DOME-T: adversarial computer vision attack on deep learning models based on Tchebichef image moments

2021 ◽  
Author(s):  
Theodore Maliamanis ◽  
George A. Papakostas
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Models ◽  
Image Moments

scholarly journals On the Depth of Deep Learning Models for Splice Site Identification

2018 ◽  
Author(s):  
Reem Elsousy ◽  
Nagarajan Kathiresan ◽  
Sabri Boughorbel
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Splice Site ◽  
Dna Sequences ◽  
Classification Performance ◽  
Model Parameters ◽  
Learning Models ◽  
Trade Off ◽  
Good Trade ◽  
Site Identification

AbstractThe success of deep learning has been shown in various fields including computer vision, speech recognition, natural language processing and bioinformatics. The advance of Deep Learning in Computer Vision has been an important source of inspiration for other research fields. The objective of this work is to adapt known deep learning models borrowed from computer vision such as VGGNet, Resnet and AlexNet for the classification of biological sequences. In particular, we are interested by the task of splice site identification based on raw DNA sequences. We focus on the role of model architecture depth on model training and classification performance.We show that deep learning models outperform traditional classification methods (SVM, Random Forests, and Logistic Regression) for large training sets of raw DNA sequences. Three model families are analyzed in this work namely VGGNet, AlexNet and ResNet. Three depth levels are defined for each model family. The models are benchmarked using the following metrics: Area Under ROC curve (AUC), Number of model parameters, number of floating operations. Our extensive experimental evaluation show that shallow architectures have an overall better performance than deep models. We introduced a shallow version of ResNet, named S-ResNet. We show that it gives a good trade-off between model complexity and classification performance.Author summaryDeep Learning has been widely applied to various fields in research and industry. It has been also succesfully applied to genomics and in particular to splice site identification. We are interested in the use of advanced neural networks borrowed from computer vision. We explored well-known models and their usability for the problem of splice site identification from raw sequences. Our extensive experimental analysis shows that shallow models outperform deep models. We introduce a new model called S-ResNet, which gives a good trade-off between computational complexity and classification accuracy.

scholarly journals On Determining Suitable Embedded Devices for Deep Learning Models

2021 ◽  
Author(s):  
Daniel Padilla ◽  
Hatem A. Rashwan ◽  
Domènec Savi Puig
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Embedded Systems ◽  
State Of The Art ◽  
Image Data ◽  
Important Change ◽  
Learning Models ◽  
Embedded Devices ◽  
And Performance ◽  
High Level

Deep learning (DL) networks have proven to be crucial in commercial solutions with computer vision challenges due to their abilities to extract high-level abstractions of the image data and their capabilities of being easily adapted to many applications. As a result, DL methodologies had become a de facto standard for computer vision problems yielding many new kinds of research, approaches and applications. Recently, the commercial sector is also driving to use of embedded systems to be able to execute DL models, which has caused an important change on the DL panorama and the embedded systems themselves. Consequently, in this paper, we attempt to study the state of the art of embedded systems, such as GPUs, FPGAs and Mobile SoCs, that are able to use DL techniques, to modernize the stakeholders with the new systems available in the market. Besides, we aim at helping them to determine which of these systems can be beneficial and suitable for their applications in terms of upgradeability, price, deployment and performance.

scholarly journals Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6655
Author(s):  
Michael Horry ◽  
Subrata Chakraborty ◽  
Biswajeet Pradhan ◽  
Manoranjan Paul ◽  
Douglas Gomes ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Computer Vision ◽  
Deep Learning ◽  
Decision Tree ◽  
Decision Trees ◽  
Lung Nodules ◽  
X Rays ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

Efficient Deep Learning Models for Categorizing Chenopodiaceae in the Wild

2021 ◽  
pp. 2152015
Author(s):  
Ahmad Heidary-Sharifabad ◽  
Mohsen Sardari Zarchi ◽  
Sima Emadi ◽  
Gholamreza Zarei
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Species Recognition ◽  
The Other ◽  
Fine Tuning ◽  
Learning Models ◽  
Natural Habitats ◽  
Biodiversity Protection ◽  
Species Analysis ◽  
In The Wild

The Chenopodiaceae species are ecologically and financially important, and play a significant role in biodiversity around the world. Biodiversity protection is critical for the survival and sustainability of each ecosystem and since plant species recognition in their natural habitats is the first process in plant diversity protection, an automatic species classification in the wild would greatly help the species analysis and consequently biodiversity protection on earth. Computer vision approaches can be used for automatic species analysis. Modern computer vision approaches are based on deep learning techniques. A standard dataset is essential in order to perform a deep learning algorithm. Hence, the main goal of this research is to provide a standard dataset of Chenopodiaceae images. This dataset is called ACHENY and contains 27030 images of 30 Chenopodiaceae species in their natural habitats. The other goal of this study is to investigate the applicability of ACHENY dataset by using deep learning models. Therefore, two novel deep learning models based on ACHENY dataset are introduced: First, a lightweight deep model which is trained from scratch and is designed innovatively to be agile and fast. Second, a model based on the EfficientNet-B1 architecture, which is pre-trained on ImageNet and is fine-tuned on ACHENY. The experimental results show that the two proposed models can do Chenopodiaceae fine-grained species recognition with promising accuracy. To evaluate our models, their performance was compared with the well-known VGG-16 model after fine-tuning it on ACHENY. Both VGG-16 and our first model achieved about 80% accuracy while the size of VGG-16 is about 16[Formula: see text] larger than the first model. Our second model has an accuracy of about 90% and outperforms the other models where its number of parameters is 5[Formula: see text] than the first model but it is still about one-third of the VGG-16 parameters.

Cloud Governance

2021 ◽  
pp. 58-71
Author(s):  
Anustup Mukherjee ◽  
Harjeet Kaur
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Deep Learning ◽  
Educational Software ◽  
Concentration Level ◽  
Online Classes ◽  
Learning Models ◽  
Voice Tone ◽  
Audio Video ◽  
The Voice

Artificial intelligence within the area of computer vision is creating a replacement genre in detection industry. Here, AI is using the power of computer vision in creating advanced educational software LMS that detects student emotions during online classes, interviews, and judges their understanding and concentration level. It also generates automated content in step with their needs. This LMS cannot only judge audio, video, and image of a student; it also judges the voice tone. Through this judgement, the AI model understands how much a student is learning, effectivity, intellect, and drawbacks. In this chapter, the power of deep learning models VGG Net and Alex Net in LMS computer vision are used. This LMS architecture will be able to work like a virtual teacher that will be taking a parental guide to students.

scholarly journals Transfer Learning for Tabular Data

2021 ◽  
Author(s):  
Leonid Joffe
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Transfer Learning ◽  
Internal Representation ◽  
The Other ◽  
Learning Models ◽  
Tabular Data ◽  
Convolutional Network ◽  
Feature Interactions

Deep learning models for tabular data are restricted to a specific table format. Computer vision models, on the other hand, have a broader applicability; they work on all images and can learn universal features. This allows them to be trained on enormous corpora and have very wide transferability and applicability. Inspired by these properties, this work presents an architecture that aims to capture useful patterns across arbitrary tables. The model is trained on randomly sampled subsets of features from a table, processed by a convolutional network. This internal representation captures feature interactions that appear in the table. Experimental results show that the embeddings produced by this model are useful and transferable across many commonly used machine learning benchmarks datasets. Specifically, that using the embeddings produced by the network as additional features, improves the performance of a number of classifiers.

scholarly journals Transfer Learning for Tabular Data

2021 ◽  
Author(s):  
Leonid Joffe
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Transfer Learning ◽  
Internal Representation ◽  
The Other ◽  
Learning Models ◽  
Tabular Data ◽  
Convolutional Network ◽  
Feature Interactions

Deep learning models for tabular data are restricted to a specific table format. Computer vision models, on the other hand, have a broader applicability; they work on all images and can learn universal features. This allows them to be trained on enormous corpora and have very wide transferability and applicability. Inspired by these properties, this work presents an architecture that aims to capture useful patterns across arbitrary tables. The model is trained on randomly sampled subsets of features from a table, processed by a convolutional network. This internal representation captures feature interactions that appear in the table. Experimental results show that the embeddings produced by this model are useful and transferable across many commonly used machine learning benchmarks datasets. Specifically, that using the embeddings produced by the network as additional features, improves the performance of a number of classifiers.

scholarly journals Deep learning and computer vision will transform entomology

2020 ◽  
Author(s):  
Toke T. Høye ◽  
Johanna Ärje ◽  
Kim Bjerge ◽  
Oskar L. P. Hansen ◽  
Alexandros Iosifidis ◽  
...  
Keyword(s):  
Computer Vision ◽  
Big Data ◽  
Deep Learning ◽  
Phenotypic Traits ◽  
Learning Tools ◽  
Learning Models ◽  
Ecological Information ◽  
Wide Range ◽  
Automated Imaging ◽  
Insect Populations

ABSTRACTMost animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is still sparse. Insect populations are challenging to study and most monitoring methods are labour intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors that can effectively, continuously, and non-invasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the lab. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behaviour, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to the big data outputs to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) Validation of image-based taxonomic identification, 2) generation of sufficient training data, 3) development of public, curated reference databases, and 4) solutions to integrate deep learning and molecular tools.Significance statementInsect populations are challenging to study, but computer vision and deep learning provide opportunities for continuous and non-invasive monitoring of biodiversity around the clock and over entire seasons. These tools can also facilitate the processing of samples in a laboratory setting. Automated imaging in particular can provide an effective way of identifying and counting specimens to measure abundance. We present examples of sensors and devices of relevance to entomology and show how deep learning tools can convert the big data streams into ecological information. We discuss the challenges that lie ahead and identify four focal areas to make deep learning and computer vision game changers for entomology.

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