Accurate deep and direction classification model based on the antiprism graph pattern feature generator using underwater acoustic for defense system

Yaman, Orhan; Tuncer, Türker

doi:10.1007/s11042-022-13196-1

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…An individual's optimal learning speed and the amount of sound parameters are both affected by the regions of the brain responsible for frequency identification, which include the auditory cortex, auditory midbrain, and the hearing centre [19]. Based on these findings, it seems that the auditory pathway's time-domain acoustic data might be separated into spectrum analysis [20]. One possible interpretation of frequency content fragmentation in frequency-domain acoustic signals is as a filtering product [21].…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok,

Latha

2024

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Section: Related Workmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok,

Latha

2024

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“…SVM and KNN algorithms have been used to classify the extracted features. SVM and KNN algorithms are commonly preferred machine learning algorithms in the literature (Pooja, Das, & Kanchana, 2018;Yaman, Ertam, Tuncer, & Firat Kilincer, 2020;Yaman & Tuncer, 2021). In order to choose these classifiers (KNN and SVM), five shallow classifiers have been used to test: Decision Tree (DT), Linear Discriminant (LD), Cubic SVM, Fine KNN, and Ensemble Boosted Trees (EBT).…”

Section: Preprocessingmentioning

confidence: 99%

Plant Classification Method Using Histogram and Machine Learning for Smart Agriculture Applications

Yaman¹,

Tuncer²

2023

acin

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Due to its high potential and value, the Internet of things (IoT) has been used in various areas such as information security, industry 4.0, and smart agriculture. IoT is used in agriculture through the use of sensors, unmanned aerial vehicles (UAV), satellite technologies, robots, image processing, and artificial intelligence technologies. These smart agricultural practices increase production and quality and lead to savings in irrigation, thereby reducing environmental pollution during production. This study proposes an ultra-lightweight automated plant species classification method for smart agriculture applications. A UAV is used to acquire a new image dataset. An ultra-lightweight classification method is then used to classify the acquired plant species images. Our proposed ultra-lightweight computer vision model presents a histogram-based simple feature extraction function. The presented feature extractor uses histogram extraction and median filter in conjunction. The generated features are fed to two shallow classifiers, which are the support vector machine (SVM), and k nearest neighbor (KNN). The utilized SVM and KNN classifiers have attained 96.45% and 94.11% accuracies consecutively. The results demonstrate that this model is very capable of plant image classification and is ready for use in a physical agriculture environment.

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“…The parts of the hearing centre, auditory cortex, & auditory midbrain that have been involved in frequency detection could modify the number of parameters of sound and also the ideal velocity to finish a learning goal [19]. These observations of the auditory system suggest that time-domain acoustic information in the auditory pathway could be divided into spectral analysis [20]. For frequency-domain acoustic signals, the fragmentation of frequency content could be understood as a filtration product [21].…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok¹,

Latha²

2022

Preprint

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Due to the complex and interdependent world, underwater acoustic source detection is a difficult challenge. It can be extremely difficult to perform Underwater Acoustic Target Recognition (UATR) utilizing ship-radiated noise due to the complicated aquatic organisms. This paper presents a new deep neural network called Audio Perspective based RCNN (APRCNN), learned by initial underwater acoustic waves with Depth Wise Separable (DWS) Convolutional Deep Neural Network. The APRCNN would then be implemented for underwater acoustic signal prediction and classification. The proposed harvester learning features & integration layers of the SPDNN system were motivated by perceptual processing, and the time-dilated convolution was influenced by machine translation. In this article, the researchers provide a methodology for identifying underwater targets using a thick DNN model. The proposed framework was created to reallocate all previous feature maps to maximize the categorization rate while minimizing the calculation burden in various degraded scenarios. Additionally, the proposed technique allows using the actual audio signal in the transfer function as the right network input information rather than using time-frequency spectrum analyzer images. Based on research findings assessed on a real-world collection of passive sonar, our classification method beats other cutting-edge DNN systems & classic ML methods, achieving an accuracy level of 98.4% at 0-dB SNR.

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Accurate deep and direction classification model based on the antiprism graph pattern feature generator using underwater acoustic for defense system

Cited by 3 publications

References 34 publications

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Plant Classification Method Using Histogram and Machine Learning for Smart Agriculture Applications

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

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