scholarly journals Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning

Molecules ◽  
2020 ◽  
Vol 25 (8) ◽  
pp. 1797
Author(s):  
Peng Gu ◽  
Yao-Ze Feng ◽  
Le Zhu ◽  
Li-Qin Kong ◽  
Xiu-ling Zhang ◽  
...  
Keyword(s):  
Culture Media ◽  
Bacterial Species ◽  
Luria Bertani ◽  
Classification Model ◽  
Plate Count ◽  
Support Vector ◽  
Universal Method ◽  
Grasshopper Optimization Algorithm ◽  
Agar Media

A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive classification of three kinds of bacterial colonies (Escherichia coli, Staphylococcus aureus and Salmonella) cultured on three kinds of agar media (Luria–Bertani agar (LA), plate count agar (PA) and tryptone soy agar (TSA)). Based on the extracted spectral data, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were employed to established classification models. The parameters of SVM models were optimized by comparing genetic algorithm (GA), particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). The best classification model was GOA-SVM, where the overall correct classification rates (OCCRs) for calibration and prediction of the full-wavelength GOA-SVM model were 99.45% and 98.82%, respectively, and the Kappa coefficient for prediction was 0.98. For further investigation, the CARS, SPA and GA wavelength selection methods were used to establish GOA-SVM simplified model, where CARS-GOA-SVM was optimal in model accuracy and stability with the corresponding OCCRs for calibration and prediction and the Kappa coefficients of 99.45%, 98.73% and 0.98, respectively. The above results demonstrated that it was feasible to classify bacterial colonies on different agar media and the unified model provided a continent and accurate way for bacterial classification.

scholarly journals Impact of Different Carbon Sources on the in vitro Growth and Viability of Escherichia coli (SUBE01) and Salmonella spp. (SUBS01) Cells

2016 ◽  
pp. 39-44
Author(s):  
Ifra Tun Nur ◽  
Jannatun Tahera ◽  
Md Sakil Munna ◽  
M Majibur Rahman ◽  
Rashed Noor
Keyword(s):  
Escherichia Coli ◽  
Bacterial Growth ◽  
Culture Media ◽  
Bacterial Species ◽  
Carbon Sources ◽  
Growth Dynamics ◽  
Luria Bertani ◽  
Tween 20 ◽  
Salmonella Spp

With a previous observation of Escherichia coli growth cessation along with temperature variation within three different bacteriological culture media (nutrient agar, Luria-Bertani agar and minimal agar), current investigation further depicted on the possible growth dynamics of Escherichia coli (SUBE01) and Salmonella (SUBS01) growth and viability upon supplementation of different carbon sources (dextrose, sucrose, lactose, glycerol and tween 20) at 37°C under the aeration of 100 rpm. Viability of the tested bacterial species was assessed through the enumeration of the colony forming unit (cfu) appeared upon prescribed incubation for 12-24 hours on different agar plates consisting of the above mentioned carbon sources. Besides, to inspect the cellular phenotypic changes, morphological observations were conducted under the light microscope. Variations in bacterial growth (either growth acceleration or cessation) were further noticed through the spot tests on the agar plates. Considerable shortfalls in the culturable cells of E. coli and Salmonella spp. were noted in the minimal media separately consisting of sucrose, lactose, glycerol or tween 20 while an opposite impact of accelerated growth was noticed in the media supplied with dextrose. The data revealed a hierarchy of consequence of carbon sources as nutrient generators whereby the favourable bacterial growth and survival order of the carbon sources was estimated as dextrose > glycerol > lactose > tween 20 > sucrose.Bangladesh J Microbiol, Volume 32, Number 1-2,June-Dec 2015, pp 39-44

scholarly journals A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

2019 ◽  
Vol 8 (4) ◽  
pp. 160 ◽  
Author(s):  
Bingxin Liu ◽  
Ying Li ◽  
Guannan Li ◽  
Anling Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Oil Film ◽  
One Dimensional

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

Biomimetic Pattern Recognition for Classification of Proteomic Profile

2013 ◽  
Vol 791-793 ◽  
pp. 1961-1964
Author(s):  
Xiao Li Yang ◽  
Qiong He
Keyword(s):  
Pattern Recognition ◽  
Cancer Diagnosis ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Proteomic Profile ◽  
Satisfactory Model ◽  
Over Time

We propose a biomimetic pattern recognition (BPR) approach for classification of proteomic profile. The proposed approach preprocess profile using iterative minimum in adaptive setting window (IMASW) method for baseline correction, discrete wavelet transform (DWT) for fitting and smoothing, and average total ion normalization (ATIN) for remove the influence of vary amount of sample and degradation over time. Then principal component analysis (PCA) and BPR build classification model. With an optimization of the parameters involved in the modeling, we obtain a satisfactory model for cancer diagnosis in three proteomic profile datasets. The predicted results show that BPR technique is more reliable and efficient than support vector machine (SVM) method.

scholarly journals Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

2020 ◽  
Vol 13 (1-2) ◽  
pp. 43-52
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

scholarly journals An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Hongyu Zhang ◽  
Limin Jiang ◽  
Jijun Tang ◽  
Yijie Ding
Keyword(s):  
Lung Cancer ◽  
Feature Representation ◽  
The Cancer Genome Atlas ◽  
Classification Model ◽  
Kernel Learning ◽  
Support Vector ◽  
Cancer Subtypes ◽  
Operation Speed ◽  
Kernel Fusion

In recent years, cancer has become a severe threat to human health. If we can accurately identify the subtypes of cancer, it will be of great significance to the research of anti-cancer drugs, the development of personalized treatment methods, and finally conquer cancer. In this paper, we obtain three feature representation datasets (gene expression profile, isoform expression and DNA methylation data) on lung cancer and renal cancer from the Broad GDAC, which collects the standardized data extracted from The Cancer Genome Atlas (TCGA). Since the feature dimension is too large, Principal Component Analysis (PCA) is used to reduce the feature vector, thus eliminating the redundant features and speeding up the operation speed of the classification model. By multiple kernel learning (MKL), we use Kernel target alignment (KTA), fast kernel learning (FKL), Hilbert-Schmidt Independence Criterion (HSIC), Mean to calculate the weight of kernel fusion. Finally, we put the combined kernel function into the support vector machine (SVM) and get excellent results. Among them, in the classification of renal cell carcinoma subtypes, the maximum accuracy can reach 0.978 by using the method of MKL (HSIC calculation weight), while in the classification of lung cancer subtypes, the accuracy can even reach 0.990 with the same method (FKL calculation weight).

scholarly journals Research on Automatic Classification Method of Ethnic Music Emotion Based on Machine Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Zijin Wu
Keyword(s):  
Folk Music ◽  
Classification Accuracy ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Method ◽  
Support Vector ◽  
Ethnic Music ◽  
Huge Impact ◽  
Speed Up

With the development of the country’s economy, there is a flourishing situation in the field of culture and art. However, the diversification of artistic expressions has not brought development to folk music. On the contrary, it brought a huge impact, and some national music even fell into the dilemma of being lost. This article is mainly aimed at the recognition and classification of folk music emotions and finds the model that can make the classification accuracy rate as high as possible. The classification model used in this article is mainly after determining the use of Support Vector Machine (SVM) classification method, a variety of attempts have been made to feature extraction, and good results have been achieved. Explore the Deep Belief Network (DBN) pretraining and reverse fine-tuning process, using DBN to learn the fusion characteristics of music. According to the abstract characteristics learned by them, the recognition and classification of folk music emotions are carried out. The DBN is improved by adding “Dropout” to each Restricted Boltzmann Machine (RBM) and adjusting the increase standard of weight and bias. The improved network can avoid the overfitting problem and speed up the training of the network. Through experiments, it is found that using the fusion features proposed in this paper, through classification, the classification accuracy has been improved.

scholarly journals Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods

2020 ◽  
Vol 10 (19) ◽  
pp. 6724
Author(s):  
Youngwook Seo ◽  
Ahyeong Lee ◽  
Balgeum Kim ◽  
Jongguk Lim
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Partial Least Square ◽  
Hyperspectral Images ◽  
Classification Model ◽  
Support Vector ◽  
Chemometric Methods ◽  
Infrared Wavelength ◽  
Linear Discriminant

(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated.

Quality Assessment in Vocational Education Based on a Hierarchical Classification Technique

2011 ◽  
Vol 403-408 ◽  
pp. 3724-3728
Author(s):  
Chantima Ekwong ◽  
Sageemas Na Wichain ◽  
Choochart Haruechaiyasak
Keyword(s):  
Quality Assessment ◽  
Vocational Education ◽  
Hierarchical Classification ◽  
Unstructured Data ◽  
Classification Model ◽  
Support Vector ◽  
Education Standards ◽  
National Education ◽  
Vector Machines

According to the laws of education in Thailand, the Office for National Education Standards and Quality Assessment is responsible for assessing the external educational institutes in order to develop the quality and educational standards. The external quality assessment reports are represented in both structured and unstructured data. In this paper, we focus on the analysis of unstructured data, i.e., to automatically classify strength and weakness points. We propose and evaluate two different classification models: Flat Classification and Hierarchical Classification. Three algorithms, Naive Bayes, Support Vector Machines (SVM) and Decision Tree, were used in the experiments. The results showed that classification viathe Hierarchical Classification model by using the SVM yielded the best performance. The classification of strength and weakness points yielded the F-measure equal to 0.843 and 0.893, respectively. The proposed approach can be applied as a decision support function for quality assessment in vocational education.

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