Abdelaziz I. Hammouri

Abdelaziz I. Hammouri

Al-Balqa' Applied University

H-index: 22

Asia-Jordan

Professor Information

University

Al-Balqa' Applied University

Position

Associate Professor of Computer Science .

Citations(all)

2391

Citations(since 2020)

2328

Cited By

773

hIndex(all)

22

hIndex(since 2020)

21

i10Index(all)

32

i10Index(since 2020)

30

Email

University Profile Page

Al-Balqa' Applied University

Research & Interests List

Artificial Intelligence

Optimization Methods

Metaheuristics

Data Mining

Scheduling Problems

Top articles of Abdelaziz I. Hammouri

Software effort estimation modeling and fully connected artificial neural network optimization using soft computing techniques

In software engineering, the planning and budgeting stages of a software project are of great importance to all stakeholders, including project managers as well as clients. The estimated costs and scheduling time needed to develop any software project before and/or during startup form the basis of a project’s success. The main objective of soft- ware estimation techniques is to determine the actual effort and/or time required for project development. The use of machine learning methods to address the estimation problem has, in general, proven remarkably successful for many engineering problems. In this study, a fully connected neural network (FCNN) model and a metaheuristic, gray wolf optimizer (GWO), called GWO-FC, is proposed to tackle the software development effort estimation (SEE) problem. The GWO is integrated with FCNN to optimize the FCNN parameters in order to enhance the accuracy of the …

Authors

Sofian Kassaymeh,Mohammed Alweshah,Mohammed Azmi Al-Betar,Abdelaziz I Hammouri,Mohammad Atwah Al-Ma’aitah

Journal

Cluster Computing

Published Date

2024/2

Feature Selection based nature inspired Capuchin Search Algorithm for solving classification problems

Identification of the optimal subset of features for Feature Selection (FS) problems is a demanding problem in machine learning and data mining. A trustworthy optimization approach is required to cope with the concerns involved in such a problem. Here, a Binary version of the Capuchin Search Algorithm (CSA), referred to as BCSA, was developed to select the optimal feature combination. Owing to the imbalance of parameters and random nature of BCSA, it may sometimes fall into the trap of an issue called local maxima. To beat this problem, the BCSA could be further improved with the resettlement of its individuals by adopting some methods of repopulating the individuals during foraging. Lévy flight was applied to augment the exploitation and exploration abilities of BCSA, a method referred to as LBCSA. A Chaotic strategy is used to reinforce search behavior for both exploration and exploitation potentials of …

Authors

Malik Braik,Abdelaziz Hammouri,Hussein Alzoubi,Alaa Sheta

Journal

Expert Systems with Applications

Published Date

2024/1/1

A binary hybrid sine cosine white shark optimizer for feature selection

Feature Selection (FS), a pre-processing step used in the majority of big data processing applications, aims to eliminate irrelevant and redundant features from the data. Its purpose is to select a final set of data characteristics that best represent the data as a whole. To achieve this, it explores every potential solution in order to identify the optimal subset. Meta-heuristic algorithms have been found to be particularly effective in solving FS problems, especially for high-dimensional datasets. This work adopts a recently developed meta-heuristic called the White Shark Optimizer (WSO) due to its simplicity and low computational overhead. However, WSO faces challenges in effectively balancing exploration and exploitation, particularly in complex multi-peak search problems. It tends to converge prematurely and get stuck in local optima, which can lead to poor search performance when dealing with FS problems. To …

Authors

Abdelaziz I Hammouri,Malik Sh Braik,Heba H Al-hiary,Rawan A Abdeen

Journal

Cluster Computing

Published Date

2024/4/3

An improved hybrid chameleon swarm algorithm for feature selection in medical diagnosis

Feature selection (FS) is generally associated with the process of using a probabilistic method to select optimal feature combinations during pre-processing steps in data mining. This technique can optimize the datasets’ features that need to be considered to heighten the performance of classification on the grounds of the selected optimal feature set. In this paper, a hybridization model is evolved and applied to select the optimal feature subset based on a binary version of the Hybrid Memory Improved Chameleon Swarm Algorithm (CSA) (HMICSA) and the k-Nearest Neighbor (k-NN) classifier. In this FS model, the following are proposed and applied: (1) Four kinds of transfer functions, (2) Amendments to the velocity of the CSA’s individuals, (3) Addition of internal memory to the CSA’s individuals, and (4) Hybridization of CSA with Ali baba and the Forty Thieves (AFT) algorithm. These actions are aimed to strike an …

Authors

Khalaf Khtatneh ik Shehadeh Braik,Abdelaziz I Hammouri,Mohammed A Awadallah,Mohammed Azmi Al-Betar

Journal

Biomedical Signal Processing and Control

Published Date

2023/8/1

A non-convex economic load dispatch problem using chameleon swarm algorithm with roulette wheel and levy flight methods

An Enhanced Chameleon Swarm Algorithm (ECSA) by integrating roulette wheel selection and Lé vy flight methods is presented to solve non-convex Economic Load Dispatch (ELD) problems. CSA has diverse strategies to move towards the optimal solution. Even so, this algorithm’s performance faces some hurdles, such as early convergence and slumping into local optimum. In this paper, several enhancements were made to this algorithm. First, it’s position updating process was slightly tweaked and took advantage of the chameleons’ randomization as well as adopting several time-varying functions. Second, the Lévy flight operator is integrated with roulette wheel selection method and both are combined with ECSA to augment the exploration behavior and lessen its bias towards exploitation. Finally, an add-on position updating strategy is proposed to develop a further balance between exploration and …

Authors

Malik Sh Braik,Mohammed A Awadallah,Mohammed Azmi Al-Betar,Abdelaziz I Hammouri,Raed Abu Zitar

Journal

Applied Intelligence

Published Date

2023/7

Improved versions of snake optimizer for feature selection in medical diagnosis: a real case COVID-19

Classification of medical data is largely dependent on the effective identification of key features of the data that can be used to aid in the diagnosis of related diseases. This goal can be achieved through feature selection methods that endeavor to get rid of redundant and irrelevant features to ameliorate classification accuracy. This is the aim of this work where a new meta-heuristic, referred to as snake optimizer, was adopted for the purpose of boosting the performance of existing feature selection methods. This optimizer may smoothly fall into local optimal solutions, which may present weak search performance and slow convergence speeds in handling feature selection problems. On this basis, this paper presents three improved adaptive versions of this optimizer, each of which has increased search performance over the basic optimizer. This optimizer was improved using three mathematical models named …

Authors

Malik Sh Braik,Abdelaziz I Hammouri,Mohammed A Awadallah,Mohammed Azmi Al-Betar,Omar A Alzubi

Journal

Soft Computing

Published Date

2023/12

Intrusion detection for the internet of things (IoT) based on the emperor penguin colony optimization algorithm

In the Internet of Things (IoT), the data that are sent via devices are sometimes unrelated, duplicated, or erroneous, which makes it difficult to perform the required tasks. Hence transmitted data need to be filtered and selected to suit the nature of the problem being dealt with in order to achieve the highest possible level of security. Feature selection is the process of identifying the suitable characteristics needed from a dataset's whole data set for usage in a certain task (FS). This study proposes a novel wrapper FS model that uses the emperor penguin colony (EPC) method to explore the issue space and a K-nearest neighbor classifier to solve FS for IoT challenges. In experiments, the proposed EPC model was applied to nine well-known IoT datasets in order to evaluate its performance. The results showed that the model had clear superiority over the multi-objective particle swarm optimization (MOPSO) and MOPSO …

Authors

Mohammed Alweshah,Abdelaziz Hammouri,Saleh Alkhalaileh,Omar Alzubi

Journal

Journal of Ambient Intelligence and Humanized Computing

Published Date

2023/5

Cognitively enhanced versions of capuchin search algorithm for feature selection in medical diagnosis: A COVID-19 case study

Feature selection (FS) is a crucial area of cognitive computation that demands further studies. It has recently received a lot of attention from researchers working in machine learning and data mining. It is broadly employed in many different applications. Many enhanced strategies have been created for FS methods in cognitive computation to boost the performance of the methods. The goal of this paper is to present three adaptive versions of the capuchin search algorithm (CSA) that each features a better search ability than the parent CSA. These versions are used to select optimal feature subset based on a binary version of each adapted one and the k-Nearest Neighbor (k-NN) classifier. These versions were matured by applying several strategies, including automated control of inertia weight, acceleration coefficients, and other computational factors, to ameliorate search potency and convergence speed of CSA. In …

Authors

Malik Braik,Mohammed A Awadallah,Mohammed Azmi Al-Betar,Abdelaziz I Hammouri,Omar A Alzubi

Journal

Cognitive Computation

Published Date

2023/11

Professor FAQs

What is Abdelaziz I. Hammouri's h-index at Al-Balqa' Applied University?

The h-index of Abdelaziz I. Hammouri has been 21 since 2020 and 22 in total.

What are Abdelaziz I. Hammouri's research interests?

The research interests of Abdelaziz I. Hammouri are: Artificial Intelligence, Optimization Methods, Metaheuristics, Data Mining, Scheduling Problems

What is Abdelaziz I. Hammouri's total number of citations?

Abdelaziz I. Hammouri has 2,391 citations in total.

What are the co-authors of Abdelaziz I. Hammouri?

The co-authors of Abdelaziz I. Hammouri are Majdi Mafarja, Salwani Abdullah, Iyad Abu Doush, ABET PEV, Bilal Zahran.

Co-Authors

H-index: 47
Majdi Mafarja

Majdi Mafarja

Birzeit University

H-index: 37
Salwani Abdullah

Salwani Abdullah

Universiti Kebangsaan Malaysia

H-index: 26
Iyad Abu Doush, ABET PEV

Iyad Abu Doush, ABET PEV

American University of Kuwait

H-index: 15
Bilal Zahran

Bilal Zahran

Al-Balqa' Applied University

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