Efficient semi-supervised learning model for limited otolith data using generative adversarial networks

International audienceOtolith shape recognition is one of the relevant tool to ensure the sustainability of maritime resources. It is used to study taxonomy, age estimation and discrimination of stocks of fish species. The most performant otolith image classification models are based on convolutional neural network approaches. To build an efficient system, these models require a large number of labeled images, which is hard to obtain. The lack of data became a big challenge, and a real problem of otolith images classification models, it causes the over-fitting issue, which is the main trouble of deep convolutional neural network based models. In this paper, we present a relevant solution for the insufficiency of data. We propose a new semi-supervised classification model based on generative adversarial network. Our results showed that the model is more efficient and also perform better than convolutional neural network system even with a small training dataset. With this efficiency and performance, we found in addition that the accuracy of the model reached 80% on training set of say, 75 images compared to other models such as a convolutional neural network model which accuracy is limited to 60%

Decision Boundary to Improve the Sensitivity of Deep Neural Networks Models

International audienceIn spite of their performance and relevance on various image classification fields, deep neural network classifiers encounter real difficulties face of minor information perturbations. In particular, the presence of contradictory examples causes a big weakness and insufficiency of deep learning models in many areas, such as illness recognition. The aim of our paper is to improve the robustness of deep neural network models to small input perturbations using standard training and adversial training to maximize the distance between predict instances and the boundary decision area. We shows the decision boundary performance of deep neural networks during model training, the minimum distance of the input images from the decision boundary area and how this distance develops during the deep neural network training. The results shows that the distance between the images and the decision boundary decreases during standard training. However, adversarial training increases this distance, which improve the performance of our model. Our work presents a new solution to the deep neural networks sensitivity problem. We found a very strong relationship between the efficiency of the deep neural networks model and the training phase. We can say that the efficiency is created during training, it is not predetermined by the initialization or architecture

System segmentation of Lungs in images chest x-ray using the generative adversarial network

One of the most common medical imaging methods is a chest x-ray, as it contributes to the early detection of lung cancer compared to other methods. this work presents the use of a generative adversarial network to perform lung chest x-ray image segmentation. The network is two frameworks neural (generator and discriminator). In our work the generator is trained to generate a mask for the input of a given original image, the discriminator distinguishes between the original mask and the generated mask, the final objective is to generate masks for the input. The model is trained and evaluated, well generalized experimental results of the JSRT dataset reveal that the proposed model can a dice score of 0.9778, which is better than other reported state-of-the-art results

Sentiment Analysis Decision System for Tracking Climate Change Opinion in Twitter

International audienceUnlabelled - Yield and quality are two crucial breeding objects of wheat therein grain weight and grain protein content (GPC) are two key relevant factors correspondingly. Investigations of their genetic mechanisms represent special significance for breeding. In this study, 199 F plants and corresponding F families derived from Nongda3753 (ND3753) and its EMS-generated mutant 564 (M564) were used to investigate the genetic basis of larger grain and higher GPC of M564. QTL analysis identified a total of 33 environmentally stable QTLs related to thousand grain weight (TGW), grain area (GA), grain circle (GC), grain length (GL), grain width (GW), and GPC on chromosomes 1B, 2A, 2B, 4D, 6B, and 7D, respectively, among which , , , and shared overlap confidence interval on chromosome 6B. This interval contained the gene playing the same role as the QTLs, so was cloned and sequenced. Sequence alignment revealed two G/A SNPs between two parents, among which the SNP in the seventh exon led to a premature termination in M564. A KASP marker was developed based on the SNP, and single-marker analysis on biparental populations showed that the mutant allele could significantly increase GW and TGW, but had no effect on GPC. Distribution detection of the mutant allele through KASP marker genotyping and sequence alignment against databases ascertained that no materials harbored this allele within natural populations. This allele was subsequently introduced into three different varieties through molecular marker-assisted backcrossing, and it was revealed that the allele had a significant effect on simultaneously increasing GW, TGW, and even GPC in all of three backgrounds. Summing up the above, it could be concluded that a novel elite allele of was artificially created and might play an important role in wheat breeding for high yield and quality. Supplementary information - The online version contains supplementary material available at 10.1007/s11032-024-01455-y

A performant deep learning model for sentiment analysis of climate change

International audienceClimate change is one of the most trend topics of the decade in the world. The recent years were the warmest in 139 years, however identifying deniers and believers of this subject still a very big issue. The challenge is to have an efficient tool to detect deniers in order to deploy the appropriate strategy facing this phenomenon. Moreover, Bidirectional Encoder Representations from Transformers (BERT) pre-trained model has taken Natural Language Processing tasks results so far. In this paper we presented an efficient technological tool based on deep learning model and BERT model for detecting people's opinions on climate change on social media platforms. We used convolutional neural network targeting the public opinions on climate change on Twitter. The results showed that our model outperforms the machine learning approaches: Naive Bays, Support Vector Machine and Logistic Regression. This model is able to analyze people's behavior and detect believers and deniers of this disaster with high accuracy results (98% for believers and 90% for deniers). Our model could be a powerful citizen sensing tool that can be used by governments for monitoring and governance, especially for smart cities

Predictive System of Semiconductor Failures based on Machine Learning Approach

International audienceMaintenance in manufacturing has been developed and researched in the last few decades at a very rapid rate. It's a major step in process control to build a decision tool that detects defects in equipment or processes as quickly as possible to maintain high process efficiencies. However, the high complexity of machines, and the increase in data available in almost all areas, makes research on improving the accuracy of fault detection via data-mining more and more challenging issue in this field. In our paper we present a new predictive model of semiconductor failures, based on machine learning approach, for predictive maintenance in industry 4.0. The framework of our model includes: Dataset and data acquisition, data preprocessing in three phases (over-sampling, data cleaning, and attribute reduction with principal component analysis (PCA) technique and CfsSubsetEval technique), data modeling, evaluation model and implementation model. We used SECOM dataset to develop four different models based on four algorithms (Naive Bayesian, C4.5 Decision tree, Multilayer perceptron (MLP), Support vector machine), according to the five metrics (True Positive rate, False Positive rate, Precision, F-Mesure and Accuracy). We implemented our new predictive model with 91, 95% of accuracy, as a new efficient predictive model of semiconductor failures

Intelligent System Based on GAN Model for Decision Support in Brain Tumor Segmentation

International audienceThe most prevalent malignant brain tumors are gliomas, with a variety of grades, and each grade has a significant impact on a patient's chances of survival. Low-grade gliomas are usually found in the human brain and spinal cord. Low-grade glioma may be accurately diagnosed and detected early, lowering the risk of mortality for patients. In the examination gliomas of low grade, segmentation of MRI images is critical. The result, manual of Segmentation Techniques takes a long time and require a lot of pathology knowledge. in our study, we provide a unique generative adversarial network-based approach for segmenting images of tumors in the brain. The network is a structure between two neurons the generator and the discriminator. The generator is taught to construct an input mask of a take original image, The discriminator can tell the difference between the original and created masks, the end goal is to create masks for the input. The suggested model achieves a dice result of 0.97 in generalized experimental results from the TCGA LGG dataset, with a loss coefficient of 0.030, which is more effective and efficient than the compared approaches

Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopy

Artificial intelligence is nowadays used for cell detection and classification in optical microscopy, during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart, to make acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced dataset due to cost and time to prepare the samples and have the datasets annotated by experts. We propose here a real-time image processing, compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning for the sake of understanding the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher’s linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without any significant loss in accuracy, offering a substantial gain in execution time. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4 % accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into 8 phases of the cell cycle using 12 feature-groups and operating a consumer market ARM-based embedded system. Interestingly, a simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit

Neural network fast‐classifies biological images through features selecting to power automated microscopy

International audienceArtificial intelligence is nowadays used for cell detection and classification in optical microscopy during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart by making acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced data set due to cost and time to prepare the samples and have the data sets annotated by experts. We propose a real-time image processing compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning to understand the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without significant accuracy loss, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4% accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into eight phases of the cell cycle, using 12 feature groups and operating a consumer market ARM-based embedded system. A simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimizing these algorithms for smart microscopy