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Found 9 papers, 0 papers with code
Search-Based Fairness Testing: An Overview
Artificial Intelligence (AI) has demonstrated remarkable capabilities in domains such as recruitment, finance, healthcare, and the judiciary.
Machine Learning for Stock Prediction Based on Fundamental Analysis
A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks historical data.
Automatic Recall of Software Lessons Learned for Software Project Managers
Our case study results confirm the effectiveness of the automatic LL recall solution.
Neural Network Models for Stock Selection Based on Fundamental Analysis
Application of neural network architectures for financial prediction has been actively studied in recent years.
Enhancing Use Case Points Estimation Method Using Soft Computing Techniques
Use case points method relies on the use case diagram to estimate the size and effort of software projects.
A Hybrid Intelligent Model for Software Cost Estimation
Although many techniques and algorithmic models have been developed and implemented by practitioners, accurate software development effort prediction is still a challenging endeavor in the field of software engineering, especially in handling uncertain and imprecise inputs and collinear characteristics.
A Comparison Between Decision Trees and Decision Tree Forest Models for Software Development Effort Estimation
Accurate software effort estimation has been a challenge for many software practitioners and project managers.
A Neuro-Fuzzy Method to Improving Backfiring Conversion Ratios
This study introduces a method to improve the accuracy of backfiring.
Neuro-Fuzzy Algorithmic (NFA) Models and Tools for Estimation
We made use of the Constructive Cost Model (COCOMO), Analysis of Variance (ANOVA), and Function Point Analysis as the algorithmic models and validated the accuracy of the Neuro-Fuzzy Algorithmic (NFA) Model in software cost estimation using industrial project data.
