Detecting Information on the Spread of Dengue on Twitter Using Articial Neural Networks

Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques.Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media.The amount of data generated by social media platforms,such as Twitter,that can be used to track diseases is increasing rapidly.This paper proposes a method for the classication of tweets related to the outbreak of dengue using machine learning algorithms.An articial neural network(ANN)-based method is developed using Global Vector(GloVe)embedding to use the data in tweets for the automatic and efcient identication and classication of dengue.The proposed method classies tweets related to the outbreak of dengue into positives and negatives.Experiments were conducted to assess the proposed ANN model based on performance evaluation matrices(confusion matrices).The results show that the GloVe vectors can efciently capture a sufcient amount of information for the classier to accurately identify and classify tweets as relevant or irrelevant to dengue outbreaks.The proposed method can help healthcare professionals and researchers track and analyze epidemic outbreaks through social media in real time.

Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.

Prospective of extracellular matrix and drug correlations in disease management

The extracellular matrix(ECM)comprises of many structural molecules that constitute the extracellular environment.ECM molecules are characterized by specific features like diversity,complexity and signaling,which are also results of improvement or development of disease mediated by some physiological changes.Several drugs have also been used to manage diseases and they have been reported to modulate ECM assembly,including physiological changes,beyond their primary targets and ECM metabolism.This review highlights the alteration of ECM environment for diseases and effect of different classes of drugs like nonsteroidal anti-inflammatory drugs,immune suppressant drug,steroids on ECM or its components.Thus,it is summarized from previously conducted researches that diseases can be managed by targeting specific components of ECM which are involved in the pathophysiology of diseases.Moreover,the drug delivery focused on targeting the ECM components also has the potential for the discovery of targeted and site specific release of drugs.Therefore,ECM or its components could be future targets for the development of new drugs for controlling various disease conditions including neurodegenerative diseases and cancers.

Supervised Machine Learning-Based Prediction of COVID-19

COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the world.As yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are underway.Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment.In this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new technologies.The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease.The state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection schemes.This study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential threat.The proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation testing.The experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation strategy.The comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.

Fractional Order Environmental and Economic Model Investigations Using Artificial Neural Network

The motive of these investigations is to provide the importance and significance of the fractional order(FO)derivatives in the nonlinear environmental and economic(NEE)model,i.e.,FO-NEE model.The dynamics of the NEE model achieves more precise by using the form of the FO derivative.The investigations through the non-integer and nonlinear mathematical form to define the FO-NEE model are also provided in this study.The composition of the FO-NEEmodel is classified into three classes,execution cost of control,system competence of industrial elements and a new diagnostics technical exclusion cost.The mathematical FO-NEE system is numerically studied by using the artificial neural networks(ANNs)along with the Levenberg-Marquardt backpropagation method(ANNs-LMBM).Three different cases using the FO derivative have been examined to present the numerical performances of the FO-NEE model.The data is selected to solve the mathematical FO-NEE system is executed as 70%for training and 15%for both testing and certification.The exactness of the proposed ANNs-LMBM is observed through the comparison of the obtained and the Adams-Bashforth-Moulton database results.To ratify the aptitude,validity,constancy,exactness,and competence of the ANNs-LMBM,the numerical replications using the state transitions,regression,correlation,error histograms and mean square error are also described.

A novel polyherbal formulation containing thymoquinone attenuates carbon tetrachloride-induced hepatorenal injury in a rat model

Objective:To evaluate a novel polyherbal formulation(BSVT)containing the standardized extracts from the leaves of Boerhavia diffusa,Solidago virgaurea,Vitex negundo,and thymoquinone in CCl4 induced hepatorenal toxicity in rats.Methods:A total of 36 rats were divided into six groups including normal control,CCl4(2 mL/kg,i.p.),CCl4(2 mL/kg,i.p.)+Cystone?(750 mg/kg p.o.),CCl4(2 mL/kg,i.p.)+BSVT(25 mg/kg,p.o.),CCl4(2 mL/kg,i.p.)+BSVT(50 mg/kg,p.o.),and CCl4(2 mL/kg,i.p.)+BSVT(100 mg/kg,p.o.).All treatments were given for four weeks.Serum levels of aspartate transaminase,alanine transaminase,alkaline phosphatase,cholesterol,total protein,serum urea,blood urea nitrogen and creatinine were assessed.Superoxide dismutase,malondialdehyde,and glutathione peroxidase were evaluated in tissue homogenate.The histopathological study of liver and kidney tissues was also done.Results:Aspartate transaminase,alanine transaminase,alkaline phosphatase,cholesterol,serum urea,blood urea nitrogen and creatinine were significantly elevated(P<0.001)while total protein was considerably reduced in the CCl4 group as compared to the normal control(P<0.001),which indicated hepatorenal toxicity.In addition,superoxide dismutase and glutathione peroxidase activities were significantly decreased(P<0.001)while malondialdehyde levels were increased markedly(P<0.001).Treatment with BSVT formulation recovered these parameters towards a normal level in a dose-dependent manner.Conclusions:BSVT formulation ameliorates the hepatorenal toxicity in a dose-dependent manner.Furthermore,clinical studies are required to confirm its efficacy.

Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

The particle swarm optimization(PSO)algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and sh.PSO is essentially an unconstrained algorithm and requires constraint handling techniques(CHTs)to solve constrained optimization problems(COPs).For this purpose,we integrate two CHTs,the superiority of feasibility(SF)and the violation constraint-handling(VCH),with a PSO.These CHTs distinguish feasible solutions from infeasible ones.Moreover,in SF,the selection of infeasible solutions is based on their degree of constraint violations,whereas in VCH,the number of constraint violations by an infeasible solution is of more importance.Therefore,a PSO is adapted for constrained optimization,yielding two constrained variants,denoted SF-PSO and VCH-PSO.Both SF-PSO and VCH-PSO are evaluated with respect to ve engineering problems:the Himmelblau’s nonlinear optimization,the welded beam design,the spring design,the pressure vessel design,and the three-bar truss design.The simulation results show that both algorithms are consistent in terms of their solutions to these problems,including their different available versions.Comparison of the SF-PSO and the VCHPSO with other existing algorithms on the tested problems shows that the proposed algorithms have lower computational cost in terms of the number of function evaluations used.We also report our disagreement with some unjust comparisons made by other researchers regarding the tested problems and their different variants.

An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefore,the objective of text extraction is to attain reduced expressive contents from the text documents.Text summarization has two main areas such as abstractive,and extractive summarization.Extractive text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding principles.All such text extractions have limitations in providing the basic theme of the underlying documents.In this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word embedding.The study compared these forms of the text summarizations with the actual text,by calculating cosine similarities.Furthermore,TF-IDF based PageRank keywords are extracted from the other two extractive summarizations.An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed.This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document.This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text.It also solves the issue of deciding the number of representative keywords for a specific text document.To evaluate the technique,the study used a sample of more than eighteen hundred text documents.The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization.The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.