Abstract In digital signal processing systems, the multiplication products cause parasitic noise which affect the system properties. Creating low sensitivity in the digital signal processing system cause the systems to reduce the noise and ultimately improve the performance of the system. In this paper, we have used the low sensitivity recursive first order digital systems to study the multiplication of products... In digital signal processing systems, the multiplication products cause parasitic noise which affect the system properties. Creating low sensitivity in the digital signal processing system cause the systems to reduce the noise and ultimately improve the performance of the system. In this paper, we have used the low sensitivity recursive first order digital systems to study the multiplication of products at poles. Thus, this work can enable the best performed fields of the studied digital signal processing systems. Read More Read Less

Abstract Digital technology has produced the Blockchain systems which is expected to improve the system performance. But still, there are impediments in registering progress. We need to assess the extent to which the digital system requires the consequences of blockchain technology for attaining progress. In this work we studied the requirements to achieve the success in the blockchain technology with the... Digital technology has produced the Blockchain systems which is expected to improve the system performance. But still, there are impediments in registering progress. We need to assess the extent to which the digital system requires the consequences of blockchain technology for attaining progress. In this work we studied the requirements to achieve the success in the blockchain technology with the introduction of some measures. We did a brief review of a few areas such as Finance, Business and Marketing, Energy Systems, Food Industry, Healthcare Systems, Science and Education, Engineering and Construction, Environment, Technology, and Crimes to determine the influence of Blockchain technology. Read More Read Less

Abstract Optimization algorithms are extensively used in machine learning where optimization techniques are deployed. With the use of deep learning, optimization approaches are widely used with the development of new features in Stochastic Gradient Descent to convex and non-convex and derivative-free approaches. The deep learning models can able to produce systems with speed and final performance that will improve the convexity... Optimization algorithms are extensively used in machine learning where optimization techniques are deployed. With the use of deep learning, optimization approaches are widely used with the development of new features in Stochastic Gradient Descent to convex and non-convex and derivative-free approaches. The deep learning models can able to produce systems with speed and final performance that will improve the convexity principles. Highly enhanced optimizers can increase the complexity of the depth and data sets become larger that require good optimization. Thus, in this paper, we have studied the most used optimizer algorithm in a practical way. We have experimented it in MNIST and AutoEncoder data sets. We tested in a variety of applications that can document the common features and differences and suitability of applications. Further, we have presented new variants of optimizers. Finally, we are able to find the better optimizer with the help of extensive analyses. Read More Read Less