Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics. INDEX TERMS Speech recognition, deep neural network, systematic review.
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tSoftware estimation is a tedious and daunting task in project management and software development. Software estimators are notorious in predicting software effort and they have been struggling in the past decades to provide new models to enhance software estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. This paper presents a novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams. A fuzzy logic approach is used to calibrate the productivity factor in the regression model. Moreover, a multilayer perceptron (MLP) neural network model was developed to predict software effort based on the software size and team productivity. Experiments show that the proposed approach outperforms the original UCP model. Furthermore, a comparison between the MLP and log-linear regression models was conducted based on the size of the projects. Results demonstrate that the MLP model can surpass the regression model when small projects are used, but the log-linear regression model gives better results when estimating larger projects.
Blockchain is a key technology that has the potential to decentralize the way we store, share, and manage information and data. One of the more recent blockchain platforms that has emerged is Hyperledger Fabric, an open source, permissioned blockchain that was introduced by IBM, first as Hyperledger Fabric v0.6, and then more recently, in 2017, IBM released Hyperledger Fabric v1.0. Although there are many blockchain platforms, there is no clear methodology for evaluating and assessing the different blockchain platforms in terms of their various aspects, such as performance, security, and scalability. In addition, the new version of Hyperledger Fabric was never evaluated against any other blockchain platform. In this paper, we will first conduct a performance analysis of the two versions of Hyperledger Fabric, v0.6 and v1.0. The performance evaluation of the two platforms will be assessed in terms of execution time, latency, and throughput, by varying the workload in each platform up to 10,000 transactions. Second, we will analyze the scalability of the two platforms by varying the number of nodes up to 20 nodes in each platform. Overall, the performance analysis results across all evaluation metrics, scalability, throughput, execution time, and latency, demonstrate that Hyperledger Fabric v1.0 consistently outperforms Hyperledger Fabric v0.6. However, Hyperledger Fabric v1.0 platform performance did not reach the performance level in current traditional database systems under high workload scenarios.
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