Viral outbreaks and communicable health hazards due to devastating floods in Pakistan

Pakistan is a developing country that has a population of 190 million people and faces a huge burden of viral diseases. Every year during monsoon season heavy rain fall and lack of disaster management skills potentially increase the transmission of waterborne diseases, vector borne diseases and viral outbreaks. Due to severe flooding, thousands of people lose their lives and millions are displaced each year. In most of the cases the children who lose their family members are forced into illegal professions of begging, child labor and prostitution which make them prone to sexually transmitted infections. Up to date, no scientific study has been conducted nationwide to illustrate epidemiological patterns of waterborne diseases, vector borne diseases and viral epidemics during flash flood. Mosquito sprays would not be a sufficient approach for dengue eradication; mass awareness, larvicide and biological control by Guppy fishes are also effective strategies to overcome dengue problem. International health bodies and non-governmental organizations must take note of this alerting situation and take adequate steps such as financial/medical aid in order to defeat the after-effects of flood.

Artificial Humming Bird Optimization with Siamese Convolutional Neural Network Based Fruit Classification Model

Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from pictures.But,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing scale.Recently,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human life.An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts.CNN’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several domains.Considering the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)model.In the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the image.In addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature vectors.For fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of fruits.The performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit images.Extensive comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.