An efficient convergence-boosted salp swarm optimizer-based artificial neural network for the development of software fault prediction models

M Al-Laham, S Kassaymeh, MA Al-Betar… - Computers and …, 2023 - Elsevier
Abstract Machine learning (ML) approaches were employed to tackle the software fault
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 …

GeoZ: a region-based visualization of clustering algorithms

K ElHaj, D Alshamsi, A Aldahan - Journal of Geovisualization and Spatial …, 2023 - Springer
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 …

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 …

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 …

A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron

M Mustaqeem, S Mustajab, M Alam - International Journal of …, 2024 - emerald.com
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 …

Cognitive complexity and graph convolutional approach over control flow graph for software defect prediction

M Gupta, K Rajnish, V Bhattacharjee - IEEE Access, 2022 - ieeexplore.ieee.org
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 …

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 …

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 …

The Effect of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application

SM Suhaili, N Salim, MN Jambli - Baghdad Science Journal, 2024 - bsj.uobaghdad.edu.iq
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 …