This paper aims to study the Conceptual Tools used in Conceptual Modeling
of Database, so that Benefit of Extending these Tool used in Effective Relational
Database Design could be found out.
In this study, we investigated two application examples
, through which two
Conceptual diagrams were used to build Relational Database.
The main result of this study was, the benefit from the extending the
Conceptual Tools for designing a Relational Database could be achieved when
this extension to Tools used in building is applicable to the Database. Since the
Conceptual Modeling stage of the Database, has to be simple and clear, the
extension is not recommended for modeling Conceptual diagrams of large
systems, which has time limits.
This work seeks to enhance the quality of the educational process output through ensure the quality of its inputs.
This thesis focused on composing two models built by using Neural Networks techniques for increasing the accuracy of predicting result represented by stock value in stock market, where the proposed solution includes two modalities of processing: in f
irst one, we build two neural networks which we want to compose, one of the two networks deals with previous stock’s prices and the other one with indecators of technical analysis, then we will compose the outputs of these two networks into third neural network which represents the second modality of processing.
ظهرت المحاججة كمقاربة مهمة في الذكاء الصنعي لحل مسائل دعم القرار بسبب قدرتها على إعطاء مبررات
للقرار المتخذ من خلال تقديم الحجج الداعمة و الحجج الداحضة التي ستقنع كلاً من متخذي القرار و المعنيين
به، إضافة إلى قدرة المحاججة على حل مسائل دعم القرار ف
ي حالات المعلومات غير الكاملة، و لأنها تمثل
الطريقة الأكثر قرباً لتلك التي يتداول فيها الأشخاص واقعياً لاتخاذ قراراتهم. تسمح المحاججة أيضاً بمعالجة
مسألة دعم القرار بوجود معلومات متناقضة، و لهذه الميزة أهمية خاصة في حالة القرار الجماعي، لأن كل مشارك في اتخاذ قرار يستند إلى جملة معارف و اعتبارات قد تكون مختلفة ( و أحياناً متناقضة ) عن تلك التي تكون لدى مشاركٍ آخر.
