scholarly journals Maximizing the quality of NMR automatic metabolite profiling by a machine learning based prediction of signal parameters

2018 ◽  
Author(s):  
Daniel Cañueto ◽  
Miriam Navarro ◽  
Mónica Bulló ◽  
Xavier Correig ◽  
Nicolau Cañellas
Keyword(s):  
Machine Learning ◽  
Quality Indicators ◽  
Metabolite Profiling ◽  
Complex Spectrum ◽  
Multiple Sources ◽  
Low Intensity ◽  
Signal Parameters ◽  
The Matrix ◽  
Expected Signal

AbstractThe quality of automatic metabolite profiling in NMR datasets in complex matrices can be compromised by the multiple sources of variability in the samples. These sources cause uncertainty in the metabolite signal parameters and the presence of multiple low-intensity signals. Lineshape fitting approaches might produce suboptimal resolutions or distort the fitted signals to adapt them to the complex spectrum lineshape. As a result, tools tend to restrict their use to specific matrices and strict protocols to reduce this uncertainty. However, the analysis and modelling of the signal parameters collected during a first profiling iteration can further reduce the uncertainty by the generation of narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted and the performance of automatic profiling can be maximized. Thanks to the ability of our workflow to learn and model the sample properties, restrictions in the matrix or protocol and limitations of lineshape fitting approaches can be overcome.

Distributed Intelligence for Constructing Economic Models

2012 ◽  
pp. 1538-1550
Author(s):  
Ting Yu
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Intelligent System ◽  
Economic Models ◽  
Data Retrieval ◽  
Multiple Sources ◽  
Matrix Estimation ◽  
Distributed Intelligence ◽  
Large Size ◽  
The Matrix

This paper presents an integrated and distributed intelligent system being capable of automatically estimating and updating large-size economic models. The input-output model of economics uses a matrix representation of a nation’s (or a region’s) economy to predict the effect of changes in one industry on others and by consumers, government, and foreign suppliers on the economy (Miller & Blair, 1985). To construct the model reflecting the underlying industry structure faithfully, multiple sources of data are collected and integrated together. The system in this paper facilitates this estimation process by integrating a series of components with the purposes of data retrieval, data integration, machine learning, and quality checking. More importantly, the complexity of national economy leads to extremely large-size models to represent every detail of an economy, which requires the system to have the capacity for processing large amounts of data. This paper demonstrates that the major bottleneck is the memory allocation, and to include more memory, the machine learning component is built on a distributed platform and constructs the matrix by analyzing historical and spatial data simultaneously. This system is the first distributed matrix estimation package for such a large-size economic matrix.

Distributed Intelligence for Constructing Economic Models

2010 ◽  
Vol 1 (3) ◽  
pp. 20-33
Author(s):  
Ting Yu
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Intelligent System ◽  
Economic Models ◽  
Data Retrieval ◽  
Multiple Sources ◽  
Matrix Estimation ◽  
Distributed Intelligence ◽  
Large Size ◽  
The Matrix

This paper presents an integrated and distributed intelligent system being capable of automatically estimating and updating large-size economic models. The input-output model of economics uses a matrix representation of a nation’s (or a region’s) economy to predict the effect of changes in one industry on others and by consumers, government, and foreign suppliers on the economy (Miller & Blair, 1985). To construct the model reflecting the underlying industry structure faithfully, multiple sources of data are collected and integrated together. The system in this paper facilitates this estimation process by integrating a series of components with the purposes of data retrieval, data integration, machine learning, and quality checking. More importantly, the complexity of national economy leads to extremely large-size models to represent every detail of an economy, which requires the system to have the capacity for processing large amounts of data. This paper demonstrates that the major bottleneck is the memory allocation, and to include more memory, the machine learning component is built on a distributed platform and constructs the matrix by analyzing historical and spatial data simultaneously. This system is the first distributed matrix estimation package for such a large-size economic matrix.

Automatically Identifying the Quality of Developer Chats for Post Hoc Use

2021 ◽  
Vol 30 (4) ◽  
pp. 1-28
Author(s):  
Preetha Chatterjee ◽  
Kostadin Damevski ◽  
Nicholas A. Kraft ◽  
Lori Pollock
Keyword(s):  
Machine Learning ◽  
Quality Indicators ◽  
Expert Knowledge ◽  
Support Tools ◽  
Software Engineers ◽  
Programming Support ◽  
Automated Technique ◽  
Post Hoc ◽  
F Measure

Software engineers are crowdsourcing answers to their everyday challenges on Q&A forums (e.g., Stack Overflow) and more recently in public chat communities such as Slack, IRC, and Gitter. Many software-related chat conversations contain valuable expert knowledge that is useful for both mining to improve programming support tools and for readers who did not participate in the original chat conversations. However, most chat platforms and communities do not contain built-in quality indicators (e.g., accepted answers, vote counts). Therefore, it is difficult to identify conversations that contain useful information for mining or reading, i.e., conversations of post hoc quality. In this article, we investigate automatically detecting developer conversations of post hoc quality from public chat channels. We first describe an analysis of 400 developer conversations that indicate potential characteristics of post hoc quality, followed by a machine learning-based approach for automatically identifying conversations of post hoc quality. Our evaluation of 2,000 annotated Slack conversations in four programming communities (python, clojure, elm, and racket) indicates that our approach can achieve precision of 0.82, recall of 0.90, F-measure of 0.86, and MCC of 0.57. To our knowledge, this is the first automated technique for detecting developer conversations of post hoc quality.

Distributed Intelligence for Constructing Economic Models

2012 ◽  
pp. 206-219
Author(s):  
Ting Yu
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Intelligent System ◽  
Economic Models ◽  
Data Retrieval ◽  
Multiple Sources ◽  
Matrix Estimation ◽  
Distributed Intelligence ◽  
Large Size ◽  
The Matrix

This paper presents an integrated and distributed intelligent system being capable of automatically estimating and updating large-size economic models. The input-output model of economics uses a matrix representation of a nation’s (or a region’s) economy to predict the effect of changes in one industry on others and by consumers, government, and foreign suppliers on the economy (Miller & Blair, 1985). To construct the model reflecting the underlying industry structure faithfully, multiple sources of data are collected and integrated together. The system in this paper facilitates this estimation process by integrating a series of components with the purposes of data retrieval, data integration, machine learning, and quality checking. More importantly, the complexity of national economy leads to extremely large-size models to represent every detail of an economy, which requires the system to have the capacity for processing large amounts of data. This paper demonstrates that the major bottleneck is the memory allocation, and to include more memory, the machine learning component is built on a distributed platform and constructs the matrix by analyzing historical and spatial data simultaneously. This system is the first distributed matrix estimation package for such a large-size economic matrix.

Criteria for evaluation of the coordinate measurements quality of parts geometric parameters at pilot-experimental production

2019 ◽  
pp. 3-8
Author(s):  
N.Yu. Bobrovskaya ◽  
M.F. Danilov
Keyword(s):  
Quality Indicators ◽  
Quality Management System ◽  
Technical Specification ◽  
Experimental Production ◽  
Technological Heredity ◽  
Coordinate Measurements ◽  
Reasonable Choice ◽  
The Stability ◽  
Geometric Elements

The criteria of the coordinate measurements quality at pilot-experimental production based on contemporary methods of quality management system and traditional methods of the measurements quality in Metrology are considered. As an additional criterion for quality of measurements, their duration is proposed. Analyzing the problem of assessing the quality of measurements, the authors pay particular attention to the role of technological heredity in the analysis of the sources of uncertainty of coordinate measurements, including not only the process of manufacturing the part, but all stages of the development of design and technological documentation. Along with such criteria as the degree of confidence in the results of measurements; the accuracy, convergence, reproducibility and speed of the results must take into account the correctness of technical specification, and such characteristics of the shape of the geometric elements to be controlled, such as flatness, roundness, cylindrical. It is noted that one of the main methods to reduce the uncertainty of coordinate measurements is to reduce the uncertainty in the initial data and measurement conditions, as well as to increase the stability of the tasks due to the reasonable choice of the basic geometric elements (measuring bases) of the part. A prerequisite for obtaining reliable quality indicators is a quantitative assessment of the conditions and organization of the measurement process. To plan and normalize the time of measurements, the authors propose to use analytical formulas, on the basis of which it is possible to perform quantitative analysis and optimization of quality indicators, including the speed of measurements.

Using the alveograph in developing requirements for the quality of flour for wafer sheet production

2020 ◽  
Vol 29 (12) ◽  
pp. 52-58
Author(s):  
E.P. Meleshkina ◽  
◽  
S.N. Kolomiets ◽  
A.S. Cheskidova ◽  
◽  
...  
Keyword(s):  
Rheological Properties ◽  
Quality Indicators ◽  
Wheat Flour ◽  
Mathematical Statistics ◽  
Intended Purpose ◽  
The Future ◽  
Relationship Of ◽  
The Relationship

Objectively and reliably determined indicators of rheological properties of the dough were identified using the alveograph device to create a system of classifications of wheat and flour from it for the intended purpose in the future. The analysis of the relationship of standardized quality indicators, as well as newly developed indicators for identifying them, differentiating the quality of wheat flour for the intended purpose, i.e. for finished products. To do this, we use mathematical statistics methods.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Pollutants in Organic Chemistry and Medicinal Chemistry Education Laboratory. Experimental and Machine Learning Studies

2020 ◽  
Vol 20 (9) ◽  
pp. 720-730
Author(s):  
Iker Montes-Bageneta ◽  
Urtzi Akesolo ◽  
Sara López ◽  
Maria Merino ◽  
Eneritz Anakabe ◽  
...  
Keyword(s):  
Organic Chemistry ◽  
Machine Learning ◽  
Chemistry Education ◽  
Organic Waste ◽  
Computational Modelling ◽  
University Education ◽  
Academic Factors ◽  
Academic Year ◽  
Statistical Analysis Software

Aims: Computational modelling may help us to detect the more important factors governing this process in order to optimize it. Background: The generation of hazardous organic waste in teaching and research laboratories poses a big problem that universities have to manage. Methods: In this work, we report on the experimental measurement of waste generation on the chemical education laboratories within our department. We measured the waste generated in the teaching laboratories of the Organic Chemistry Department II (UPV/EHU), in the second semester of the 2017/2018 academic year. Likewise, to know the anthropogenic and social factors related to the generation of waste, a questionnaire has been utilized. We focused on all students of Experimentation in Organic Chemistry (EOC) and Organic Chemistry II (OC2) subjects. It helped us to know their prior knowledge about waste, awareness of the problem of separate organic waste and the correct use of the containers. These results, together with the volumetric data, have been analyzed with statistical analysis software. We obtained two Perturbation-Theory Machine Learning (PTML) models including chemical, operational, and academic factors. The dataset analyzed included 6050 cases of laboratory practices vs. practices of reference. Results: These models predict the values of acetone waste with R2 = 0.88 and non-halogenated waste with R2 = 0.91. Conclusion: This work opens a new gate to the implementation of more sustainable techniques and a circular economy with the aim of improving the quality of university education processes.

scholarly journals Mimicking Anti-Viruses with Machine Learning and Entropy Profiles

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 513 ◽  
Author(s):  
Héctor D. Menéndez ◽  
José Luis Llorente
Keyword(s):  
Machine Learning ◽  
Classification Algorithms ◽  
Security Breach

The quality of anti-virus software relies on simple patterns extracted from binary files. Although these patterns have proven to work on detecting the specifics of software, they are extremely sensitive to concealment strategies, such as polymorphism or metamorphism. These limitations also make anti-virus software predictable, creating a security breach. Any black hat with enough information about the anti-virus behaviour can make its own copy of the software, without any access to the original implementation or database. In this work, we show how this is indeed possible by combining entropy patterns with classification algorithms. Our results, applied to 57 different anti-virus engines, show that we can mimic their behaviour with an accuracy close to 98% in the best case and 75% in the worst, applied on Windows’ disk resident malware.

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