Essam Said Hanandeh
@zu.edu.jo
Faculty of Information Technology - Computer Science
Zarqa University
RESEARCH INTERESTS
Database,Information Retrieval,Data Mining
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Laith Abualigah, Saba Hussein Ahmed, Mohammad H. Almomani, Raed Abu Zitar, Anas Ratib Alsoud, Belal Abuhaija, Essam Said Hanandeh, Heming Jia, Diaa Salama Abd Elminaam, and Mohamed Abd Elaziz
Springer Science and Business Media LLC
Laith Abualigah, Sahar M. Alshatti, Abiodun M. Ikotun, Raed Abu Zitar, Anas Ratib Alsoud, Nima Khodadadi, Absalom E. Ezugwu, Essam Said Hanandeh, Heming Jia, and Mohsen Zare
Elsevier
Laith Abualigah, Roa’a Abualigah, Abiodun M. Ikotun, Raed Abu Zitar, Anas Ratib Alsoud, Nima Khodadadi, Absalom E. Ezugwu, Essam Said Hanandeh, and Heming Jia
Elsevier
Laith Abualigah, Eman Abu-Dalhoum, Abiodun M. Ikotun, Raed Abu Zitar, Anas Ratib Alsoud, Nima Khodadadi, Absalom E. Ezugwu, Essam Said Hanandeh, and Heming Jia
Elsevier
Laith Abualigah, Batool Sbenaty, Abiodun M. Ikotun, Raed Abu Zitar, Anas Ratib Alsoud, Nima Khodadadi, Absalom E. Ezugwu, Essam Said Hanandeh, and Heming Jia
Elsevier
Laith Abualigah, Worod Hawamdeh, Raed Abu Zitar, Shadi AlZu’bi, Ala Mughaid, Essam Said Hanandeh, Anas Ratib Alsoud, and El-Sayed M. El-kenawy
Elsevier
Laith Abualigah, Ashraf Ababneh, Abiodun M. Ikotun, Raed Abu Zitar, Anas Ratib Alsoud, Nima Khodadadi, Absalom E. Ezugwu, Essam Said Hanandeh, and Heming Jia
Elsevier
Laith Abualigah, Mohammad Al-Zyod, Abiodun M. Ikotun, Mohammad Shehab, Mohammed Otair, Absalom E. Ezugwu, Essam Said Hanandeh, Ali Raza, and El-Sayed M. El-kenawy
Elsevier
Laith Abualigah, Aya Abusaleem, Abiodun M. Ikotun, Raed Abu Zitar, Anas Ratib Alsoud, Nima Khodadadi, Absalom E. Ezugwu, Essam Said Hanandeh, and Heming Jia
Elsevier
Laith Abualigah, Suhier Odah, Abiodun M. Ikotun, Anas Ratib Alsoud, Agostino Forestiero, Absalom E. Ezugwu, Essam Said Hanandeh, Heming Jia, and Mohsen Zare
Elsevier
Laith Abualigah, Essam Said Hanandeh, Raed Abu Zitar, Cuong-Le Thanh, Samir Khatir, and Amir H. Gandomi
Elsevier BV
Laith Abualigah, Mahmoud Habash, Essam Said Hanandeh, Ahmad MohdAziz Hussein, Mohammad Al Shinwan, Raed Abu Zitar, and Heming Jia
Springer Science and Business Media LLC
Ali Raza, Mohammad Rustom Al Nasar, Essam Said Hanandeh, Raed Abu Zitar, Ahmad Yacoub Nasereddin, and Laith Abualigah
MDPI AG
Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.
Al Refai Mohammed N., Taani M.K. K., Ahmed Ali Otoom, Ghassan Samara, Hanandeh E. S, and Hayel Khafajeh
IEEE
This study examines decision-making in software re-engineering within businesses, focusing on the challenges and considerations involved. Financial constraints, time, and staff efforts are critical factors in these decisions. The study identifies difficulties faced by top-level management with legacy systems, existing systems, underused systems, and packaged software due to user resistance and customer dissatisfaction. The primary goal of the study is to assess the importance of software quality factors, especially from the users' perspective, in making re-engineering decisions. It uses the ISO 9126 Model to analyze quality elements in the case study software, aiming to provide guidance for deciding whether to proceed with re-engineering. The study explores functionality, reliability, usability, efficiency, maintainability, and portability as factors influencing re-engineering decisions, based on the ISO 9126 model. To understand user interactions and program performance, the study used unstructured participant observation in the case study software, highlighting the challenges faced by top management due to user resistance. These findings were further confirmed through face-to-face interviews with software users and department employees, offering insights into their perspectives on software quality elements. The results reveal that, despite some flaws, the system has valuable features and capabilities. Most employees express a desire to undergo re-engineering to enhance service quality, align with ISO 9126 standards, and leverage the expertise of competent staff for re-engineering activities.
Essam Hanandeh and Mohamed Shajahan
Springer Nature Switzerland
Aref abu Awad, Essam Hanandeh, and Halah Nasseif
Springer Nature Switzerland
Laith Abualigah, Sayel Abualigah, Mothanna Almahmoud, Agostino Forestiero, Gagan Sachdeva, and Essam S. Hanandeh
Springer Nature Switzerland
Yazan Alaya AL-Khassawneh, Essam Said Hanandeh, and Sattam Almatarneh
Springer Nature Singapore
Yazan Alaya AL-Khassawneh and Essam Said Hanandeh
MDPI AG
With the noteworthy expansion of textual data sources in recent years, easy, quick, and precise text processing has become a challenge for key qualifiers. Automatic text summarization is the process of squeezing text documents into shorter summaries to facilitate verification of their basic contents, which must be completed without losing vital information and features. The most difficult information retrieval task is text summarization, particularly for Arabic. In this research, we offer an automatic, general, and extractive Arabic single document summarizing approach with the goal of delivering a sufficiently informative summary. The proposed model is based on a textual graph to generate a coherent summary. Firstly, the original text is converted to a textual graph using a novel formulation that takes into account sentence relevance, coverage, and diversity to evaluate each sentence using a mix of statistical and semantic criteria. Next, a sub-graph is built to reduce the size of the original text. Finally, unwanted and less weighted phrases are removed from the summarized sentences to generate a final summary. We used Recall-Oriented Research to Evaluate Main Idea (RED) as an evaluative metric to review our proposed technique and compare it with the most advanced methods. Finally, a trial on the Essex Arabic Summary Corpus (EASC) using the ROUGE index showed promising results compared with the currently available methods.
Ali Khazalah, Boppana Prasanthi, Dheniesh Thomas, Nishathinee Vello, Suhanya Jayaprakasam, Putra Sumari, Laith Abualigah, Absalom E. Ezugwu, Essam Said Hanandeh, and Nima Khodadadi
Springer International Publishing
Essam S. Hanandeh, Aref Abu Awwad, and Yazan Khassawneh
IEEE
The researchers of this study chose 242 Arabic abstract doucments. Computer science and information systems are mentioned in all of these abstracts. The researchers created an Arabic-specific autonomous information retrieval system, the system was written in the C# NET programming language and its compatible with IBM/PCs and other microcomputers. For this corpus, The researchers used an automatic indexing strategy. The system was created using the Vector Space Model (VSM). In this model, the researcher take all measurements and utilize the Cosine, Dice, Jaccard, and Inner Product Similarity measures. Using the Vector Space Model, the researchers compared the retrieval results. In Arabic documents, the researchers discovered that the retrieval result for cosine is better than the retrieval result for other measures.
Laith Mohammad Abualigah, Essam Said Hanandeh, Ahamad Tajudin Khader, Mohammed Abdallh Otair, and Shishir Kumar Shandilya
Bentham Science Publishers Ltd.
Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.
Laith Mohammad Abualigah, Ahamad Tajudin Khader, and Essam Said Hanandeh
Springer International Publishing
For the purpose of improving the search strategy of the krill herd algorithm (KHA) , an improved robust approach is proposed to address the function optimization problems, namely, modified krill herd algorithm (MKHA) . In MKHA method, the modification of krill herd algorithm focuses on genetic operators (GOs) and it occurs in the ordering of procedures of the basic krill herd algorithm, where the crossover and mutation operators are employed after the updating process of the krill individuals position, the krill herd (KH) motion calculations, is finished. This modification is conducted because the genetic operators insignificantly exploit to enhance the global exploration search in the basic krill herd algorithm so as to speed up convergence. Several versions of benchmark functions are applied to verify the proposed method (MKHA) and it is showed that, in most cases, the proposed algorithm (MKHA) obtained better results in comparison with the basic KHA and other comparative methods.
Laith Mohammad Abualigah, Ahamad Tajudin Khader, and Essam Said Hanandeh
Springer Science and Business Media LLC
In this paper, a novel text clustering method, improved krill herd algorithm with a hybrid function, called MMKHA, is proposed as an efficient clustering way to obtain promising and precise results in this domain. Krill herd is a new swarm-based optimization algorithm that imitates the behavior of a group of live krill. The potential of this algorithm is high because it performs better than other optimization methods; it balances the process of exploration and exploitation by complementing the strength of local nearby searching and global wide-range searching. Text clustering is the process of grouping significant amounts of text documents into coherent clusters in which documents in the same cluster are relevant. For the purpose of the experiments, six versions are thoroughly investigated to determine the best version for solving the text clustering. Eight benchmark text datasets are used for the evaluation process available at the Laboratory of Computational Intelligence (LABIC). Seven evaluation measures are utilized to validate the proposed algorithms, namely, ASDC, accuracy, precision, recall, F-measure, purity, and entropy. The proposed algorithms are compared with the other successful algorithms published in the literature. The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.
Laith Mohammad Abualigah, Ahamad Tajudin Khader, and Essam Said Hanandeh
Elsevier BV
Abstract Krill herd (KH) algorithm is a novel swarm-based optimization algorithm that imitates krill herding behavior during the searching for foods. It has been successfully used in solving many complex optimization problems. The potency of this algorithm is very high because of its superior performance compared with other optimization algorithms. Hence, the applicability of this algorithm for text document clustering is investigated in this work. Text document clustering refers to the method of clustering an enormous amount of text documents into coherent and dense clusters, where documents in the same cluster are similar. In this paper, a combination of objective functions and hybrid KH algorithm, called, MHKHA, is proposed to solve the text document clustering problem. In this version, the initial solutions of the KH algorithm are inherited from the k-mean clustering algorithm and the clustering decision is based on two combined objective functions. Nine text standard datasets collected from the Laboratory of Computational Intelligence are used to evaluate the performance of the proposed algorithms. Five evaluation measures are employed, namely, accuracy, precision, recall, F-measure, and convergence behavior. The proposed versions of the KH algorithm are compared with other well-known clustering algorithms and other thirteen published algorithms in the literature. The MHKHA obtained the best results for all evaluation measures and datasets used among all the clustering algorithms tested.