High-dimensional microarray dataset classification using an improved adam optimizer (iAdam)

Khaire, Utkarsh Mahadeo; Dhanalakshmi, R.

doi:10.1007/s12652-020-01832-3

Cited by 45 publications

(22 citation statements)

References 46 publications

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“…Our deep learning network parameters are initialized with Xavier initialization [ 54 ]. Adam optimizer [ 55 ] with learning rate of 3e-4 and 24 as our mini-batch size are used to minimize the network loss function from Eq (1) . The learning rate is gradually decayed to 25e-6 during the training process.…”

Section: Experiments and Resultsmentioning

confidence: 99%

High-throughput image-based plant stand count estimation using convolutional neural networks

et al. 2022

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The landscape of farming and plant breeding is rapidly transforming due to the complex requirements of our world. The explosion of collectible data has started a revolution in agriculture to the point where innovation must occur. To a commercial organization, the accurate and efficient collection of information is necessary to ensure that optimal decisions are made at key points of the breeding cycle. In particular, recent technology has enabled organizations to capture in-field images of crops to record color, shape, chemical properties, and disease susceptibility. However, this new challenge necessitates the need for advanced algorithms to accurately identify phenotypic traits. This work, advanced the current literature by developing an innovative deep learning algorithm, named DeepStand, for image-based counting of corn stands at early phenological stages. The proposed method adopts a truncated VGG-16 network to act as a feature extractor backbone. We then combine multiple feature maps with different dimensions to ensure the network is robust against size variation. Our extensive computational experiments demonstrate that our DeepStand framework accurately identifies corn stands and out-performs other cutting-edge methods.

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Section: Experiments and Resultsmentioning

confidence: 99%

High-throughput image-based plant stand count estimation using convolutional neural networks

et al. 2022

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“…e effective input vector is then passed to the fully connected layer, which functions similarly to the MLP. In the final section of the deep convolution layers, Softmax [17], classification layers perform classification using ADAM (adaptive moment optimizer) [18]; the lost function is shown in the following equation:…”

Section: Deep Convolutionmentioning

confidence: 99%

COVID‐19 Detection Using Deep Convolutional Neural Networks and Binary Differential Algorithm‐Based Feature Selection from X‐Ray Images

2021

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The new COVID-19 is rapidly spreading and has already claimed the lives of numerous people. The virus is highly destructive to the human lungs, and early detection is critical. As a result, this paper presents a hybrid approach based on deep convolutional neural networks that are very effective tools for image classification. The feature vectors were extracted from the images using a deep convolutional neural network, and the binary differential metaheuristic algorithm was used to select the most valuable features. The SVM classifier was then given these optimized features. For the study, a database containing images from three categories, including COVID-19, pneumonia, and a healthy category, included 1092 X-ray samples, was used. The proposed method achieved a 99.43% accuracy, a 99.16% sensitivity, and a 99.57% specificity. Our findings indicate that the proposed method outperformed recent studies on COVID-19 detection using X-ray images.

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“…The OSCC data used in this research is accumulated from the National Center for Biotechnology Information (NCBI). 52 The rest of the dataset is collected from http://csse.szu.edu.cn/staff/ahuzx/Datasets.html. All six datasets used in this research have two labels in their target variable.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Excogitating marine predators algorithm based on random opposition‐based learning for feature selection

Balakrishnan

Dhanalakshmi

Khaire

2021

Concurrency and Computation

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Obtaining precise information from a high-dimensional dataset is one of the most difficult tasks as datasets contain more features and fewer samples. The high-dimensionality of the dataset reduces predictive capability and increases the computational complexity of the analytical model. The widespread employment of meta-heuristic methods to handle the challenge of high-dimensional datasets has been exceptional in recent years. The marine predators algorithm (MPA) is a recently developed meta-heuristic algorithm based on the "survival-of-the-fittest" notion. This research critique overcomes the drawbacks of the existing MPA and proposes a feature selection model using random opposition-based learning (ROBL). The searching for the optimum solution in a single direction of the traditional MPA reduces its performance. The incorporation of ROBL in the MPA enhances its ability to reconnoiter bigger search space. The proposed algorithm generates a new population based on the initial and random opposite population. The performance of ROBL-MPA is inspected on six high-dimensional microarray datasets. The results of the proposed ROBL-MPA are compared to traditional MPA and opposition based MPA (OBL-MPA). The proposed ROBL-MPA outperforms traditional MPA based on several benchmark performance analysis tests.

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High-dimensional microarray dataset classification using an improved adam optimizer (iAdam)

Cited by 45 publications

References 46 publications

High-throughput image-based plant stand count estimation using convolutional neural networks

High-throughput image-based plant stand count estimation using convolutional neural networks

COVID‐19 Detection Using Deep Convolutional Neural Networks and Binary Differential Algorithm‐Based Feature Selection from X‐Ray Images

Excogitating marine predators algorithm based on random opposition‐based learning for feature selection

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