Papers by Mohammed Al-Weshah
The impact of information and communication technologies on the application of knowledge management: an empirical study on commercial banks operating in the Jordanian capital city 'Amman
International Journal of Technology, Policy and Management, 2017
The study investigates the effect and success of using various information and communication tech... more The study investigates the effect and success of using various information and communication technology (ICT) in the application of knowledge management (KM) in commercial Jordanian banks in Amman. A 35-item questionnaire directed at all executive and supervisory levels was distributed to 34 branches belonging to 12 commercial banks in Amman. A total of 235 questionnaires were distributed; 221 were collected and 14 of these were excluded as inappropriate for analysis. Thus, 208 questionnaires (a response rate of 88.51%) were analysed by SPSS. Respondents indicated high degrees of approval for the use of ICT techniques. Moreover, ICT was found to have a high effect of 54% in applying KM. Different factors were found to affect ICT's application of KM, i.e., age, academic background, job description and years of experience. The study recommends advocating the importance of keeping abreast with technological advancements and providing the proper infrastructure to maintain and utilise ICT. It also recommends that KM be incorporated and disseminated into all administrative levels. Finally, staff should be familiarised, encouraged and motivated to employ KM via incentives and access to information should be made available to all staff through the use of data management systems.
Journal of Information and Communication Technology
Automated classification of prostate histopathology images includes the identification of multipl... more Automated classification of prostate histopathology images includes the identification of multiple classes, such as benign and cancerous (grades 3 & 4). To address the multiclass classification problem in prostate histopathology images, breakdown approaches are utilized, such as one-versus-one (OVO) and oneversus-all (Ovall). In these approaches, the multiclass problem is decomposed into numerous binary subtasks, which are separately addressed. However, OVALL introduces an artificial class imbalance, which degrades the classification performance, while in the case of OVO, the correlation between different classes not regarded as a multiclass problem is broken into multiple independent binary problems. This paper proposes a new multiclass approach called multi-level (hierarchical) learning architecture (MLA). It addresses the binary classification tasks within the framework of a hierarchical strategy. It does so by accounting for the interaction between several classes and the domain knowledge. The proposed approach relies on the 'divide-
IEEE Access
An imbalanced classification problem is one in which the distribution of instances across defined... more An imbalanced classification problem is one in which the distribution of instances across defined classes is uneven or biased in one direction or another. In data mining, the probabilistic neural network (PNN) classifier is a well-known technology that has been successfully used to solve a variety of classification difficulties. On the other hand, metaheuristic optimization approaches offer an excellent means by which to deal with this problem. Therefore, this work combines two metaheuristic algorithms-the Ali Baba and the Forty Thieves (AFT) algorithm and the Water Strider Algorithm (WSA)-in order to alter the weights of a PNN classifier for imbalanced datasets. This article introduces a self-contained multiple-search approach for parallel metaheuristics that may be used in a variety of situations. Most implementations begin many search processes, all of which utilize the same search algorithm, with a set of starting parameters that are all generated separately. Most implementations pick a processor to collect data and verify the data for compliance with some stopping criteria, with the latter being the default. In the proposed AFT-WSA parallel method, the two algorithms begin simultaneously, and the fitness value is communicated in each iteration to find the best classification accuracy in the smallest number of iterations, thereby allowing the weight of the PNN classifier to be adjusted. In this study, ten imbalanced public datasets were used to test the performance of the proposed approach in terms of classification accuracy, standard deviation, and F-measure. INDEX TERMS Ali Baba and the Forty Thieves algorithm, imbalanced data, parallel metaheuristic, PNN classifier, water strider algorithm.
An enhanced salp swarm optimizer boosted by local search algorithm for modelling prediction problems in software engineering
Artificial Intelligence Review
Knowledge Based Systems, 2022
Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on ... more Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Research Square (Research Square), Sep 2, 2022
Sentiment analysis (SA) is the process of assessing the sentiment and attitude of digital audienc... more Sentiment analysis (SA) is the process of assessing the sentiment and attitude of digital audiences toward a range of topics and subjects. The aim of this research is to propose an effective approach for nding good-quality solutions for dialectal Arabic SA problems by addressing inherent challenges in an optimal way. This is achieved by determining the polarities of review texts by using the k-means clustering algorithm in a lexicon-based model and also applying a ML model where necessary in a hybrid approach. In this research, a sentiment lexicon (senti-lexicon) corpus of 3,824 positive and negative words/terms is used in a deep feature extraction process to convert the text into feature vectors. The experimental results showed that the k-means clustering model worked better after separating the observations with relative score values and moving them to be classi ed using the lexicon-based model. The k-means clustering model part of the hybrid model yielded high-performance results in terms of accuracy, recall, and F1 score metrics, especially in the positive and negative score value features and total score. Each technique has shortcomings, the hybrid model; as the results that are shared will represent; prove that it is an ideal and more exible solution and approach to conducting SA in an effective and self-improving manner.
Novel File-Checksum Method for Data Duplication Removal of Patients
Nowadays, with rapid technology development, individuals have become capable of storing a large a... more Nowadays, with rapid technology development, individuals have become capable of storing a large amount of information in virtual storage. Even medical organizations use virtual repositories to store important patient data, including personal data, medical histories, and records for medical services. With the database, medical organizations process the available data and take measures to improve the quality of services. When processing statistical and customer review information, there may be inaccuracies due to duplication of patient data. Duplication of data usually occurs through the mistakes of staff members. In this paper, we introduce a novel checksum method for data duplication (CMDD). The checksum method is implemented using Java Platform. Based on the testing results, we are capable of handling data duplication.
Software effort estimation modeling and fully connected artificial neural network optimization using soft computing techniques
Cluster Computing, Feb 12, 2023
A Non-convex Economic Load Dispatch Using Hybrid Salp Swarm Algorithm
Arabian journal for science and engineering, Apr 28, 2021
In this paper, the economic load dispatch (ELD) problem with valve point effect is tackled using ... more In this paper, the economic load dispatch (ELD) problem with valve point effect is tackled using a hybridization between salp swarm algorithm (SSA) as a population-based algorithm and $$\beta $$ -hill climbing optimizer as a single point-based algorithm. The proposed hybrid SSA is abbreviated as HSSA. This is to achieve the right balance between the intensification and diversification of the ELD search space. ELD is an important problem in the power systems which is concerned with scheduling the generation units in active generators in optimal way to minimize the fuel cost in accordance with equality and inequality constraints. The proposed HSSA is evaluated using six real-world ELD systems: 3-unit generator, two cases of 13-unit generator, 40-unit generator, 80-unit generator, and 140-unit generator system. These ELD systems are well circulated in the previous literature. The comparative results against 66 well-regarded algorithms are conducted. The results show that the proposed HSSA is able to produce viable and competitive solutions for ELD problems.
Journal of Big Data, May 28, 2023
The incorporation of data analytics in the healthcare industry has made significant progress, dri... more The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.
Neural Computing and Applications, Jul 27, 2020
Feature selection (FS) is considered to be a hard optimization problem in data mining and some ar... more Feature selection (FS) is considered to be a hard optimization problem in data mining and some artificial intelligence fields. It is a process where rather than studying all of the features of a whole dataset, some associated features of a problem are selected, the aim of which is to increase classification accuracy and reduce computational time. In this paper, a recent optimization algorithm, the monarch butterfly optimization (MBO) algorithm, is implemented with a wrapper FS method that uses the k-nearest neighbor (KNN) classifier. Experiments were implemented on 18 benchmark datasets. The results showed that, in comparison with four metaheuristic algorithms (WOASAT, ALO, GA and PSO), MBO was superior, giving a high rate of classification accuracy of, on average, 93% for all datasets as well as reducing the selection size significantly. Therefore, the use of the MBO to solve the FS problems has been proven through the results obtained to be effective and highly efficient in this field, and the results have also proven the strength of the balance between global and local search of MBO.
International Journal of Data Mining, Modelling and Management, 2017
Classification is a task of supervised learning whose aim is to identify to which of a set of cat... more Classification is a task of supervised learning whose aim is to identify to which of a set of categories a new input element belongs. Probabilistic neural network is a variant of artificial neural network, which is simple in structure, easy for training and often used in classification problems. In this paper, the authors propose an improved probabilistic neural network model that employs biogeography-based optimisation to enhance the accuracy of the classification. The proposed approach was tested on 11 standard benchmark medical datasets from the machine-learning repository. Results show that the classification accuracy of the proposed improved probabilistic neural network model outperforms that of the traditional probabilistic neural network model.
arXiv (Cornell University), Jul 8, 2022
The incorporation of data analytics in the healthcare industry has made significant progress, dri... more The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-ofthe-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.
Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), 2010
Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony searc... more Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.
Nature-inspired metaheuristic algorithms
... In 2010, the author of this book developed a bat-inspired algorithm for continuous optimizati... more ... In 2010, the author of this book developed a bat-inspired algorithm for continuous optimization, and its efficiency is quite promising. ... 28. XS Yang, A new metaheuristic bat-inspired algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (Eds. ...
Applied Intelligence, Nov 26, 2020
Feature selection (FS) is used to solve hard optimization problems in artificial intelligence and... more Feature selection (FS) is used to solve hard optimization problems in artificial intelligence and data mining. In the FS process, some, rather than all of the features of a dataset are selected in order to both maximize classification accuracy and minimize the time required for computation. In this paper a FS wrapper method that uses K-nearest Neighbor (KSN) classification is subjected to two modifications using a current improvement algorithm, the Monarch Butterfly Optimization (MBO) algorithm. The first modification, named MBOICO, involves the utilization of an enhanced crossover operator to improve FS. The second, named MBOLF, integrates the Lévy flight distribution into the MBO to improve convergence speed. Experiments are carried out on 25 benchmark data sets using the original MBO, MBOICO and MBOLF. The results show that MBOICO is superior, so its performance is also compared against that of four metaheuristic algorithms (PSO, ALO, WOASAT, and GA). The results indicate that it has a high classification accuracy rate of 93% on average for all datasets and significantly reduces the selection size. Hence, the findings demonstrate that the MBOICO outperforms the other algorithms in terms of classification accuracy and number of features chosen (selection size).
African buffalo algorithm: Training the probabilistic neural network to solve classification problems
Journal of King Saud University - Computer and Information Sciences, May 1, 2022
Abstract Classification is used to categorize data and produce decisions for several domains. To ... more Abstract Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective manner. The hybridization of algorithms is now commonplace and has resulted in the creation of novel methods that are more effective in comparison with those that employ a sole algorithm. Therefore, in this paper, a hybridization approach is employed to utilize the African buffalo optimization (ABO) algorithm as an optimizer to adjust the weights of the probabilistic neural network (PNN). The effectiveness of the proposed (ABO-PNN) method is investigated by applying it to several different classification problems. The efficiency of the ABO algorithm is assessed based on the PNN training results produced and its performance is compared with that of different types of optimization algorithm. The performance of the proposed algorithm in terms of classification accuracy is tested on 11 benchmark datasets. The results show that the ABO is better than the firefly algorithm (FA) in terms of both classification accuracy and convergence speed.
Neural Computing and Applications, Feb 28, 2018
Classification is a data mining task that assigns items in a collection to predefined categories ... more Classification is a data mining task that assigns items in a collection to predefined categories or classes, also referred to as supervised learning. The goal of classification is to accurately predict the target class for each case in the data. A review of the literature shows that many algorithms, including statistical and machine learning algorithms, have been successfully used to handle classification problems in different areas, but their performance varies considerably. Even though the neural network is effective in addressing a wide range of problems, to date no specific neural network approach has been found that can ensure that the optimal solution is arrived at when solving classification problems. Some of the important challenges include finding the most appropriate weight parameter for the classifier through the implementation of population-based approaches; attaining a balance between the processes of exploration and exploitation by employing hybridization methods; and obtaining fast convergence by controlling random movement and by generating good initial solutions. This study investigates how can good initial populations drive higher convergence speed and better classification accuracy in solving classification problems. Local search (in this case, the simulated annealing algorithm) is used to produce an initial solution for the classification problem and then a heuristic initialization hybridized with biogeographybased optimization is applied. The proposed approaches are tested on 11 standard benchmark datasets. This is a new approach in the classification arena, and it represents an approach that outperforms the current state of the art on most of the tested benchmark datasets.
Hybridization of Arithmetic Optimization with Great Deluge Algorithms for Feature Selection Problems in Medical Diagnoses
Jordanian journal for computers and information technology, 2022
On detecting distributed denial of service attacks using fuzzy inference system
Cluster Computing, Aug 3, 2022
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Papers by Mohammed Al-Weshah