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Image enhancement is a vital process that serves as a tool for improving the quality of a lot of real-life applications. Fractional calculus can be utilized in enhancing images using fractional order kernels, adding more controllability to the system, due to the flexible choice of the fractional order parameter, which adds extra degrees of freedom. The proposed system merges two fractional order kernels which helps in image enhancement techniques, and the contribution of this work is based on the study of how to optimize this process. The optimization of the two fractional kernels was done using the neural network optimization algorithm (NNA) to utilize the best order for the two kernels. In this paper, three fractional kernels are studied to highlight the performance of image enhancement using fractional kernels against different metrics. Furthermore, three different combinations of two kernels are combined and studied to enhance the metrics score by utilizing two different fractional orders for each kernel. Various optimization algorithms are used to obtain the optimum fractional order for both single and combined kernels. Using the constrained NNA, the evaluation metrics of the image enhancement show a 33% increase in measure of enhancement metric (EME), 21% increase in contrast, and 4% increase in average gradient compared to the best-achieved metrics by the literature while keeping the similarity metric above 0.75. [ABSTRACT FROM AUTHOR]

With the development of science and technology, the application of the Internet of Things (IOT) is becoming more and more widespread. Applying BP neural network algorithms (NNA) to the IOT system will help improve the performance of the IOT system. The research purpose of this paper is to solve the problems of long-parameter measurement cycle and untimely feedback of the existing IOT online measurement system. In this paper, a multi-parameter (MP) IOT online measurement system based on BP NNA is designed, and a simulation test experiment is performed. The MP online measurement IOT system based on the BP NNA completes the parameter collection, analysis, and display through the perception layer, network transmission layer, and application layer. The core is that the system application layer adds the BP NNA to optimize real-time acquisition parameters, processing to reduce parameter measurement time. It can be known from algorithm simulation experiments that the online measurement system based on the BP NNA proposed in this paper uses the BP NNA to predict the absolute error value of the final moisture content and the measured moisture content within 0.3, and the absolute error of the moisture content value in actual production. It is acceptable in the range of 0.5, which speeds up data collection time. This system has a very good effect on improving the feedback adjustment speed of the manufacturing process system. [ABSTRACT FROM AUTHOR]