Rafid Sagban
University of Babylon
Classification ruleMetaheuristicStatistical classificationAlgorithmSwarm intelligenceTabu searchData miningIterated local searchMathematical optimizationQuadratic equationLocal search (optimization)Optimization problemLocal optimumCentroidArtificial intelligenceTravelling salesman problemSet (abstract data type)Swarm behaviourSensitivity (control systems)OverfittingRule inductionTask (project management)Parallel metaheuristicPopulationAnt colony optimization algorithmsComputer scienceAnt colonyPruning (decision trees)Cluster analysisSelection (genetic algorithm)Genetic algorithmClassifier (UML)Process (computing)
21Publications
5H-index
62Citations
Publications 21
Newest
#1Ayad Mohammed Jabbar (Shatt Al-Arab University College)
#1Ayad Mohammed Jabbar (Shatt Al-Arab University College)H-Index: 4
Last. Rafid Sagban (University of Babylon)H-Index: 5
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A fundamental problem in data clustering is how to determine the correct number of clusters. The null null k null -adaptive medoid set ant colony optimization (ACO) clustering (METACOC-K) algorithm is superior in solving clustering problems. However, METACOC-K does not guarantee in finding the best number of clusters. It assumed the number of clusters based on an adaptive parameter strategy that lacks feedback learning. This has restrained the algorithm in producing compact clusters and the opti...
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Content-Centric Networking (CCN) is a novel modern architecture for the Internet in the future. This architecture concentrates on content retrieval and dissemination solution for communication models. Forwarding strategies are decision-making strategies whose aim is to define the forwarding destination, i.e., where and when request packets will be redirected. They are considered as the most crucial component in a network environment because of their contribution to determining which strategy is ...
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#2Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 9
Last. Rafid Sagban (University of Babylon)H-Index: 5
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Ant colony optimization (ACO) is a well-known algorithm from swarm intelligence that plays an essential role in obtaining rich solutions to complex problems with wide search space. ACO is successfully applied to different application problems involving rules-based classification through an ant-miner classifier. However, in the ant-miner classifier, rule-pruning suffers from the problem of nesting effect origins from the method of greedy Sequential Backward Selection (SBS) in term selection, ther...
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#1Sanju MishraH-Index: 7
#2Rafid Sagban (Information Technology University)H-Index: 5
Last. Niketa GandhiH-Index: 10
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In an era of the industrial internet of things (IoT), data transferred or saved is always vulnerable to attacks. The IoT networks are needed for implementing security in IoT devices. The IoT networ...
18 CitationsSource
#1Rafid Sagban (University of Babylon)H-Index: 5
Last. Raaid Alubady (University of Babylon)H-Index: 4
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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...
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#2Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 9
Last. Rafid Sagban (University of Babylon)H-Index: 5
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In this study, a hybrid rule-based classifier namely, ant colony optimization/genetic algorithm ACO/GA is introduced to improve the classification accuracy of Ant-Miner classifier by using GA. The Ant-Miner classifier is efficient, useful and commonly used for solving rule-based classification problems in data mining. Ant-Miner, which is an ACO variant, suffers from local optimization problem which affects its performance. In our proposed hybrid ACO/GA algorithm, the ACO is responsible for gener...
2 CitationsSource
#2Rafid Sagban (University of Babylon)H-Index: 5
Last. Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 9
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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...
4 CitationsSource
#2Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 9
Last. Rafid Sagban (University of Babylon)H-Index: 5
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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...
3 CitationsSource
#2Ku Ruhana Ku-Mahamud (UUM: Universiti Utara Malaysia)H-Index: 9
Last. Rafid Sagban (University of Babylon)H-Index: 5
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...
2 CitationsSource
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...
4 CitationsSource