A Systematic Approach for Contextualizing Focal Structure Analysis in Social Networks

Focal Structures are key sets of individuals who may be responsible for coordinating events, protests, or leading citizen engagement efforts on social media networks. Discovering focal structures that can promote online social campaigns is important but complex. Unlike influential individuals, focal structures can effect large-scale complex social processes. In our prior work, we applied a greedy algorithm and bi-level decomposition optimization solution to identify focal structures in social media networks. However, the outcomes lacked a contextual representation of the focal structures that affected interpretability. In this research, we present a novel Contextual Focal Structure Analysis (CFSA) model to enhance the discovery and the interpretability of the focal structures to provide the context in terms of the content shared by individuals in the focal structures through their communication network. The CFSA model utilizes multiplex networks, where the first layer is the users-users network based on mentions, replies, friends, and followers, and the second layer is the hashtag co-occurrence network. The two layers have interconnections based on the user hashtag relations. The model's performance was evaluated on real-world datasets from Twitter related to domestic extremist groups spreading information about COVID-19 and the Black Lives Matter (BLM) social movement during the 2020–2021 time. The model identified Contextual Focal Structure (CFS) sets revealing the context regarding individuals’ interests. We then evaluated the model's efficacy by measuring the influence of the CFS sets in the network using various network structural measures such as the modularity method, network stability, and average clustering coefficient values. The ranking Correlation Coefficient (RCC) was used to conduct a comparative evaluation with real-world scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Lung Ultrasound COVID-19 Detection Using Deep Feature Recursive Neural Network

Coronavirus disease (COVID-19) is a universal illness that has been prevalent since December 2019. COVID-19 causes a disease that extends to more serious illnesses than the flu and is formulated from a large group of viruses. COVID-19 has been announced as a global epidemic that has greatly affected the global economy and society. Recent studies have great promise for lung ultrasound (LU) imaging, subjects infected by COVID-19. Extensively, the growth of an impartial, fast, and accurate automated method for evaluating LU images is still in its infancy. The present algorithms provide results of LU detecting COVID-19, are very time consuming, and provide high false rate for early detection and treatment of affected patients. Today, accurate detection of COVID-19 usually takes a long time and is prone to human error. To resolve this problem, Information Gain Feature Selection (IGFS) based on Deep Feature Recursive Neural Network (DFRNN) algorithm is proposed to detect the COVID-19 automatically at an early stage. The LU images are preprocessed using Gaussian filter approach, then quality enhanced by Watershed Segmentation (WS) algorithm, and later trained into IGFS algorithm to detect the finest features of COVID-19 to improve classification performance. Thus, the proposed algorithm detects whether the person is COVID-19 affected or not, from his LU image, in an efficient manner. The proposed experimental results show improved precision, recall, F-measure, and classification performance with low time complexity and less false rate performance, compared to the previous algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Exploiting a knowledge base for intelligent decision tree construction to enhance classification power

Decision Trees are a common approach used for classifying unseen data into defined classes. The Information Gain is usually applied as splitting criteria in the node selection process for constructing the decision tree. However, bias in selecting the multi-variation attributes is a major limitation of using this splitting condition, leading to unsatisfactory classification performance. To deal with this problem, a new decision tree algorithm called “Knowledge-Based Decision Tree (KDT)” is proposed which exploits the knowledge in an ontology to assist the decision tree construction. The novelty of the study is that an ontology is applied to determine the attribute importance values using the PageRank algorithm. These values are used to modify the Information Gain to obtain appropriate attributes to be nodes in the decision tree. Four different datasets, Soybean, Heart disease, Dengue fever, and COVID-19 dataset, were employed to evaluate the proposed approach. The experimental results show that the proposed method is superior to the other decision tree algorithms, such as the traditional ID3 and the Mutual Information Decision tree (MIDT), and also performs better than a non-decision tree algorithm, e.g., the k-Nearest Neighbors. © 2022, Paulus Editora. All rights reserved.

Classifying COVID-19 based on amino acids encoding with machine learning algorithms.

COVID-19 disease causes serious respiratory illnesses. Therefore, accurate identification of the viral infection cycle plays a key role in designing appropriate vaccines. The risk of this disease depends on proteins that interact with human receptors. In this paper, we formulate a novel model for COVID-19 named "amino acid encoding based prediction" (AAPred). This model is accurate, classifies the various coronavirus types, and distinguishes SARS-CoV-2 from other coronaviruses. With the AAPred model, we reduce the number of features to enhance its performance by selecting the most important ones employing statistical criteria. The protein sequence of SARS-CoV-2 for understanding the viral infection cycle is analyzed. Six machine learning classifiers related to decision trees, k-nearest neighbors, random forest, support vector machine, bagging ensemble, and gradient boosting are used to evaluate the model in terms of accuracy, precision, sensitivity, and specificity. We implement the obtained results computationally and apply them to real data from the National Genomics Data Center. The experimental results report that the AAPred model reduces the features to seven of them. The average accuracy of the 10-fold cross-validation is 98.69%, precision is 98.72%, sensitivity is 96.81%, and specificity is 97.72%. The features are selected utilizing information gain and classified with random forest. The proposed model predicts the type of Coronavirus and reduces the number of extracted features. We identify that SARS-CoV-2 has similar physicochemical characteristics in some regions of SARS-CoV. Also, we report that SARS-CoV-2 has similar infection cycles and sequences in some regions of SARS CoV indicating the affectedness of vaccines on SARS-CoV-2. A comparison with deep learning shows similar results with our method.

Two-step Feature Selection for Predicting Mortality Risk in COVID-19 Patients

COVID-19 pandemic is causing serious impact on our society. The whole world is suffering from financial, social, psychological, and other health crisis. One of the various challenges faced is the lack of health and medical facilities around the globe. It is very crucial to properly manage the available resources to save the lives of COVID-19 affected patients. This study proposes an intelligent model to facilitate the hospitals and medical facilities to diagnose which patients are in serious conditions and needs priority health services. The proposed model is based on feature selection-based mechanism, where most dominating features are identified to best discriminate among the serious patients and the less affected patients. We adopted two-step strategy, where filter measure is applied to rank the features according to their relevance in the first step, and Genetic Algorithm is applied with Decision Tree classifier to find the best feature subset in the second step. The results are reported in terms of classification accuracy and the most dominating features are also identified to help the medical practitioners. © 2021 IEEE.

Ordinal Decision-Tree-Based Ensemble Approaches: The Case of Controlling the Daily Local Growth Rate of the COVID-19 Epidemic.

In this research, we develop ordinal decision-tree-based ensemble approaches in which an objective-based information gain measure is used to select the classifying attributes. We demonstrate the applicability of the approaches using AdaBoost and random forest algorithms for the task of classifying the regional daily growth factor of the spread of an epidemic based on a variety of explanatory factors. In such an application, some of the potential classification errors could have critical consequences. The classification tool will enable the spread of the epidemic to be tracked and controlled by yielding insights regarding the relationship between local containment measures and the daily growth factor. In order to benefit maximally from a variety of ordinal and non-ordinal algorithms, we also propose an ensemble majority voting approach to combine different algorithms into one model, thereby leveraging the strengths of each algorithm. We perform experiments in which the task is to classify the daily COVID-19 growth rate factor based on environmental factors and containment measures for 19 regions of Italy. We demonstrate that the ordinal algorithms outperform their non-ordinal counterparts with improvements in the range of 6-25% for a variety of common performance indices. The majority voting approach that combines ordinal and non-ordinal models yields a further improvement of between 3% and 10%.