Applications of Artificial Neural Network

Many artificial neural network techniques have been adopted in the research, academics and industries to address the challenges in computer vision, speech and pattern recognitions, face alignment, and detection. These include;

Speech recognition



Artificial Neural Networks have capability successfully applied in speech or communication recognition. In the past decades, ML algorithms have applied widely in areas like acoustic modeling and automatic
speech recognition.

Computer vision



Computer vision aims at making computers to accurately understand and process visual data efficiently like videos and images. Computer vision alludes to the logical control which studies how to
separate data from images in artificial frameworks. Sub domains of computer vision include object detection and object recognition, object estimation, object position, event detection, scene reconstruction, image restoration, image editing, video enhancement, and statistical learning. Hence, in computer vision, ANN models are very useful.

Pattern recognition

The recent improvement in deep learning models has given novel ways to deal with
the issue in recognition of a pattern or pattern recognition (PR). PR is a scientific area
that focus in identification of sequence in each input. PR is a general concept
that surrounds various subdomains such as speech tagging, regression, sequence labeling and classification. There are rapidly increasing needs for information process-
ing and output, due to industrial development, that has new trend and challenges to
PR.

Face alignment

Face alignment plays a role that is significant in diverse visual applications. In recent
times ANNs has claimed successes in face alignment and face recognition and other models have shown successes. Interestingly DL techniques can be applying to explain genetic variants to identify pathogenic variants. Usually, combined annotation dependent depletion algorithm is popularly applied to interpret the coding and non-coding variants.

Detection

Detection in medical diagnosis, security, image objects, financial irregularity, a fault
in a system, are being enhanced through ANNs application. Thus, ANN plays an
essential role in the detection, particularly when applied to breast cancer. The performance of ANN can be relatively compared with other approaches in crime detection such as DNA and activity profiling and the use of big data for financial crime detection

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