An Efficient Optimization Technique for Training Deep Neural Networks

Mehmood, Faisal; Ahmad, Shabir; Whangbo, Taeg Keun

doi:10.3390/math11061360

Cited by 41 publications

(10 citation statements)

References 28 publications

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“…Machine learning models have increasingly incorporated statistical features from EEG data to enhance classification and diagnostic capabilities. Deep Neural Networks (DNN) [ 20 , 21 ], with their capacity for feature learning, have shown promise in deciphering complex patterns from Hjorth parameters and similar statistics. The hierarchical nature of DNNs allows them to distill high-level abstractions from raw data, which is particularly beneficial for identifying subtle neurological differences between various cognitive tasks or pathological states.…”

Section: Resultsmentioning

confidence: 99%

Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis

Alawee,

Basem,

Al-Haddad

2023

Journal of Electrical Bioimpedance

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Biomedical engineering stands at the forefront of medical innovation, with electroencephalography (EEG) signal analysis providing critical insights into neural functions. This paper delves into the utilization of EEG signals within the MILimbEEG dataset to explore their potential for machine learning-based task recognition and diagnosis. Capturing the brain’s electrical activity through electrodes 1 to 16, the signals are recorded in the time-domain in microvolts. An advanced feature extraction methodology harnessing Hjorth Parameters—namely Activity, Mobility, and Complexity—is employed to analyze the acquired signals. Through correlation analysis and examination of clustering behaviors, the study presents a comprehensive discussion on the emergent patterns within the data. The findings underscore the potential of integrating these features into machine learning algorithms for enhanced diagnostic precision and task recognition in biomedical applications. This exploration paves the way for future research where such signal processing techniques could revolutionize the efficiency and accuracy of biomedical engineering diagnostics.

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Section: Resultsmentioning

confidence: 99%

Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis

Alawee,

Basem,

Al-Haddad

2023

Journal of Electrical Bioimpedance

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“…The proposal also involves modeling a neural network (NN) [51] model to classify primary and non-primary objects in an image. For modeling an efficient model [52], a good network and premeditated features [53] are required. Regarding feature design, metrics affecting model performance need to be included in the training.…”

Section: Methodology and Major Contributionsmentioning

confidence: 99%

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas

Mehmood,

Ko,

Kim

et al. 2024

Sensors

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Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects captured in a scene makes their identification difficult. Addressing this limitation is necessary because the artificial retina can utilize a very limited number of pixels to represent vision information. This problem in a multi-object scenario can be mitigated by enhancing images such that only the major objects are considered to be shown in vision. Although simple techniques like edge detection are used, they fall short in representing identifiable objects in complex scenarios, suggesting the idea of integrating primary object edges. To support this idea, the proposed classification model aims at identifying the primary objects based on a suggested set of selective features. The proposed classification model can then be equipped into the artificial retina system for filtering multiple primary objects to enhance vision. The suitability of handling multi-objects enables the system to cope with real-world complex scenarios. The proposed classification model is based on a multi-label deep neural network, specifically designed to leverage from the selective feature set. Initially, the enhanced images proposed in this research are compared with the ones that utilize an edge detection technique for single, dual, and multi-object images. These enhancements are also verified through an intensity profile analysis. Subsequently, the proposed classification model’s performance is evaluated to show the significance of utilizing the suggested features. This includes evaluating the model’s ability to correctly classify the top five, four, three, two, and one object(s), with respective accuracies of up to 84.8%, 85.2%, 86.8%, 91.8%, and 96.4%. Several comparisons such as training/validation loss and accuracies, precision, recall, specificity, and area under a curve indicate reliable results. Based on the overall evaluation of this study, it is concluded that using the suggested set of selective features not only improves the classification model’s performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios. Therefore, the proposed classification model designed on the basis of selective features is considered to be a very useful tool in supporting the idea of optimizing image enhancement.

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“…The choice of optimizer can be influenced by the particulars of the training job and the kind of data since each optimizer has advantages and uses [65]. For example, Adagrad or RMSprop may be favored for jobs with sparse data, although Adam is frequently used due to its broad usefulness across various situations.…”

Section: Model Optimizermentioning

confidence: 99%

A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform

Mutawa,

Al-Sabti,

Raizada

et al. 2024

Preprint

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Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.

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An Efficient Optimization Technique for Training Deep Neural Networks

Cited by 41 publications

References 28 publications

Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis

Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas

A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform

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