Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{145732,
        author = {R.Mamatha and N.Vinayasree},
        title = {A novel multitask learning frame work for forecasting models of the locations},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {11},
        pages = {68-70},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145732},
        abstract = {Spatial occasion determining from online networking is conceivably amazingly helpful however experiences basic difficulties, for example, the dynamic examples of highlights (catchphrases) and geographic heterogeneity (e.g., spatial relationships, imbalanced examples, and unique populaces in various areas). Most existing methodologies (e.g., LASSO relapse, dynamic question extension, and burst identification) address a few, yet not all, of these difficulties. Here we propose a novel multi-errand learning system that intends to simultaneously address every one of the difficulties included. In particular, given a gathering of areas (e.g., urban communities), anticipating models are worked for all the areas all the while by extricating and using proper shared data that viably expands the example estimate for each area, in this way enhancing the estimating execution. The new model consolidates both static highlights got from a predefined vocabulary by area specialists and dynamic highlights produced from dynamic inquiry extension in a multi-errand include learning structure. Distinctive systems to adjust homogeneity and decent variety amongst static and dynamic terms are likewise explored. Furthermore, productive calculations in light of Iterative Group Hard Thresholding are created to accomplish proficient and successful model preparing and expectation. Broad trial assessments on Twitter information from common distress and flu episode datasets show the adequacy and productivity of our proposed approach.},
        keywords = {Twitter, event detection, earthquake, LASSO},
        month = {},
        }