scholarly journals The classification of Crithidia luciliae immunofluorescence test (CLIFT) using a novel automated system

2014 ◽  
Vol 16 (2) ◽  
pp. R71 ◽  
Author(s):  
Francesca Buzzulini ◽  
Amelia Rigon ◽  
Paolo Soda ◽  
Leonardo Onofri ◽  
Maria Infantino ◽  
...  
Keyword(s):  
Automated System ◽  
Immunofluorescence Test ◽  
Crithidia Luciliae

Automated System Design for Classification of Chronic Lung Viruses using Non-Linear Dynamic System Features and K-Nearest Neighbour

2021 ◽  
Author(s):  
Misha Urooj Khan ◽  
Ayesha Farman ◽  
Asad Ur Rehman ◽  
Nida Israr ◽  
Muhammad Zulqarnain Haider Ali ◽  
...  
Keyword(s):  
Dynamic System ◽  
System Design ◽  
Linear Dynamic System ◽  
Automated System ◽  
Nearest Neighbour ◽  
Linear Dynamic ◽  
Non Linear

scholarly journals THE CORRELATION BETWEEN LEAN MEAT PERCENTAGE IN PRIMAL CUTS AND TOTAL LEAN MEAT PERCENTAGE IN CARCASS

2018 ◽  
Vol 3 (2) ◽  
pp. 33-39
Author(s):  
Andrey V. Pavlov ◽  
Andrey I. Rud ◽  
Maxim A. Zankevich
Keyword(s):  
Muscle Tissue ◽  
Conflict Of Interest ◽  
Correlation Coefficients ◽  
Automated System ◽  
Lean Meat ◽  
Additional Weight ◽  
A Value ◽  
Slaughter Pigs ◽  
Equal Contribution

With the help of the automated system for the classification of carcasses of pigs, AutoFOM ultrasound have been processed 56682 carcasses of slaughter pigs with an average carcass weight of 94.3 kg. The mass and yield of muscle tissue from the main cuts in the carcass is shown. Correlation coefficients between the mass and the content of muscle tissue in the carcass and the main (premium) cuts (ham, neck, shoulder, belly, and loin) were studied. It is shown how the increase in the weight of each of the cuts affects the content of muscle tissue in the carcass and in the cut. For example, it was found that when the weight of the belly is increased by 10 kg (from 6 to 16 kg), the percentage of muscle tissue from carcass is reduced by 3.3% (from 54.5 to 51.8%), which is approximately 0.33% for 1 kg of additional weight of the belly. With an increase in the weight of the loin from 4 to 14 kg, the yield of muscle tissue from the carcass on the contrary increased by 11.6%, i.е. 1.16% for each additional kg of loin weight. A value (in absolute and relative units) of the main cuts is given. The conclusion is made about the prospects of using the obtained data in the creation of a specialized terminal line of pigs, characterized by an increased content of weight of premium cuts in the carcass.ContributionAll authors bear responsibility for the work and presented data. All authors made an equal contribution to the work. The authors were equally involved in writing the manuscript and bear the equal responsibility for plagiarism.Conflict of interestThe authors declare no conflict of interest.

scholarly journals Automated Negative Lightning Return Strokes Classification System

2021 ◽  
Vol 2107 (1) ◽  
pp. 012022
Author(s):  
F. Abdul Haris ◽  
M.Z.A. Ab Kadir ◽  
S. Sudin ◽  
D. Johari ◽  
J. Jasni ◽  
...  
Keyword(s):  
Classification System ◽  
Automatic System ◽  
Human Error ◽  
Processing Time ◽  
Visual Inspection ◽  
Automated System ◽  
Graphic User Interface ◽  
Data Comparison ◽  
Good Agreement

Abstract Over the years, many studies have been conducted to measure and classify the lightning-generated electric field waveform for a better understanding of the lightning physics phenomenon. Through measurement and classification, the features of the negative lightning return strokes can be accessed and analysed. In most studies, the classification of negative lightning return strokes was performed using a conventional approach based on manual visual inspection. Nevertheless, this traditional method could compromise the accuracy of data analysis due to human error, which also required a longer processing time. Hence, this study developed an automated negative lightning return strokes classification system using MATLAB software. In this study, a total of 115 return strokes was recorded and classified automatically by using the developed system. The data comparison with the Tenaga Nasional Berhad Research (TNBR) lightning report showed a good agreement between the lightning signal detected from this study with those signals recorded from the report. Apart from that, the developed automated system was successfully classified the negative lightning return strokes which this parameter was also illustrated on Graphic User Interface (GUI). Thus, the proposed automatic system could offer a practical and reliable approach by reducing human error and the processing time while classifying the negative lightning return strokes.

scholarly journals Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Sufian A. Badawi ◽  
Muhammad Moazam Fraz
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Retinal Vessel ◽  
Automated System ◽  
Retinal Vasculature ◽  
Disease Prognosis ◽  
Learning Techniques ◽  
Retinal Blood Vessels

The arterioles and venules (AV) classification of retinal vasculature is considered as the first step in the development of an automated system for analysing the vasculature biomarker association with disease prognosis. Most of the existing AV classification methods depend on the accurate segmentation of retinal blood vessels. Moreover, the unavailability of large-scale annotated data is a major hindrance in the application of deep learning techniques for AV classification. This paper presents an encoder-decoder based fully convolutional neural network for classification of retinal vasculature into arterioles and venules, without requiring the preliminary step of vessel segmentation. An optimized multiloss function is used to learn the pixel-wise and segment-wise retinal vessel labels. The proposed method is trained and evaluated on DRIVE, AVRDB, and a newly created AV classification dataset; and it attains 96%, 98%, and 97% accuracy, respectively. The new AV classification dataset is comprised of 700 annotated retinal images, which will offer the researchers a benchmark to compare their AV classification results.

An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images

2018 ◽  
Vol 65 (6) ◽  
pp. 1382-1390 ◽  
Author(s):  
Kedir M. Adal ◽  
Peter G. van Etten ◽  
Jose P. Martinez ◽  
Kenneth W. Rouwen ◽  
Koenraad A. Vermeer ◽  
...  
Keyword(s):  
Automated System ◽  
Fundus Images

A SYNERGISTIC MODEL FOR REPRESENTING AND INTERPRETING HUMAN ACTIVITIES AND EVENTS FROM VIDEO

2003 ◽  
Vol 12 (01) ◽  
pp. 101-116 ◽  
Author(s):  
NIKOLAOS BOURBAKIS ◽  
JIM R. GATTIKER ◽  
GEORGE BEBIS
Keyword(s):  
Law Enforcement ◽  
Image Features ◽  
Automated System ◽  
Adaptive Model ◽  
Functional Behavior ◽  
Structural Image ◽  
Functional Knowledge ◽  
The Government ◽  
Free Environment

This paper describes a new and innovative and approach for representing, recognizing and interpreting human activity from video, contributing to an automated system capable of recognition of complex human behaviors. This technology is directly applicable for monitoring public safety and law enforcement, and capturing of activities is crucial for supporting virtual collaborations between citizens, and between citizens and government. Digital video stores of terabytes are now common, and will continue to increase until they dominate stored data. The government of the future will have to manage, organize, recall, and interpret information from this resource, This paper addresses one important facet of this. The approach presented here is a model based on the hierarchical synergy of three other models: the Local/Global (L-G) graph, the Stochastic Petri Net (SPN) graph and a neural network (NN) model. The application focus is the description of activity of actors in a video (or multi-sensor) scene, from the snapshot state description through higher levels of organization into events. The concept of importance is the distinction and interaction between structural knowledge, or knowledge about physical state, and functional knowledge, knowledge about change and events. The L-G graph provides a powerful description of the structural image features presented in an event, and the SPN model offers a description of the functional behavior. The NN (or other adaptive) model provides the capability of leaning behavioral patterns for classification of posture and activity, and forecasting possible events in a free environment.

scholarly journals Identification and Classification of Animal Kingdom using Image Processing and Artificial Neural Networks

2019 ◽  
Vol 8 (3) ◽  
pp. 4645-4650
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Bilateral Symmetry ◽  
Automated System ◽  
Back Propagation Algorithm ◽  
Animal Kingdom ◽  
Artificial Neural ◽  
Eukaryotic Organisms

The biological kingdom ‘Animalia’ is composed of multi cellular eukaryotic organisms. Most of the animal species exhibit bilateral symmetry. The hierarchy of biological classification has eight taxonomy ranks. The top position in the hierarchy is occupied by the ‘domain’ and ending with the lowest position occupied by ‘species’. The classification of animal kingdom includes, Porifera, Coelenterata, Platyhelminthes, Aschelminthes, Annelida, Arthropoda, Mollusca, Echinodermata and Chordata. Manual identification of Phylum or class for each and every species, is very tedious, because there exists nearly a millions of species categorized under various classes. Hence an automated system is proposed to be developed using image segmentation and Artificial Neural Networks (ANN) trained with Back Propagation Algorithm (BPA) which is capable of assisting the scientists and researchers for class identification. This system will be useful in Museums and Archeological departments, where a huge variety of species are maintained. The classification efficiency of the proposed system is 89.1%.

scholarly journals Automated System for Grading Apples using Convolutional Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 1458-1464
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Automated System ◽  
Test Accuracy ◽  
Fruit Processing ◽  
Fruit Grading ◽  
Increasing Demand

Fruit grading is a process that affect quality control and fruit-processing industries to meet the efficiency of its production and society. However, these industries have suffered from lack of standards in quality control, higher time of grading and low product output because of the use of manual methods. To meet the increasing demand of quality fruit products, fruit-processing industries must consider automating their fruit grading process. Several algorithms have been proposed over the years to achieve this purpose and their works were based on color, shape and inability to handle large dataset which resulted in slow recognition accuracy. To mitigate these flaws, we develop an automated system for grading and classification of apple using Convolutional Neural Network (CNN) used in image recognition and classification. Two models were developed from CNN using ResNet50 as its convolutional base, a process called transfer learning. The first model, the apple checker model (ACM) performs the recognition of the image with two output connections (apple and non-apple) while the apple grader model (AGM) does the classification of the image that has four output classes (spoiled, grade A, grade B & grade C) if the image is an apple. A comparison evaluation of both models were conducted and experimental results show that the ACM achieved a test accuracy of 100% while the AGM obtained recognition rate of 99.89%.The developed system may be employed in food processing industries and related life applications.

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