Blood vessel detection using enhanced <scp>DeepJoint</scp> fuzzy clustering algorithm with deep Maxout network for glaucoma detection

Rajeswari, R.; Prabhakar, M.; Padmapriya, G.; Kumar, B. Santhosh

doi:10.1002/cpe.7480

Cited by 2 publications

(1 citation statement)

References 32 publications

(78 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the article published by Goodfellow et al [38], the Maxout activation function has demonstrated its effectiveness for training with Dropout, as well as its robustness for image classification [39]. Each feature map is dimensionally reduced using a Maxpooling layer with a pool size of 2x2 to preserve the most relevant features identified and avoid unnecessary computations.…”

Section: Learning Spatiotemporal Featuresmentioning

confidence: 99%

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

Korti

Slimane

Lakhdari

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

Radar-based hand gesture recognition is an important research area that provides suitable support for various applications, such as human-computer interaction and healthcare monitoring. Several deep learning algorithms for gesture recognition using Impulse Radio Ultra-Wide Band (IR-UWB) have been proposed. Most of them focus on achieving high performance, which requires a huge amount of data. The procedure of acquiring and annotating data remains a complex, costly, and time-consuming task. Moreover, processing a large volume of data usually requires a complex model with very large training parameters, high computation, and memory consumption. To overcome these shortcomings, we propose a simple data processing approach along with a lightweight multi-input hybrid model structure to enhance performance. We aim to improve the existing state-of-the-art results obtained using an available IR-UWB gesture dataset consisting of range-time images of dynamic hand gestures. First, these images are extended using the Sobel filter, which generates low-level feature representations for each sample. These represent the gradient images in the x-direction, the y-direction, and both the x- and y-directions. Next, we apply these representations as inputs to a three-input Convolutional Neural Network- Long Short-Term Memory- Support Vector Machine (CNN-LSTM-SVM) model. Each one is provided to a separate CNN branch and then concatenated for further processing by the LSTM. This combination allows for the automatic extraction of richer spatiotemporal features of the target with no manual engineering approach or prior domain knowledge. To select the optimal classifier for our model and achieve a high recognition rate, the SVM hyperparameters are tuned using the Optuna framework. Our proposed multi-input hybrid model achieved high performance on several parameters, including 98.27% accuracy, 98.30% precision, 98.29% recall, and 98.27% F1-score while ensuring low complexity. Experimental results indicate that the proposed approach improves accuracy and prevents the model from overfitting.

show abstract

Section: Learning Spatiotemporal Featuresmentioning

confidence: 99%

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

Korti

Slimane

Lakhdari

2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

show abstract

An Empirical Study and Analysis of Artificial Intelligence, Machine Learning, and Big Data for Crypto Healthcare Industries

Ananthavadivel,

Anto Arockia Rosaline,

Vinod

et al. 2024

Machine Learning and Cryptographic Solutions for Data Protection and Network Security

View full text Add to dashboard Cite

In the contemporary era, cryptocurrencies have emerged as a vital digital asset class. This abstract provides a succinct overview of a chapter dedicated to the empirical examination of artificial intelligence (AI), machine learning (ML), and big data within the context of crypto healthcare industry. This chapter commences by recognizing the growing importance of cryptocurrencies as digital assets in the modern landscape. Within the healthcare domain, AI-powered diagnostic tools take center stage. These sophisticated software programs utilize AI algorithms to analyze patient data and identify patterns indicative of diseases such as cancer, heart conditions, and diabetes. In conclusion, this offers a comprehensive perspective on how AI, ML, and big data can catalyse transformation within the crypto healthcare industry. Despite challenges, further exploration and development in these domains hold the promise of enriching the patient's lifetime globally. This analysis targets to accelerate an adoption by these technologies and advance the crypto healthcare landscape.

show abstract

Blood vessel detection using enhanced DeepJoint fuzzy clustering algorithm with deep Maxout network for glaucoma detection

Cited by 2 publications

References 32 publications

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

An Empirical Study and Analysis of Artificial Intelligence, Machine Learning, and Big Data for Crypto Healthcare Industries

Contact Info

Product

Resources

About