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Showing 203 results for Type of Study: Original
Dr Mohammad Mohammadi, Dr Davood Darvishi,
Volume 16, Issue 1 (3-2025)
Abstract
Prostate cancer is the most common cancer in men and the second leading cause of cancer-related death worldwide. Over the years, researchers from various fields, beyond medicine, have sought to expand their understanding of the disease to develop more effective treatments. Treatment planning for high-dose-rate (HDR) brachytherapy involves designing the trajectory of the radiation source to deliver sufficient doses to the target area while minimizing exposure to surrounding organs at risk (OAR) within clinically safe limits. Since the exact tumor volume is not known, the model uses gray numbers instead of tumor volume, which provides more accurate results.
In this study, four powerful multi-objective evolutionary algorithms (MOEAs) NSGA1-II, PESA2-II, SPEA3-II, and MOPSO4 are employed. Instead of yielding a single best solution, these algorithms produce a set of Pareto-optimal solutions, each representing a trade-off where no one solution is definitively better than the rest. However, they demonstrate improved performance compared to other optimization methods. The results show that the MOPSO algorithm performs better than the other three powerful algorithms in terms of solution quality and maintaining diversity among solutions.
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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