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Increased industrialization demand using synthetic dyes in the newspaper, cosmetics, textiles, food, and leather industries. As a consequence, harmful chemicals from dye industries are released into water reservoirs with numerous structural components of synthetic dyes, which are hazardous to the ecosystem, plants and humans. The discharge of synthetic dye into various aquatic environments has a detrimental effect on the balance and integrity of ecological systems. Moreover, numerous inorganic dyes exhibit tolerance to degradation and repair by natural and conventional processes. So, the present condition requires the development of efficient and effective waste management systems that do not exacerbate environmental stress or endanger other living forms. Numerous biological systems, including microbes and plants, have been studied for their ability to metabolize dyestuffs. To minimize environmental impact, bioremediation uses endophytic bacteria, which are plant beneficial bacteria that dwell within plants and may improve plant development in both normal and stressful environments. Moreover, Phytoremediation is suitable for treating dye contaminants produced from a wide range of sources. This review article proves a comprehensive evaluation of the most frequently utilized plant and microbes as dye removal technologies from dye-containing industrial effluents. Furthermore, this study examines current existing technologies and proposes a more efficient, cost-effective method for dye removal and decolorization on a big scale. This study also aims to focus on advanced degradation techniques combined with biological approaches, well regarded as extremely effective treatments for recalcitrant wastewater, with the greatest industrial potential.

Diabetic retinopathy (DR), a major cause of vision loss and it raises a major issue among diabetes people. DR considerably affect the financial condition of the society specially in medicinal sector. Once proper treatment is given to the DR patients, roughly 90% of patients can be saved from vision loss. So, it is needed to develop a DR classification model for classifying the stages and severity level of DR to offer better treatment. This article develops a novel Particle Swarm Optimization (PSO) algorithm based Convolutional Neural Network (CNN) Model called PSO-CNN model to detect and classify DR from the color fundus images. The proposed PSO-CNN model comprises three stages namely preprocessing, feature extraction and classification. Initially, preprocessing is carried out as a noise removal process to discard the noise present in the input image. Then, feature extraction process using PSO-CNN model is applied to extract the useful subset of features. Finally, the filtered features are given as input to the decision tree (DT) model for classifying the set of DR images. The simulation of the PSO-CNN model takes place using a benchmark DR database and the experimental outcome stated that the PSO-CNN model has outperformed all the compared methods in a significant way. The outcome of the simulation process indicated that the PSO-CNN model has offered maximum results.