A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology.

Currently, countries across the world are suffering from a prominent viral infection called COVID-19. Most countries are still facing several issues due to this disease, which has resulted in several fatalities. The first COVID-19 wave caused devastation across the world owing to its virulence and led to a massive loss in human lives, impacting the country's economy drastically. A dangerous disease called mucormycosis was discovered worldwide during the second COVID-19 wave, in 2021, which lasted from April to July. The mucormycosis disease is commonly known as "black fungus," which belongs to the fungus family Mucorales. It is usually a rare disease, but the level of destruction caused by the disease is vast and unpredictable. This disease mainly targets people already suffering from other diseases and consuming heavy medication to counter the disease they are suffering from. This is because of the reduction in antibodies in the affected people. Therefore, the patient's body does not have the ability to act against fungus-oriented infections. This black fungus is more commonly identified in patients with coronavirus disease in certain country. The condition frequently manifests on skin, but it can also harm organs such as eyes and brain. This study intends to design a modified neural network logic for an artificial intelligence (AI) strategy with learning principles, called a hybrid learning-based neural network classifier (HLNNC). The proposed method is based on well-known techniques such as convolutional neural network (CNN) and support vector machine (SVM). This article discusses a dataset containing several eye photographs of patients with and without black fungus infection. These images were collected from the real-time records of people afflicted with COVID followed by the black fungus. This proposed HLNNC scheme identifies the black fungus disease based on the following image processing procedures: image acquisition, preprocessing, feature extraction, and classification; these procedures were performed considering the dataset training and testing principles with proper performance analysis. The results of the procedure are provided in a graphical format with the precise specification, and the efficacy of the proposed method is established.

Comparative study on bioremediation of heavy metals by solitary ascidian, Phallusia nigra, between Thoothukudi and Vizhinjam ports of India.

Ascidians belonging to the sub-phylum Uro-chordata are used as potential model organisms in various parts of the world for biosorption of metals. The sedentary nature, filter feeding habits, presence of vanadocytes and the absence of kidneys cause them to accumulate metals. The present study was aimed to compare biosorption of metals such as cadmium, copper, lead, mercury and vanadium in test and mantle body of solitary ascidian Phallusia nigra between two ecologically significant stations such as Thoothukudi (Station 1) and Vizhinjam (Station 2) ports of India. Monthly samplings of water and P. nigra were done for a period of one year from September 2010 to August 2011 and subjected to analysis of metal accumulation. The average metal concentrations except mercury in the Thoothukudi water were found to be higher of comparable magnitudes than the Vizhinjam water. One-way ANOVA showed significant differences between the stations. A comparison of average metal concentrations in the test and mantle body of P. nigra between two stations showed that the enrichment of V, Cd, Pb, Cu and Hg in the Thoothukudi samples may be due to high bioaccumulation factors of these elements as compared to other species of ascidians. The bioaccumulation factors were in the order of V>Pb>Cd>Cu> Hg for the test and mantle body in stations 1 and 2. Application of one-way ANOVA for the concentration of these metals between test and mantle body showed significant differences in both stations. Similarly, ANOVA for biosorption of these trace metals by P. nigra showed significant difference between stations. Metal concentrations recorded in this ascidian could effectively be used as good reference material for monitoring metal contamination in Indian sea waters.