Analysis study about (MFCC and Endpoint) algorithms and the extent of their impact in voice recognition rates

Voice recognition includes two basic parts: speech and speaker recognition. These recognition processes consider as the most important processes of modern technologies, many systems has been developed that differ in the methods used to extract features and classification ways to support recognition systems of this type. The study was conducted in this research on the previous subject, where the system is designed to recognize the speaker and his voice orders and focus on several complementary algorithms to carry out the research. we conducted an analytical study on MFCC algorithm used in the extraction of features, and it has been studying two parameters the number of filters in the filters bank and the number of features that taken from each frame and the impact of these two parameters in the recognition rate and the relationship of these two parameters on each other. It was the use of feed forwarding back propagation neural networks performance analysis as characteristics and we analyze the performance of the network to gain access to the best features and components to the process of achieving recognition. And it has been studying Endpoint algorithm that used to remove periods of silence and its impact on voice recognition rates.

Neural networks and their role in the sales forecasting accuracy (Case study of ALFANAR Company)

أهداف البحث: -1 دراسة نظرية عن أهمية و أثر الدقة في التنبؤ بالمبيعات على خطط الإنتاج و التسويق و التوزيع. -2 دراسة مرجعية عن التنقيب في البيانات و التنبؤ باستخدام السلاسل الزمنية و الشبكات العصبونية. -3 استخدام الشبكات العصبية الصناعية في زيادة دقة التنبؤ بحجم المبيعات الشهرية لشركة الفنار. -4 اختبار تفوق الشبكات العصبية في التنبؤ على نموذجي المتوسطات المتحركة و الانحدار.

Artificial intelligence techniques for the development of statistical Automatic learning

The word "massive data" spread in 2017 and became the most common in the industry of advanced technology, it uses automated learning that allows computers to analyze past data and predict future data widely in familiar places. Non-automated learning professionals can use it too. To study the analytical method of statistical Automatic learning, it is necessary to identify the concept of artificial intelligence and its main classification and analytical techniques included and represent in automatic learning and deep learning. Automatic learning has developed thanks to some breakthroughs in artificial intelligence. It is an awareness of the efficient teaching of computers in addition to the invention of the Internet. Neural networks have an important role to play in teaching computers, such as humans, where they use data they can access to make decisions. There are many algorithms for learning about automatic learning. In our study, we demonstrate the methods and applications of automated statistical analysis, such as regression analysis, decision tree, middle method k and association analysis.