scholarly journals Application of Nature Inspired Soft Computing Techniques for Gene Selection: A Novel Frame Work for Classification of Cancer.

2022 ◽  
Author(s):  
Rabia Musheer Aziz
Keyword(s):  
Gene Selection ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Cancer Classification ◽  
Prediction Errors ◽  
Bee Colony ◽  
Proposed Model ◽  
Comparison Results ◽  
Soft Computing Techniques

Abstract A modified Artificial Bee Colony (ABC) metaheuristics optimization technique is applied for cancer classification, that reduces the classifier's prediction errors and allows for faster convergence by selecting informative genes. Cuckoo search (CS) algorithm was used in the onlooker bee phase (exploitation phase)of ABC to boost performance by maintaining the balance between exploration and exploitation of ABC. Tuned the modified ABC algorithm by using Naïve Bayes (NB) classifiers to improve the further accuracy of the model. Independent Component Analysis (ICA) is used for dimensionality reduction. In the first step, the reduced dataset is optimized by using Modified ABC and after that, in the second step, the optimized dataset is used to train the NB classifier. Extensive experiments were performed for comprehensive comparative analysis of the proposed algorithm with well-known metaheuristic algorithms, namely Genetic Algorithm (GA) when used with the same framework for the classification of six high-dimensional cancer datasets. The comparison results showed that the proposed model with the CS algorithm achieves the highest performance as maximum classification accuracy with less count of selected genes. This shows the effectiveness of the proposed algorithm which is validated using ANOVA for cancer classification.

scholarly journals Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

2018 ◽  
Vol 8 (9) ◽  
pp. 1569 ◽  
Author(s):  
Shengbing Wu ◽  
Hongkun Jiang ◽  
Haiwei Shen ◽  
Ziyi Yang
Keyword(s):  
Logistic Regression ◽  
Gene Selection ◽  
Classification Performance ◽  
Cancer Classification ◽  
Sparse Logistic Regression ◽  
The Subject ◽  
Selection For ◽  
Microarray Datasets ◽  
Sparse Methods

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.

scholarly journals Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Nivedhitha Mahendran ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang
Keyword(s):  
Gene Expression ◽  
Gene Selection ◽  
Imaging Techniques ◽  
Optimization Technique ◽  
Gene Expression Omnibus ◽  
Deep Belief Network ◽  
Bayesian Optimization ◽  
Belief Network ◽  
Feature Selection Techniques

Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer’s dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.

Non-convex Economic Load Dispatch using Cuckoo Search Algorithm

2017 ◽  
Vol 5 (1) ◽  
pp. 48 ◽  
Author(s):  
N. Karthik ◽  
A.K. Parvathy ◽  
R. Arul
Keyword(s):  
Fossil Fuel ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Economic Load Dispatch ◽  
Transmission Losses ◽  
Soft Computing Techniques ◽  
Valve Point Loading Effect ◽  
Optimisation Technique

<p>This paper presents cuckoo search algorithm (CSA) for solving non-convex economic load dispatch (ELD) problems of fossil fuel fired generators considering transmission losses and valve point loading effect. CSA is a new meta-heuristic optimisation technique inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. The strength of the proposed meta-heuristic optimization technique CSA has been tested and validated on the standard IEEE 14-bus, 26-bus and 30-bus system with several heuristic load patterns. The results have indicated that the proposed approach is able to obtain significant economic load dispatch solutions than those of Firefly Algorithm (FFA) and other soft computing techniques reported in the literature.</p>

scholarly journals LogSum + L2 penalized logistic regression model for biomarker selection and cancer classification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiao-Ying Liu ◽  
Sheng-Bing Wu ◽  
Wen-Quan Zeng ◽  
Zhan-Jiang Yuan ◽  
Hong-Bo Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cancer Classification ◽  
Classification Problems ◽  
Proposed Model ◽  
Different Types ◽  
Penalized Logistic Regression ◽  
Biomarker Selection

AbstractBiomarker selection and cancer classification play an important role in knowledge discovery using genomic data. Successful identification of gene biomarkers and biological pathways can significantly improve the accuracy of diagnosis and help machine learning models have better performance on classification of different types of cancer. In this paper, we proposed a LogSum + L2 penalized logistic regression model, and furthermore used a coordinate decent algorithm to solve it. The results of simulations and real experiments indicate that the proposed method is highly competitive among several state-of-the-art methods. Our proposed model achieves the excellent performance in group feature selection and classification problems.

scholarly journals Classification of Plants Leaf Diseases using Convolutional Neural Network

2021 ◽  
Vol 24 (2) ◽  
pp. 64-71
Author(s):  
Reem Mohammed Jasim Al-Akkam ◽  
◽  
Mohammed Sahib Mahdi Altaei ◽  
Keyword(s):  
Neural Network ◽  
Agricultural Production ◽  
Optimization Technique ◽  
Plant Diseases ◽  
Cross Entropy ◽  
Training Phase ◽  
Proposed Model ◽  
Rgb Images ◽  
Deep Cnn

Agriculture is one of the most important professions in many countries, including Iraq, as the Iraqi financial system depends on agricultural production and great attention should be paid to concerns about agricultural production. Because plants are exposed to many diseases and monitoring plant diseases with the help of specialists in the agricultural region can be very expensive. There is a need for a system capable of automatically detecting diseases. The aim of the research proposed is to create a model that classifies and predicts leaf diseases in plants. This model is based on a convolution network, which is a kind of deep learning. The dataset used in this study called (Plant Village) was downloaded from the kaggle website. The dataset contains 34,934 RGB images, and the deep CNN model can efficiently classify 15 different classes of healthy and diseased plants using the leaf images. The model used techniques to augment data and dropout. The Soft max output layer was used with the categorical cross-entropy loss function to apply the CNN model proposed with the Adam optimization technique. The results obtained by the proposed model were 97.42% in the training phase and 96.18% in the testing phase.

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