Identifying Phasic dopamine releases using DarkNet-19 Convolutional Neural Network

Al‐Haija, Qasem Abu; Smadi, Mahmoud A.; Al-Bataineh, Osama M.

doi:10.1109/iemtronics52119.2021.9422617

Cited by 24 publications

(14 citation statements)

References 9 publications

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“…Results achieved by research lead towards investigating further optimization of hyperparameters of CNN and the integration of additional deep learning models for advanced detection performance. In the future, we will consider tuning our proposed model to perform high-performance classification tasks for other medical images such as lung cancer images and phasic dopamine releases [40]. Besides, the proposed system can be customized to provide detection in advance with high accuracy for several other health risks such as breast cancer detection [41], Tuberculosis Disease Diagnosis [42], and early-stage diabetes risk prediction [43].…”

Section: Conclusion and Future Scopementioning

confidence: 99%

Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

Alnabhan

Habboush

Al‐Haija

et al. 2022

Mobile Information Systems

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This research utilizes metaheuristic optimization inspired by the Egyptian Vulture Optimization (EVO) technique. Biomedical image segregation is developed to reduce the complex association of hyperparameters of Convolutional Neural networks (CNN). The complex attributes of CNN include the type of kernel, size of the kernel, size of the batch, epoch counts, momentum, learning rate, activation function, convolution layer, and dropout. However, the life cycle of an Egyptian vulture influences the optimization technique to resolve complexity and increase the accuracy of CNN. The proposed CNN-based EVO model was evaluated in comparison to ANN-based and deep learning-based classifiers utilizing brain MRI image datasets. The results achieved have confirmed the efficiency and performance of the proposed CNN-based EVO model, in which the average detection accuracy and precision were 93% and 95%, respectively.

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Section: Conclusion and Future Scopementioning

confidence: 99%

Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

Alnabhan

Habboush

Al‐Haija

et al. 2022

Mobile Information Systems

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“…Moreover, Figure 11 illustrates the performance analysis using confusion matrix records for (a) the ODT-based detection system and (b) the ODT-based classification system (since ODT performed better, we show only the confusion matrix for ODT models). The confusion matrix is a summarized table of the number of correct and incorrect predictions yielded by the classification model for binary/multi-classification tasks [35]. Specifically, the confusion matrix provides records for four performance indicator metrics for every predicted class label: namely, the true positive record, which counts the number of samples the model correctly predicts for the positive class; the true negative record, which counts the number of samples the model correctly predicts for the negative class; a false positive record, which counts the number of samples the model incorrectly predicts for the positive class when the actual class is negative; and the false negative record, which counts the number of samples the model incorrectly predicts for the negative class when the actual class is positive [35].…”

Section: Resultsmentioning

confidence: 99%

“…The confusion matrix is a summarized table of the number of correct and incorrect predictions yielded by the classification model for binary/multi-classification tasks [35]. Specifically, the confusion matrix provides records for four performance indicator metrics for every predicted class label: namely, the true positive record, which counts the number of samples the model correctly predicts for the positive class; the true negative record, which counts the number of samples the model correctly predicts for the negative class; a false positive record, which counts the number of samples the model incorrectly predicts for the positive class when the actual class is negative; and the false negative record, which counts the number of samples the model incorrectly predicts for the negative class when the actual class is positive [35]. Since the good classification model will normally generate confusion matrix results with large values across the diagonal and small values off the diagonal, this shows that our predictive models are high performant models for both detection and classification, especially for the models build using optimizable decision trees.…”

Section: Resultsmentioning

confidence: 99%

“…Please note that TP and FP correspond to the ransomware records (the minority class) while TN and FN correspond to the normal records (the majority class). Furthermore, based on the results obtained for the confusion matrix, Table 3 rizes the model evaluation metrics [35] Furthermore, based on the results obtained for the confusion matrix, Table 3 summarizes the model evaluation metrics [35] The obtained results exhibit the extraordinary performance indicators of our predictive models with greater figures obtained for the predictive models based ODT of both detection (two-class) and classification (multiclass) systems scoring accuracy values of 99.9% and 99.4%, respectively, compared to the accuracy of the detection and classification systems based SNN, which scored 98.6% and 98.4%, respectively.…”

Section: Resultsmentioning

confidence: 99%

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High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks

Al‐Haija

Alsulami

2021

Electronics

Self Cite

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The Bitcoin cryptocurrency is a worldwide prevalent virtualized digital currency conceptualized in 2008 as a distributed transactions system. Bitcoin transactions make use of peer-to-peer network nodes without a third-party intermediary, and the transactions can be verified by the node. Although Bitcoin networks have exhibited high efficiency in the financial transaction systems, their payment transactions are vulnerable to several ransomware attacks. For that reason, investigators have been working on developing ransomware payment identification techniques for bitcoin transactions’ networks to prevent such harmful cyberattacks. In this paper, we propose a high performance Bitcoin transaction predictive system that investigates the Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks. Specifically, our system makes use of two supervised machine learning methods to learn the distinguishing patterns in Bitcoin payment transactions, namely, shallow neural networks (SNN) and optimizable decision trees (ODT). To validate the effectiveness of our solution approach, we evaluate our machine learning based predictive models on a recent Bitcoin transactions dataset in terms of classification accuracy as a key performance indicator and other key evaluation metrics such as the confusion matrix, positive predictive value, true positive rate, and the corresponding prediction errors. As a result, our superlative experimental result was registered to the model-based decision trees scoring 99.9% and 99.4% classification detection (two-class classifier) and accuracy (multiclass classifier), respectively. Hence, the obtained model accuracy results are superior as they surpassed many state-of-the-art models developed to identify ransomware payments in bitcoin transactions.

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“…Firstly, we use Darknet53 [18] as the backbone network for keypoint detection, which has higher training accuracy than Darknet19 [42][43][44] and higher efficiency than ResNet101 [45,46] and ResNet152 [47][48][49][50] networks. Combining the characteristics of ResNet [51], Darknet53 avoids the gradient problem caused by the deep network while ensuring the strong expression of features.…”

Section: The Knob Key Point Detection Based On Darknet53-duc-dsntmentioning

confidence: 99%

Recognition Method of Knob Gear in Substation Based on YOLOv4 and Darknet53-DUC-DSNT

Qin

Hua

Sun

et al. 2022

Sensors

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When unattended substations are popular, the knob is a vital monitoring object for unattended substations. However, in the actual scene of the substation, the recognition method of a knob gear has low accuracy. The main reasons are as follows. Firstly, the SNR of knob images is low due to the influence of lighting conditions, which are challenging to extract image features. Secondly, the image deviates from the front view affected by the shooting angle; that knob has a certain deformation, which causes the feature judgment to be disturbed. Finally, the feature distribution of each kind of knob is inconsistent, which interferes with image extraction features and leads to weak spatial generalization ability. For the above problems, we propose a three-stage knob gear recognition method based on YOLOv4 and Darknet53-DUC-DSNT models for the first time and apply key point detection of deep learning to knob gear recognition for the first time. Firstly, YOLOv4 is used as the knob area detector to find knobs from a picture of a cabinet panel. Then, Darknet53, which can extract features, is used as the backbone network for keypoint detection of knobs, combined with DUC structure to recover detailed information and DSNT structure to enhance feature extraction and improve spatial generalization ability. Finally, we obtained the knob gear by calculating the angle between the line of the rotating center point and the pointing point and horizontal direction. The experimental results show that this method effectively solves the above problems and improves the performance of knob gear detection.

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Identifying Phasic dopamine releases using DarkNet-19 Convolutional Neural Network

Cited by 24 publications

References 9 publications

Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks

Recognition Method of Knob Gear in Substation Based on YOLOv4 and Darknet53-DUC-DSNT

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