PREPARE: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems

2012 ◽  
Author(s):  
Yongmin Tan ◽  
Hiep Nguyen ◽  
Zhiming Shen ◽  
Xiaohui Gu ◽  
Chitra Venkatramani ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Cloud Systems ◽  
Performance Anomaly

Distributed predictive performance anomaly detection for virtualised platforms

2018 ◽  
Vol 11 (4) ◽  
pp. 279 ◽  
Author(s):  
Ali Imran Jehangiri ◽  
Ramin Yahyapour ◽  
Edwin Yaqub ◽  
Philipp Wieder
Keyword(s):  
Anomaly Detection ◽  
Predictive Performance ◽  
Performance Anomaly

Distributed predictive performance anomaly detection for virtualised platforms

2018 ◽  
Vol 11 (4) ◽  
pp. 279
Author(s):  
Philipp Wieder ◽  
Edwin Yaqub ◽  
Ramin Yahyapour ◽  
Ali Imran Jehangiri
Keyword(s):  
Anomaly Detection ◽  
Predictive Performance ◽  
Performance Anomaly

Radar analysis of cloud systems and their rainfall yield in Israel

2004 ◽  
Vol 53 (2) ◽  
pp. 63-76 ◽  
Author(s):  
Yair Goldreich ◽  
Hanan Mozes ◽  
Daniel Rosenfeld
Keyword(s):  
Cloud Systems

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Machine Learning-Based Scoring Functions. Development and Applications with SAnDReS.

2020 ◽  
Vol 27 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Camila Rizzotto ◽  
Walter Filgueira de Azevedo Junior
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Drug Targets ◽  
Computational Models ◽  
Factor Xa ◽  
Coagulation Factor ◽  
Predictive Performance ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Molegro Virtual Docker

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs. Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes. Method: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding, and thermodynamic data to create targeted scoring functions. Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases, and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions. Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker, and AutoDock Vina.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

Optimised retinopathy of prematurity screening guideline in China based on a 5-year cohort study

2020 ◽  
pp. bjophthalmol-2020-316401
Author(s):  
Qian Yang ◽  
Xiaohong Zhou ◽  
Yingqin Ni ◽  
Haidong Shan ◽  
Wenjing Shi ◽  
...  
Keyword(s):  
Cohort Study ◽  
Retinopathy Of Prematurity ◽  
Neonatal Intensive Care ◽  
Predictive Performance ◽  
Screening Guideline ◽  
Postmenstrual Age ◽  
The Mean ◽  
Type 1 Rop ◽  
Rop Screening

PurposesTo develop an optimised retinopathy of prematurity (ROP) screening guideline by adjusting the screening schedule and thresholds of gestational age (GA) and birth weight (BW).MethodsA multicentre retrospective cohort study was conducted based on data from four tertiary neonatal intensive care units in Shanghai, China. The medical records of enrolled infants, born from 2012 to 2016 who underwent ROP examinations, were collected and analysed. The incidence and risk factors for ROP were analysed in all infants. Postnatal age (PNA) and postmenstrual age (PMA) of infants, detected to diagnose ROP for the first time, were compared with the present examination schedule. The predictive performance of screening models was evaluated by internally validating sensitivity and specificity.ResultsOf the 5606 eligible infants, ROP was diagnosed in 892 (15.9%) infants; 63 (1.1%) of them received treatment. The mean GA of ROP patients was 29.4±2.4 weeks, and the mean BW was 1260±330 g. Greater prematurity was associated with an older PNA at which ROP developed. The minimum PMA and PNA at which diagnosis of treatable ROP occurred were 32.43 and 3 weeks, respectively. The optimised criteria (GA <32 weeks or BW <1600 g) correctly predicted 98.4% type 1 ROP infants, reducing the infants requiring examinations by 43.2% when internally validated.ConclusionsThe incidence of type 1 ROP and the mean GA and BW of ROP infants have decreased in China. The suggested screening threshold and schedule may be reliably used to guide the modification of ROP screening guideline and decrease medical costs.

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