scholarly journals Deep Learning Classification of Lake Zooplankton

2021 ◽  
Vol 12 ◽  
Author(s):  
Sreenath P. Kyathanahally ◽  
Thomas Hardeman ◽  
Ewa Merz ◽  
Thea Bulas ◽  
Marta Reyes ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Frequency ◽  
Ecosystem Health ◽  
Manual Annotation ◽  
Classification Models ◽  
Automated Imaging ◽  
Freshwater Habitats ◽  
Plankton Communities ◽  
Better Than

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, Imaging FlowCytobot, and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

scholarly journals Deep Learning Classification of Lake Zooplankton

2021 ◽  
Author(s):  
Sreenath Pruthviraj Kyathanahally ◽  
Tommy Hardeman ◽  
Ewa Merz ◽  
Thea Kozakiewicz ◽  
Marta reyes ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Frequency ◽  
Ecosystem Health ◽  
Manual Annotation ◽  
Classification Models ◽  
Automated Imaging ◽  
Freshwater Habitats ◽  
Plankton Communities ◽  
Better Than

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor- intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, FlowCytobot and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Investigating Deep Learning Approaches on the Security Analysis of Cryptographic Algorithms

Cryptography ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 30
Author(s):  
Bang Yuan Chong ◽  
Iftekhar Salam
Keyword(s):  
Deep Learning ◽  
Security Analysis ◽  
Data Encryption ◽  
Classification Model ◽  
Learning Approaches ◽  
Secret Key ◽  
Data Encryption Standard ◽  
Random Guess ◽  
Better Than

This paper studies the use of deep learning (DL) models under a known-plaintext scenario. The goal of the models is to predict the secret key of a cipher using DL techniques. We investigate the DL techniques against different ciphers, namely, Simplified Data Encryption Standard (S-DES), Speck, Simeck and Katan. For S-DES, we examine the classification of the full key set, and the results are better than a random guess. However, we found that it is difficult to apply the same classification model beyond 2-round Speck. We also demonstrate that DL models trained under a known-plaintext scenario can successfully recover the random key of S-DES. However, the same method has been less successful when applied to modern ciphers Speck, Simeck, and Katan. The ciphers Simeck and Katan are further investigated using the DL models but with a text-based key. This application found the linear approximations between the plaintext–ciphertext pairs and the text-based key.

scholarly journals Research on Classification Method of Maize Seed Defect Based on Machine Vision

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Sheng Huang ◽  
Xiaofei Fan ◽  
Lei Sun ◽  
Yanlu Shen ◽  
Xuesong Suo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Heat Map ◽  
Deep Learning Algorithm ◽  
Quality Classification ◽  
Visualization Technology ◽  
Better Than

Traditionally, the classification of seed defects mainly relies on the characteristics of color, shape, and texture. This method requires repeated extraction of a large amount of feature information, which is not efficiently used in detection. In recent years, deep learning has performed well in the field of image recognition. We introduced convolutional neural networks (CNNs) and transfer learning into the quality classification of seeds and compared them with traditional machine learning algorithms. Experiments showed that deep learning algorithm was significantly better than the machine learning algorithm with an accuracy of 95% (GoogLeNet) vs. 79.2% (SURF+SVM). We used three classifiers in GoogLeNet to demonstrate that network accuracy increases as the depth of the network increases. We used the visualization technology to obtain the feature map of each layer of the network in CNNs and used the heat map to represent the probability distribution of the inference results. As an end-to-end network, CNNs can be easily applied for automated seed manufacturing.

scholarly journals ProtTox: Toxin identification from Protein Sequences

2020 ◽  
Author(s):  
Sathappan Muthiah ◽  
Debanjan Datta ◽  
Mohammad Raihanul Islam ◽  
Patrick Butler ◽  
Andrew Warren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Synthetic Biology ◽  
Real World ◽  
Protein Sequences ◽  
Structural Diversity ◽  
Manual Annotation ◽  
Real World Applications ◽  
Automated Machine Learning

AbstractToxin classification of protein sequences is a challenging task with real world applications in healthcare and synthetic biology. Due to an ever expanding database of proteins and the inordinate cost of manual annotation, automated machine learning based approaches are crucial. Approaches need to overcome challenges of homology, multi-functionality, and structural diversity among proteins in this task. We propose a novel deep learning based method ProtTox, that aims to address some of the shortcomings of previous approaches in classifying proteins as toxins or not. Our method achieves a performance of 0.812 F1-score which is about 5% higher than the closest performing baseline.

A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images

2021 ◽  
pp. 1-13
Author(s):  
Malathi Murugesan ◽  
Kalaiselvi Kaliannan ◽  
Shankarlal Balraj ◽  
Kokila Singaram ◽  
Thenmalar Kaliannan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Lung Tumor ◽  
Median Filter ◽  
Lung Nodule ◽  
Rapid Identification ◽  
Lung Nodules ◽  
Classification Models ◽  
Deep Learning Model

Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities.

scholarly journals Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties

Molecules ◽  
2019 ◽  
Vol 24 (18) ◽  
pp. 3268 ◽  
Author(s):  
Zhu ◽  
Zhou ◽  
Gao ◽  
Bao ◽  
He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Cotton Seed ◽  
Near Infrared ◽  
Principal Component ◽  
Critical Factor ◽  
Support Vector ◽  
Classification Models ◽  
Cotton Seeds ◽  
Better Than

Cotton seed purity is a critical factor influencing the cotton yield. In this study, near-infrared hyperspectral imaging was used to identify seven varieties of cotton seeds. Score images formed by pixel-wise principal component analysis (PCA) showed that there were differences among different varieties of cotton seeds. Effective wavelengths were selected according to PCA loadings. A self-design convolution neural network (CNN) and a Residual Network (ResNet) were used to establish classification models. Partial least squares discriminant analysis (PLS-DA), logistic regression (LR) and support vector machine (SVM) were used as direct classifiers based on full spectra and effective wavelengths for comparison. Furthermore, PLS-DA, LR and SVM models were used for cotton seeds classification based on deep features extracted by self-design CNN and ResNet models. LR and PLS-DA models using deep features as input performed slightly better than those using full spectra and effective wavelengths directly. Self-design CNN based models performed slightly better than ResNet based models. Classification models using full spectra performed better than those using effective wavelengths, with classification accuracy of calibration, validation and prediction sets all over 80% for most models. The overall results illustrated that near-infrared hyperspectral imaging with deep learning was feasible to identify cotton seed varieties.

scholarly journals Investigating deep feedforward neural networks for classification of transposon-derived piRNAs

2021 ◽  
Author(s):  
Alisson Hayasi da Costa ◽  
Renato Augusto Corrêa dos Santos ◽  
Ricardo Cerri
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Gene Expression Regulation ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Advantages And Disadvantages ◽  
Large Application ◽  
Non Coding Rnas ◽  
Better Than

AbstractPIWI-interacting RNAs (piRNAS) form an important class of non-coding RNAs that play a key role in gene expression regulation and genome integrity by silencing transposable elements. However, despite the importance of piRNAs and the large application of deep learning in computational biology, there are few studies of deep learning for piRNAs prediction. Still, current methods focus on using advanced architectures like CNN and variations. This paper presents an investigation on deep feedforward network models for classification of human transposon-derived piRNAs. We developed a lightweight predictor (when compared to other deep learning methods) and we show by practical evidence that simple neural networks can perform as well as better than complex neural networks when using the appropriate hyperparameters. For that, we train, analyze and compare the results of a multilayer perceptron with different hyperparameter choices, such as numbers of hidden layers, activation functions and optimizers, clarifying the advantages and disadvantages of each choice. Our proposed predictor reached a F-score of 0.872, outperforming other state-of-the-art methods for human transposon-derived piRNAs classification. In addition, to better access the generalization of our proposal, we also showed it achieved competitive results when classifying piRNAs of other species.

scholarly journals Classification of Indramayu’s Mangoes Based on Shape Features Using Neural Network and Rule Based Programming

2017 ◽  
Vol 1 ◽  
pp. 245-248
Author(s):  
Sumarudin Sumarudin ◽  
Iryanto Iryanto ◽  
Eka Ismantohadi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Learning Approach ◽  
Shape Features ◽  
Rule Based ◽  
General Classification ◽  
Result Show ◽  
Better Than

Object classification using image processing simplifies the process. Many approaches have been used to classify the object. In general, classification of mangoes uses image of leaves. In this research, we do a slightly different approach using image of mango itself. Here, two kinds of method are used to classify the object.  Implementations of deep learning using neural network and rule based programming are used in the process. Comparative study of the methods are presented in the article. Our result show that accuracy of deep learning approach is better than the rule based programming. The accuracy is 80% and 8% for neural network and rule based programming, respectively.

scholarly journals Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1319
Author(s):  
Muhammad Hammad Saleem ◽  
Johan Potgieter ◽  
Khalid Mahmood Arif
Keyword(s):  
Deep Learning ◽  
Comparative Evaluation ◽  
Plant Disease ◽  
Performance Metrics ◽  
State Of The Art ◽  
Disease Classification ◽  
Agricultural Applications ◽  
Learning Architectures ◽  
Better Than

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.

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