Match!
Rafid Sagban
University of Babylon
MetaheuristicSwarm intelligenceTravelling salesman problemAnt colony optimization algorithmsComputer science
10Publications
3H-index
20Citations
What is this?
Publications 14
Newest
#1Rafid Sagban (University of Babylon)H-Index: 3
Last. Raaid Alubady (University of Babylon)H-Index: 2
view all 3 authors...
Rule-based classification in the health field using artificial intelligence went away to rendering solutions in decision-making problems in different domains. The most important of these challenges is access to good and fast health facilities, which pose a major threat to injure the disease. Cervical cancer is one of the most frequent causes of death to the female. The diagnosis methods for cervical cancer used in health centers are costly and time-consuming. In this paper, Bat Algorithm for Fea...
Source
#2Rafid Sagban (University of Babylon)H-Index: 3
Last. Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 8
view all 3 authors...
Pruning is the popular framework for preventing the dilemma of overfitting noisy data. This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-AntMiner. A key aspect of this algorithm is the selection of an appropriate number of terms to be included in the classification rule. ACS-AntMiner introduces a new parameter called importance rate (IR) which is a pre-pruning criterion based on the probability (heuristic and pheromone) amount. This crite...
Source
#2Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 8
Last. Rafid Sagban (University of Babylon)H-Index: 3
view all 3 authors...
Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. Each object in every cluster exhibits sufficient similarity to its neighbourhood, whereas objects with insufficient similarity are found in other clusters. Data clustering techniques minimise intra-cluster similarity in each cluster and maximise inter-cluster dissimilarity amongst different clusters. Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the...
Source
#2Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 8
Last. Rafid Sagban (University of Babylon)H-Index: 3
view all 3 authors...
Ant colony optimization (ACO) was successfully applied to data mining classification task through ant-mining algorithms. Exploration and exploitation are search strategies that guide the learning process of a classification model and generate a list of rules. Exploitation refers to the process of intensifying the search for neighbors in good regions, whereas exploration aims towards new promising regions during a search process. The existing balance between exploration and exploitation in the ru...
Source
Ant Colony Optimization (ACO) is a generic algorithm, which has been widely used in different application domains due to its simplicity and adaptiveness to different optimization problems. The key component that governs the search process in this algorithm is the management of its memory model. In contrast to other algorithms, ACO explicitly utilizes an adaptive memory, which is important to its performance in terms of producing optimal results. The algorithm’s memory records previous search reg...
Source
#1Hayder Naser Khraibet Al-Behadili (UUM: Universiti Utara Malaysia)H-Index: 1
#2Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 8
Last. Rafid Sagban (University of Babylon)H-Index: 3
view all 3 authors...
Rule-based classification is considered an important task of data classification. The ant-mining rule-based classification algorithm, inspired from the ant colony optimization algorithm, shows a comparable performance and outperforms in some application domains to the existing methods in the literature. One problem that often arises in any rule-based classification is the overfitting problem. Rule pruning is a framework to avoid overfitting. Furthermore, we find that the influence of rule prunin...
1 CitationsSource
#1Ayad Mohammed Jabbar (UUM: Universiti Utara Malaysia)
#2Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 8
Last. Rafid Sagban (University of Babylon)H-Index: 3
view all 3 authors...
Data clustering is used in a number of fields including statistics, bioinformatics, machine learning exploratory data analysis, image segmentation, security, medical image analysis, web handling and mathematical programming. Its role is to group data into clusters with high similarity within clusters and with high dissimilarity between clusters. This paper reviews the problems that affect clustering performance for deterministic clustering and stochastic clustering approaches. In deterministic c...
Source
May 1, 2017 in CIT (Computer and Information Technology)
#1Rafid Sagban (College of Information Technology)H-Index: 3
#2Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 8
Last. Muhamad Shahbani Abu Bakar (UUM: Universiti Utara Malaysia)H-Index: 3
view all 3 authors...
This intensification and diversification in Ant Colony Optimization (ACO) is the search strategy to achieve a trade-off between learning a new search experience (exploration) and earning from the previous experience (exploitation). The automation between the two processes is maintained using reactive search. However, existing works in ACO were limited either to the management of pheromone memory or to the adaptation of few parameters. This paper introduces the reactive ant colony optimization (R...
1 CitationsSource
#1Rafid Sagban (University of Babylon)H-Index: 3
view all 3 authors...
AbstractAnt colony optimization is a successful metaheuristic for solving combinatorial optimization problems. However, the drawback of premature exploitation arises in ant colony optimization when coupled with local searches, in which the neighborhood’s structures of the search space are not completely traversed. This paper proposes two algorithmic components for solving the premature exploitation, i.e. the reactive heuristics and recursive local search technique. The resulting algorithm is tes...
3 CitationsSource
#1Rafid SagbanH-Index: 3
view all 3 authors...
Ant colony optimization (ACO) is a stochastic search method for solving NP-hard problems. The exploration versus exploitation dilemma rises in ACO search.Reactive max-min ant system algorithm is a recent proposition to automate the exploration and exploitation.It memorizes the search regions in terms of reactive heuristics to be harnessed after restart, which is to avoid the arbitrary exploration later.This paper examined the assumption that local heuristics are useless when combined with local ...
1 Citations
12