Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithms

Zhang, Xuecong; Chen, Zhong; Abualigah, Laith

doi:10.1007/s00500-022-07526-6

Cited by 7 publications

(5 citation statements)

References 60 publications

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“…Performance measures, namely classification accuracy, sensitivity, precision, and F1 score evaluation metrics, were used. The SVM parameters were adjusted using a hyperparameter search algorithm [ 32 ]. And the number of nearest neighbors and the number of selected features using the ReliefF algorithm were set to 5 and 7000.…”

Section: Resultsmentioning

confidence: 99%

LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals

Atila

Deniz

Arı

et al. 2023

Sensors

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Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam’s spiral and Sophia Germain’s prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time–frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time–frequency representation is saved as a time–frequency image, and a non-overlapping n × n sliding window is applied to this image for patch extraction. An n × n Ulam’s spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain’s primes are located in Ulam’s spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children’s neurological disorders.

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Section: Resultsmentioning

confidence: 99%

LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals

Atila

Deniz

Arı

et al. 2023

Sensors

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“…Differential evolution algorithm is an efficient global optimization algorithm, which is implemented by heuristic search method based on population [21,22]. The implementation steps are as follows: random generation of initial population → construction of fitness function → mutation operation → crossover operation → selection operation.…”

Section: Improved Differential Evolution Algorithm Designmentioning

confidence: 99%

Optimal Differential Control for Accurate Positioning of Medical Electronic Wristband

2024

Teh. vjesn.

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In this paper, an improved differential evolution algorithm is proposed to improve the positioning accuracy of electronic wristband system. The wristband system is composed of STM32F103 microcontroller, Beidou positioning module, LORA communication module and so on. In the traditional differential evolution algorithm, premature convergence to local optimal will reduce the accuracy of the algorithm. To solve this problem, the method introduces the distance information between known and unknown points and between all unknown points in the fitness function and overall initialization. This reduces the premature convergence of the algorithm and improves the optimization ability of the algorithm. Experimental results using the CICDDoS2019 dataset show that the localization accuracy of the proposed algorithm is improved by 7.6% compared with traditional differential evolution. The method also has good stability. This provides an effective way to improve the positioning accuracy of the wristband, which can be used for applications such as patient monitoring.

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“…Bhatt and Gor (2022) [3] review applications of quantum harmonic oscillator model in financial mathematics and discussed about different applications of quantum harmonic oscillator and its characteristics. Zhang (2022) [11] enhances the IOAS algorithm's neighbourhood and out-of-bounds movement rules. It then suggests the DETS support vector regression algorithm, which is based on enhanced tabu search and differential evolution and uses error indicators to compare related algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring value at risk of cryptocurrency using quantum harmonic oscillator

Bhatt¹,

Gor²

2023

Int. J. Stat. Appl. Math.

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Cryptocurrency, also known as crypto is a type of digital currency that operates as an exchange mechanism over a computer network and is not supported or maintained by any governing authority. Compared to other equities and bonds, the price of crypto-currencies is highly volatile in nature. Its fluctuations are highly influenced by supply and demand, sentiments of investors and media hype. This paper introduces quantum harmonic oscillator model to measure the value at risk for the fix amount of investment to identify the maximum risk behaviour of cryptocurrency.

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Foreign exchange forecasting and portfolio optimization strategy based on hybrid-molecular differential evolution algorithms

Cited by 7 publications

References 60 publications

LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals

LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain’s Primes on Ulam’s Spiral-Based Features with Electroencephalogram Signals

Optimal Differential Control for Accurate Positioning of Medical Electronic Wristband

Measuring value at risk of cryptocurrency using quantum harmonic oscillator

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