8th International Conference on Advanced Computer Science and Information Technology (ICAIT 2019)

March 30-31, 2019, Zurich, Switzerland

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Accepted Papers


Constructing mixed failure model and estimating its fuzzy hazard rate function by different methods using simulation

Dr.Inaam Rikan Hassan,Dr. Jane Jaleel Stephan,Alaa Hamza Omran ,University of information technology and communications,Iraq

ABSTRACT

In this paper we work on estimating parameters of compound three parameters (Burr-XII) which is one of time to failure model distribution with two shapes parameters (α,r) and one scale parameter (λ), the three parameters (α,r,λ) are estimated by methods of moments and method of maximum likelihood and also method of Least square.

KEYWORDS

fuzzy hazard, Burr-XII, failure model, maximum likelihood method, moment method.


THE SYSTEM DESIGN FOR FATIGUE DRIVING DETECTIONBY BRAINWAVES ANALYSIS IN SMARTPHONE

Yung Gi Wu1, Jwu Jenq Chen2 and Rui Hsin Wang3 ,1,2,3Department of Computer Science & Information Engineering, Chang Jung Christian University, Taiwan.

ABSTRACT

The car accidents caused by fatigue driving have been reporting from the news. In order to avoid the accidents mentioned above, we survey some published methods that used to detect fatigue and find that every method has its own specified purposes. Therefore, this research design the system to detect fatigue and alert driver with light weight brainwave detector and smartphone that everyone has for most car drivers. In this system, the driver wear a light weight head mounted brainwave device and the signal is transmitted to the smartphone.Our algorithm calculates the driver’s fatigue index by focus, eyes blink frequency, α-wave, β-wave, δ-wave and θ-wave to judge the driver’s condition. The experimental results show that the system can not only remind driverwhen they are actually fatigued but also find the fatigue period by examine historical records. This research can reduce the occurrence of car accident caused by driver’s fatigue.

KEYWORDS

Brainwave Detecting,Fatigue Detecting, APP.


A MULTI-AGENT SYSTEM FOR INTELLIGENT GENERATION OF UNIVERSITY TIME SCHEDULES

Abderrahim Siam1, and Samir Safir2 ,1ICOSI Lab, Abbes Laghrour University, Khenchela, Algeria,2Department of Mathematics and Computer Engineering, Abbes Laghrour University, Khenchela, Algeria

ABSTRACT

The car accidents caused by fatigue driving have been reporting from the news. In order to avoid the accidents mentioned above, we survey some published methods that used to detect fatigue and find that every method has its own specified purposes. Therefore, this research design the system to detect fatigue and alert driver with light weight brainwave detector and smartphone that everyone has for most car drivers. In this system, the driver wear a light weight head mounted brainwave device and the signal is transmitted to the smartphone.Our algorithm calculates the driver’s fatigue index by focus, eyes blink frequency, α-wave, β-wave, δ-wave and θ-wave to judge the driver’s condition. The experimental results show that the system can not only remind driverwhen they are actually fatigued but also find the fatigue period by examine historical records. This research can reduce the occurrence of car accident caused by driver’s fatigue.

KEYWORDS

Brainwave Detecting,Fatigue Detecting, APP.


EFFICIENT TECHNIQUE FOR COURSES SCHEDULING

Abdoul Rjoub1, Renad Haddad2,and Rayeh Alghsoon3 ,1Department of Computer Engineering,Jordan University of Science and Technology, Irbid, Jordan,2Department of Mathematics and Computer Engineering, Abbes Laghrour University, Khenchela, Algeria, 3University of Jordan, Amman, Jordan

ABSTRACT

In addition to its monotony and time-consuming, manual school timetabling leads to have more than one class are assigned to the same instructor. Moreover, more than one instructor are assigned to the same class at the same time slot. In this paper school timetable generation process is developed and new technique is adopted to address the challenges of creating school timetable. Hill climbing algorithm has been used to transact hard and soft constraints. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first iteration and by 50% from the second iteration. While, the optimal solution will be achieved after 15 iteration ensuring that more than 50% of scientific courses will take place in the early time slots.

KEYWORDS

Course Schedule, Hill Climbing Algorithm, School Timetable, Scheduling, Timetabling.