Classification
M. Rohani; H. Farsi; S. Mohamadzadeh
Abstract
Background and Objectives: Recent advancements in race classification from facial images have been significantly propelled by deep learning techniques. Despite these advancements, many existing methodologies rely on intricate models that entail substantial computational costs and exhibit slow processing ...
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Background and Objectives: Recent advancements in race classification from facial images have been significantly propelled by deep learning techniques. Despite these advancements, many existing methodologies rely on intricate models that entail substantial computational costs and exhibit slow processing speeds. This study aims to introduce an efficient and robust approach for race classification by utilizing transfer learning alongside a modified Efficient-Net model that incorporates attention-based learning.Methods: In this research, Efficient-Net is employed as the base model, applying transfer learning and attention mechanisms to enhance its efficacy in race classification tasks. The classifier component of Efficient-Net was strategically modified to minimize the parameter count, thereby enhancing processing speed without compromising classification accuracy. To address dataset imbalance, we implemented extensive data augmentation and random oversampling techniques. The modified model was rigorously trained and evaluated on a comprehensive dataset, with performance assessed through accuracy, precision, recall, and F1 score metrics.Results: The modified Efficient-Net model exhibited remarkable classification accuracy while significantly reducing computational demands on the UTK-Face and FairFace datasets. Specifically, the model achieved an accuracy of 88.19% on UTK-Face and 66% on FairFace, reflecting a 2% enhancement over the base model. Additionally, it demonstrated a 9-14% reduction in memory consumption and parameter count. Real-time evaluations revealed a processing speed 14% faster than the base model, alongside achieving the highest F1-score results, which underscores its effectiveness for practical applications. Furthermore, the proposed method enhanced test accuracy in classes with approximately 50% fewer training samples by about 5%.Conclusion: This study presents efficient race classification model grounded in a modified Efficient-Net that utilizes transfer learning and attention-based learning to attain state-of-the-art performance. The proposed approach not only sustains high accuracy but also ensures rapid processing speeds, rendering it ideal for real-time applications. The findings indicate that this lightweight model can effectively rival more complex and computationally intensive recent methods, providing a valuable asset for practical race classification endeavors.
Classification
I. Kadoun; H. Khaleghi
Abstract
Background and Objectives: Intelligent receivers, automatically detect the digital modulation type of the received signals for demodulation purposes where is well known as Automatic Modulation Classification (AMC) module. The performance of AMC algorithms depends on the channel conditions where for example, ...
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Background and Objectives: Intelligent receivers, automatically detect the digital modulation type of the received signals for demodulation purposes where is well known as Automatic Modulation Classification (AMC) module. The performance of AMC algorithms depends on the channel conditions where for example, in fading channel its performance gets worse than the AWGN channel.Methods: We propose a new algorithm for improving the AMC classification accuracy in flat fading channels. The proposed algorithm consists of an optimizable nonlinear preprocess followed by Linear Discriminant Analysis (LDA) technique. Two Lemmas have been found for extracting the optimization rule. And an optimization algorithm has been built based on the previous Lemmas. Results: The simulation results show that the proposed algorithm improves the classification accuracy between 8-Phase Shift Keying (8PSK) and 16PSK (as an example of M-array PSK (MPSK) inter-class) for Signal-to-noise ratio (SNR) values greater than 13 dB, and between 16- quadrature amplitude shift modulation (16QAM) and 64QAM (as an example of M-array QAM (MQAM) inter-class) for SNR values greater than 4 dB.Conclusion: By using the proposed optimization algorithm, the AMC classification accuracy has been improved. Other classification problems can use this algorithm. And other nonlinear preprocess functions or optimization algorithms may be found in future work.
Classification
K. Kiaei; H. Omranpour
Abstract
Background and Objectives: Time series classification (TSC) means classifying the data over time and based on their behavior. TSC is one of the main machine learning tasks related to time series. Because the classification accuracy is of particular importance, we have decided to increase it in this research.Methods: ...
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Background and Objectives: Time series classification (TSC) means classifying the data over time and based on their behavior. TSC is one of the main machine learning tasks related to time series. Because the classification accuracy is of particular importance, we have decided to increase it in this research.Methods: In this paper, we proposed a simple method for TSC problems to achieve higher classification accuracy than other existing methods. Fast Fourier transform is a method that uses in raw time series data preprocess. In this study, we apply the fast Fourier transform (FFT) over the raw datasets. Then we use the polar form of a complex number to create a histogram. The proposed method consists of three steps: preprocessing using FFT, feature extraction by histogram computation, and decision making using a random forest classifier.Results: The presented method was tested on 12 datasets of the UCR time series classification archive from different domains. Evaluation of our method was performed using k-fold cross-validation and classification accuracy. The experimental results state that our model has been achieved classification accuracy higher or comparable than related methods. Computational complexity has also been significantly reduced.Conclusion: In the latest years, the TSC problems have been increased. In this work, we proposed a simple method with extracted features from fast Fourier transforms that is efficient to gain more high accuracy.
Classification
M. Mirhosseini; M. Fazlali
Abstract
Background and Objectives: -similarity problem defined as measuring the similarity among objects and finding a group of objects from a dataset that have the most similarity to each other. This problem has been become an important issue in information retrieval and data mining. Theory of this ...
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Background and Objectives: -similarity problem defined as measuring the similarity among objects and finding a group of objects from a dataset that have the most similarity to each other. This problem has been become an important issue in information retrieval and data mining. Theory of this concept is mathematically proven, but it practically has high memory complexity and is so time consuming. Besides, the solutions found by metaheuristics are not exact.Methods: This paper is conducted to propose an exact method to solve -similarity problem reducing the memory complexity and decreasing the execution time by parallelism using Open-MP. The experiments are performed on the application of text document resemblance.Results: It has been shown that the memory complexity of the proposed method is decreased to , and the experimental results show that this method accelerates the speed of the computations about 5 times.Conclusion: The simulated results of the proposed method display a good improvement in speed, the used memory space, and scalability compared with the previous exact method.
