Inverted Exponentiated Gamma and its bivariate extension

Bassiouny, A. H. El; Abouhawwash, Mohamed; Shahen, H. S.

doi:10.26808/rs.ca.i8v3.02

Cited by 11 publications

(11 citation statements)

References 6 publications

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“…Next, [27] was implemented as an example of a light weight DNN. This model uses depthwise separable CNN [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Experiments and Resultsmentioning

confidence: 99%

An Automatic Deep Neural Network Model for Fingerprint Classification

Mahmoud¹,

Awad²,

Behery³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The accuracy of fingerprint recognition model is extremely important due to its usage in forensic and security fields. Any fingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy. To solve this problem in a unified model, this paper proposes a model that can automatically specify itself. So, it is called an automatic deep neural network (ADNN). Our algorithm can specify the appropriate architecture of the neural network used and some significant parameters of this network. These parameters are the number of filters, epochs, and iterations. It guarantees the highest accuracy by updating itself until achieving 99% accuracy then it stops and outputs the result. Moreover, this paper proposes an end-to-end methodology for recognizing a person's identity from the input fingerprint image based on a residual convolutional neural network. It is a complete system and is fully automated whether in the features extraction stage or the classification stage. Our goal is to automate this fingerprint recognition system because the more automatic the system is, the more time and effort it saves. Our model also allows users to react by inputting the initial values of these parameters. Then, the model updates itself until it finds the optimal values for the parameters and achieves the best accuracy. Another advantage of our algorithm is that it can recognize people from their thumb and other fingers and its ability to recognize distorted samples. Our algorithm achieved 99.75% accuracy on the public fingerprint dataset (SOCOFing). This is the best accuracy compared with other models.

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“…Next, [27] was implemented as an example of a light weight DNN. This model uses depthwise separable CNN [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Experiments and Resultsmentioning

confidence: 99%

An Automatic Deep Neural Network Model for Fingerprint Classification

Mahmoud¹,

Awad²,

Behery³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

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“…For job distribution among cloud VMs in HealthFog and FETCH, we were using the Bag of Tasks paradigm. FogBus is used to capture fog services, and Aneka is used to capture the public cloud in HealthFog-FE [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40].…”

Section: Framework Automationmentioning

confidence: 99%

Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

Arunkumar¹,

Masud²,

Aljahdali³

et al. 2023

Computer Systems Science and Engineering

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Nowadays, the cloud environment faces numerous issues like synchronizing information before the switch over the data migration. The requirement for a centralized internet of things (IoT)-based system has been restricted to some extent. Due to low scalability on security considerations, the cloud seems uninteresting. Since healthcare networks demand computer operations on large amounts of data, the sensitivity of device latency evolved among health networks is a challenging issue. In comparison to cloud domains, the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing solutions. Previous fog computing frameworks have various flaws, such as overvaluing response time or ignoring the accuracy of the result yet handling both at the same time compromises the network community. In this proposed work, Health Fog is integrated with the Optimized Cascaded Convolution Neural Network framework for diagnosing heart disease. Initially, the data is collected, and then pre-processing is done by Linear Discriminant Analysis. Then the features are extracted and optimized using Galactic Swarm Optimization. The optimized features are given into the Health Fog framework for diagnosing heart disease patients. It uses ensemble-based deep learning in edge computing devices, which automatically monitors real-life health networks such as heart disease analysis. Finally, the classifiers such as bagging, boosting, XGBoost, Multi-Layer Perceptron (MLP), and Partitions (PART) are used for classifying the data. Then the majority voting classifier predicts the result. This work uses FogBus architecture and evaluates the execution of power usage, bandwidth of the network, latency, execution time, and accuracy.

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“…Gabor filter is a multi-resolution filter [20][21][22][23][24][25][26][27] for the removal of noise. It can be applied in various orientations and frequencies [28][29][30][31][32][33][34][35][36][37]. The steps to remove the noises using Gabor filter based gaussian rule are stated below.…”

Section: Preprocessingmentioning

confidence: 99%

Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

Ram¹,

Kumar²,

Al-shami³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Deep learning-based approaches are applied successfully in many fields such as deepFake identification, big data analysis, voice recognition, and image recognition. Deepfake is the combination of deep learning in fake creation, which states creating a fake image or video with the help of artificial intelligence for political abuse, spreading false information, and pornography. The artificial intelligence technique has a wide demand, increasing the problems related to privacy, security, and ethics. This paper has analyzed the features related to the computer vision of digital content to determine its integrity. This method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction method. By the proposed deep belief network with loss handling, the manipulation of video/image is found by means of a pairwise learning approach. This proposed approach has improved the accuracy of the detection rate by 98% on various datasets.

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Inverted Exponentiated Gamma and its bivariate extension

Cited by 11 publications

References 6 publications

An Automatic Deep Neural Network Model for Fingerprint Classification

An Automatic Deep Neural Network Model for Fingerprint Classification

Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

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