This study has been done to develop scientific research and select talented people of
post-graduate students (master students) to continue and get doctoral degrees (degree in
PHD) at Tishreen University. The research has been prepared, which aims t
o suggest a
model for measuring the degree of creativity and talent for post-graduate students by using
one of artificial intelligence techniques such as Fuzzy Logic.
An expert system has been built that contains an inference rule which consists of
three types of tests: (Theory Test, TT), (Practice Test, PT), and (Creativity Test, CT) for
each course.
This intelligent system has also aimed to determine the ability to make decisions
which gives the rate of talent for post- graduate students.
The study has reached to an important set of results, and the most important is:
Results have shown high strength and reliability shows the validity of this proposed
model, the validity of the results have reached to 85% and 100%, by using two different
methods to defuzzificate of proposed model.
The main goal of data mining process is to extract information and
discover knowledge from huge databases, where the clustering is
one of the most important functionalities which can be done in this
area. There are many of clustering algorithms an
d methods, but
determining or estimating the number of clusters which should be
extracted from a dataset is one of the most important issues most of
these methods encounter it. This research focuses on the problem of
estimating number of clusters in the case of agglomerative
hierarchical clustering. We present an evaluation of three of the
most common methods used in estimating number of clusters.
The Research Aims:
Syrian organizations keep large amounts of information and data about their
personnel in their IT systems. This information, however, is often left unutilized or
may be analyzed through statistical methods. In this study, DM is
considered a
solution for analyzing HR data and explore knowledge from data stored in some
Syrian organization through two major stages:
Stage A: Using results of Semi-Annual performance evaluation process to build
prototype showed in (Fig. 6) to accomplish two tasks:
1. Building a models to predict appropriate job function for an employee
through majority principle and using high accuracy result to increase the
number of training data and make it self-learning model.
2. Choose most important attributes that used in classify methods to use it in
personnel selection and recruitment.
Stage B: Using data of Time & Attendance to analysis personnel activity through
clustering methods and building many meaningful groups.
الذكاء هو القدرة على فهم و تعلم الأشياء.
الذكاء الطبيعي هو كائن له دماغ, او شيء ما, يمكنه من التعلم, و الفهم, و حل المشكلات و اتخاذ القرارات.
الذكاء الصنعي علم يبحث في السلوك الذكي لغير الكائنات الحية.
In this research, a hybrid system was proposed between the
genetic algorithm and the fuzzy Kohonen clustering network ,
where the genetic algorithm is one of the methods of artificial
intelligence is one of the modern methods.
In this paper, it has
merged two techniques of the artificial intelligent, they are the
ants colony optimization algorithm and the genetic algorithm, to
The recurrent reinforcement learning trading system
optimization. The proposed trading system
is based on an ant
colony optimization algorithm and the genetic algorithm to
select an optimal group of technical indicators, and fundamental
indicators.
Recently, a class of tracking techniques called "tracking by detection" has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This
classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause drift. In this paper, we show that usingSimple Online and Realtime Tracking (SORT) which is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms