SpikeGoogle: Spiking Neural Networks with GoogLeNet‐like inception module

Wang, Xuan; Zhong, Minghong; Cheng, Hoiyuen; Xie, Junjie; Zhou, Yingchu; Ren, Jun; Liu, Mengyuan

doi:10.1049/cit2.12082

Cited by 14 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Inception Network is a variant of deep convolutional neural network that was initially developed by academics associated with Google. The GoogLeNet architecture, which was presented at the ImageNet Large-Scale Visual Detection Challenge 2014 (ILSVRC14), effectively tackled computer vision tasks such as image classification and object detection [41]. We utilized a mini batch size of 32 and employed max pooling with a stride of 2.…”

Section: ) Cnn With Optimizationmentioning

confidence: 99%

Enhancement of Recommendation Engine Technique for Bug System Fixes

Al-Bayati,

Al-Shamma,

Tawfeeq

2024

JAIT

View full text Add to dashboard Cite

This study aims to develop a recommendation engine methodology to enhance the model's effectiveness and efficiency. The proposed model is commonly used to assign or propose a limited number of developers with the required skills and expertise to address and resolve a bug report. Managing collections within bug repositories is the responsibility of software engineers in addressing specific defects. Identifying the optimal allocation of personnel to activities is challenging when dealing with software defects, which necessitates a substantial workforce of developers. Analyzing new scientific methodologies to enhance comprehension of the results is the purpose of this analysis. Additionally, developer priorities were discussed, especially their utility in allocating a problem to a specific developer. An analysis was conducted on two key areas: first, the development of a model to represent developer prioritizing within the bug repository, and second, the use of hybrid machine learning techniques to select bug reports. Moreover, we use our model to facilitate developer assignment responsibilities. Moreover, we considered the developers' backgrounds and drew upon their established knowledge and experience when formulating the pertinent objectives. An examination of two individuals' experiences with software defects and how their actions impacted their rankings as developers in a software project is presented in this study. Researchers are implementing developer categorization techniques, assessing severity, and reopening bugs. A suitable number of bug reports is used to examine the model's output. A developer's bug assignment employee has been established, enabling the program to successfully address software maintenance issues with the highest accuracy of 78.38%. Best engine performance was achieved by optimizing and cleansing data, using relevant attributes, and processing it using deep learning.

show abstract

Section: ) Cnn With Optimizationmentioning

confidence: 99%

Enhancement of Recommendation Engine Technique for Bug System Fixes

Al-Bayati,

Al-Shamma,

Tawfeeq

2024

JAIT

View full text Add to dashboard Cite

show abstract

Medical image segmentation method based on multi‐scale feature and U‐net network

Wang

2023

Internet Technology Letters

View full text Add to dashboard Cite

In medical image segmentation (MIS), better segmentation results can be obtained by training the deeper neural network. However, directly building too deep network will cause problems such as gradient disappearance, which will affect the segmentation effect. Therefore, a dilated inception U‐Net (DIU)‐net network is constructed by combining the multi‐scale feature fusion (MSFF) method and the concept of Inception in Google net based on U‐net, and its effectiveness is verified by experiments. The DIU‐net network's training accuracy has been improved in the lung computed tomography (CT) and fundus vascular CT image data sets. And the attenuation of the loss function is relatively stable, with the highest accuracy of 99.6%. In comparison of evaluation indicators, the values of different indicators of DIU‐net in the two data sets are higher than those of the comparison network. The DICE coefficient of DIU‐net in the lung CT image in the experiment is 0.986 on average, which is 0.2% higher than that of ResU‐net. SE value is 0.985, which is 1.9% higher than SegNet. Specificity value is slightly higher than the second segmentation effect. F1 score is 0.985, 0.6% higher than ResU‐net, area under curve value is 0.99, 0.7% higher than FCN‐8 s. In general, the DIU‐net network proposed in the study will not cause gradient disappearance and other problems in the experiment. At the same time, this method also shows high efficiency and has strong feasibility for the actual MIS.

show abstract