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Showing 202 results for Type of Study: Original

Dr Elham Askari,
Volume 16, Issue 1 (3-2025)
Abstract

Emotion recognition in Persian texts using data mining is a significant area within text analysis. Emotions are typically defined as individuals’ emotional reactions to situations, events, and information. Emotion recognition in text involves identifying and analyzing emotional content across various types of textual data. This paper presents a model for detecting different emotions in Persian texts using an enhanced transfer model. The proposed model comprises an encoder and a decoder, each equipped with a self-attention mechanism and RNN modules. Initially, a dataset of sentences annotated with emotional states—anger, happiness, sadness, and fear—is created by multiple users. These sentences are then converted into image representations and fed into the improved transfer model for emotion recognition. Experimental results demonstrate that the model effectively identifies the emotions of sadness, anger, happiness, and surprise with precision, accuracy, recall, and F1-score values of 90.25%, 91.4%, 91.6%, and 90.80%, respectively.
 
Dr Amir-Mohammad Golmohammadi, Hamidreza Abedsoltan,
Volume 16, Issue 1 (3-2025)
Abstract

Facility location and routing problems have attracted significant research attention since the 1960s due to their practical relevance and complexity. Efficiently establishing production facilities, optimizing vehicle routes, and implementing effective inventory systems are essential for improving organizational performance. In this study, we propose an integrated location-routing model for the pharmaceutical supply chain, designed to satisfy all retailer demands through an appropriate inventory policy, ensuring no demand is unmet. The proposed mixed-integer mathematical model considers a four-tier supply chain, including manufacturers, distributors, wholesalers, and retailers, with the objective of establishing cost-effective warehouses while fulfilling all demand requirements. Demand uncertainty is addressed using a scenario-based probabilistic approach. The model is solved using GAMS for a small-scale case study. For larger-scale instances, where exact solutions are computationally challenging, a meta-heuristic approach—specifically, a genetic algorithm—is employed to efficiently obtain near-optimal solutions.
 

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مجله انجمن ایرانی تحقیق در عملیات Iranian Journal of Operations Research
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