[HTML][HTML] Significance of machine learning in healthcare: Features, pillars and applications
Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
A study on ML-based software defect detection for security traceability in smart healthcare applications
S Mcmurray, AH Sodhro - Sensors, 2023 - mdpi.com
Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-
Cycle (SDLC). As the prevalence of software systems increases and becomes more …
Cycle (SDLC). As the prevalence of software systems increases and becomes more …
An adaptive rank aggregation-based ensemble multi-filter feature selection method in software defect prediction
Feature selection is known to be an applicable solution to address the problem of high
dimensionality in software defect prediction (SDP). However, choosing an appropriate filter …
dimensionality in software defect prediction (SDP). However, choosing an appropriate filter …
[PDF][PDF] Machine learning-based models for magnetic resonance imaging (mri)-based brain tumor classification
In the medical profession, recent technological advancements play an essential role in the
early detection and categorization of many diseases that cause mortality. The technique …
early detection and categorization of many diseases that cause mortality. The technique …
Mutation boosted salp swarm optimizer meets rough set theory: A novel approach to software defect detection
K Sekaran, SPA Lawrence - Transactions on Emerging …, 2024 - Wiley Online Library
Software defect detection (SDD) is crucial to ensure the reliability of software systems and
identify defects in classification. One of the key challenges in defect detection is to select …
identify defects in classification. One of the key challenges in defect detection is to select …
PDF Malware Detection Based on Fuzzy Unordered Rule Induction Algorithm (FURIA)
S Mejjaouli, S Guizani - Applied Sciences, 2023 - mdpi.com
The number of cyber-attacks is increasing daily, and attackers are coming up with new ways
to harm their target by disseminating viruses and other malware. With new inventions and …
to harm their target by disseminating viruses and other malware. With new inventions and …
Analysis of Tree-Family Machine Learning Techniques for Risk Prediction in Software Requirements
Risk prediction is the most sensitive and critical activity in the Software Development Life
Cycle (SDLC). It might determine whether the project succeeds or fails. To increase the …
Cycle (SDLC). It might determine whether the project succeeds or fails. To increase the …
Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique
SS Swain, TK Khura, PK Sahoo, KA Chobhe… - Scientific Reports, 2024 - nature.com
An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and
groundwater pollution reduction. However, past studies could not efficiently model nitrate …
groundwater pollution reduction. However, past studies could not efficiently model nitrate …
[PDF][PDF] Liver ailment prediction using random forest model
Today, liver disease, or any deterioration in one's ability to survive, is extremely common all
around the world. Previous research has indicated that liver disease is more frequent in …
around the world. Previous research has indicated that liver disease is more frequent in …
An empirical study on data sampling methods in addressing class imbalance problem in software defect prediction
With the growing rate of software systems and their applications in diverse walks of life,
developing a software system that has no defects is a subject that cannot be …
developing a software system that has no defects is a subject that cannot be …