Search Results - intelligence _ ((drops algorithm) OR (window algorithm))

Intelligent Examination Timetabling System Using Hybrid Intelligent Water Drops Algorithm by AlDeeb, BA, Norwawi, NM, Al-Betar, MA, Jali, MZ

Subjects:

Intelligent examination timetabling system using hybrid intelligent water drops algorithm by AlDeeb, Bashar A., Md Norwawi, Norita, Al-Betar, Mohammed A., Jali, Mohd Z.

“…This paper proposes Hybrid Intelligent Water Drops (HIWD) algorithm to solve Tamhidi programs uncapacitated examination timetabling problem in Universiti Sains Islamic Malaysia (USIM).Intelligent Water Drops algorithm (IWD) is a population-based algorithm where each drop represents a solution and the sharing between the drops during the search lead to better drops.The results of this study prove that the proposed algorithm can produce a high quality examination timetable in shorter time in comparison with the manual timetable.…”
Get full text
Get full text
Get full text

Solving university examination timetabling problem using intelligent water drops algorithm by Aldeeb B.A., Norwawi N.M., Al-Betar M.A., Jali M.Z.B.

Subjects: “…Intelligent water drops algorithm…”

Modified And Ensemble Intelligent Water Drop Algorithms And Their Applications by O. F. Alijla, Basem

“…Pertama, algoritma TAC yang diubahsuai, diperkenalkan. The Intelligent Water Drop (IWD) algorithm is a swarm-based model that is useful for undertaking optimization problems. …”
Get full text
Get full text

Hybrid Intelligent Water Drops Algorithm For Examination Timetabling Problem by Bashar A.Aldeeb, Mohammed Azmi Al-Betar, Norita Md Norwawi, Khalid A.Alissa, Mutasem K.Alsmadi, Ayman A.Hazaymeh, Malek Alzaqebah

Subjects: “…Examination Time table, Intelligent Water Drops algorithm, Metaheuristic, Locale search algorithm, Optimization…”

Solving University Examination Timetabling Problem Using Intelligent Water Drops Algorithm by Aldeeb, BA, Norwawi, NM, Al-Betar, MA, Bin Jali, MZ

Subjects:

IWDSA: a hybrid Intelligent Water Drops with a Simulated Annealing for the localization improvement in wireless sensor networks by Gumaida, Bassam, Ibrahim, Adamu Abubakar

“…The proposed algorithm, named Intelligent Water Drops with Simulated Annealing (IWDSA), combines two powerful optimization methods: Intelligent Water Drops (IWD) and Simulated Annealing (SA). …”
Get full text
Get full text
Get full text

Enhanced Intelligent Water Drops Algorithm for University Examination Timetabling Problems by Bashar AbedAl Mohdi Talal AlDeeb

“…This thesis presents an investigation of using the Intelligent Water Drops (IWD) algorithm to construct and produce good quality solutions for the UETP. …”

Hybrid model to improve the river streamflow forecasting utilizing multi-layer perceptron-based intelligent water drop optimization algorithm by Pham Q.B., Afan H.A., Mohammadi B., Ahmed A.N., Linh N.T.T., Vo N.D., Moazenzadeh R., Yu P.-S., El-Shafie A.

“…Complex networks; Drops; Forecasting; Iterative methods; Network architecture; Network layers; Optimization; Rivers; Stochastic models; Stochastic systems; Stream flow; Time series; Engineering applications; Gradient-decent algorithm; Intelligent Water Drops (IWD); Multi layer perceptron; Multi-layer perceptron neural networks; Optimization algorithms; Streamflow forecasting; Time series prediction; Multilayer neural networks…”

Intelligent signature detection for scanning Internet Worms by Rasheed M.M., Ghazali O., Norwawi N.M.

Enhancement on the modified artificial bee colony algorithm to optimize the vehicle routing problem with time windows by Sankor, Salah Mortada Shahen

“…The Artificial Bee Colony (ABC) is a popular swarm intelligence algorithm for COP. In this study, existing Modified ABC (MABC) algorithm is revised to solve the VRPTW. …”
Get full text
Get full text

Network calculus-based latency for time-triggered traffic under Flexible Window-Overlapping Scheduling (FWOS) in a Time-Sensitive Network (TSN) by Shalghum, Khaled M., Noordin, Nor Kamariah, Sali, Aduwati, Hashim, Fazirulhisyam

“…Accordingly, more relaxed scheduling algorithms are required. In this paper, we introduce the flexible window-overlapping scheduling (FWOS) algorithm that optimizes the overlapping among TT windows by three different metrics: the priority of overlapping, the position of overlapping, and the overlapping ratio (OR). …”
Get full text
Get full text

Design of intelligent Qira’at identification algorithm by Kamarudin, Noraziahtulhidayu

Get full text
Get full text

Implementation of an intelligent SINS navigator based on ANFIS by Ahjebory, Karim M., Ismaeel, Salam A., Alqaissi, Ahmed M.

“…In this work an intelligent navigator developed to overcome the limitations of existing Strapdown Inertial Navigation Systems (SINS) algorithm. …”
Get full text
Get full text

Intelligent motion planning of a mobile robot by using convolutional neural network / Siti Asmah Abdullah by Siti Asmah, Abdullah

“…Several algorithms are used to create the Windowing Grid in capturing the informative and relevant current position of the mobile robot. …”
Get full text
Get full text
Get full text

Inverse modeling of mass transferprocess in the RDC column by fuzzy approach by Maan, Normah, Talib, J., Arshad, K. A., Ahmad, T.

Get full text
Get full text

Design and Implementation of Intelligent Interoperability Framework for Heterogeneous Subsystems in Smart Home Environment by Perumal, Thinagaran

“…The third algorithm, named as Pro-Active Intelligence algorithm has inspired autonomous action triggering for each event interoperation, using a control action statement that is generated by SOAP packets required for joint execution of tasks among heterogeneous subsystems. …”
Get full text
Get full text

Hybrid artificial intelligent algorithm for call admission control in WCDMA mobile network by Tee Y.K., Tiong S.K., Johnny K.S.P., Yeoh E.C.

A study of frame timing synchronization for WiMAX applications by V., Jeoti, K.L., Ang, K.M., Yew

Get full text
Get full text
Get full text

Artificial intelligent power prediction for efficient resource management of WCDMA mobile network by Tee Y.K., Tinng S.K., Koh J., David Y.

“…A few comparative results in downlink have shown that our integrated support vector regression assists genetic algorithm (SVRaGA) is capable of predicting next interval power consumption at Node B with low prediction error and improving the quality of service (QoS) by reducing dropped calls. � 2008 IEICE.…”