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METHOD FOR EFFORT ESTIMATION OF AGILE PROJECTS
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Abstract
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Filed on 30 October 2024
Abstract
ABSTRACT A method (100) for effort estimation of agile projects, the method (100) comprising: collecting, via a processor, data pertaining to tasks to be estimated; preprocessing, via the processor, the collected data; building, via the processor, an ANFIS model using MATLAB; training, via the processor. the ANFIS model on the preprocessed data; adjusting the model parameters to minimize the error between predicted outputs and actual outputs using a suitable optimization algorithm; testing, via the processor, the ANFIS model on a separate test dataset; evaluating the performance of the ANFIS model by calculating error metrics; deploying, via the processor, the ANFIS model for practical use in agile projects; and integrating, via the processor, the trained model into project management software or utilizing it as a standalone tool for estimating effort, thereby assisting project managers in resource allocation and effort estimation.
Patent Information
Application ID | 202411083137 |
Invention Field | COMPUTER SCIENCE |
Date of Application | 30/10/2024 |
Publication Number | 46/2024 |
Inventors
Name | Address | Country | Nationality |
---|---|---|---|
RAVISH RAJ | LOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411 | India | India |
ABHINAV JAISWAL | LOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411 | India | India |
RISHABH SINGH | LOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411 | India | India |
ANJALI NAIN | LOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411 | India | India |
ADITYENDRA SRIVASTAVA | LOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411 | India | India |
Dr. MOHIT ARORA | LOVELY PROFESSIONAL UNIVERSITY, JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411 | India | India |
Applicants
Name | Address | Country | Nationality |
---|---|---|---|
LOVELY PROFESSIONAL UNIVERSITY | JALANDHAR-DELHI, G.T. ROAD, PHAGWARA, PUNJAB (INDIA) -144411 | India | India |
Specification
Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of project management and, in particular, to a method for effort estimation in agile projects utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS) within a MATLAB environment. The method aims to enhance the accuracy of effort estimation by employing advanced data analysis techniques and optimizing the interplay of neural networks and fuzzy logic.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Conventional methods for effort estimati , Claims:1. A method (100) for effort estimation of agile projects, the method (100) comprising:
collecting, via a processor, data pertaining to tasks to be estimated, the data including historical data on tasks of similar size and complexity, along with other relevant factors that influence the effort required for those tasks;
preprocessing, via the processor, the collected data, wherein the preprocessing further involves:
data cleaning to remove inaccuracies and inconsistencies,
normalization to ensure that input features are within a similar scale, and
scaling to adjust the range of input data to enhance model performance;
building, via the processor, an ANFIS model using MATLAB, wherein the model is constructed by utilizing the 'anfis' function with the preprocessed data, and specifying the number of membership functions for each input variable based on the data points and desired fuzziness;
training, via the processor. the ANFIS model on the preprocessed data, wherein the training process includes:
adjusting the
Documents
Name | Date |
---|---|
202411083137-COMPLETE SPECIFICATION [30-10-2024(online)].pdf | 30/10/2024 |
202411083137-DECLARATION OF INVENTORSHIP (FORM 5) [30-10-2024(online)].pdf | 30/10/2024 |
202411083137-DRAWINGS [30-10-2024(online)].pdf | 30/10/2024 |
202411083137-FIGURE OF ABSTRACT [30-10-2024(online)].pdf | 30/10/2024 |
202411083137-FORM 1 [30-10-2024(online)].pdf | 30/10/2024 |
202411083137-FORM-9 [30-10-2024(online)].pdf | 30/10/2024 |
202411083137-POWER OF AUTHORITY [30-10-2024(online)].pdf | 30/10/2024 |
202411083137-PROOF OF RIGHT [30-10-2024(online)].pdf | 30/10/2024 |
202411083137-REQUEST FOR EARLY PUBLICATION(FORM-9) [30-10-2024(online)].pdf | 30/10/2024 |
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