An efficient convergence-boosted salp swarm optimizer-based artificial neural network for the development of software fault prediction models
Abstract Machine learning (ML) approaches were employed to tackle the software fault
prediction (SFP) issue due to their consistent and rigorous performance. Multilayer …
prediction (SFP) issue due to their consistent and rigorous performance. Multilayer …
Artificial Neural Network Hyperparameters Optimization: A Survey.
ZS Kadhim, HS Abdullah… - International Journal of …, 2022 - search.ebscohost.com
Abstract Machine-learning (ML) methods often utilized in applications like computer vision,
recommendation systems, natural language processing (NLP), as well as user behavior …
recommendation systems, natural language processing (NLP), as well as user behavior …
GeoZ: a region-based visualization of clustering algorithms
The spatial display of clustered data using machine learning (ML) as regions (bordered
areas) is currently unfeasible. This problem is commonly encountered in various research …
areas) is currently unfeasible. This problem is commonly encountered in various research …
Global-scale biomass estimation based on machine learning and deep learning methods
S Talebiesfandarani, A Shamsoddini - Remote Sensing Applications …, 2022 - Elsevier
Modeling accurate aboveground biomass (AGB) is a critical aspect of remote sensing
research. Besides selecting the appropriate model from significant inputs, integrating optical …
research. Besides selecting the appropriate model from significant inputs, integrating optical …
Survey on identification and prediction of security threats using various deep learning models on software testing
RA Khan - Multimedia Tools and Applications, 2024 - Springer
In this research, authors give a literature analysis of the methods used to detect and
anticipate security risks in software testing by using a number of deep learning models. The …
anticipate security risks in software testing by using a number of deep learning models. The …
A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron
Purpose Software defect prediction (SDP) is a critical aspect of software quality assurance,
aiming to identify and manage potential defects in software systems. In this paper, we have …
aiming to identify and manage potential defects in software systems. In this paper, we have …
Cognitive complexity and graph convolutional approach over control flow graph for software defect prediction
The software engineering community is working to develop reliable metrics to improve
software quality. It is estimated that understanding the source code accounts for 60% of the …
software quality. It is estimated that understanding the source code accounts for 60% of the …
A novel dimensionality reduction-based software bug prediction using a bat-inspired algorithm
A Gupta, M Sharma, A Srivastava - 2023 13th International …, 2023 - ieeexplore.ieee.org
The major intention of software bug identification is to predict the defects in the software
modules for increasing the performance of testing. It helps in analyzing the bugs before …
modules for increasing the performance of testing. It helps in analyzing the bugs before …
Automatically Avoiding Overfitting in Deep Neural Networks by Using Hyper-Parameters Optimization Methods.
ZS Kadhim, HS Abdullah… - International Journal of …, 2023 - search.ebscohost.com
Overfitting is one issue that deep learning faces in particular. It leads to highly accurate
classification results, but they are fraudulent. As a result, if the overfitting problem is not fully …
classification results, but they are fraudulent. As a result, if the overfitting problem is not fully …
The Effect of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application
When optimizing the performance of neural network-based chatbots, determining the
optimizer is one of the most important aspects. Optimizers primarily control the adjustment of …
optimizer is one of the most important aspects. Optimizers primarily control the adjustment of …