Research
Automated Lead Time Estimation for Anomaly Detection using a Machine Learning Algorithm
The increasing complexity of modern business operations demands efficient and accurate lead time estimation to enhance decision-making processes. This study proposes a novel approach to automate lead time estimation using machine learning algorithms. Traditional lead time estimation methods often rely on manual calculations and historical averages, leading to inaccuracies and inefficiencies. In contrast, machine learning algorithms leverage historical data, contextual factors, and patterns to predict lead times dynamically. The automation of lead time estimation not only improves accuracy but also facilitates real-time decision-making. The system continuously learns from new data, adapting its predictions to changing business environments. A user-friendly interface is developed to allow easy input of relevant data and to visualize the lead time prediction. In this project design an automated time estimation is calculated for the usage of two algorithms and to get the accuracy for the maximum amount of iterations to be fitted by the PSO algorithm and then use the K-means clustering for the grouping the classes. From the PSO algorithm get the best features and then apply the Neural Network and Analogy Based Estimation for encrypt the data and then apply model to get the accuracy and time estimation from initialization to the end of the prediction and compare the two model for the accuracy and time comparison and also get the best features.
A Neuro Fuzzy Algorithm to Compute Software Effort Estimation
Software Effort Estimation is highly important and considered to be a primary activity in software project management. The accurate estimates are conducted in the development of business case in the earlier stages of project management. This accurate prediction helps the investors and customers to identify the total investment and schedule of the project. The project developers define process to estimate the effort more accurately with the available mythologies using the attributes of the project. The algorithmic estimation models are very simple and reliable but not so accurate. The categorical datasets cannot be estimated using the existing techniques. Also the attributes of effort estimation are measured in linguistic values which may leads to confusion. This paper looks in to the accuracy and reliability of a non-algorithmic approach based on adaptive neuro fuzzy logic in the problem of effort estimation. The performance of the proposed method demonstrates that there is a accurate substantiation of the outcomes with the dataset collected from various projects. The results were compared for its accuracy using MRE and MMRE as the metrics. The research idea in the proposed model for effort estimation is based on project domain and attribute which incorporates the model with more competence in augmenting the crux of neural network to exhibit the advances in software estimation.
