Evaluating Public Sentiments during Uttarakhand Flood: An Artificial Intelligence Techniques

Users of social networks can readily express their thoughts on websites like Twitter(now X),Facebook,and Instagram.The volume of textual data flowing from users has greatly increased with the advent of social media in comparison to traditional media.For instance,using natural language processing(NLP)methods,social media can be leveraged to obtain crucial information on the present situation during disasters.In this work,tweets on the Uttarakhand flash flood are analyzed using a hybrid NLP model.This investigation employed sentiment analysis(SA)to determine the people’s expressed negative attitudes regarding the disaster.We apply a machine learning algorithm and evaluate the performance using the standard metrics,namely root mean square error(RMSE),mean absolute error(MAE),and mean absolute percentage error(MAPE).Our random forest(RF)classifier outperforms comparable works with an accuracy of 98.10%.In order to gain a competitive edge,the study shows how Twitter(now X)data and machine learning(ML)techniques can analyze public discourse and sentiments regarding disasters.It does this by comparing positive and negative comments in order to develop strategies to deal with public sentiments on disasters.

CP-AnemiC:A conjunctival pallor dataset and benchmark for anemia detection in children

Anemia is a universal public health issue,which occurs as the result of a reduction in red blood cells.This disease is common among children in Africa and other developing countries.If not treated early,children may suffer longterm consequences such as impairment in social,emotional,and cognitive functioning.Early detection of anemia in children is highly desirable for effective treatment measures.While there has been research into the development of computer-aided diagnosis(CAD)systems for anemia diagnosis,a significant proportion of these studies encountered limitations when working with limited datasets.To overcome the existing issues,this paper proposes a large dataset,named CP-AnemiC,comprising 710 individuals(range of age,6–59 months),gathered from several hospitals in Ghana.The conjunctiva image-based dataset is supported with Hb levels(g/dL)annotations for accurate diagnosis of anemia.A joint deep neural network is developed that simultaneously classifies anemia and estimates hemoglobin levels(g/dL)based on the conjunctival pallor images.This paper conducts a comprehensive experiment on the CP-AnemiC dataset.The experimental results demonstrate the efficacy of the joint deep neural network in both the tasks of anemia classification and Hb levels(g/dL)estimation.

Detection of anemia using conjunctiva images:A smartphone application approach

Anemia is one of the public health issues that affect children and pregnant women globally.Anemia occurs when the level of red blood cells within the body is reduced.Detecting anemia requires expert blood draw for clinical analysis of hemoglobin quantity.Although this standard method is accurate,it is costive and consumes enough time,unlike the non-invasive approach which is cost-effective and takes less time.This study focused on pallor analysis and used images of the conjunctiva of the eyes to detect anemia using machine learning techniques.This study used a publicly available dataset of 710 images of the conjunctiva of the eyes acquired with a unique tool that eliminates any interference from ambient light.We combined Convolutional Neural Networks,Logistic Regression,and Gaussian Blur algorithm to develop a conjunctiva detection model and an anemia detection model which runs on a Fast API server connected to a frontend mobile app built with React Native.The developed model was embedded into a smartphone application that can detect anemia by capturing and processing a patient's conjunctiva with a sensitivity of 90%,a specificity of 95%,and an accuracy of 92.50%on average performance in about 50 s.

Application of ensemble models approach in anemia detection using images of the palpable palm

Anemia is a public health issue with serious ramifications for human health globally.Anemia particularly affects pregnant women and children from 6 to 59 months old even though every individual is at risk.Anemia occurs when the Hb level is below its normal threshold or when the red blood cells are weakened or destroyed.To discover medical remedies on time,early detection or diagnosis of anemia assist patients to understand their condition.The invasive approach for anemia detection is costive and time-consuming as compared to the non-invasive approach which is reliable and suitable for developing communities where medical resources and personnel are inadequate.This study uses palpable palm images(dataset)collected from 710 participants in selected hospitals in Ghana.The images were extracted,segmented and converted into RGB percentile to train,validate and tested the machine learning models.A hybrid model was developed with the application of ensemble learning models using the R programming language on the R Studio platform.Stacking,voting,boosting and bagging ensemble model techniques were used to build the hybrid models,the stacking ensemble model achieved an accuracy of 99.73%.The study justifies that ensemble models are efficient for medical disease diagnosis or detection such as anemia.