Microarray technology provides an enormous opportunity to measure large-scale gene expressions simultaneously. However sparse and high-dimensional feature space posed significant challenges during data analysis, mainly in learning network structure. Hence, feature selection (FS) has become an essential phase in microarray data analysis to obtain significant genes that could enhance the performance of subsequent process. This study aims to propose a hybrid FS methods based on Binary Bat Algorithm (BBA) and Dynamic Bayesian Network (DBN). The proposed method is tested on cancer gene expression dataset that is publicly available. The fold change analysis is conducted to measure a gene expression level between two diverse conditions prior to subsequent process of FS. Experimental results show that BBA has achieved better results compared to other baseline methods when trained with DBN low-order conditional independence and DBN full-order conditional independence with accuracy of 89.1
Clustering is a standard paradigm for associating some similar objects to a cluster while associating others to their respective clusters in an unsupervised manner. Different techniques are proposed in the domain of clustering depending upon the underlying methodology slightly vary from one another. Some of these are regarded as Hierarchical Clustering, K-means Clustering, Spectral Clustering, Affinity Propagation and Density based spatial Clustering. Their strengths and weaknesses are different for different datasets. In this research, we have used three benchmark datasets to analyze the merits and demerits for each of the clustering techniques. After applying these algorithms to the datasets named letter, glass and wine, we presented the critical review on why a specific clustering algorithm lacks with respect to execution time and clustering quality measured by Silhouette’s coefficient and why an algorithm performs better than others on the same dataset.
Pakistan is one of the major coal producing country and also have the most serious coal mine accidents around the world. The proposed study performs experiments on a dataset contain reviews based on accident reasons occurred during mining work to reduce the accident rate. The aim of this study achieved by categorizing the reasons into different classes on the behalf of human behavior, roof dropping, and smoke inhalation. Then perform preprocessing on the reviews to clean the data. After preprocessing, the bag-of-words and TF-IDF are used singly and in combination to preserve meaningful information in extracted feature form. Finally, the Random Forest, Naive Bayes classifier, SVM, Decision Tree, Logistic Regression and proposed ensemble LSD (LR+SVM+DT) models are used to classify the accident reasons to analyze the most occurring once. The performance of the proposed approach is evaluated using average accuracy, precision, recall, and f1 score. The experiments reveal that a combined feature improved the performance of machine learning models. Ensemble LSD outperforms the other models and achieves 94
The biggest challenge in Electronic Commerce (E-Commerce) is understanding the demand of the market and customer perception regarding the products of online stores. Online user evaluations are faster than a direct poll for obtaining customers' perceptions. To predict the quality of service provided, sentiment analysis methodology is used, which identifies and classifies product reviews into positive and negative sentiments through different classifying algorithmic models. This study has used Random Forest, Logistic Regression, and Gaussian Naive Bayes to perform sentiment analysis to analyze customers' likes and dislikes. The “Clothing Products Reviews” dataset from ‘Kaggle’ is used. Due to the significant number of positive sentiments, the results showed the dependability dimensions that cause the biasness in results. Synthetic Minority Oversampling Technique (SMOTE) is applied to balance the positive and negative sentiments. Random Forest, Naïve Bayes, and KNN classifiers are combined into one Stacking Classifier to improve the results and get 97
The COVID-19 outbreak is moving individuals globally. Monitoring social media and internet news is now vital to grasping this phenomenon and its impact. This study’s goal is to offer a method for capturing important concepts and themes addressed in the mainstream media and social networks and then apply it to the COVID-19 outbreak. This study compares articles and news, then visualizes the evolution and influence of the COVID-19 epidemic articles. The COVID-19 articles dataset from Kaggle is utilized for experiments. Various articles use the dataset to examine COVID-19 reviews. The studies use datasets and models such as Decision Tree (DT), Logistic Regression (LR), and Extra Tree Classifier (ETC), with F1 score, precision, and recall being evaluated. To improve accuracy, Term Frequency-Inverse Document Frequency (TF-IDF) is applied to the extracted keywords. The assembled model enhances this research, and the Logistic Regression (LR) model provides the highest accuracy at 90%.
