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Scheduling using Convolutional Neural Network in GPU environment

100%

Świtalski P. , Siwiak K.

Studia Informatica : systems and information technology

2024

tom vol. 1 (30)

81--96

Graphics processing units (GPU) have become the foundation of artificial intelligence. Machine learning was slow, inaccurate, and inadequate for many of today’s applications. The inclusion and utilization of GPUs made a remarkable difference in large neural networks. The numerous core processors on a GPU allow machine learning engineers to train complex models using many files relatively quickly. The ability to rapidly perform multiple computations in parallel is what makes them so effective; with a powerful processor, the model can make statistical predictions about very large amounts of data. GPUs are widely used in machine learning because they offer more power and speed than CPUs. In this paper, we show the use of GPU for solving a scheduling problem. The results show that this idea is useful, especially for large optimization problems.

A convolutional neural network machine learning based navigation of underwater vehicles under limited communication

75%

Sahoo S. P. , Pati B. B. , Das B.

Archives of Control Sciences

2024

tom Vol. 34, No. 3

537--568

This paper proposes navigation of multiple autonomous underwater vehicles (AUVs) by employing machine learning approach for wide area surveys in underwater environment. Wide area survey in underwater environment is affected by low data rate. We consider two AUVs moving in formation through clustering followed by selection of optimal path that is affected by low data rate and limited acoustical underwater communication. A state compression approach using machine learning based acoustical localization and communication (ML-ALOC) is proposed to overcome the low data rate issue in which AUV states are approximated by Hierarchical clustering followed by an optimal selection approach using Convolutional Neural Network (CNN). The performance of the proposed state compression algorithm is compared with particle state compression algorithm based on K-Means clustering at each iteration followed by Akaike information criterion (AIC) pursuing extensive simulations, in which two AUVs navigate through trajectory. It is observed from the simulations that the proposed ML-ALOC system provides better estimates when compared with acoustical localization and communication (ALOC) system using particle clustering for state compression scheme.

SpeakerNet for Cross-lingual Text-Independent Speaker Verification

63%

Habib H. , Tauseef H. , Fahiem M. A. , Farhan S. , Usman G. , Habib H. , Tauseef H. , Fahiem M. A. , Farhan S. , Usman G.