Rafid Sagban
@alayen.edu.iq
Technical Engineering College
Al-Ayen University
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Aso M. Aladdin, Jaza M. Abdullah, Kazhan Othman Mohammed Salih, Tarik A. Rashid, Rafid Sagban, Abeer Alsaddon, Nebojsa Bacanin, Amit Chhabra, S. Vimal, and Indradip Banerjee
CRC Press
Tao Hai, Biju Theruvil Sayed, Ali Majdi, Jincheng Zhou, Rafid Sagban, Shahab S. Band, and Amir Mosavi
Informa UK Limited
Bo Liu, Imran Khalid, Indrajit Patra, Oleg R. Kuzichkin, R. Sivaraman, Abduladheem Turki Jalil, Rafid Sagban, Ghassan Fadhil Smaisim, Hasan Sh. Majdi, and Maboud Hekmatifar
Elsevier BV
R. Sivaraman, Indrajit Patra, Maria Jade Catalan Opulencia, Rafid Sagban, Himanshu Sharma, Abduladheem Turki Jalil, and Abdol Ghaffar Ebadi
Elsevier BV
Abeer Raad and Rafid Sagban
IEEE
Text summarization is a process of converting a big textual information from single or multi documents into a concise text without change its semantics. Ant Colony Optimization (ACO) is a prominent framework applied successfully for text summarization. However, existing ACO-based text summarization methods did not consider three characteristics in the calculations of the heuristic function of their summarization, they are the main content coverage, the redundancy reduction, and the sentiment reflection. In this paper, the proposed SU-ACO algorithm is based on a new heuristic function that unified the three objectives in ACO-based summarization. Results showed the superiority of the proposed method over other related methods in literature.
Alaa Akram Huby, Raaid Alubady, and Rafid Sagban
IEEE
Large tankers, ships, and pipeline cracks that spew oil onto sea surfaces wreak havoc on the maritime environment. Target scenarios, such as sea and land surfaces, ships, oil spills, and lookalikes, are roughly represented by synthetic aperture radar (SAR) photographs. To assist in leak cleanups and safeguard the environment, oil spills from SAR pictures must be identified and segmented. This research introduces a deep-learning system that uses the U-Net semantic segmentation technique to detect oil spills. With the use of a Densnet201 model that was previously trained on the Imagnet dataset, the encoder portion of Unet was substituted. The Decoder component, in contrast, uses a U-Net framework. Five groups of the dataset are classified using 256256 spatial dimensions and their corresponding annotations. The U-net with the DenseNet201 backbone presented slightly better results (96% accuracy, 79% precision, 80% recall, 80% F-score, and 69% IoU). Moreover, The results of this study are very promising and provide a comparable improved IoU compared to related works.
Alaa Akram Huby, Rafid Sagban, and Raaid Alubady
IEEE
For many years, oil spills have posed a huge and inescapable threat to the seas and oceans. Hence, it is noted that oil spills caused by the purposeful or unintentional discharge of liquid petroleum hydrocarbons into water are to blame for a number of ecological disasters, which disrupt the marine life cycle as well as degrade the productivity and quality of the marine environment, posing significant environmental dangers. There exist various methods, for example, zone segmentation, edge detection and threshold segmentation to address oil spill problem. Currently Machine Learning (ML) and Deep Learning (DL) are used as one of the efforts identify oil spills based on Artificial Intelligence (AI) because they are the most significant techniques and playing a significant role in the oil spills’ monitoring and accurate detection. This paper summary the classification of modern methods for detecting oil spills in the time period (2018-2021) especially explains the utilization of ML and DL methods to address the problem through the presentation and analysis. On the other hand, describe the advantage and disadvantage of these studies. In addition to denote the ideas for a future research direction to develop oil spill detection.
Raaid Alubady, Rafid Sagban, Haydar A. Marhoon, and Ahmed Alkhayyat
Praise Worthy Prize
Ayad Mohammed Jabbar, Ku Ruhana Ku-Mahamud, and Rafid Sagban
Insight Society
A fundamental problem in data clustering is how to determine the correct number of clusters. The k -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 optimal number of clusters. In this paper, a self-adaptive ACO clustering (S-ACOC) algorithm is proposed to produce the optimal number of clusters by incorporating a self-adaptive parameter strategy. The S-ACOC algorithm is a centroid-based algorithm that automatically adjusts the number of clusters during the algorithm run. The selection of the number of clusters is based on a construction graph that reflects the influence of a pheromone in algorithm learning. Experiments were conducted on real-world datasets to evaluate the performance of the proposed algorithm. The external evaluation metrics (purity, F-measure, and entropy) were used to compare the results of the proposed algorithm with other swarm clustering algorithms, including a genetic algorithm (GA), particle swarm optimization (PSO), and METACOC-K. Results showed that S-ACOC provides higher purity (50%) and lower entropy (40%) than GA, PSO, and METACOC-K. Experiments were also performed on several predefined clusters, and results demonstrate that the S-ACOC algorithm is superior to GA, PSO, and METACOC-K. Based on the superior performance, S-ACOC can be used to solve clustering problems in various application domains.
Hayder Naser Khraibet Al-Behadili, Ku Ruhana Ku-Mahamud, and Rafid Sagban
Insight Society
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, thereby depriving the opportunity of obtaining a good pruned rule by adding/removing the terms during the pruning process. This paper presents an extension to the Ant-Miner, namely the genetic algorithm Ant-Miner (GA-Ant Miner), which incorporates the use of GA as a key aspect in the design and implementation of a new rule pruning technique. This pruning technique consists of three fundamental procedures: an initial population Ant-Miner, crossover to prune the rule, and mutation to diversify the pruned classification rule. The GA-Ant Miner performance is tested and compared with the most related ant-mining classifiers, including the original Ant-Miner, ACO/ PSO2, TACO-Miner, CAnt-Miner, and Ant-Miner with a hybrid pruner, across various public available UCI datasets. These datasets are varied in terms of instance number, feature size, class number, and the application domains. Overall, the performance results indicate that the GA-Ant Miner classifier outperforms the other five classifiers in the classification accuracy and model size. Furthermore, the experimental results using statistical test prove that GA-Ant Miner is the best classifier when considering the multi objectives (i.e., accuracy and model size ranks).
Sanju Mishra, Rafid Sagban, Ali Yakoob, and Niketa Gandhi
Informa UK Limited
ABSTRACT 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 networks are considered as secured with authentication and encryption, but these networks are not protected against cyber-attacks. Although there exist hundreds of data protection systems, but there are some shortcomings as well. Thus, anomaly detection takes the responsibility upon itself to make various kinds of attacks less vulnerable. This is achieved by making use of the power of data mining algorithms and tools to analyze and capture any anomalous network traffic. Swarm intelligence has been integrated with data mining to generate lightweight but robust methods to detect and identify the flow of data effectively. This review paper pursues a twofold goal. First is to review various swarm-based anomaly detection methods and to provide new insights in that direction. Secondly, to replenish the literature with fresh reviews on swarm-based data mining studies based on anomaly detection. Further it discusses various methods and architectures of anomaly detection based on statistical, machine learning and data mining techniques.
Rafid Sagban, Haydar A. Marhoon, and Raaid Alubady
Institute of Advanced Engineering and Science
Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy.
Hayder Naser Khraibet Al-Behadili, Rafid Sagban, and Ku Ruhana Ku-Mahamud
IAES Indonesia Section
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 criterion is responsible for adding only the important terms to each rule, thus discarding noisy data. The ACS algorithm is designed to optimize the IR parameter during the learning process of the Ant-Miner algorithm. The performance of the proposed classifier is compared with related ant-mining classifiers, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with a hybrid pruner across several datasets. Experimental results show that the proposed classifier significantly outperforms the other ant-mining classifiers.
Hayder Naser Khraibet AL-Behadili, Ku Ruhana Ku-Mahamud, and Rafid Sagban
Science Publications
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 generating classification rules and the GA improves the classification rules iteratively using the principles of multi-neighborhood structure (i.e., mutation and crossover) procedures to overcome the local optima problem. The performance of the proposed classifier was tested against other existing hybrid ant-mining classification algorithms namely, ACO/SA and ACO/PSO2 using classification accuracy, the number of discovered rules and model complexity. For the experiment, the 10-fold cross-validation procedure was used on 12 benchmark datasets from the University California Irwine machine learning repository. Experimental results show that the proposed hybridization was able to produce impressive results in all evaluation criteria.
Ayad Mohammed Jabbar, Ku Ruhana Ku-Mahamud, and Rafid Sagban
Institute of Advanced Engineering and Science
<span lang="EN-GB">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 foraging behaviour of ants. This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. However, ACOC suffers from high diversification in which the algorithm cannot search for best solutions in the local neighbourhood. To improve the ACOC, this study proposes a modified ACOC, called M-ACOC, which has a modification rate parameter that controls the convergence of the algorithm. Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms. </span>
Ayad Mohammed Jabbar, Ku Ruhana Ku-Mahamud, and Rafid Sagban
Science Publications
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 regions and is fully responsible for transferring the neighborhood of the current structures to the next iteration. Ant Colony Optimization for Clustering (ACOC) is a swarm algorithm inspired from nature to solve clustering issues as optimization problems. However, ACOC defined implicit memory (pheromone matrix) inability to retain previous information on an ant’s movements in the pheromone matrix. The problem arises because ACOC is a centroid-label clustering algorithm, in which the relationship between a centroid and instance is unstable. The label of the current centroid value changes from one iteration to another because of changes in centroid label. Thus the pheromone values are lost because they are associated with the label (position) of the centroid. ACOC cannot transfer the current clustering solution to the next iterations due to the history of the search being lost during the algorithm run. This study proposes a new centroid memory (A-ACOC) for data clustering that can retain the information of a previous clustering solution. This is possible because the pheromone is associated with the adaptive instance and not with label of the centroid. Centroids will be identified based on the adaptive instance route. A comparison of the performance of several common clustering algorithms using real-world data sets shows that the accuracy of the proposed algorithm surpasses those of its counterparts.
Ishraq Abdul Alameer and Rafid Sagban
American Scientific Publishers
Hayder Naser Khraibet AL-Behadili, Ku Ruhana Ku-Mahamud, and Rafid Sagban
Institute of Advanced Engineering and Science
<span>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, </span><span>whereas exploration aims towards new promising regions during a search process. </span><span>The existing balance between exploration and exploitation in the rule construction procedure is limited to the roulette wheel selection mechanism, which complicates rule generation. Thus, low-coverage complex rules with irrelevant terms will be generated. This work proposes an enhancement rule pruning procedure for the ACO algorithm that can be used in rule-based classification. This procedure, called the annealing strategy, is an improvement of ant-mining algorithms in the rule construction procedure. Presented as a pre-pruning technique, the annealing strategy deals first with irrelevant terms before creating a complete rule through an annealing schedule. The proposed improvement was tested through benchmarking experiments, and results were compared with those of four of the most related ant-mining algorithms, namely, Ant-Miner, CAnt-Miner, TACO-Miner, and Ant-Miner with hybrid pruner. </span><span>Results display that our proposed technique achieves better performance in terms of classification accuracy, model size, and </span><span>computational time. </span><span>The proposed annealing schedule can be used in other ACO variants for different applications to improve classification accuracy.</span>
Ayad Mohammed Jabbar, Ku Ruhana Ku-Mahamud, and Rafid Sagban
IEEE
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 clustering, the problems are caused by sensitivity to the number of provided clusters. In stochastic clustering, problems are caused either by the absence of an optimal number of clusters or by the projection of data. The review is focused on ant-based sorting and ACO-based clustering which have problems of slow convergence, un-robust results and local optima solution. The results from this review can be used as a guide for researchers working in the area of data clustering as it shows the strengths and weaknesses of using both clustering approaches.
Hayder Naser Khraibet Al-Behadili, Ku Ruhana Ku-Mahamud, and Rafid Sagban
IEEE
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 pruning in ant-miner classification algorithms is equivalent to that of local search in stochastic methods when they aim to search for more improvement for each candidate solution. In this paper, we review the history of the pruning techniques in ant-miner and its variants. These techniques are classified into post-pruning, pre-pruning and hybrid-pruning. In addition, we compare and analyse the advantages and disadvantages of these methods. Finally, future research direction to find new hybrid rule pruning techniques are provided.
Rafid Sagban, Ku Ruhana Ku-Mahamud, and Muhamad Shahbani Abu Bakar
IEEE
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 (RACO) strategy that sticks to the reactive way of automation using memory, diversity indication, and parameterization. The performance of RACO is evaluated on the travelling salesman and quadratic assignment problems from TSPLIB and QAPLIB, respectively. Results based on a comparison of relative percentage deviation revealed the superiority of RACO over other well-known metaheuristics algorithms. The output of this study can improve the quality of solutions as exemplified by RACO.
Rafid Sagban, Ku Ruhana Ku-Mahamud, and Muhamad Shahbani Abu Bakar
Computers, Materials and Continua (Tech Science Press)
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 tested on two well-known combinatorial optimization problems arising in the artificial intelligence problems field and compared experimentally to six (6) variants of ACO with local search. Results showed that the enhanced algorithm outperforms the six ACO variants.