capabilities of cloud computing infrastructure for processing very large datasets. The solution was developed over the InterCloud Data Mining Package, which is a suite of distributed classification methods, previously employed in hyperspectral image analysis. In this work we extended the functionalities of that package, by making it able to process multispectral images using the aforementioned clustering algorithms. To validate our proposal, we analysed the Ausangate glacier, located on the Andes Mountains, in Peru, by mapping the changes in such environment through a multi-temporal Remote Sensing analysis. Our results and conclusions are focused on the thematic accuracy and the computational performance achieved by our proposed solution. Thematic accuracy was assessed by comparing the automatically detected glacier areas by the clustering approaches against the manually selected ground truth data. We compared the computational load involved in executing the clustering processes sequentially and in a distributed fashion, using a local mode and cluster configuration over a cloud computing infrastructure.</p>

scholarly journals Clinical Features at Presentation for Glioblastoma Patients Impact Survival Predictions in a Machine Learning Model

2021 ◽  
Vol 23 (Supplement_4) ◽  
pp. iv18-iv18
Author(s):  
Alistair Lawrence ◽  
Rohit Sinha ◽  
Stefan Mitrasinovic ◽  
Stephen Price
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Machine Learning Algorithms ◽  
Analysis Model ◽  
Tree Model ◽  
Machine Learning Model ◽  
Validation Set ◽  
Learning Analysis ◽  
Prognostic Modelling

Abstract Aims To generate an accurate prediction model for greater than median survival using Random Forest machine learning analysis and to compare the model to a traditional logistic regression analysis model on the same Glioblastoma Dataset. Method In this single centre retrospective cohort study, all patients with histologically diagnosed primary GB from October 2014 to April 2019 were included (n=466). Machine learning algorithms encompassing multiple logistic regression and a Random Forest, Gini index-based decision tree model with 100,000 trees were used. 17 clinical, molecular and treatment specific binarily categorised variables were used. The dataset was split 70:30 into training and validating sets. Results The dataset contained 466 patients. 326 patients made up the training set and 140 the validation set. The Random Forest model’s accuracy for predicting 18-month survival was 86.4% compared to the Logistic Regression model’s accuracy of 85.7%. The top 5 factors that the Random Forest model used to predict survival over 18 months were; mean MGMT status &gt;10%, if the patient underwent gross total resection, whether the patient had adjuvant temozolomide, whether the patient had a neurological deficit on presentation, and the sex of the patient. Conclusion Machine learning can be applied in the context of GB prognostic modelling. The models show that as well as the known factors that affect GB survival, the presenting symptom may also have an impact on prognostication.

Design of a Comprehensive Sensor based Soil and Crop Analysis System Model using Machine Learning Algorithms

2021 ◽  
Author(s):  
Aafreen Sana H ◽  
Soma Prathibha ◽  
Pravin Kumar P ◽  
Shabika Fathima M ◽  
Yashwanth Krishnan B
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
System Model ◽  
Analysis System

scholarly journals Stock Market (NIFTY) Forecasting using Machine Learning Analysis on Option Chain

2021 ◽  
Vol 9 (5) ◽  
pp. 80-83
Author(s):  
Saurabh Gupta ◽  
Vaishali Vaishali ◽  
Raghuvansh Tahlan ◽  
Navya Sanjna Joshi ◽  
Ritvik Agarwal
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Long Time ◽  
Direction Of Movement ◽  
Regression Techniques ◽  
Learning Analysis

Stock market prediction is a long-time intriguing topic to researchers from different fields. Stock market data is extremely volatile and hence laborious to model. In particular, innumerable studies have been conducted to predict the movement of stock market using Machine Learning algorithms such as Regression Techniques, Time Series Forecasting, Indices Modelling, Natural Language Processing and more, but there is still room for improvement. Also, Option chain and Options have been the subjects that not many have ventured into, leading us to this subject. Mainly, NIFTY and BANKNIFTY Options account for 70% of total derivatives traded and much more turnover than all stocks combined. This research paper attempts to figure out the utility of Option Chain in predicting the direction of movement in NIFTY. We have tried how different features from Option chain can be extracted, and the resulting problem can be solved using Machine Learning techniques and Deep Learning techniques.

scholarly journals Efficient intelligent system for diagnosis pneumonia (SARS-COVID19) in X-Ray images empowered with initial clustering

2021 ◽  
Vol 22 (1) ◽  
pp. 241
Author(s):  
Salam Saad Mohamed Ali ◽  
Ali Hakem Alsaeedi ◽  
Dhiah Al-Shammary ◽  
Hassan Hakem Alsaeedi ◽  
Hadeel Wajeeh Abid
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Clustering ◽  
Intelligent System ◽  
Learning Algorithms ◽  
Image Features ◽  
Machine Learning Algorithms ◽  
Main Role ◽  
Training Process ◽  
X Ray

<span>This paper proposes efficient models to help diagnose respiratory (SARS-COVID19) infections by developing new data descriptors for standard machine learning algorithms using X-Ray images. As COVID-19 is a significantly serious respiratory infection that might lead to losing life, artificial intelligence plays a main role through machine learning algorithms in developing new potential data classification. Data clustering by K-Means is applied in the proposed system advanced to the training process to cluster input records into two clusters with high harmony. Principle Component Analysis PCA, histogram of orientated gradients (HOG) and hybrid PCA and HOG are developed as potential data descriptors. The wrapper model is proposed for detecting the optimal features and applied on both clusters individually. This paper proposes new preprocessed X-Ray images for dataset featurization by PCA and HOG to effectively extract X-Ray image features. The proposed systems have potentially empowered machine learning algorithms to diagnose Pneumonia (SARS-COVID19) with accuracy up to %97.</span>

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