Object Recognition
E. Ghasemi Bideskan; S.M. Razavi; S. Mohamadzadeh; M. Taghippour
Abstract
Background and Objectives: The recognition of facial expressions using metaheuristic algorithms is a research topic in the field of computer vision. This article presents an approach to identify facial expressions using an optimized filter developed by metaheuristic algorithms. Methods: The entire process ...
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Background and Objectives: The recognition of facial expressions using metaheuristic algorithms is a research topic in the field of computer vision. This article presents an approach to identify facial expressions using an optimized filter developed by metaheuristic algorithms. Methods: The entire process of feature extraction hinges on using a filter optimally configured by metaheuristic algorithms. Essentially, the purpose of utilizing this metaheuristic algorithm is to determine the optimal weights for feature extraction filters. Once the optimal weights for the filter have been determined by the metaheuristic algorithm, optimal filter sizes have also been determined. As an initial step, the k-nearest neighbor classifier is employed due to its simplicity and high accuracy. Following the initial stage, a final model is presented, which integrates results from both filterbank and Multilayer Perceptron neural networks.Results: An analysis of the existing instances in the FER2013 database has been conducted using the method proposed in this article. This model achieved a recognition rate of 78%, which is superior to other algorithms and methods while requiring less training time than other algorithms and methods.In addition, the JAFFE database, a Japanese women's database, was utilized for validation. On this dataset, the proposed approach achieved a 94.88% accuracy rate, outperforming other competitors.Conclusion: The purpose of this article is to propose a method for improving facial expression recognition by using an optimized filter, which is implemented through a metaheuristic algorithm based on the KA. In this approach, optimized filters were extracted using the metaheuristic algorithms kidney, k-nearest neighbor, and multilayer perceptron. Additionally, by employing this approach, the optimal size and number of filters for facial state recognition were determined in order to achieve the highest level of accuracy in the extraction process.
Computational Intelligence
M.R. Esmaeili; S.H. Zahiri; S.M. Razavi
Abstract
Background and Objectives: High-level synthesis (HLS) is one of the substantial steps in designing VLSI digital circuits. The primary purpose of HLS is to minimize the digital units used in the system to improve their power, delay, and area.Methods: In the modified MFO algorithm presented in this paper, ...
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Background and Objectives: High-level synthesis (HLS) is one of the substantial steps in designing VLSI digital circuits. The primary purpose of HLS is to minimize the digital units used in the system to improve their power, delay, and area.Methods: In the modified MFO algorithm presented in this paper, a hyperbolic spiral is chosen as the update mechanism of moths. Also, by presenting a new approach, a paramount issue involved in applying meta-heuristic methods for solving HLS problems of VLSI circuits has been disentangled.Results: By comparing the performance of the proposed method with Genetic algorithm (GA)-based method and particle swarm optimization (PSO)-based method for the synthesis of the digital filters, it is concluded that the proposed method has the higher ability in the HLS of data path in digital filters. The best improvement is 2.78% for the delay (latency), 6.51% for the occupied area of the chip and 6.93% in power consumption. Another feature of the proposed method is its high-speed in finding optimal solutions, in a manner which, more than 21.6% and 12.9% faster than the GA-based and PSO-based methods, respectively on average.Conclusion: The most important very large scale integration (VLSI) circuits are digital filters and transformers, which are widely used in audio and video processing, medical signal processing, and telecommunication systems. The complex, expansive, and discrete nature of design space in high-level synthesis problems has made them one of the most difficult problems in VLSI circuit design.
Data Mining
I. Behravan; S.H. Zahiri; S.M. Razavi; R. Trasarti
Abstract
Background and Objectives: Big data referred to huge datasets with high number of objects and high number of dimensions. Mining and extracting big datasets is beyond the capability of conventional data mining algorithms including clustering algorithms, classification algorithms, feature selection methods ...
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Background and Objectives: Big data referred to huge datasets with high number of objects and high number of dimensions. Mining and extracting big datasets is beyond the capability of conventional data mining algorithms including clustering algorithms, classification algorithms, feature selection methods and etc. Methods: Clustering, which is the process of dividing the data points of a dataset into different groups (clusters) based on their similarities and dissimilarities, is an unsupervised learning method which discovers useful information and hidden patterns from raw data. In this research a new clustering method for big datasets is introduced based on Particle Swarm Optimization (PSO) algorithm. The proposed method is a two-stage algorithm which first searches the solution space for proper number of clusters and then searches to find the position of the centroids. Results: the performance of the proposed method is evaluated on 13 synthetic datasets. Also its performance is compared to X-means through calculating two evaluation metrics: Rand index and NMI index. The results demonstrate the superiority of the proposed method over X-means for all of the synthetic datasets. Furthermore, a biological microarray dataset is used to evaluate the proposed method deeper. Finally, 2 real big mobility datasets, including the trajectories traveled by several cars in the city of Pisa, are analyzed using the proposed clustering method. The first dataset includes the trajectories recorded in Sunday and the second one contains the trajectories recorded in Monday during 5 weeks. The achieved results showed that people choose more diverse destinations in Sunday although it has fewer trajectories. Conclusion: Finding the number of clusters is a big challenge especially fir big datasets. The results achieved for the proposed method showed its fabulous performance in detecting the number of clusters for high dimensional and massive datasets. Also, the results demonstrate the power and effectiveness of the swarm intelligence methods in solving hard and complex optimization problems.======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
