Titlebook: Advances in Computer Communication and Computational Sciences; Proceedings of IC4S Sanjiv K. Bhatia,Shailesh Tiwari,Munesh C. Trivedi Conf

自由职业者

Sanjiv K. Bhatia,Shailesh Tiwari,Munesh C. TrivediContains high-quality peer-reviewed papers on communication, computational sciences and technology.Provides interesting data and new results in computational sciences.Serves as a reference material fo

Acetaldehyde

JECT

拱形面包

Business Modeling and Software Designbeen growing at an enormous rate ever since the development of computers and information technology. Many methods and algorithms have been developed in the last half-century to evaluate data and extract useful information to help develop faster. Due to the wide variety of algorithms and different ap

无能力之人

https://doi.org/10.1007/978-3-662-69518-0are vulnerabilities. The vulnerabilities bring about attacks. If attacks are launched before the public exposure of the targeted vulnerability, they are called zero-day attacks. These attacks damage system and economy seriously. One such attack is buffer overflow attack which is a threat to the soft

防止

Business Process Management Methodologye response (FIR) filter is presented. Equalizers form an inherent part of receiver in communication system that help in eliminating distortions in the received data to counterbalance the interference and nonlinearity that appears in practical channels using a simple and proficient optimization algor

尾巴

尊严

Pander

Business Process Management Architecturescharacteristic defect frequencies in the envelope power spectrum or by developing discriminative models for features extracted from the signals through time and frequency domain analysis. This issue not only requires expert knowledge to design discriminative features but also requires appropriate fe

LAP

Francesco Vinci,Massimiliano de Leonitect because of the complexity in components of acquired acoustic emission signals. To address this issue, this paper proposes a deep learning method that can effectively detect the combined defects in bearings. The proposed deep neural network (DNN) is based on the stacked denoising autoencoder (SD