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Comparison of decolorization of reactive azo dyes by microorganisms isolated from various sources 被引量:6
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作者 S.Padamavathy s.sandhya +2 位作者 K.Swaminathan Y.V.Subrahmanyam S.N.Kaul 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2003年第5期628-632,共5页
Azo dyes are among the oldest man made chemicals and they are still widely used in the textile, printing and the food industries. About 10%-15% of the total dyes used in the industry is released into the environment ... Azo dyes are among the oldest man made chemicals and they are still widely used in the textile, printing and the food industries. About 10%-15% of the total dyes used in the industry is released into the environment during the manufacturing and usage. Some dyes and some of their N substituted aromatic bio transformation products are toxic and/or carcinogenic and therefore these dyes are considered to be environmental pollutants and health hazards. These azo dyes are degraded by physico chemical and biological methods. Of these, biological methods are considered to be the most economical and efficient. In this work, attempts were made to degrade these dyes aerobically. The organisms which were efficient in degrading the following azo dyes Red RB, Remazol Red, Remazol Blue, Remazol Violet, Remazol Yellow, Golden Yellow, Remazol Orange, Remazol Black were isolated from three different sources viz., wastewater treatment plant, paper mill effluent treatment plant and tannery wastewater treatment plant. The efficiency of azo dye degradation by mixed cultures from each source was analyzed. It was found that mixed cultures from tannery treatment plant worked efficiently in decolorizing Remazol Red, Remazol Orange, Remazol Blue and Remazol Violet, while mixed cultures from the paper mill effluent worked efficiently in decolorizing Red RB, Golden Yellow and Remazol Yellow. The mixed cultures from wastewater treatment plant efficiently decolorized Remazol Black. 展开更多
关键词 azo dyes DECOLORIZATION aerobic transformation
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Deep Learning Based Face Detection and Identification of Criminal Suspects
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作者 s.sandhya A.Balasundaram Ayesha Shaik 《Computers, Materials & Continua》 SCIE EI 2023年第2期2331-2343,共13页
Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is co... Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is committed.As most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their identity.Hence,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed.After detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the CriminalDatabase.The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric.After plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above. 展开更多
关键词 Deep learning OPENCV deep neural network single shot multi-box detector auto-encoder cosine similarity
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