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نتیجه جستجو - Language recognition

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Sign Language Recognition: A Deep Survey
شناخت زبان اشاره: یک مرور عمیق-2021
Sign language, as a different form of the communication language, is important to large groups of people in society. There are different signs in each sign language with variability in hand shape, motion profile, and position of the hand, face, and body parts contributing to each sign. So, visual sign language recognition is a complex research area in computer vision. Many models have been proposed by different researchers with significant improvement by deep learning approaches in recent years. In this survey, we review the vision- based proposed models of sign language recognition using deep learning approaches from the last five years. While the overall trend of the proposed models indicates a significant improvement in recognition accuracy in sign language recognition, there are some challenges yet that need to be solved. We present a taxonomy to categorize the proposed models for isolated and continuous sign language recognition, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field.
Keywords: Sign language recognition | Pose estimation | Deep learning | Computer Vision | Face recognition | Application
مقاله انگلیسی
2 Data mining algorithm for pre-processing biopharmaceutical drug product manufacturing records
الگوریتم داده کاوی برای سوابق تولید دارویی بیوشیمیایی قبل از پردازش-2019
The quality of data plays a crucial role in providing a reliable decision-making process when improving processes and operations under uncertainty. We present a data mining-based algorithm for robustly pre- processing the manufacturing records of biopharmaceutical batch processes. The algorithm can identify the time intervals in which the process is in commercial operation, and can characterize process fail- ures automatically. An approximate string-matching algorithm, a decision tree classifier and a constrained clustering is applied to sequence the raw data, to classify the noise and identify each single batches; fi- nally process failure are characterized. The algorithm was applied to the records of the process named as “cleaning- and sterilizing-in-place”, which is an essential process in manufacturing environment, in a case study. The algorithm was training on state of the art manual pre-processing outcome and was ap- plied reducing the execution time of the activity down to 11.7% while maintaining high data quality and integrity.
Keywords: GMP | Noise Filtering | Language recognition | Supervised machine learning | Semi-supervised machine learning | Ishikawa fishbone diagram
مقاله انگلیسی
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