Enhancing kidney disease prediction with optimized forest and ECG signals data

M Binsawad - Heliyon, 2024 - cell.com
To improve the early detection of Chronic Kidney Disease (CKD) utilizing electrocardiogram
(ECG) data, this study explores the use of the Optimized Forest (Opt-Forest) model …

Cross Project Software Defect Prediction Using Machine Learning: A Review

MS Saeed, M Saleem - … Journal of Computational and Innovative Sciences, 2023 - ijcis.com
Software defect prediction is a crucial area of study focused on enhancing software quality
and cutting down on software upkeep expenses. Cross Project Defect Prediction (CPDP) is …

Software Defect Prediction Using an Intelligent Ensemble-Based Model

M Ali, T Mazhar, Y Arif, S Al-Otaibi, YY Ghadi… - IEEE …, 2024 - ieeexplore.ieee.org
Software defect prediction plays a crucial role in enhancing software quality while achieving
cost savings in testing. Its primary objective is to identify and send only defective modules to …

FEPP: Advancing Software Risk Prediction in Requirements Engineering Through Innovative Rule Extraction and Multi-Class Integration

M Binsawad, B Khan - IEEE Access, 2024 - ieeexplore.ieee.org
The increasing complexity of software projects makes it difficult to predict risks in software
requirements, which is a crucial and essential part of the Software Development Life Cycle …

A Classification Framework to Detect Sars Covid-19 Disease Using Feature Selection and Variant-Based Ensemble Learning

A Akhtar - International Journal of Computational and Innovative …, 2023 - ijcis.com
The hazardous COVID-19 pandemic has caused millions of deaths worldwide which depicts
the significance of an early screening of this infection in order to stop it from spreading. Real …

Software Defect Prediction Based on Optimized Machine Learning Models: A Comparative Study

MZFN Siswantoro, UL Yuhana - Teknika, 2023 - ejournal.ikado.ac.id
Software defect prediction is crucial used for detecting possible defects in software before
they manifest. While machine learning models have become more prevalent in software …

Comparison of Hidden Markov Model with other Machine Learning Techniques in Software Defect Prediction

R Malhotra, C Singla… - 2022 IEEE 7th International …, 2022 - ieeexplore.ieee.org
In the field of Software Engineering, defect prediction is the most comprehensive and active
area of study. It determines which modules are prone to errors and requires rigorous testing …

Prediction and Correction of Software Defects in Message-Passing Interfaces Using a Static Analysis Tool and Machine Learning

NA Al-Johany, FE Eassa, SA Sharaf, AY Noaman… - IEEE …, 2023 - ieeexplore.ieee.org
The Software Defect Prediction (SDP) method forecasts the occurrence of defects at the
beginning of the software development process. Early fault detection will decrease the …

ML-Based Software Defect Prediction in Embedded Software for Telecommunication Systems (Focusing on the Case of SAMSUNG ELECTRONICS)

H Kang, S Do - Electronics, 2024 - mdpi.com
Software stands out as one of the most rapidly evolving technologies in the present era,
characterized by its swift expansion in both scale and complexity, which leads to challenges …

Improved mayfly optimization deep stacked sparse auto encoder feature selection scorched gradient descent driven dropout XLM learning framework for software …

M Anbu - Concurrency and Computation: Practice and …, 2022 - Wiley Online Library
Software testing is the process of improving software quality by classifying and removing
defects in the software development. Previously, several methods were used for software …