Acoustic Detection and Recognition of Dolphins using Swarm Intelligence Neural Networks

Wu, Jianhua; Khishe, Mohammad; Mohammadi, Mokhtar; Karim, Sarkhel H. Taher; Shams, Mojtaba

doi:10.1016/j.apor.2021.102837

Cited by 16 publications

(11 citation statements)

References 65 publications

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“…The analytical results demonstrate the robustness and sensitivity of the parameters to input. Four different parameter values were used in the corresponding tests (Wu et al, 2021a , b ). Numerous parameter combinations were established using an orthogonal array, as illustrated in Table 10 .…”

Section: Results Of the Experiments And Discussionmentioning

confidence: 99%

“…Due to the widespread of COVID-19 and its variations in all nations, the timely and exact diagnosis of COVID-19 is essential. The initial stage in the COVID-19 diagnosis is to take the reverse transcription-polymerase chain reaction (RT-PCR) (Hu et al, 2021a ); however, in a few situations, the RT-PCR test of a subject is negative, yet the associated patient obtains difficulty in breathing (Wu et al, 2021a , b ). The chest CT scan is advised as a second-line diagnostic in this situation.…”

Section: Introductionmentioning

confidence: 99%

“…DCNN design complexity has been reduced by metaheuristics (Khishe and Mosavi, 2020a , b ; Mosavi et al, 2017 ; Qiao et al, 2021 ), which develop an architecture without the assistance of a human designer (Rastogi and Choudhary, 2019 ). Numerous metaheuristics have been effectively studied and applied, including genetic programming (GP) (Suganuma et al, 2017 ), particle swarm optimization (PSO) (Mosavi and Khishe, 2017 ; Wang et al, 2018 ; J. Wu et al, 2021a , b ), Position-transitional particle swarm optimization (Luo et al, 2020 ), sine–cosine algorithm (SCA) (Wang et al, 2020 ; C. Wu et al, 2021a , b ), chimp optimization algorithm (ChOA) (Hu et al, 2021b ), salp swarm algorithm (Khishe and Mohammadi, 2019 ), and genetic algorithms (GAs) (Liu et al, 2022a , b ). However, learning from large data sets is impracticable due to the high cost of computing and the time of the learning process (Yuan et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolving deep convolutional neural networks by IP-based marine predator algorithm for COVID-19 diagnosis using chest CT scans

Liu

Nie

et al. 2022

J Ambient Intell Human Comput

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This paper proposes an optimal structured deep convolutional neural network (DCNN) based on the marine predator algorithm (MPA) to construct a novel automatic diagnosis platform that may help radiologists identify COVID-19 and non-COVID-19 patients based on CT scan categorization and analysis. The goal is met with the help of three modifications based on the regular MPA. First, a novel encoding scheme based on Internet Protocol (IP) addresses is proposed, followed by introducing an Enfeebled layer to build a variable-length DCNN. Finally, the learning process divides big datasets into smaller chunks that are randomly evaluated. The proposed model is compared to the COVID-CT and SARS-CoV-2 datasets to undertake a complete evaluation. Following that, the performance of the developed model (DCNN-IPMPA) is compared to that of a typical DCNN and seven variable-length models using five well-known comparison metrics, as well as the receiver operating characteristic and precision-recall curves. The results show that the DCNN-IPMPA outperforms other benchmarks, with a final accuracy of 97.21% on the SARS-CoV-2 dataset and 97.94% on the COVID-CT dataset. Also, timing analysis indicates that the DCNN processing time is the best among all benchmarks as expected; however, DCNN-IPMPA represents a competitive result compared to the standard DCNN.

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Section: Results Of the Experiments And Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolving deep convolutional neural networks by IP-based marine predator algorithm for COVID-19 diagnosis using chest CT scans

Liu

Nie

et al. 2022

J Ambient Intell Human Comput

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“…The signal-noise ratio, often shortened as SNR and defined as the ratio of signal power to noise power, is typically expressed in decibels. The field of machine learning, which may be described as the process of allowing computers to make persuasive predictions based on previous experiences, has witnessed phenomenal development in recent years due to the fast rise in computer storage capacity and processing power [2,3].…”

Section: Introductionmentioning

confidence: 99%

Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach

Jader

Jumaa

2022

J. Stud. Sci. Eng.

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Everyone has paid much attention to modulation-type recognition in the past few years. There are many ways to find the modulation type, but only a few good ways to deal with signals with a lot of noise. This study comes up with a way to test how well different machine learning algorithms can handle noise when detecting digital and analogue modulations. This study looks at the four most common digital and analogue modulations: Phase Shift Keying, Quadrature Phase Shift Keying, Amplitude Modulation, and Morse Code. A signal noise rate from -10dB to +25dB is used to find these modulations. We used machine learning algorithms to determine the modulation type like Decision Tree, Random Forest, Support Vectors Machine, and k-nearest neighbours. After the IQ samples had been converted to the amplitude of samples and radio frequency format, the accuracy of each method looked good. Still, in the format of the sample phase, each algorithm's accuracy was less. The results show that the proposed method works to find the signals that have noises. When there is less noise, the random forest algorithm gives better results than SVM, but SVM gives better results when there is more noise.

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“…However, decision trees need knowledge of the properties of the optimization issue and some information about the gradient. In recent years, the use of metaheuristic algorithms (MAs) in optimization problems has grown in popularity [5,6].…”

Section: Introductionmentioning

confidence: 99%

Application Research of Petroleum Basic Data Mining System Based on Intelligent Computing and Decision Tree Algorithm

Wang

Liang

et al. 2022

Wireless Communications and Mobile Computing

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Recent improvements in data mining technologies, besides the IoT, enable the implementation of a strategy for boosting oil output from oil wells. As a regularly employed improved oil recovery technology, steam flood injection takes use of thermodynamic and gravitational capabilities to deploy and neutralize oil on-site to raise oil output. Instead of relying on conventional physics to model steam floods, this research proposes using a combination of a chimp optimization algorithm (ChOA) and a decision tree to better represent steam flood performance. We present a method for dealing with a particular type of petroleum time series data using ChOA in conjunction with decision trees and IoT. It is shown that the method is useful in predicting oil production in steam floods. Even more impressive is the 4.02 percent increase in oil output that may be achieved via the use of a new optimization system that offers the best possible steam allocation plan. Our objective has been to develop a cloud-based minimum viable product capable of data collection and storage and also training and deployment of a cloud ChOA model. Predictive maintenance, for example, might benefit from this workflow’s ability to analyze time series data.

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Acoustic Detection and Recognition of Dolphins using Swarm Intelligence Neural Networks

Cited by 16 publications

References 65 publications

Evolving deep convolutional neural networks by IP-based marine predator algorithm for COVID-19 diagnosis using chest CT scans

Evolving deep convolutional neural networks by IP-based marine predator algorithm for COVID-19 diagnosis using chest CT scans

Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach

Application Research of Petroleum Basic Data Mining System Based on Intelligent Computing and Decision Tree Algorithm

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