Identification Method of Fault Level Based on Deep Learning for Open Source Software

2016 ◽  
pp. 65-75 ◽  
Author(s):  
Yoshinobu Tamura ◽  
Satoshi Ashida ◽  
Mitsuho Matsumoto ◽  
Shigeru Yamada
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Open Source Software ◽  
Identification Method

scholarly journals IoT-based group size prediction and recommendation system using machine learning and deep learning techniques

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Deepti Chopra ◽  
Arvinder Kaur
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Open Source ◽  
Group Size ◽  
Open Source Software ◽  
Multilayer Perceptron ◽  
Recommendation System ◽  
Regression Trees ◽  
Learning Models ◽  
Information Foraging

AbstractIn an open source software development environment, it is hard to decide the number of group members required for resolving software issues. Developers generally reply to issues based totally on their domain knowledge and interest, and there are no predetermined groups. The developers openly collaborate on resolving the issues based on many factors, such as their interest, domain expertise, and availability. This study compares eight different algorithms employing machine learning and deep learning, namely—Convolutional Neural Network, Multilayer Perceptron, Classification and Regression Trees, Generalized Linear Model, Bayesian Additive Regression Trees, Gaussian Process, Random Forest and Conditional Inference Tree for predicting group size in five open source software projects developed and managed using an open source development framework GitHub. The social information foraging model has also been extended to predict group size in software issues, and its results compared to those obtained using machine learning and deep learning algorithms. The prediction results suggest that deep learning and machine learning models predict better than the extended social information foraging model, while the best-ranked model is a deep multilayer perceptron((R.M.S.E. sequelize—1.21, opencv—1.17, bitcoin—1.05, aseprite—1.01, electron—1.16). Also it was observed that issue labels helped improve the prediction performance of the machine learning and deep learning models. The prediction results of these models have been used to build an Issue Group Recommendation System as an Internet of Things application that recommends and alerts additional developers to help resolve an open issue.

scholarly journals Review on Different Software Tools for Deep Learning

2022 ◽  
Vol 10 (1) ◽  
pp. 565-571
Author(s):  
Anshuja Anand Meshram
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Open Source Software ◽  
Review Paper ◽  
Learning Model ◽  
Software Tools ◽  
Advantages And Disadvantages ◽  
Learning Frameworks ◽  
Deep Learning Model

Abstract: Deep Learning Applications are being applied in various domains in recent years. Training a deep learning model is a very time consuming task. But, many open source frameworks are available to simplify this task. In this review paper we have discussed the features of some popular open source software tools available for deep learning along with their advantages and disadvantages. Software tools discussed in this paper are Tensorflow, Keras, Pytorch, Microsoft Cognitive Toolkit (CNTK). Keywords: Deep Learning, Frameworks, Open Source, Tensorflow, Pytorch, Keras, CNTK

scholarly journals DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation

2020 ◽  
Author(s):  
Ilya Belevich ◽  
Eija Jokitalo
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Open Source Software ◽  
Light And Electron Microscopy ◽  
Learning Approaches ◽  
Learning Network ◽  
Deep Learning Network ◽  
User Friendly ◽  
2D And 3D ◽  
Biological Image

AbstractDeep learning approaches are highly sought after solutions for coping with large amounts of collected datasets and are expected to become an essential part of imaging workflows. However, in most cases, deep learning is still considered as a complex task that only image analysis experts can master. DeepMIB addresses this problem and provides the community with a user-friendly and open-source tool to train convolutional neural networks and apply them to segment 2D and 3D light and electron microscopy datasets.

scholarly journals DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation

2021 ◽  
Vol 17 (3) ◽  
pp. e1008374
Author(s):  
Ilya Belevich ◽  
Eija Jokitalo
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Open Source ◽  
Open Source Software ◽  
Learning Network ◽  
Matlab Code ◽  
Deep Learning Network ◽  
User Friendly ◽  
2D And 3D ◽  
Biological Image

We present DeepMIB, a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy datasets on any workstation. We demonstrate its successful application for segmentation of 2D and 3D electron and multicolor light microscopy datasets with isotropic and anisotropic voxels. We distribute DeepMIB as both an open-source multi-platform Matlab code and as compiled standalone application for Windows, MacOS and Linux. It comes in a single package that is simple to install and use as it does not require knowledge of programming. DeepMIB is suitable for everyone interested of bringing a power of deep learning into own image segmentation workflows.

scholarly journals A Means of Assessing Deep Learning-Based Detection of ICOS Protein Expression in Colon Cancer

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3825
Author(s):  
Md Mostafa Kamal Sarker ◽  
Yasmine Makhlouf ◽  
Stephanie G. Craig ◽  
Matthew P. Humphries ◽  
Maurice Loughrey ◽  
...  
Keyword(s):  
Deep Learning ◽  
T Cell ◽  
Protein Expression ◽  
Open Source ◽  
Open Source Software ◽  
Digital Pathology ◽  
Response To Therapy ◽  
Cell Segmentation ◽  
Nuclear Staining ◽  
Annotation Process

Biomarkers identify patient response to therapy. The potential immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS), expressed on regulating T-cell activation and involved in adaptive immune responses, is of great interest. We have previously shown that open-source software for digital pathology image analysis can be used to detect and quantify ICOS using cell detection algorithms based on traditional image processing techniques. Currently, artificial intelligence (AI) based on deep learning methods is significantly impacting the domain of digital pathology, including the quantification of biomarkers. In this study, we propose a general AI-based workflow for applying deep learning to the problem of cell segmentation/detection in IHC slides as a basis for quantifying nuclear staining biomarkers, such as ICOS. It consists of two main parts: a simplified but robust annotation process, and cell segmentation/detection models. This results in an optimised annotation process with a new user-friendly tool that can interact with1 other open-source software and assists pathologists and scientists in creating and exporting data for deep learning. We present a set of architectures for cell-based segmentation/detection to quantify and analyse the trade-offs between them, proving to be more accurate and less time consuming than traditional methods. This approach can identify the best tool to deliver the prognostic significance of ICOS protein expression.

scholarly journals Open-source deep-learning software for bioimage segmentation

2021 ◽  
Vol 32 (9) ◽  
pp. 823-829
Author(s):  
Alice M. Lucas ◽  
Pearl V. Ryder ◽  
Bin Li ◽  
Beth A. Cimini ◽  
Kevin W. Eliceiri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Open Source Software ◽  
Learning Tools ◽  
Complex Information ◽  
Biological Structures ◽  
Bioimage Analysis ◽  
Learning Software ◽  
Dynamic Relationships ◽  
Microscopy Images

Microscopy images are rich in information about the dynamic relationships among biological structures. However, extracting this complex information can be challenging, especially when biological structures are closely packed, distinguished by texture rather than intensity, and/or low intensity relative to the background. By learning from large amounts of annotated data, deep learning can accomplish several previously intractable bioimage analysis tasks. Until the past few years, however, most deep-learning workflows required significant computational expertise to be applied. Here, we survey several new open-source software tools that aim to make deep-learning–based image segmentation accessible to biologists with limited computational experience. These tools take many different forms, such as web apps, plug-ins for existing imaging analysis software, and preconfigured interactive notebooks and pipelines. In addition to surveying these tools, we overview several challenges that remain in the field. We hope to expand awareness of the powerful deep-learning tools available to biologists for image analysis.

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