scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Machine Learning and Deep Learning Techniques, Features and Obstacles in the Cataract Diagnosis

2020 ◽  
Vol 9 (3) ◽  
pp. 87-93
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Congenital Cataract ◽  
Low Cost ◽  
Current Situation ◽  
Learning Techniques ◽  
Research Questions ◽  
New Research

Cataract is a degenerative condition that, according to estimations, will rise globally. Even though there are various proposals about its diagnosis, there are remaining problems to be solved. This paper aims to identify the current situation of the recent investigations on cataract diagnosis using a framework to conduct the literature review with the intention of answering the following research questions: RQ1) Which are the existing methods for cataract diagnosis? RQ2) Which are the features considered for the diagnosis of cataracts? RQ3) Which is the existing classification when diagnosing cataracts? RQ4) And Which obstacles arise when diagnosing cataracts? Additionally, a cross-analysis of the results was made. The results showed that new research is required in: (1) the classification of “congenital cataract” and, (2) portable solutions, which are necessary to make cataract diagnoses easily and at a low cost.

scholarly journals Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Gabriel Cifuentes-Alcobendas ◽  
Manuel Domínguez-Rodrigo
Keyword(s):  
Machine Learning ◽  
Accurate Method ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Accurate Identification ◽  
Cut Marks ◽  
Eating Meat ◽  
Microscopic Features ◽  
Immense Potential

AbstractAccurate identification of bone surface modifications (BSM) is crucial for the taphonomic understanding of archaeological and paleontological sites. Critical interpretations of when humans started eating meat and animal fat or when they started using stone tools, or when they occupied new continents or interacted with predatory guilds impinge on accurate identifications of BSM. Until now, interpretations of Plio-Pleistocene BSM have been contentious because of the high uncertainty in discriminating among taphonomic agents. Recently, the use of machine learning algorithms has yielded high accuracy in the identification of BSM. A branch of machine learning methods based on imaging, computer vision (CV), has opened the door to a more objective and accurate method of BSM identification. The present work has selected two extremely similar types of BSM (cut marks made on fleshed an defleshed bones) to test the immense potential of artificial intelligence methods. This CV approach not only produced the highest accuracy in the classification of these types of BSM until present (95% on complete images of BSM and 88.89% of images of only internal mark features), but it also has enabled a method for determining which inconspicuous microscopic features determine successful BSM discrimination. The potential of this method in other areas of taphonomy and paleobiology is enormous.

scholarly journals Speaker Awareness for Speech Emotion Recognition

2020 ◽  
Vol 16 (04) ◽  
pp. 15 ◽  
Author(s):  
Gustavo Assunção ◽  
Paulo Menezes ◽  
Fernando Perdigão
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Considerable Improvement ◽  
Speech Emotion Recognition ◽  
Emotional States ◽  
Emotional Prosody ◽  
Emotional Speech ◽  
Front End ◽  
Machine Learning Model

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>The idea of recognizing human emotion through speech (SER) has recently received considerable attention from the research community, mostly due to the current machine learning trend. Nevertheless, even the most successful methods are still rather lacking in terms of adaptation to specific speakers and scenarios, evidently reducing their performance when compared to humans. In this paper, we evaluate a largescale machine learning model for classification of emotional states. This model has been trained for speaker iden- tification but is instead used here as a front-end for extracting robust features from emotional speech. We aim to verify that SER improves when some speak- er</span><span>’</span><span>s emotional prosody cues are considered. Experiments using various state-of- the-art classifiers are carried out, using the Weka software, so as to evaluate the robustness of the extracted features. Considerable improvement is observed when comparing our results with other SER state-of-the-art techniques.</span></p></div></div></div>

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

scholarly journals Learning similarity measures from data

2019 ◽  
Vol 9 (2) ◽  
pp. 129-143 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Agnar Aamodt ◽  
Kerstin Bach ◽  
Helge Langseth
Keyword(s):  
Machine Learning ◽  
Similarity Measure ◽  
State Of The Art ◽  
Similarity Measures ◽  
Learning System ◽  
Case Based Reasoning ◽  
Training Time ◽  
Domain Experts ◽  
Trained Classifier ◽  
Clustering Data

Abstract Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, datasets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features; thus, they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working toward this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced toward this goal which relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze the current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs: The first design uses a pre-trained classifier as basis for a similarity measure, and the second design uses as little modeling as possible while learning the similarity measure from data and keeping training time low. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state-of-the-art performance. Finally, the evaluation shows that our fully data-driven similarity measure design outperforms state-of-the-art methods while keeping training time low.

scholarly journals Colorimetric detection on paper analytical device using machine learning

2021 ◽  
Author(s):  
Bidur Khanal ◽  
Pravin Pokhrel ◽  
Bishesh Khanal ◽  
Basant Giri
Keyword(s):  
Machine Learning ◽  
Enzyme Inhibition ◽  
Prediction Accuracy ◽  
Low Cost ◽  
Colorimetric Detection ◽  
Field Testing ◽  
Support Vector ◽  
Ann Model ◽  
Fine Grained ◽  
Imaging Conditions

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. The PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary resulting in less accurate results. Recently, machine learning (ML) assisted models have been used in image analysis. We evaluated a combinations of four ML models - logistic regression, support vector machine, random forest, and artificial neural network, and three image color spaces - RGB, HSV, and LAB for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras, and users for food color and enzyme inhibition assays to create training and test datasets. Prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML model and colorspace combinations. All models better predicted coarse level classification than fine grained concentration labels. ML models using sample color along with a reference color increased the models’ ability in predicting the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration label prediction accuracy obtained for food color was 0.966 when using ANN model and LAB colorspace. The accuracy for enzyme inhibition assay was 0.908 when using SVM model and LAB colorspace. Appropriate model and colorspace combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

scholarly journals Simulation and Analysis of Bionanopore Dna Sequencing Signals for Genetic Mutations Detection

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Iryna M. Ievdoshchenko ◽  
Kateryna Olehivna Ivanko ◽  
Nataliia Heorhiivna Ivanushkina ◽  
Vishwesh Kulkarni
Keyword(s):  
Machine Learning ◽  
Dna Sequencing ◽  
Noise Level ◽  
Nearest Neighbors ◽  
High Accuracy ◽  
Low Noise ◽  
Nucleotide Sequences ◽  
Learning Methods ◽  
Machine Learning Methods

The application of genomic signal processing methods to the problem of modeling and analysis of nanoporous DNA sequencing signals is considered in the paper. Based on the nucleotide sequences in the norm and in the case of mutations, 1200 signals are simulated, which represent 4 classes: norm, missense mutation, insertion mutation and deletion mutation. Correlation analysis was used to determine the similarity of nanoporous DNA sequencing signals using a cross-correlation function between two current signals in the protein nanopore, specifically signal in norm and in the presence of mutation. The location of the correlation peak determines the type of mutation (insertion or deletion), as well as the alignment of the same nucleotide sequences using a defined signal shift. The results of applying machine learning methods to the problem of classification of nanoporous DNA sequencing signals significantly depend on the noise level of the registered current signals through the protein nanopore and the type of mutation. Given a relatively low noise level, when the values of the ion current through a protein nanopore for different nucleotides do not intersect, the classification accuracy reaches 100%. In the case of increasing the standard deviation of the law of distribution of noise components, there is an overlap of the levels of current values in the nanopore in the case of its blocking by nucleotides of the close size. As a result, errors in the definition of normal and single nucleotide mutations (missense or nonsense) often occur, especially if the levels of current steps in the nanopore for two nucleotides are similar (for example, guanine and thymine, thymine and adenine, adenine and cytosine) and noise masks their contribution to reduction current in the nanopore. Mutations of insertion and deletion of a certain nucleotide sequence are often classified without errors, because these mutations are characterized by a shift of several nucleotides between normal signals and pathology, which increases the distance between these signals. Among the machine learning methods that have demonstrated the high accuracy of classification of the signals of nanopore-based DNA sequencing, the methods of linear discriminant, k-nearest neighbors classifier (with Euclidean distance and the sufficient number of nearest neighbors), as well as the method of reference vectors should be mentioned. The best results were obtained for the classification method of support vector machines. The use of linear, quadratic and cubic kernel functions shows the high accuracy of correctly classified signals - from 93 to 100%.

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