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2 2022

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Computational intelligence based design of biomaterials

Vinoth Arulraj, Datta Shubhabrata

Computer Methods in Materials Science

2022

Vol. 22, No. 4

229--262

This paper presents an overview of the applications of computational intelligence techniques, viz. artificial neural networks, fuzzy inference systems, and genetic algorithms, for the design of biomaterials with improved performance. These techniques are basically used for developing data-driven models and for optimization. The paper introduces the domain of biomaterials and how they can be designed using computational intelligence techniques. Then a brief description of the tools is made, followed by the applications of the tools in various domains of biomaterials. The applications range in all classes of materials ranging from alloys to composites. There are examples of applications for the surface treatment of biomaterials, materials for drug delivery systems, materials for scaffolds and even in implant design. It is found the tools can be effectively used for designing new and improved biomaterials.

Computational intelligence for speech enhancement using deep neural network

Hepsiba D., Justin Judith

Computer Assisted Methods in Engineering and Science

2022

Vol. 29, no. 1-2 spec.

71--85

In real time, the speech signal received contains noise produced in the background andreverberations. These disturbances reduce the quality of speech; therefore, it is importantto eliminate the noise and increase the intelligibility and quality of speech signal. Speechenhancement is the primary task in any real-time application that handles speech signals.In the proposed method, the most effective and challenging noise, i.e., babble noise, isremoved, and the clean speech is recovered. The enhancement of the corrupted speechsignal is done by applying a deep neural network-based denoising algorithm in which theideal ratio mask is used to mask the noisy speech and separate the clean speech signal.In the proposed system, the speech signal corrupted by noise is enhanced. Evaluation ofenhanced speech signal by performance metrics such as short time objective intelligibilityand signal to noise ratio of the denoised speech show that the speech intelligibility andspeech quality are improved by the proposed method.