H.3. Artificial Intelligence
Mohammad Aalishahi; Mohammad Hadi Bokaei; Abolfazl Nadi
Abstract
Given the importance of Named Entity Recognition (NER), numerous studies have been conducted in this field. However, most research has focused on languages such as English, French, and Arabic. In contrast, studies on Persian remain limited, despite Persian being one of the most widely spoken languages ...
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Given the importance of Named Entity Recognition (NER), numerous studies have been conducted in this field. However, most research has focused on languages such as English, French, and Arabic. In contrast, studies on Persian remain limited, despite Persian being one of the most widely spoken languages in West Asia, necessitating the development of NER methods for it. In this study, using Active Learning, a corpus of 1,351 advertisements from the Official Gazette was annotated. The GEMMA2b model was then fine-tuned on this data, achieving approximately 95% accuracy. This model was employed to extract around 13 types of named entities and their relationships within the advertisement texts. The primary advantage of this method is the model’s high accuracy compared to other approaches. Additionally, the use of Persian data—which, unlike languages such as English or Arabic, has fewer resources—is another notable feature of this research.
S. Shadravan; H. Naji; V. Khatibi
Abstract
The SailFish Optimizer (SFO) is a metaheuristic algorithm inspired by a group of hunting sailfish that alternates their attacks on group of prey. The SFO algorithm takes advantage of using a simple method for providing the dynamic balance between exploration and exploitation phases, creating the swarm ...
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The SailFish Optimizer (SFO) is a metaheuristic algorithm inspired by a group of hunting sailfish that alternates their attacks on group of prey. The SFO algorithm takes advantage of using a simple method for providing the dynamic balance between exploration and exploitation phases, creating the swarm diversity, avoiding local optima, and guaranteeing high convergence speed. Nowadays, multi agent systems and metaheuristic algorithms can provide high performance solutions for solving combinatorial optimization problems. These methods provide a prominent approach to reduce the execution time and improve of the solution quality. In this paper, we elaborate a multi agent based and distributed method for sailfish optimizer (DSFO), which improves the execution time and speedup of the algorithm while maintaining the results of optimization in high quality. The Graphics Processing Units (GPUs) using Compute Unified Device Architecture (CUDA) are used for the massive computation requirements in this approach. In depth of the study, we present the implementation details and performance observations of DSFO algorithm. Also, a comparative study of distributed and sequential SFO is performed on a set of standard benchmark optimization functions. Moreover, the execution time of distributed SFO is compared with other parallel algorithms to show the speed of the proposed algorithm for solving unconstrained optimization problems. The final results indicate that the proposed method is executed about maximum 14 times faster than other parallel algorithms and shows the ability of DSFO for solving non-separable, non-convex and scalable optimization problems.
