Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches

Ahmadi, Mohsen; Ghoushchi, Saeid Jafarzadeh; Taghizadeh, Rahim; Sharifi, Abbas

doi:10.1007/s00521-019-04417-0

Cited by 49 publications

(30 citation statements)

References 24 publications

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“…Multilayer perceptron (MLP) is a popular feed-forward neural network architecture. MLP consists of three layers, namely, input, output, and hidden layers (Ahmadi et al 2019 ). More details about MLP is found in Ahmadi et al ( 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…MLP consists of three layers, namely, input, output, and hidden layers (Ahmadi et al 2019). More details about MLP is found in Ahmadi et al (2019). Based on trial and error method, MLP technique, with 10 hidden neurons and using 70% of the data for training, 15% testing and 15% for validation, was conducted to obtain the best model with R = 70.5% for the initial wave of COVID-19 transmission as shown in Fig.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

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The effect of weather data on the spread of COVID-19 in Jordan

Dabbour

Hamdan

2021

Environ Sci Pollut Res

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Section: Resultsmentioning

confidence: 99%

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

The effect of weather data on the spread of COVID-19 in Jordan

Dabbour

Hamdan

2021

Environ Sci Pollut Res

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“…From Table 7, we can observe that the simulation results are more reasonable than that of Table 5. For instance，Guangdong (19) is not adjacent to Hubei Province, but it leaps from the third level dangerous city in Table 5，to the second level dangerous city under the inverse distance matrix, which is more consistent with the real situation. The number of errors in the three dangerous provinces or provincial municipalities of the predicted results and the real epidemic situation level has changed from 7 in Table 5 to in…”

Section: Epidemic Prediction Based On Spatial Euclidean Inverse Distancementioning

confidence: 60%

Peer Review #1 of "Regional infectious risk prediction of COVID-19 based on geo-spatial data (v0.1)"

Ahmadi¹

2020

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“…The more MRI data, the more copies are duplicated. In this algorithm, reproduction is plural, especially like a crossover in the genetic algorithm [ 12 , 13 ]. Antibodies focus on modern quantum memory detection systems in mass segmentation in real time and examine detection and cross-sectional states against the MRI image structure.…”

Section: Proposed Approachmentioning

confidence: 99%

QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network

Ahmadi

Sharifi

Hassantabar

et al. 2021

BioMed Research International

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Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many cases, the presented methods’ reliability is at a high level, and such systems are used directly. In recent decades, several methods of segmentation of various images, such as MRI, CT, and PET, have been proposed for brain tumors. Advanced brain tumor segmentation has been a challenging issue in the scientific community. The reason for this is the existence of various tumor dimensions with disproportionate boundaries in medical imaging. This research provides an optimized MRI segmentation method to diagnose tumors. It first offers a preprocessing approach to reduce noise with a new method called Quantum Matched-Filter Technique (QMFT). Then, the deep spiking neural network (DSNN) is implemented for segmentation using the conditional random field structure. However, a new algorithm called the Quantum Artificial Immune System (QAIS) is used in its SoftMax layer due to its slowness and nonsegmentation and the identification of suitable features for selection and extraction. The proposed approach, called QAIS-DSNN, has a high ability to segment and distinguish brain tumors from MRI images. The simulation results using the BraTS2018 dataset show that the accuracy of the proposed approach is 98.21%, average error-squared rate is 0.006, signal-to-noise ratio is 97.79 dB, and lesion structure criteria including the tumor nucleus are 80.15%. The improved tumor is 74.50%, and the entire tumor is 91.92%, which shows a functional advantage over similar previous methods. Also, the execution time of this method is 2.58 seconds.

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Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches

Cited by 49 publications

References 24 publications

The effect of weather data on the spread of COVID-19 in Jordan

The effect of weather data on the spread of COVID-19 in Jordan

Peer Review #1 of "Regional infectious risk prediction of COVID-19 based on geo-spatial data (v0.1)"

QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network

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