Abeer Aljohani - Academia.edu (original) (raw)

Papers by Abeer Aljohani

Advances in intelligent systems and computing, Sep 30, 2022

Wireless Communications and Mobile Computing

Mobile computing and technology are becoming more common in many parts of private life and public... more Mobile computing and technology are becoming more common in many parts of private life and public services, and they are playing an increasingly important role in healthcare, not just for sensory devices but also for communication, recording, and display. They are used for more than only sensory devices but also for communications, recording, and display. Numerous medical indications and postoperative days must be monitored carefully. As a result, the most recent development in Internet of Things- (IoT-) based healthcare communication has been embraced. The Internet of Things (IoT), which is employed in a wide range of applications, is a catalyst for the healthcare industry. Healthcare data is complicated, making it difficult to handle and evaluate in order to derive useful information for decision-making. On the other hand, data security is a vital requirement in a healthcare data systems. Determining the need for a smart and secure IoT platform for healthcare applications, we crea...

Processes

With the extremely rapid growth of data in various industries, big data is gradually recognized a... more With the extremely rapid growth of data in various industries, big data is gradually recognized and valued by people. Medical big data, which can best reflect the significance of big data value, has also received attention from various parties. In Saudi Arabia, healthcare quality assessment is mostly based on human experience and basic statistical methods. In this paper, we proposed a healthcare quality assessment model based on medical big data in a region of Saudi Arabia, which integrated traditional evaluation methods and machine learning based techniques. Healthcare data has been accurate and effective after noise processing, and the outliers could reflect certain medical quality information. An improved k-nearest neighbors (KNN) algorithm has been proposed and its time complexity have been reduced to be more suitable for big data processing. An outlier indicator has been established based on statistical methods and the improved KNN algorithm. Experimental results showed that th...

The basic idea of ssHC is to leverage domain knowledge in the form of triple-wise constraints to ... more The basic idea of ssHC is to leverage domain knowledge in the form of triple-wise constraints to group data into clusters. In this paper, we perform extensive experiments in order to evaluate the effects of different distance metrics, linkages measures and constraints on the performance of two ssHC algorithms: IPoptim and UltraTran. The algorithms are implemented with varying proportions of constraints in the different datasets, ranging from 10% to 60%. We found that both IPoptim and UltraTran performed almost equally across the seven datasets. An interesting observation is that an increase in constraint does not always show an improvement in ssHC performance. It can also be observed that the inclusion of too many classes degrades the performance of clustering. The experimental results show that the ssHC with Canberra distance perform well, apart from ssHC with well-known distances such as Euclidean and Standard Euclidean distances. Together with complete linkages and small amount o...

Constraints-based hierarchical clustering (HC) has emerged as an important improvement over the e... more Constraints-based hierarchical clustering (HC) has emerged as an important improvement over the existing clustering algorithms. Triple-wise relative constraints are suitable to be applicable for HC, enabling the derivation of a cluster hierarchy instead of a flat partition. This paper proposes Constrained Ward’s Hierarchical Agglomerative Clustering algorithm (CWHAC). It is a novel variation of Ward’s hierarchical agglomerative clustering method based on the ideas of triple-wise relative constraints. The algorithm is proposed based on the ultra-metric transformation of the dissimilarity matrix which exploits the triple-wise relative constraints as background knowledge to create a new metric for data similarity. IPoptim and UltraTran methods are introduced to address the triple-wise relative constraints to modify and update the similarity metric for the proposed algorithm. This study addresses the issue of non-satisfaction of triple-wise relative constraints with HC to improve the ef...

Clustering algorithms with constraints (also known as semi-supervised clustering algorithms) have... more Clustering algorithms with constraints (also known as semi-supervised clustering algorithms) have been introduced to the field of machine learning as a significant variant to the conventional unsupervised clustering learning algorithms. They have been demonstrated to achieve better performance due to integrating prior knowledge during the clustering process, that enables uncovering relevant useful information from the data being clustered. However, the research conducted within the context of developing semi-supervised hierarchical clustering techniques are still an open and active investigation area. Majority of current semi-supervised clustering algorithms are developed as partitional clustering (PC) methods and only few research efforts have been made on developing semi-supervised hierarchical clustering methods. The aim of this research is to enhance hierarchical clustering (HC) algorithms based on prior knowledge, by adopting novel methodologies. [Continues.]

Computers & Industrial Engineering, 2017

This paper deals with a two-machine flowshop problem in which the machine at the first stage requ... more This paper deals with a two-machine flowshop problem in which the machine at the first stage requires preventive maintenance activities that have to be started within a given cumulative working time limit after the previous maintenance. That is, a maintenance activity can be started at any time unless the cumulative working time after the end of the previous maintenance exceeds the given limit. For the problem with the objective of minimizing total tardiness, we develop dominance properties and lower bounds for this scheduling problem as well as a heuristic algorithm, and suggest a branch and bound algorithm in which these properties, lower bounds, and heuristic algorithm are used. Computational experiments are performed to evaluate the algorithm and the results are reported.

2008 5th International Conference on Broadband Communications, Networks and Systems, 2008

We address the problem of transmit beamforming under channel uncertainty for a multiuser (MU), si... more We address the problem of transmit beamforming under channel uncertainty for a multiuser (MU), single carrier frequency domain equalization multiple input multiple output (SC-FDE-MIMO) system. SC-FDE-MIMO scheme is effective solution with relative low complexity to combat inter -symbol interference (ISI) whilst exploiting multi antennas diversity gain. In our system, the signal to leakage ratio (SLR) criterion is used to design

Advances in intelligent systems and computing, Sep 30, 2022

Wireless Communications and Mobile Computing

Mobile computing and technology are becoming more common in many parts of private life and public... more Mobile computing and technology are becoming more common in many parts of private life and public services, and they are playing an increasingly important role in healthcare, not just for sensory devices but also for communication, recording, and display. They are used for more than only sensory devices but also for communications, recording, and display. Numerous medical indications and postoperative days must be monitored carefully. As a result, the most recent development in Internet of Things- (IoT-) based healthcare communication has been embraced. The Internet of Things (IoT), which is employed in a wide range of applications, is a catalyst for the healthcare industry. Healthcare data is complicated, making it difficult to handle and evaluate in order to derive useful information for decision-making. On the other hand, data security is a vital requirement in a healthcare data systems. Determining the need for a smart and secure IoT platform for healthcare applications, we crea...

Processes

With the extremely rapid growth of data in various industries, big data is gradually recognized a... more With the extremely rapid growth of data in various industries, big data is gradually recognized and valued by people. Medical big data, which can best reflect the significance of big data value, has also received attention from various parties. In Saudi Arabia, healthcare quality assessment is mostly based on human experience and basic statistical methods. In this paper, we proposed a healthcare quality assessment model based on medical big data in a region of Saudi Arabia, which integrated traditional evaluation methods and machine learning based techniques. Healthcare data has been accurate and effective after noise processing, and the outliers could reflect certain medical quality information. An improved k-nearest neighbors (KNN) algorithm has been proposed and its time complexity have been reduced to be more suitable for big data processing. An outlier indicator has been established based on statistical methods and the improved KNN algorithm. Experimental results showed that th...

The basic idea of ssHC is to leverage domain knowledge in the form of triple-wise constraints to ... more The basic idea of ssHC is to leverage domain knowledge in the form of triple-wise constraints to group data into clusters. In this paper, we perform extensive experiments in order to evaluate the effects of different distance metrics, linkages measures and constraints on the performance of two ssHC algorithms: IPoptim and UltraTran. The algorithms are implemented with varying proportions of constraints in the different datasets, ranging from 10% to 60%. We found that both IPoptim and UltraTran performed almost equally across the seven datasets. An interesting observation is that an increase in constraint does not always show an improvement in ssHC performance. It can also be observed that the inclusion of too many classes degrades the performance of clustering. The experimental results show that the ssHC with Canberra distance perform well, apart from ssHC with well-known distances such as Euclidean and Standard Euclidean distances. Together with complete linkages and small amount o...

Constraints-based hierarchical clustering (HC) has emerged as an important improvement over the e... more Constraints-based hierarchical clustering (HC) has emerged as an important improvement over the existing clustering algorithms. Triple-wise relative constraints are suitable to be applicable for HC, enabling the derivation of a cluster hierarchy instead of a flat partition. This paper proposes Constrained Ward’s Hierarchical Agglomerative Clustering algorithm (CWHAC). It is a novel variation of Ward’s hierarchical agglomerative clustering method based on the ideas of triple-wise relative constraints. The algorithm is proposed based on the ultra-metric transformation of the dissimilarity matrix which exploits the triple-wise relative constraints as background knowledge to create a new metric for data similarity. IPoptim and UltraTran methods are introduced to address the triple-wise relative constraints to modify and update the similarity metric for the proposed algorithm. This study addresses the issue of non-satisfaction of triple-wise relative constraints with HC to improve the ef...

Clustering algorithms with constraints (also known as semi-supervised clustering algorithms) have... more Clustering algorithms with constraints (also known as semi-supervised clustering algorithms) have been introduced to the field of machine learning as a significant variant to the conventional unsupervised clustering learning algorithms. They have been demonstrated to achieve better performance due to integrating prior knowledge during the clustering process, that enables uncovering relevant useful information from the data being clustered. However, the research conducted within the context of developing semi-supervised hierarchical clustering techniques are still an open and active investigation area. Majority of current semi-supervised clustering algorithms are developed as partitional clustering (PC) methods and only few research efforts have been made on developing semi-supervised hierarchical clustering methods. The aim of this research is to enhance hierarchical clustering (HC) algorithms based on prior knowledge, by adopting novel methodologies. [Continues.]

Computers & Industrial Engineering, 2017

This paper deals with a two-machine flowshop problem in which the machine at the first stage requ... more This paper deals with a two-machine flowshop problem in which the machine at the first stage requires preventive maintenance activities that have to be started within a given cumulative working time limit after the previous maintenance. That is, a maintenance activity can be started at any time unless the cumulative working time after the end of the previous maintenance exceeds the given limit. For the problem with the objective of minimizing total tardiness, we develop dominance properties and lower bounds for this scheduling problem as well as a heuristic algorithm, and suggest a branch and bound algorithm in which these properties, lower bounds, and heuristic algorithm are used. Computational experiments are performed to evaluate the algorithm and the results are reported.

2008 5th International Conference on Broadband Communications, Networks and Systems, 2008

We address the problem of transmit beamforming under channel uncertainty for a multiuser (MU), si... more We address the problem of transmit beamforming under channel uncertainty for a multiuser (MU), single carrier frequency domain equalization multiple input multiple output (SC-FDE-MIMO) system. SC-FDE-MIMO scheme is effective solution with relative low complexity to combat inter -symbol interference (ISI) whilst exploiting multi antennas diversity gain. In our system, the signal to leakage ratio (SLR) criterion is used to design