Objective
This research aimed to describe several areas in which AI could play a role in the development of Personalized Medicine and Drug Screening, and the transformations it has created in the field of biology and therapy. It also addressed the l
imitations faced by the application of artificial intelligence techniques and make suggestions for further research.
Methods
We have conducted a comprehensive review of research and papers related to the role of AI in personalized medicine and drug screening, and filtered the list of works for those relevant to this review.
Results
Artificial Intelligence can play an important role in the development of personalized medicines and drug screening at all clinical phases related to development and implementation of new customized health products, starting with finding the appropriate medicines to testing their usefulness. In addition, expertise in the use of artificial intelligence techniques can play a special role in this regard.
Discussion
The capacity of AI to enhance decision-making in personalized medicine and drug screening will largely depend on the accuracy of the relevant tests and the ways in which the data produced is stored, aggregated, accessed, and ultimately integrated.
Conclusion
The review of the relevant literature has revealed that AI techniques can enhance the decision-making process in the field of personalized medicine and drug screening by improving the ways in which produced data is aggregated, accessed, and ultimately integrated. One of the major obstacles in this field is that most hospitals and healthcare centers do not employ AI solutions, due to healthcare professionals lacking the expertise to build successful models using AI techniques and integrating them with clinical workflows.
Flight delays are frequent all over the world (about 20% of airline flights arrive more than 15 minutes
late) and they are estimated to have an annual cost of several tens of billion dollars. This scenario makes
the prediction of flight delays a pr
imary issue for airlines and travelers. The main goal of this work is to
implement a predictor of the arrival delay of a scheduled flight due to weather conditions. The predicted
arrival delay takes into consideration both flight information (origin airport, destination airport, scheduled
departure and arrival time) and weather conditions at origin airport and destination airport according to
the flight timetable. Airline flights and weather observations datasets have been analyzed and mined using
parallel algorithms implemented as MapReduce programs executed on a Cloud platform. The results show
a high accuracy in predicting delays above a given threshold. For instance, with a delay threshold of 15
minutes we achieve an accuracy of 74.2% and 71.8% recall on delayed flights, while with a threshold of
60 minutes the accuracy is 85.8% and the delay recall is 86.9%. Furthermore, the experimental results
demonstrate the predictor scalability that can be achieved performing data preparation and mining tasks
as MapReduce applications on the Cloud.
تعرض المحاضرة شرح عن علم البيانات وعلاقته بعلم الإحصاء والتعلم الآلي وحالتين دراسيتين عن دور عالم البيانات في تصميم حلول تعتمد على استخراج المعرفة من حجم كبير من البيانات المتوفرة, كما يتم عرض أهم المهام في المؤتمرات العلمية التي يمكن المشاركة بها لطلاب المعلوماتية المهتمين بهذا المجال
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.
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.
Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; cof-ounder and CEO of Tesla and SpaceX
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.
This research work presents fuzzy pitch controller design of wind turbine to get the maximum power in
addition to decrease the losses caused by acceleration and deceleration in turbine rotation. And thus
optimize power coefficient of turbine throug
h artificial intelligence and in particular fuzzy logic, because
the fuzzy controller doesn’t need a complex mathematical pattern of the controlled system.
A fuzzy controller is designed and compared with conventional controller for the same purpose in a wind
turbine system described by its transfer function and membership function has been chosen for error and
accumulation errors signals by using MATLAB. Results have been compared and showed better response
by using the fuzzy controller.
