Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model

Vaiyapuri, Thavavel; Syed, Liyakathunisa; Alaskar, Haya; Aljohani, Eman; Shridevi, S; Hussain, Abir Jaafar

doi:10.3390/app12094172

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…Moreover, Vaiyapuri et al [ 244 ] developed a hybridized red fox optimizer with a deep-learning-enabled microarray gene expression classification (RFODL-MGEC) method. Genes were the features, and the study aimed to advance classification performance by choosing suitable features.…”

Section: Metaheuristic Algorithms For Multiclass Feature Selectionmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

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Section: Metaheuristic Algorithms For Multiclass Feature Selectionmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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“…However, a 3D map of 32 × 32 × 32 has 32,768 voxels [ 15 ]. In contrast, 2D can increase the computation time as well the probability of overfitting, especially when the data are limited [ 16 ]. A vector with such a large input poses many problems, and the requirement for representative image features requires significant manual engineering effort.…”

Section: Deep Neural Network (Dnns)mentioning

confidence: 99%

Deep Learning Approaches for Automatic Localization in Medical Images

Alaskar

Hussain

ِAlmaslukh

et al. 2022

Computational Intelligence and Neuroscience

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Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy. These achievements have shown great promise in a wide variety of fields, especially in medical image analysis by creating opportunities to diagnose and treat diseases earlier. In recent years, the application of the DNN for object localization has gained the attention of researchers due to its success over conventional methods, especially in object localization. As this has become a very broad and rapidly growing field, this study presents a short review of DNN implementation for medical images and validates its efficacy on benchmarks. This study presents the first review that focuses on object localization using the DNN in medical images. The key aim of this study was to summarize the recent studies based on the DNN for medical image localization and to highlight the research gaps that can provide worthwhile ideas to shape future research related to object localization tasks. It starts with an overview on the importance of medical image analysis and existing technology in this space. The discussion then proceeds to the dominant DNN utilized in the current literature. Finally, we conclude by discussing the challenges associated with the application of the DNN for medical image localization which can drive further studies in identifying potential future developments in the relevant field of study.

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“…Li, Y., Qian, B et al have proposed GNDP, a disease prediction model which is based on a graph convolutional network. It exploits the spatial structure of the EHR data and the temporal dependencies of the entities to predict the patient's future diagnosis which is similar to [8,10]. Sun, Z., Dong, W. et al propose a Reinforcement Learning mechanism that would take random walks over the knowledge graph with respect to the patient's symptoms and then propose the most likely disease.…”

Section: Related Workmentioning

confidence: 99%

“…However, most of the above-mentioned papers fail to utilise the temporal features of the symptoms that are associated with the diseases. Some papers [8], [9] make use of GNNs tailored for this specific purpose especially in [9], [10] where the authors use patient data to first construct a dependency of the patient with symptoms on each visit and a final diagnosis and then model a graph from which is fed to the network proposed by them. This model could achieve an accuracy of a little over 85% at the most favourable conditions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Spatial-Temporal Graph Model for Disease Prediction

Senthilkumar,

Gupte,

2022

IJACSA

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Advances in the field of Neural Networks, especially Graph Neural Networks (GNNs) has helped in many fields, mainly in the areas of Chemistry and Biology where recognizing and utilising hidden patterns is of much importance. In Graph Neural Networks, the input graph structures are exploited by using the dependencies formed by the nodes. The data can also be transformed in the form of graphs which can then be used in such models. In this paper, a method is proposed to make appropriate transformations and then to use the structure to predict diseases. Current models in disease prediction do not fully use the temporal features that are associated with diseases, such as the order of the occurrence of symptoms and their significance. In the proposed work, the presented model takes into account the temporal features of a disease and represents it in terms of a graph to fully utilize the power of Graph Neural Networks and Spatial-Temporal models which take into consideration of the underlying structure that change over time. The model can be efficiently used to predict the most likely disease given a set of symptoms as input. The model exhibits the best algorithm based on its accuracy. The accuracy of the algorithm is determined by the performance on the given dataset. The proposed model is compared with the existing baseline models and proves to be outstanding and more promising in the disease prediction.

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Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model

Cited by 15 publications

References 20 publications

Multiclass feature selection with metaheuristic optimization algorithms: a review

Multiclass feature selection with metaheuristic optimization algorithms: a review

Deep Learning Approaches for Automatic Localization in Medical Images

Dynamic Spatial-Temporal Graph Model for Disease Prediction

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