Deep Learning Approach for Object Features Detection

Mitra, Ambik; Mohanty, Debasis; Ijaz, Muhammad Fazal; Rana, Abu ul Hassan S.

doi:10.1007/978-981-16-2911-2_27

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Weights associated with features must be optimized regularly for better performance of the model. These weights are optimized concerning the loss function and model parameters associated with each parameter in the training dataset [ 70 , 71 ]. In the current study, the input-target pair

and the

denote the training set.…”

Section: Methodsmentioning

confidence: 99%

Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data

Srinivasu

Shafi

Krishna³

et al. 2022

Diagnostics

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The development of genomic technology for smart diagnosis and therapies for various diseases has lately been the most demanding area for computer-aided diagnostic and treatment research. Exponential breakthroughs in artificial intelligence and machine intelligence technologies could pave the way for identifying challenges afflicting the healthcare industry. Genomics is paving the way for predicting future illnesses, including cancer, Alzheimer’s disease, and diabetes. Machine learning advancements have expedited the pace of biomedical informatics research and inspired new branches of computational biology. Furthermore, knowing gene relationships has resulted in developing more accurate models that can effectively detect patterns in vast volumes of data, making classification models important in various domains. Recurrent Neural Network models have a memory that allows them to quickly remember knowledge from previous cycles and process genetic data. The present work focuses on type 2 diabetes prediction using gene sequences derived from genomic DNA fragments through automated feature selection and feature extraction procedures for matching gene patterns with training data. The suggested model was tested using tabular data to predict type 2 diabetes based on several parameters. The performance of neural networks incorporating Recurrent Neural Network (RNN) components, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) was tested in this research. The model’s efficiency is assessed using the evaluation metrics such as Sensitivity, Specificity, Accuracy, F1-Score, and Mathews Correlation Coefficient (MCC). The suggested technique predicted future illnesses with fair Accuracy. Furthermore, our research showed that the suggested model could be used in real-world scenarios and that input risk variables from an end-user Android application could be kept and evaluated on a secure remote server.

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and the

denote the training set.…”

Section: Methodsmentioning

confidence: 99%