Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease

In the emerging field of image segmentation,Fully Convolutional Networks(FCNs)have recently become prominent.However,their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters,which can often be a cumbersome manual task.The main aim of this study is to propose a more efficient,less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus images.To this end,our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network(FCEDN).The optimization is handled by a novel Genetic Grey Wolf Optimization(G-GWO)algorithm.This algorithm employs the Genetic Algorithm(GA)to generate a diverse set of initial positions.It leverages Grey Wolf Optimization(GWO)to fine-tune these positions within the discrete search space.Testing on the Indian Diabetic Retinopathy Image Dataset(IDRiD),Diabetic Retinopathy,Hypertension,Age-related macular degeneration and Glacuoma ImageS(DR-HAGIS),and Ocular Disease Intelligent Recognition(ODIR)datasets showed that the G-GWO method outperformed four other variants of GWO,GA,and PSO-based hyperparameter optimization techniques.The proposed model achieved impressive segmentation results,with accuracy rates of 98.5%for IDRiD,98.7%for DR-HAGIS,and 98.4%,98.8%,and 98.5%for different sub-datasets within ODIR.These results suggest that the proposed hyperparameter-optimized FCEDN model,driven by the G-GWO algorithm,is more efficient than recent deep-learning models for image segmentation tasks.It thereby presents the potential for increased automation and accuracy in the segmentation of fundus images,mitigating the need for extensive manual hyperparameter adjustments.

Applications of beneficial plant growth promoting rhizobacteria and mycorrhizae in rhizosphere and plant growth:A review

Because of climate change and the highly growing world population,it becomes a huge challenge to feed the whole population.To overcome this challenge and increase the crop yield,a large number of fertilizers are applied but these have many side effects.Instead of these,scientists have discovered beneficial rhizobacteria,which are environmentally friendly and may increase crop yield and plant growth.The microbial population of the rhizosphere shows a pivotal role in plant development by inducing its physiology.Plant depends upon the valuable interactions among the roots and microbes for the growth,nutrients availability,growth promotion,disease suppression and other important roles for plants.Recently numerous secrets of microbes in the rhizosphere have been revealed due to huge development in molecular and microscopic technologies.This review illustrated and discussed the current knowledge on the development,maintenance,interactions of rhizobacterial populations and various proposed mechanisms normally used by PGPR in the rhizosphere that encouraging the plant growth and alleviating the stress conditions.In addition,this research reviewed the role of single and combination of PGPR,mycorrhizal fungi in plant development and modulation of the stress as well as factors affecting the microbiome in the rhizosphere.