Joint Scattering for Automatic Chick Call Recognition

Wang, Changhong; Benetos, Emmanouil; Wang, Shuge; Versace, Elisabetta

doi:10.23919/eusipco55093.2022.9909738

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Four classifiers were tested at the 1,000 ms frame level, and the accuracy of these classifiers varies from 63% to 83%, depending on the age group. Wang et al [37] proposed an effective audio-based system for automatically identifying and recognizing the different types of chick vocalizations, including begging, peeping, and chirping.…”

Section:  Issn: 2502-4752mentioning

confidence: 99%

Poultry disease early detection methods using deep learning technology

Yajie,

Md Johar,

Iqbal Hajamydeen

2023

IJEECS

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<span>Poultry production is a pivotal contributor to global economic growth, playing a central role in promoting human ecosystem sustainability. It offers affordable and readily accessible protein sources, encompassing meat, eggs, and other by-products. Beyond its direct nutritional benefits, poultry production enhances household income, bolsters food security, and aids in poverty reduction, making it integral to worldwide economic advancement. However, as the global population surges, so does the demand for poultry meat and eggs. Concurrently, poultry disease management emerges as a paramount challenge, leading to significant threats to food security and economic stability. Leveraging cutting-edge technology offers promising avenues to devise strategies that not only bolster farm profitability but also mitigate environmental impacts and foster the well-being of both animals and humans. This study systematically reviews the latest literature concerning poultry disease diagnosis based on deep learning techniques, elucidating the clinical manifestations associated with various ailments. The analysis indicates that emerging technological solutions, especially image processing and deep learning (DL), substantially outperform conventional manual inspection methods in early disease detection and warning in the poultry sector. Such innovations underscore their potential for revolutionizing poultry health management and disease mitigation.</span>

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Section:  Issn: 2502-4752mentioning

confidence: 99%

Poultry disease early detection methods using deep learning technology

Yajie,

Md Johar,

Iqbal Hajamydeen

2023

IJEECS

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Optimizing poultry audio signal classification with deep learning and burn layer fusion

Hassan,

Elbedwehy,

Shams

et al. 2024

J Big Data

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This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model robustness. The methodology integrates digital audio signal processing, convolutional neural networks (CNNs), and the innovative Burn Layer, which injects controlled random noise during training to reinforce the model's resilience to input signal variations. The proposed architecture is streamlined, with convolutional blocks, densely connected layers, dropout, and an additional Burn Layer to fortify robustness. The model demonstrates efficiency by reducing trainable parameters to 191,235, compared to traditional architectures with over 1.7 million parameters. The proposed model utilizes a Burn Layer with burn intensity as a parameter and an Adamax optimizer to optimize and address the overfitting problem. Thorough evaluation using six standard classification metrics showcases the model's superior performance, achieving exceptional sensitivity (96.77%), specificity (100.00%), precision (100.00%), negative predictive value (NPV) (95.00%), accuracy (98.55%), F1 score (98.36%), and Matthew’s correlation coefficient (MCC) (95.88%). This research contributes valuable insights into the fields of audio signal processing, animal health monitoring, and robust deep-learning classification systems. The proposed model presents a systematic approach for developing and evaluating a deep learning-based poultry audio classification system. It processes raw audio data and labels to generate digital representations, utilizes a Burn Layer for training variability, and constructs a CNN model with convolutional blocks, pooling, and dense layers. The model is optimized using the Adamax algorithm and trained with data augmentation and early-stopping techniques. Rigorous assessment on a test dataset using standard metrics demonstrates the model's robustness and efficiency, with the potential to significantly advance animal health monitoring and disease detection through audio signal analysis.

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ViT-PSO-SVM: Cervical Cancer Predication Based on Integrating Vision Transformer with Particle Swarm Optimization and Support Vector Machine

AlMohimeed,

Shehata,

El-Rashidy

et al. 2024

Bioengineering

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Cervical cancer (CCa) is the fourth most prevalent and common cancer affecting women worldwide, with increasing incidence and mortality rates. Hence, early detection of CCa plays a crucial role in improving outcomes. Non-invasive imaging procedures with good diagnostic performance are desirable and have the potential to lessen the degree of intervention associated with the gold standard, biopsy. Recently, artificial intelligence-based diagnostic models such as Vision Transformers (ViT) have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). This paper studies the effect of applying a ViT to predict CCa using different image benchmark datasets. A newly developed approach (ViT-PSO-SVM) was presented for boosting the results of the ViT based on integrating the ViT with particle swarm optimization (PSO), and support vector machine (SVM). First, the proposed framework extracts features from the Vision Transformer. Then, PSO is used to reduce the complexity of extracted features and optimize feature representation. Finally, a softmax classification layer is replaced with an SVM classification model to precisely predict CCa. The models are evaluated using two benchmark cervical cell image datasets, namely SipakMed and Herlev, with different classification scenarios: two, three, and five classes. The proposed approach achieved 99.112% accuracy and 99.113% F1-score for SipakMed with two classes and achieved 97.778% accuracy and 97.805% F1-score for Herlev with two classes outperforming other Vision Transformers, CNN models, and pre-trained models. Finally, GradCAM is used as an explainable artificial intelligence (XAI) tool to visualize and understand the regions of a given image that are important for a model’s prediction. The obtained experimental results demonstrate the feasibility and efficacy of the developed ViT-PSO-SVM approach and hold the promise of providing a robust, reliable, accurate, and non-invasive diagnostic tool that will lead to improved healthcare outcomes worldwide.

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Joint Scattering for Automatic Chick Call Recognition

Cited by 4 publications

References 13 publications

Poultry disease early detection methods using deep learning technology

Poultry disease early detection methods using deep learning technology

Optimizing poultry audio signal classification with deep learning and burn layer fusion

ViT-PSO-SVM: Cervical Cancer Predication Based on Integrating Vision Transformer with Particle Swarm Optimization and Support Vector Machine

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