Papers by Emel Şeyma Küçükaşcı
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2021
Multiple instance learning (MIL) aims to classify objects with complex structures and covers a wi... more Multiple instance learning (MIL) aims to classify objects with complex structures and covers a wide range of real-world data mining applications. In MIL, objects are represented by a bag of instances instead of a single instance, and class labels are provided only for the bags. Some of the earlier MIL methods focus on solving MIL problem under the standard MIL assumption, which requires at least one positive instance in positive bags and all remaining instances are negative. This study proposes a linear programming framework to learn instance level contributions to bag label without emposing the standart assumption. Each instance of a bag is mapped to a pseudo-class membership estimate and these estimates are aggregated to obtain the bag-level class membership in an optimization framework. A simple linear mapping enables handling various MIL assumptions with adjusting instance contributions. Our experiments with instance-dissimilarity based data representations verify the effectiveness of the proposed MIL framework. Proposed mathematical models can be solved efficiently in polynomial time.
Journal of Global Optimization
Optimization is one of the most important key factors in nowadays manufacturing processes. Since ... more Optimization is one of the most important key factors in nowadays manufacturing processes. Since the new economic conditions forcing the competition in a multidimensional global paths, economically survival companies should improve their processes, adapt manufacturing tag times, lower their operation costs and increase the overall quality also focusing on CRM programs This paper introduces an alternative and dynamic algorithm for improving and optimizing the control of AGV’s that used to transfer equipment from stock zones to manufacturing zones as a part of ordinary and unplanned Kanban operations for a vehicle manufacturing factory by using particle swarm optimization. Key success factors for this operation are decreasing tag time per car and also eliminating the waiting time for unplanned equipment transfer within the factory area.
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Papers by Emel Şeyma Küçükaşcı