The aim of this research is subjected to discover The Dictation mistakes which occur
widely in the Arabic Language written by Iraqi – News papers.
The research classified These Mistakes in Three groups as Follows:
1. The first group dealing with distinguishing between the Arabic – letter ) ا ( When Written
or ) أ ( at The beginning The word and as ) ئ ( which differ in meaning from each other .
2. The second group dealing with the writing of The Arabic ) ٙ( and as ) ٛ( at the end of The
word which differ in meaning from each other.
3. The Third group dealing with distinguishing in written The Arabic letter as ) ض( or as ) )ظ
which differ in meaning From each other.
4. The fourth group dealing with The writing The one Arabic – letter sometime as ) ٕ ( and
other time as ) ح( which differ each other .
Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c
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Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization
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