H.3.15.3. Evolutionary computing and genetic algorithms
Homa Mehtarizadeh; Najme Mansouri; Behnam Mohammad Hasani Zade; Mohammad Mehdi Hosseini
Abstract
Accurate and reliable stock price prediction is both a formidable and essential task in financial markets, requiring the use of advanced techniques. This paper presents an innovative approach that integrates Long Short-Term Memory (LSTM) networks with Modified Complex Variational Mode Decomposition (MCVMD) ...
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Accurate and reliable stock price prediction is both a formidable and essential task in financial markets, requiring the use of advanced techniques. This paper presents an innovative approach that integrates Long Short-Term Memory (LSTM) networks with Modified Complex Variational Mode Decomposition (MCVMD) for preprocessing and the Secretary Bird Optimization Algorithm (SBOA) for hyperparameter optimization. In the preprocessing phase, MCVMD decomposes stock price time series into intrinsic mode functions, effectively capturing complex patterns and reducing noise. To enhance predictive performance, SBOA optimizes both the hyperparameters of the LSTM network and the decomposition parameters of MCVMD. The proposed methodology is evaluated on datasets from six companies: Ferrari NV (RACE) and Intesa Sanpaolo (ISP) from Italy, Amadeus IT (AMA) and Repsol (REP) from Spain, and Hitachi Ltd (6501) and Chugai Pharmaceutical Co., Ltd. (4519) from Japan. Results show that the LSTM-MCVMD-SBOA model achieves lower error values compared with conventional benchmarks including ARIMA-GARCH, vanilla LSTM, Long Short-Term Memory-Particle Swarm Optimization (LSTM-PSO), and Long Short-Term Memory-Sine Cosine Algorithm (LSTM-SCA). Compared with these alternatives, SBOA was selected because of its superior balance between exploration and exploitation, inspired by secretary bird hunting and evasion behavior, which enables efficient search in complex optimization landscapes. Overall, the proposed model demonstrates significantly improved predictive accuracy over conventional methods, highlighting the efficacy of combining advanced decomposition with nature-inspired optimization for stock market forecasting.
I.3.6. Electronics
Samira Mavaddati; Mohammad Razavi
Abstract
Rice is one of the most important staple crops in the world and provides millions of people with a significant source of food and income. Problems related to rice classification and quality detection can significantly impact the profitability and sustainability of rice cultivation, which is why the importance ...
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Rice is one of the most important staple crops in the world and provides millions of people with a significant source of food and income. Problems related to rice classification and quality detection can significantly impact the profitability and sustainability of rice cultivation, which is why the importance of solving these problems cannot be overstated. By improving the classification and quality detection techniques, it can be ensured the safety and quality of rice crops, and improving the productivity and profitability of rice cultivation. However, such techniques are often limited in their ability to accurately classify rice grains due to various factors such as lighting conditions, background, and image quality. To overcome these limitations a deep learning-based classification algorithm is introduced in this paper that combines the power of convolutional neural network (CNN) and long short-term memory (LSTM) networks to better represent the structural content of different types of rice grains. This hybrid model, called CNN-LSTM, combines the benefits of both neural networks to enable more effective and accurate classification of rice grains. Three scenarios are demonstrated in this paper include, CNN, CNN in combination with transfer learning technique, and CNN-LSTM deep model. The performance of the mentioned scenarios is compared with the other deep learning models and dictionary learning-based classifiers. The experimental results demonstrate that the proposed algorithm accurately detects different rice varieties with an impressive accuracy rate of over 99.85%, and 99.18% to identify quality for varying combinations of rice varieties with an average accuracy of 99.18%.
A. Lakizadeh; Z. Zinaty
Abstract
Aspect-level sentiment classification is an essential issue in sentiment analysis that intends to resolve the sentiment polarity of a specific aspect mentioned in the input text. Recent methods have discovered the role of aspects in sentiment polarity classification and developed various techniques to ...
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Aspect-level sentiment classification is an essential issue in sentiment analysis that intends to resolve the sentiment polarity of a specific aspect mentioned in the input text. Recent methods have discovered the role of aspects in sentiment polarity classification and developed various techniques to assess the sentiment polarity of each aspect in the text. However, these studies do not pay enough attention to the need for vectors to be optimal for the aspect. To address this issue, in the present study, we suggest a Hierarchical Attention-based Method (HAM) for aspect-based polarity classification of the text. HAM works in a hierarchically manner; firstly, it extracts an embedding vector for aspects. Next, it employs these aspect vectors with information content to determine the sentiment of the text. The experimental findings on the SemEval2014 data set show that HAM can improve accuracy by up to 6.74% compared to the state-of-the-art methods in aspect-based sentiment classification task.
