No abstract

Now-a-days, the medical industry is growing a lot with the adaptation of latest technologies as well as the logical evaluation and security norms provides a robust platform to enhance the effectiveness of the industry at a drastic level. In this paper, a digital bio-medical image processing based Pneumonia disease identification system is introduced with enhanced security features. Due to improving the efficiency of the application, a well-known watermarking based security constraint is included to provide the protection to the respective hospital environment and patients as well. To avoid these issues, some sort of security aspects need to be followed so that this paper included watermarking based security to provide a rich level of protection to the images going to be tested. The main intention of this paper is to introduce a novel security enabled digital image processing scheme to identify the Pneumonic disease in earlier stages with respect to the proper classification principles. In this paper, a novel deep learning algorithm is introduced called enhanced Dynamic Learning Neural Network in which it is a hybrid algorithm with the combinations of conventional DLNN algorithm and the Support Vector Classification algorithm. This proposed approach effectively identifies the Pneumonia disease in earlier stages but the security inspection on the testing stage is so important to analyze the disease. The respective testing image is properly watermarked with the logo of the corresponding hospital; the image is processed otherwise the proposed approach skips the image to process. These kinds of security features emphasize the medical industry and boost up the levels more as well as the patients can get an appropriate error free care with the help of such technology. A proper Chest X-Ray based Kaggle dataset is considered to process the system as well as which contains 5856 Chest X-Ray images under two different categories such as Pneumonia and Normal. With respect to processing these images and identifying the Pneumonia disease effectively as well as the proposed watermarking enabled security features provide a good impact in the medical field protection system. The resulting section provides the proper proof to the effectiveness of the proposed approach and its prediction efficiency.

Ag is abundant in nature and is employed in practically every aspect of life. Furthermore, Ag+ contamination poses a severe hazard to human and environmental health due to the extensive usage of Ag products. Traditional Ag+ detection techniques include drawbacks such as high operational costs, sophisticated operating units and instruments, and strong technical demands. The use of fluorescence copper nanoparticles in pollution detection has received a lot of attention in recent times. The development of copper nanoparticles and the detecting of Ag+ are the major topics of this research. Utilizing fluorescence copper nanoparticles produced utilizing glucose (Glc) as a reduction mediator like a fluorescent probe, and a simple approach for determining Ag+ in water was devised. Due to its appealing properties, including such dissolution rate, widespread availability, simplicity of synthesis process, and excellent biocompatibility, fluorescence copper nanoparticles (F-CuNPs) have sparked a lot of interest, and a lot of time and effort has gone into their synthesis and usage. The slightly elevated metallophilic Ag+ contact served as such sensing element, efficiently quenching the fluorescent of AuAg NCs. Moreover, these fluorescence nanoprobes could have been used to identify Ag+ in the atmosphere, implying that they might be used as practical, dual-functional, fast-responding, and label-free fluorescent sensors for health and environmental assessment. The experiment’s analytical methodology would be that silver ions could fast and efficiently extinguish the fluorescent of Glucose-CuNPs. In the Ag+ region at 100 mol/L–600 mol/L ( R = 0   9845 ), a strong linear relation was discovered; the color is progressively improved below the observable region and visually colorimetrical measurement. Furthermore, the Glucose-CuNP instrument only detected Ag+ and was unaffected by other metal ions, demonstrating that Glucose-CuNPs have strong sensitivity for Ag+ sensing. Glucose-CuNP, as a result, accomplishes the identification of substantial metal Ag+ ions, and it has promising future applicability in environmental monitoring.