scholarly journals Pixel-Based Machine Learning and Image Reconstitution for Dot-ELISA Pathogen Diagnosis in Biological Samples

2021 ◽  
Vol 12 ◽  
Author(s):  
Cleo Anastassopoulou ◽  
Athanasios Tsakris ◽  
George P. Patrinos ◽  
Yiannis Manoussopoulos
Keyword(s):  
Machine Learning ◽  
False Negative ◽  
Color Discrimination ◽  
Supervised Machine Learning ◽  
Multivariate Logistic Regression Model ◽  
Enzyme Linked Immunosorbent Assay ◽  
Novel Approach ◽  
Image Pixels ◽  
Diagnostic Applications ◽  
Dot Elisa

Serological methods serve as a direct or indirect means of pathogen infection diagnosis in plant and animal species, including humans. Dot-ELISA (DE) is an inexpensive and sensitive, solid-state version of the microplate enzyme-linked immunosorbent assay, with a broad range of applications in epidemiology. Yet, its applicability is limited by uncertainties in the qualitative output of the assay due to overlapping dot colorations of positive and negative samples, stemming mainly from the inherent color discrimination thresholds of the human eye. Here, we report a novel approach for unambiguous DE output evaluation by applying machine learning-based pattern recognition of image pixels of the blot using an impartial predictive model rather than human judgment. Supervised machine learning was used to train a classifier algorithm through a built multivariate logistic regression model based on the RGB (“Red,” “Green,” “Blue”) pixel attributes of a scanned DE output of samples of known infection status to a model pathogen (Lettuce big-vein associated virus). Based on the trained and cross-validated algorithm, pixel probabilities of unknown samples could be predicted in scanned DE output images, which would then be reconstituted by pixels having probabilities above a cutoff. The cutoff may be selected at will to yield desirable false positive and false negative rates depending on the question at hand, thus allowing for proper dot classification of positive and negative samples and, hence, accurate diagnosis. Potential improvements and diagnostic applications of the proposed versatile method that translates unique pathogen antigens to the universal basic color language are discussed.

scholarly journals Pixel-based machine learning and image reconstitution for dot-ELISA pathogen serodiagnosis

2020 ◽  
Author(s):  
Cleo Anastassopoulou ◽  
Athanasios Tsakris ◽  
George P. Patrinos ◽  
Yiannis Manoussopoulos
Keyword(s):  
Machine Learning ◽  
False Negative ◽  
Color Discrimination ◽  
Supervised Machine Learning ◽  
Multivariate Logistic Regression Model ◽  
Enzyme Linked Immunosorbent Assay ◽  
Novel Approach ◽  
Image Pixels ◽  
Diagnostic Applications ◽  
Dot Elisa

AbstractSerological methods serve as a direct or indirect means of pathogen infection diagnosis in plant and animal species, including humans. Dot-ELISA (DE) is an inexpensive and sensitive, solid-state version of the microplate enzyme-linked immunosorbent assay, with a broad range of applications in epidemiology. Yet, its applicability is limited by uncertainties in the qualitative output of the assay due to overlapping dot colorations of positive and negative samples, stemming mainly from the inherent color discrimination thresholds of the human eye. Here, we report a novel approach for unambiguous DE output evaluation by applying machine learning-based pattern recognition of image pixels of the blot using an impartial predictive model rather than human judgment. Supervised machine learning was used to train a classifier algorithm through a built multivariate logistic regression model based on the RGB (“Red”, “Green”, “Blue”) pixel attributes of a scanned DE output of samples of known infection status to a model pathogen (Lettuce big-vein associated virus). Based on the trained and cross-validated algorithm, pixel probabilities of unknown samples could be predicted in scanned DE output images which would then be reconstituted by pixels having probabilities above a cutoff that may be selected at will to yield desirable false positive and false negative rates depending on the question at hand, thus allowing for proper dot classification of positive and negative samples and, hence, accurate diagnosis. Potential improvements and diagnostic applications of the proposed versatile method that translates unique pathogen antigens to the universal basic color language are discussed.

scholarly journals A Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze the Moisture of Buildings

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2285 ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski ◽  
Edward Kozłowski
Keyword(s):  
Machine Learning ◽  
Spatial Analysis ◽  
Building Materials ◽  
Supervised Machine Learning ◽  
Electrical Tomography ◽  
High Processing Speed ◽  
Image Pixels ◽  
Architectural Significance ◽  
Building Walls ◽  
Non Destructive

This article presents the results of research on a new method of spatial analysis of walls and buildings moisture. Due to the fact that destructive methods are not suitable for historical buildings of great architectural significance, a non-destructive method based on electrical tomography has been adopted. A hybrid tomograph with special sensors was developed for the measurements. This device enables the acquisition of data, which are then reconstructed by appropriately developed methods enabling spatial analysis of wet buildings. Special electrodes that ensure good contact with the surface of porous building materials such as bricks and cement were introduced. During the research, a group of algorithms enabling supervised machine learning was analyzed. They have been used in the process of converting input electrical values into conductance depicted by the output image pixels. The conductance values of individual pixels of the output vector made it possible to obtain images of the interior of building walls as both flat intersections (2D) and spatial (3D) images. The presented group of algorithms has a high application value. The main advantages of the new methods are: high accuracy of imaging, low costs, high processing speed, ease of application to walls of various thickness and irregular surface. By comparing the results of tomographic reconstructions, the most efficient algorithms were identified.

Sentiment Analysis on UAV-aided Product Comments Based on Machine Learning: From Sentence to Document Level

2020 ◽  
Author(s):  
JINGYANG CAO ◽  
Shirong Yin ◽  
Guoxu Zhang
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Accurate Result ◽  
Supervised Machine Learning ◽  
Hotel Management ◽  
Novel Approach ◽  
Online Comments ◽  
New Perspective ◽  
The Relationship ◽  
Document Level

Abstract This paper presents a novel approach to analyze the sentiment of the product comments from sentence to document level and apply to the customers sentiment analysis on UAV-aided product comments for hotel management. In order to realize the effiffifficient sentiment analysis, a cascaded sentence-to-document sentiment classifification method is investigated. Initially, a supervised machine learning method is applied to explore the sentiment polarity of the sentence (SPS). Afterward, the contribution of the sentence to document (CSD) is calculated by using various statistical algorithms. Lastly, the sentiment polarity of the document (SPD) is determined by the SPS as well as its contribution. Comparative experiments have been established on the basis of hotel online comments, and the outcomes indicate that the proposed method not only raises the effiffifficiency in attaining a more accurate result but also assists immensely in regards to the B5G wireless communication supported by the UAV. The fifindings provide a new perspective that sentence position and its sentiment similarity with document (sentiment condition) dramatically disclose the relationship between sentence and document.

scholarly journals Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze Moisture of Buildings

2018 ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski ◽  
Edward Kozłowski
Keyword(s):  
Machine Learning ◽  
Spatial Analysis ◽  
Building Materials ◽  
Supervised Machine Learning ◽  
Electrical Tomography ◽  
High Processing Speed ◽  
Image Pixels ◽  
Architectural Significance ◽  
Building Walls ◽  
Non Destructive

The article presents the results of research on a new method of spatial analysis of walls and buildings moisture. Due to the fact that destructive methods are not suitable for historical buildings of great architectural significance, a non-destructive method based on electrical tomography has been adopted. A hybrid tomograph with special sensors was developed for the measurements. This device enables the acquisition of data, which are then reconstructed by appropriately developed methods enabling spatial analysis of wet buildings. Special electrodes that ensure good contact with the surface of porous building materials such as bricks and cement were introduced. During the research, a group of algorithms enabling supervised machine learning was analyzed. They have been used in the process of converting input electrical values into conductance depicted by the output image pixels. The conductance values of individual pixels of the output vector made it possible to obtain images of the interior of building walls, both flat intersections (2D) and spatial (3D) images. The presented group of algorithms has a high application value. The main advantages of the new methods are: high accuracy of imaging, low costs, high processing speed, ease of application to walls of various thickness and irregular surface. By comparing the results of tomographic reconstructions, the most efficient algorithms were identified.

scholarly journals Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning

2018 ◽  
Vol 11 (4) ◽  
pp. 70 ◽  
Author(s):  
Jung-sik Hong ◽  
Hyeongyu Yeo ◽  
Nam-Wook Cho ◽  
Taeuk Ahn
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Area Under The Curve ◽  
High Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Machine Learning Technique ◽  
Novel Approach ◽  
Learning Technique

Since not all suppliers are to be managed in the same way, a purchasing strategy requires proper supplier segmentation so that the most suitable strategies can be used for different segments. Most existing methods for supplier segmentation, however, either depend on subjective judgements or require significant efforts. To overcome the limitations, this paper proposes a novel approach for supplier segmentation. The objective of this paper is to develop an automated and effective way to identify core suppliers, whose profit impact on a buyer is significant. To achieve this objective, the application of a supervised machine learning technique, Random Forests (RF), to e-invoice data is proposed. To validate the effectiveness, the proposed method has been applied to real e-invoice data obtained from an automobile parts manufacturer. Results of high accuracy and the area under the curve (AUC) attest to the applicability of our approach. Our method is envisioned to be of value for automating the identification of core suppliers. The main benefits of the proposed approach include the enhanced efficiency of supplier segmentation procedures. Besides, by utilizing a machine learning method to e-invoice data, our method results in more reliable segmentation in terms of selecting and weighting variables.

scholarly journals Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas M. Clarke ◽  
Sasha K. Whitmarsh ◽  
Jenna L. Hounslow ◽  
Adrian C. Gleiss ◽  
Nicholas L. Payne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Body Position ◽  
High Amplitude ◽  
Pelagic Fish ◽  
Supervised Machine Learning ◽  
Accelerometer Data ◽  
Direct Observations ◽  
Novel Approach ◽  
Free Ranging

Abstract Background Tri-axial accelerometers have been used to remotely describe and identify in situ behaviours of a range of animals without requiring direct observations. Datasets collected from these accelerometers (i.e. acceleration, body position) are often large, requiring development of semi-automated analyses to classify behaviours. Marine fishes exhibit many “burst” behaviours with high amplitude accelerations that are difficult to interpret and differentiate. This has constrained the development of accurate automated techniques to identify different “burst” behaviours occurring naturally, where direct observations are not possible. Methods We trained a random forest machine learning algorithm based on 624 h of accelerometer data from six captive yellowtail kingfish during spawning periods. We identified five distinct behaviours (swim, feed, chafe, escape, and courtship), which were used to train the model based on 58 predictive variables. Results Overall accuracy of the model was 94%. Classification of each behavioural class was variable; F1 scores ranged from 0.48 (chafe) – 0.99 (swim). The model was subsequently applied to accelerometer data from eight free-ranging kingfish, and all behaviour classes described from captive fish were predicted by the model to occur, including 19 events of courtship behaviours ranging from 3 s to 108 min in duration. Conclusion Our findings provide a novel approach of applying a supervised machine learning model on free-ranging animals, which has previously been predominantly constrained to direct observations of behaviours and not predicted from an unseen dataset. Additionally, our findings identify typically ambiguous spawning and courtship behaviours of a large pelagic fish as they naturally occur.

scholarly journals Non-Destructive System Based on Electrical Tomography and Machine Learning to Analyze Moisture of Buildings

2018 ◽  
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski ◽  
Edward Kozłowski
Keyword(s):  
Machine Learning ◽  
Spatial Analysis ◽  
Building Materials ◽  
Supervised Machine Learning ◽  
Electrical Tomography ◽  
High Processing Speed ◽  
Image Pixels ◽  
Architectural Significance ◽  
Building Walls ◽  
Non Destructive

The article presents the results of research on a new method of spatial analysis of walls and buildings moisture. Due to the fact that destructive methods are not suitable for historical buildings of great architectural significance, a non-destructive method based on electrical tomography has been adopted. A hybrid tomograph with special sensors was developed for the measurements. This device enables the acquisition of data, which are then reconstructed by appropriately developed methods enabling spatial analysis of wet buildings. Special electrodes that ensure good contact with the surface of porous building materials such as bricks and cement. During the research, a group of algorithms enabling supervised machine learning was analyzed. They have been used in the process of converting input electrical values into conductance depicted by the output image pixels. The conductance values of individual pixels of the output vector made it possible to obtain images of the interior of building walls, both flat intersections (2D) and spatial (3D) images. The presented group of methods has a high application value. The main advantages of the new methods are: high accuracy of imaging, low costs, high processing speed, ease of application to walls of various thickness and irregular surface. By comparing the results of tomographic reconstructions, the most efficient algorithms were identified.

scholarly journals Credit Risk Rating Using State Machines and Machine Learning

2020 ◽  
Vol 11 (6) ◽  
pp. 163-168
Author(s):  
Behnam Sabeti ◽  
◽  
Hossein Abedi Firouzjaee ◽  
Reza Fahmi ◽  
Saeid Safavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Learning Task ◽  
Supervised Machine Learning ◽  
State Machines ◽  
Data Set ◽  
Credit Score ◽  
Novel Approach ◽  
User Data ◽  
Number Of Customers

Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed state machine is used to convert historical user data to a credit score which generates a data-set for training supervised models. We have explored several classification models in our experiments and illustrated the effectiveness of our modeling approach.

scholarly journals Supervised Machine Learning Techniques for Predicting Sugarcane Yield

2019 ◽  
Vol 8 (2) ◽  
pp. 5662-5668
Keyword(s):  
Machine Learning ◽  
Vegetation Index ◽  
Weather Condition ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accuracy Score ◽  
Sugarcane Crop ◽  
Novel Approach ◽  
Wide Range ◽  
Sugarcane Yield

Agriculture is the most important sector in the Indian economy and contributes 18% of Gross Domestic Product(GDP). India is the second largest producer of sugarcane crop and produces about 20% of the world's sugarcane. Sugarcane is cultivated in tropics and subtropic regions, on a wide range of soils from fertile well-drained mollisols to through heavy cracking vertisols, infertile acid oxisols, peaty histosols, to rocky andisols. Minimum moisture of 60cms, rich water supply and plenty of sunshine. In this paper, a novel approach to sugarcane yield forecasting in Karnataka, India region using Long Term Time Series(LTTS), weather-and-soil attributes, Normalized Vegetation Index(NDVI) and Supervised Machine Learning(SML) algorithms have been proposed. Sugarcane cultivation life cycle(SCLC) in the Karnataka region is about 12 months, with plantation beginning at three different seasons in weather condition. Our approach has been verified using historical dataset and results have shown that our approach has successfully modeled crop prediction. The application of the Custom-Kernel gives us a considerable boost in accuracy with SVM-Kernel Multiple giving 86.31% of accuracy, SVM-RBF kernel in second with an accuracy of 83.40%, GPR producing an accuracy score of 81.75%, Lasso giving an accuracy score of 26.81% and Kernel Ridge-RBF with the least accuracy score of 21.46% for final yield prediction.

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