Publications by authors named "Zulfiqar Ali Memon"
The primary goal of this study is to develop a deep neural network for action recognition that enhances accuracy and minimizes computational costs. In this regard, we propose a modified EMO-MoviNet-A2* architecture that integrates Evolving Normalization (EvoNorm), Mish activation, and optimal frame selection to improve the accuracy and efficiency of action recognition tasks in videos. The asterisk notation indicates that this model also incorporates the stream buffer concept.
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- This paper introduces the Improved Transient Search Optimization Algorithm (ITSOA), designed for optimizing unbalanced distribution networks by integrating multiobjective optimization strategies.
- ITSOA enhances the conventional Transient Search Optimization Algorithm (TSOA) through opposition learning and nonlinear decreasing techniques to better balance local and global search for optimal solutions.
- The results demonstrate that ITSOA effectively improves various objectives like power loss reduction and voltage stability in both 13-bus and 118-bus networks while outperforming traditional optimization methods like PSO and GWO.
Aim Of The Study: Management of metaphyseal bone loss in complex primary and revision TKA is a challenge for surgeons. Out of various types of bony defects, large metaphyseal bone loss (AORI types IIB and III) requires special augments in the form of cones or sleeves. The aim of this study is to assess the reliability of metaphyseal sleeves, in dealing with massive bone defects to provide stability for immediate weight bearing and also to check short to mid-term survivorship of metaphyseal sleeves in Asian population by assessing various parameters and complications.
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