arXiv subjects

Bryar A. Hassan

Publications and source records attributed to Bryar A. Hassan.

30 records · Page 2Linked to original sources

arXiv2022

Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application

cs.NE↗

arXiv2021

A Novel Cluster Detection of COVID-19 Patients and Medical Disease Conditions Using Improved Evolutionary Clustering Algorithm Star

With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically; however, these definitions have not been effective in grouping and analysing medical diseases. The use of evolutionary clustering algorithms may help to effectively cluster these diseases. On this presumption, we improved the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners: (i) utilising the elbow method to find the correct number of clusters; (ii) cleaning and processing data as part of iECA* to apply it to multivariate and domain-theory datasets; (iii) using iECA* for real-world applications in clustering COVID-19 and medical disease datasets. Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms using performance and validation measures (validation measures, statistical benchmarking, and performance ranking framework). The results demonstrate three primary findings. First, iECA* was more effective than other algorithms in grouping the chosen medical disease datasets according to the cluster validation criteria. Second, iECA* exhibited the lower execution time and memory consumption for clustering all the datasets, compared to the current clustering methods analysed. Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets. Further research is required on real-world multi-dimensional data containing complex knowledge fields for experimental verification of iECA* compared to evolutionary algorithms.

cs.NE↗

arXiv2021

Artificial Intelligence Algorithms for Natural Language Processing and the Semantic Web Ontology Learning

Evolutionary clustering algorithms have considered as the most popular and widely used evolutionary algorithms for minimising optimisation and practical problems in nearly all fields. In this thesis, a new evolutionary clustering algorithm star (ECA*) is proposed. Additionally, a number of experiments were conducted to evaluate ECA* against five state-of-the-art approaches. For this, 32 heterogeneous and multi-featured datasets were used to examine their performance using internal and external clustering measures, and to measure the sensitivity of their performance towards dataset features in the form of operational framework. The results indicate that ECA* overcomes its competitive techniques in terms of the ability to find the right clusters. Based on its superior performance, exploiting and adapting ECA* on the ontology learning had a vital possibility. In the process of deriving concept hierarchies from corpora, generating formal context may lead to a time-consuming process. Therefore, formal context size reduction results in removing uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this work aims to propose a framework to reduce the ambiguity of the formal context of the existing framework using an adaptive version of ECA*. In turn, an experiment was conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reduction of formal context size, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context was evaluated to the original one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the original one.

cs.AI↗

arXiv2021

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using FCA. However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is required to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising FCA; (2) A framework to decrease the formal contexts ambiguity of the first framework using an adaptive version of ECA*. Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.

cs.AI↗

arXiv2021

A New Framework to Adopt Multidimensional Databases for Organizational Information System Strategies

As information becomes increasingly sizable for organizations to maintain the challenge of organizing data still remains. More importantly, the on-going process of analysing incoming data occurs on a continual basis and organizations should employ existing procedures that may not be adequate or efficient when attempting to access specific information to analyse. In these latter days of technological advancement, organizations can offer their customers extensive data resources to utilize and thus accomplish individual objectives and maintain competitiveness; however, it remains a challenge in providing data in a format that serves each clients suited needs. For some, the complexity of a data model can be overwhelming to utilize. Furthermore, companies should secure an understanding of the purchasing power used by specific consumer groups to remain competitive and ease the operation of data analysis. This research paper is to examine the use of multi-dimensional models within a business environment and how it may provide customers and managers with generating queries that will provide accurate and relevant data for effective analysis. It also provides a new framework that can aid various types of organisations using sizable database systems to create their own multidimensional model from relational databases and present the data in multidimensional views. It also defines the requirements. Despite the availability of set tools, the complexity of utilizing the conceptions discourages customers as they may become apprehensive about exploring these options for analytical purposes. This could be done by conducting a query.

cs.DB↗

arXiv2021

Towards Semantic Web: Challenges and Needs

There has been recently a growth of interest in developing the current machine-readable Web towards the next generation of machine-understandable Web - Semantic Web. The development of the Web to a global business was reasonably fast, whereas Semantic Web development has taken time from a plan to be used as the mainstream Web. It is also important to note that the use of Semantic Web would only be successful in small technologies. However, the goal of Semantic Web is to be used in big technologies and to be the mainstream Web. Some challenges may impede make further progress of Semantic Web. In this review paper, an overview of the current status and future needs of Semantic Web will be presented. Specifically, the challenges and needs of Semantic Web in the hope of shedding some light on the adoption or infusion of Semantic Web in the future will be discussed. Then, a critical evaluation of these challenges and needs will be presented. Semantic Web has a clear vision. It is moving, in line with this vision, towards overcoming the challenges and usability in real world applications.

cs.SE↗

arXiv2021

Evaluating e-Government Services in Kurdistan Institution for Strategic Studies and Scientific Research Using the EGOVSAT Model

Office automation is an initiative used to digitally deliver services to citizens, private and public sectors. It is used to digitally collect, store, create, and manipulate office information as a need of accomplishing basic tasks. Azya Office Automation has been implemented as a pilot project in Kurdistan Institution for Strategic Studies and Scientific Research (KISSR) since 2013. The efficiency of governance in Kurdistan Institution for Strategic Studies and Scientific Research has been improved, thanks to its implementation. The aims of this research paper is to evaluate user satisfaction of this software and identify its significant predictors using EGOVSAT Model. The user satisfaction of this model encompasses five main parts, which are utility, reliability, efficiency, customization, and flexibility. For that purpose, a detailed survey is conducted to measure the level of user satisfaction. A total of sixteen questions have distributed among forty one users of the software in KISSR. In order to evaluate the software, three measurement have been used which are reliability test, regression analysis and correlation analysis. The results indicate that the software is successful to a decent extent based on user satisfaction feedbacks obtained by using EGOVSAT Model.

cs.IR↗

arXiv2021

An Optimized Framework to Adopt Computer Laboratory Administrations for Operating System and Application Installations

Nowadays, in most of the fields, task automation is area of interest and research due to that manual execution of a task is error prone, time consuming, involving more human resources and focus concerning. In the area of Computer laboratory administration, the old fashioned administration cannot run with todays growth, where the Operating System (OS) and required applications are installed on all the machines one by one. Therefore, a framework for automating Lab administration in regards of Operating Systems and Application installations will be proposed in this research. Affordability, simplicity, usability are taken into major consideration. All the parts of the framework are implemented and illustrated in detail which promotes a great enhancement in the area of Computer Lab Administration.

cs.NI↗

arXiv2021

Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets

This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ) , expectation maximisation (EM) , K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.

cs.NE↗

arXiv2021

A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets

Clustering is a commonly used method for exploring and analysing data where the primary objective is to categorise observations into similar clusters. In recent decades, several algorithms and methods have been developed for analysing clustered data. We notice that most of these techniques deterministically define a cluster based on the value of the attributes, distance, and density of homogenous and single-featured datasets. However, these definitions are not successful in adding clear semantic meaning to the clusters produced. Evolutionary operators and statistical and multi-disciplinary techniques may help in generating meaningful clusters. Based on this premise, we propose a new evolutionary clustering algorithm (ECAStar) based on social class ranking and meta-heuristic algorithms for stochastically analysing heterogeneous and multiple-featured datasets. The ECAStar is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques, such as quartiles and percentiles, as well as the Euclidean distance of the K-means algorithm. Experiments are conducted to evaluate the ECAStar against five conventional approaches: K-means (KM), K-meansPlusPlus (KMPlusPlus), expectation maximisation (EM), learning vector quantisation (LVQ), and the genetic algorithm for clusteringPlusPlus (GENCLUSTPlusPlus).

cs.LG↗

arXiv2020

CSCF: a chaotic sine cosine firefly Algorithm for practical application problems

Recently, numerous meta-heuristic based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity etc. However, several optimization algorithms namely firefly algorithm, sine cosine algorithm, particle swarm optimization algorithm have few drawbacks such as computational complexity, convergence speed etc. So to overcome such shortcomings, this paper aims in developing a novel Chaotic Sine Cosine Firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine cosine algorithm (SCA) and the Firefly (FF) algorithms are integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping is selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm.

cs.NE↗

arXiv2019

Operational Framework for Recent Advances in Backtracking Search Optimisation Algorithm: A Systematic Review and Performance Evaluation

The experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems. This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA; therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are two-fold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces.

cs.NE↗