A novel community detection based genetic algorithm for feature selection

Rao

et al. 2022

Multimed Tools Appl

In the current world, the disorders occurring in dermatological images are among the foremost widespread diseases. Despite being common, its identification is tremendously hard because of the complexities like skin tone and color variation due to the presence of hair regions. Therefore the type of skin disease prediction is not accurately achieved in many pieces of research. To deal with mentioned concerns, a novel optimal probability-based deep neural network is proposed to assist medical professionals in appropriately diagnosing the type of skin disease. Initially, the input dataset is fed into the pre-processing stage, which helps to remove unwanted contents in the image. Afterward, features extracted for all the pre-processed images are subjected to the proposed Optimal Probability-Based Deep Neural Network (OP-DNN) for the training process. This classification algorithm classifies incoming clinical images as different skin diseases with the help of probability values. While learning OP-DNN, it is essential to determine the optimal weight values for reducing the training error. For optimizing weight in OP-DNN structure, an optimization approach is implemented in this research. For that, whale optimization is utilized because it works faster than other methods. The proposed multi-type skin disease prediction model is implemented in MatLab software and achieved 95% of accuracy, 0.97 of specificity, and 0.91 of sensitivity. This exposes the superiority of the proposed multi-type skin disease prediction model using an effective OP-DNN based feature extraction approach to attain a high accuracy rate and also it predict several kinds of skin disease than the previous models, which can protect the patients survives as well as can assist the physicians in making a decision certainly.

Section: Literature Reviewmentioning

confidence: 99%

Multi-type skin diseases classification using OP-DNN based feature extraction approach

Rao

et al. 2022

Multimed Tools Appl

“…The method seeks to maximize modularity function. In [126], Rostami et al developed a new CD based on GA for feature selection. The method consists of three steps: the features are computed and selected, then these features are clustered; finally select the best clustering by GA.…”

Section: B Community Detection Based On Meta-heuristic Algorithmsmentioning

confidence: 99%

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

et al. 2021

Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, Parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions. INDEX TERMSCommunity detection; Deep learning; Complex networks; Meta-heuristic algorithms; Parallel computing.

“…In other words, in many of medical and microarray datasets, it is possible that many genes are irrelevant or redundant for machine learning algorithm [29][30][31][32]. Feature selection or gene selection is a popular and powerful approach in medical datasets to overcome this shortcoming [33][34][35]. In gene selection, to decrease the microarray data dimensions, by eliminating the irrelevant and similar genes, only a subset of relevant and dissimilar genes that are strongly related to the objective function are selected [36].…”

Section: Feature Selectionmentioning

confidence: 99%

Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

et al. 2021

Self Cite

Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.